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Viroids are self replicating non-coding RNAs capable of infecting a wide range of plant hosts . They do not encode any proteins , thus the mechanism by which they escape plant defenses remains unclear . RNAi silencing is a major defense mechanism against virus infections , with the four DCL proteins being principal components of the pathway . We have used Nicotiana benthamiana as a model to study Potato spindle tuber viroid infection . This viroid is a member of the Pospiviroidae family and replicates in the nucleus via an asymmetric rolling circle mechanism . We have created knock-down plants for all four DCL genes and their combinations . Previously , we showed that DCL4 has a positive effect on PSTVd infectivity since viroid levels drop when DCL4 is suppressed . Here , we show that PSTVd levels remain decreased throughout infection in DCL4 knockdown plants , and that simultaneous knockdown of DCL1 , DCL2 or DCL3 together with DCL4 cannot reverse this effect . Through infection of plants suppressed for multiple DCLs we further show that a combined suppression of DCL2 and DCL3 has a major effect in succumbing plant antiviral defense . Based on our results , we further suggest that Pospoviroids may have evolved to be primarily processed by DCL4 as it seems to be a DCL protein with less detrimental effects on viroid infectivity . These findings pave the way to delineate the complexity of the relationship between viroids and plant RNA silencing response .
Viroids are infectious , naked circular RNAs sized from 246 to 401 nucleotides ( nt ) , capable of infecting a wide range of hosts , causing important economic loses [1] . They are divided into two families , Pospiviroidae and Avsunviroidae [1–3] . Potato spindle tuber viroid ( PSTVd ) , a type species of the Pospiviroidae family , has a rod-like secondary structure with five distinct domains , and replicates in the nucleus through an asymmetric rolling circle mechanism using RNA polymerase II ( RNAPII ) [1 , 4–6] . It is known to infect crop plants of the Solanaceae family such as tomato and potato , as well as some ornamental plants of the Scrophulariaceae and Asteracae family , but does not infect the plant model Arabidopsis thaliana systemically . Since viroids do not encode any protein they rely on plant available resources and / or mechanisms for their infectivity . One of the mechanisms they have been proposed to exploit is RNA interference ( RNAi ) , especially because of their particular double stranded RNA structures ( dsRNA ) [4 , 5] . RNAi is an epigenetic process that regulates gene expression at transcriptional and post-transcriptional level and is one of the major plant defense mechanisms against viruses [7 , 8] . In plants , endogenous or exogenous double stranded or aberrant RNAs are recognized by specific proteins named Dicer-Like ( DCL ) , and digested into double stranded small interfering RNAs ( siRNAs ) of 21 to 24nt [9] . These siRNAs are then incorporated into the RNA induced silencing complex ( RISC ) whose major components are Argonaute proteins ( AGO ) , after which one of the strands is removed [9] . These complexes can recognize single stranded RNAs with good complementarity driving their degradation . In addition , they can serve as primers for the synthesis of dsRNA by an RNA-dependent RNA polymerase ( RdRP ) , which leads to the production of secondary siRNAs thus amplifying the suppressive phenomenon [9] . In Nicotiana benthamiana , four DCL proteins have been described , with close homology with the ones in A . thaliana [10] . All four proteins contain six specific domains: DEAD-Helicase , Helicase C , DSRD , PAZ , RNAseIII and dsRBD [11] . Each DCL holds a specific and rather defined role . DCL1 is involved in the miRNA ( micro-RNAs ) biogenesis pathway and produces 21nt small RNAs from precursors named primary miRNA ( pri-miRNA ) ( reviewed in [12] ) . DCL1 has also been proposed to affect DNA methylation , contribute to the silencing of certain transposons and finally facilitate the biogenesis of DNA virus siRNAs by other DCLs [13 , 14] . DCL2 generates 22nt long siRNAs of exogenous origin and 22nt natural antisense siRNAs [15 , 16] . DCL2 is involved in the production of secondary siRNAs which trigger the phenomenon of transitivity [17 , 18] . In addition , a role in the antiviral defense together with DCL4 is well established [15 , 19 , 20] . The main reported role of DCL3 is to form 24nt-mers related to RNA-directed DNA methylation , however its involvement in the production of 23 to 25nt long-miRNAs generated by miRNAs precursors has also been shown [21] . DCL4 is in charge of processing endogenous 21nt trans-acting RNAs ( tasi-RNAs ) but also of specific miRNAs in A . thaliana such as mir822 and mir839 [22 , 23] . Recently , a role in transcription termination was also described [24 , 25] . Nevertheless , the principal role of DCL4 is considered to be the antiviral capacity of this protein [19 , 20] . Of particular note is that even though the role of each DCL seem to be rather specific , redundancy between these pathways has been proposed [13 , 19 , 20 , 26–28] . RNAs are then incorporated into AGO containing complexes . Ten different AGO proteins are found in A . thaliana and seven in N . benthamiana [10 , 29 , 30] . In A . thaliana , AGO1 loads DCL1 products while AGO2 loads trans-acting RNAs and repeat associated RNAs [29] . Both AGO1 and AGO2 are major plant antiviral proteins depending on the virus [30] . For instance , AGO1 is involved in the defense against Turnip crinkle virus ( TCV-Family: Tombusviridae , Genus: Carmovirus ) and Cucumber mosaic virus ( CMV-Family: Bromoviridae , Genus: Cucumovirus ) whereas AGO2 in the defense of Tobacco rattle virus ( TRV-Family: Virgaviridae , Genus: Tobravirus ) and Turnip mosaic virus ( Family: Potyviridae , Genus: Potyvirus ) [31–34] . An additional minor role in plant defense has been attributed to AGO5 , AGO7 and AGO10 [30 , 34] . 24nt are loaded into AGO4 and possibly AGO6 , and are involved in RNA-directed DNA methylation [29] . AGO4 has also been involved in antiviral defense against RNA or DNA viruses , but most of its actions are related to perturbations of DNA methylation processes [30] . It is to note that recent findings propose an interaction of AGO4 with RNAPII in the plant nucleus [35] . The relation of viroids to the gene silencing mechanisms has been rather puzzling since contradictory reports about a positive or a negative role of gene silencing , have been published . In 2001 , two independent studies suggested that viroids are targeted by plant defense , since small RNAs derived from the viroid genome ( vd-siRNAs ) could be detected in the host plants [36 , 37] . However in 2003 , Chang et al . demonstrated in vitro that human Dicer was unable to cleave PSTVd [38] . A year later Wang et al . showed that tomato plants expressing RNA hairpins against PSTVd presented phenotypic similarities to infected plants suggesting an important role of vd-siRNAs in targeting endogenous transcripts but not the PSTVd genome [39] . This was further supported by two independent studies in 2007 , where it was suggested that viroid genome is not targeted upon infection [40 , 41] . In contrast , a research study from Schmind et al . ( 2009 ) proposed that RNAi is able to counteract PSTVd infection [42] . The authors of this work showed that transgenic plants expressing a hairpin against PSTVd cannot be infected by the viroid which suggests that the viroid is targeted by the produced vd-siRNAs [42] . Targeting of specific endogenous genes by vd-siRNAs is now accepted; however , the question of if and how the targeting of viroid genome is achieved is still under investigation [43 , 44] . On the other hand , there were studies showing an involvement of specific RNAi components in the defense against viroids . RDR6 was shown to delay PSTVd infection , since plants with decreased RDR6 levels presented increased viroid titer compared to WT plants [45] . This increase was only visible in early time points , showing that RDR6 suppression is important mainly in the initial steps of PSTVd infection [45] . In 2013 , we showed that DCL4 protein is somehow involved in the infection caused by PSTVd [46] . Specifically , knocked-down N . benthamiana plants for each and every DCL protein were produced and infected with the viroid . At 3 weeks post infection ( wpi ) plants with decreased DCL4 levels ( DCL4i ) repeatedly presented lower viroid levels compared to WT plants . This observation was in striking contrast to what is observed in viral infections , since when this protein is decreased or absent , a viral enhancement is usually observed . This indicated that the interplay between the silencing pathway and viroids does not follow current theory for anti-viral responses [13 , 19 , 20] . A recent publication by Minoia and colleagues showed that different AGO proteins bind vd-siRNAs [47] . AGO1 , AGO2 and AGO3 bind 21 and 22nt vd-siRNAs , whereas AGO4 , AGO5 and AGO9 additionally bind 24nt vd-siRNAs [47] . In this work they also showed that overexpression of A . thaliana AGO1 , AGO2 , AGO4 and AGO5 in infected plants drives a decrease in PSTVd levels , suggesting a targeting of the viroid by vd-siRNAs [47] . Lastly , advances of deep sequencing technologies showed that vd-siRNAs are of both polarities and are produced from the entire viroid genome [48–50] . In the present work , we attempt to revisit the complex interplay that viroids have with the RNAi silencing machinery . We have focused on detailing the effect of individual DCL genes and their combinations on PSTVd infectivity . We provide , for the first time , evidence that it is the combined activity of the DCL2 and DCL3 pathways that are required to efficiently suppress PSTVd . In addition , we suggest that viroids may have evolved to be primarily targeted by DCL4 , a DCL protein with less detrimental effects on its infectivity , to avoid the more potent anti-viroid effect of the DCL2-DCL3 pathways .
In order to further investigate our previous observation of the reduction of PSTVd levels in DCL4i plants [46] , we first tested potential limitations of the experimental design that could interfere with the observed phenomenon . Firstly , we asked whether the hairpin itself is likely to interfere with the detected reduction in viroid levels by directly targeting the viroid . We used a blastn approach and evaluated whether artificially generated 21nt siRNAs derived from the DCL4 . 9i hairpin ( DCL4hp ) sequence ( 336 sequences in total; S1 Table ) might target the PSTVdNB genome . Only seven blastn-hits had an alignment length of more than 15 nucleotides ( DCL4_hairpin_123-DCL4_hairpin_129; S1 Table ) and all seven blastn hits had mismatches in the siRNA guide strand seed region . It is known that mismatches at this region perturb their function . In addition , leaves of PSTVdNB infected plants were agroinfiltrated in one half with GFP and the other half with DCL4hp for three days , time sufficient for the hairpin to be expressed but not for the targeted protein to be significantly reduced . No significant difference in viroid levels were observed in the presence of DCL4hp ( S1 Fig ) , suggesting that the hairpin does not directly target the viroid sequence . Next , we used tissue print hybridization to detect possible effects of DCL4 suppression on the spatial distribution of PSTVd . No significant differences were observed in the infection of individual leaves between WT and DCL4i plants ( S2 Fig ) We have also investigated whether the agroinfiltration method used for infection could influence our results . For this reason , we have used mechanical infections in WT and DCL4 . 9i plants with either in vitro transcribed viroid RNA or total RNA from infected tissue and monitored PSTVd levels . We detected similar results to what we have observed before with agroinfiltration ( S3 Fig ) . Finally , PSTVd levels were monitored in the course of infection in a 7 week period . We reasoned that if DCL4 suppression had an effect only at the initial events of the infection , viroid titer in DCL4i plants would eventually recover to WT levels . Three plants of each condition ( WT and DCL4i ) were infected with PSTVd and tissue of young leaves was collected at 1 , 2 , 3 , 4 , 5 and 7 wpi . As shown in Fig 1A , throughout infection , viroid levels are lower in DCL4i plants compared to WT plants . This suggests that DCL4 knock-down affects viroid accumulation throughout infection . Taken together these results imply that the observed reduction of viroid levels in the DCL4i plant line is sustained through the course of the infection and does not seem to be caused by an indirect effect of DCL4hp produced siRNAs or by problematic spatial PSTVd distribution . Next , we asked whether this phenomenon extends beyond PSTVd . Firstly , we tested whether the observed phenomenon in DCL4i plants is observed for another PSTVd strain additionally to the strain PSTVdNB . To this end , WT and DCL4i plants were infected with PSTVdKF493732 . 1 [51] ) , which was isolated from an infected tomato and differs from the NB strain in 20nt ( S4 Fig ) . Then we extended our analysis to an additional member of the Pospiviroidae family of the same genus , Tomato apical stunt viroid ( TASVdKF484878 . 1 [51] ) , an important viroid , recently ( 2014 ) added to the quarantine alert pest list of the European and Mediterranean plant protection organization ( EPPO ) [52] . This strain was isolated from a Solanum jasminoides plant , and present 75% identity to PSTVdNB ( S4 Fig ) . Both strains induce mild symptoms in N . benthamiana . Plants were mechanically infected and 5wpi upper leaves were collected and analyzed by northern blots , for viroid levels . As shown in Fig 1B , in both cases lower levels of viroid accumulation were observed in DCL4i plants . Additionally , we have infected by agroinfiltration WT and DCL4i plants with Hop stunt viroid ( HSVdY09352-Family: Pospiviroid , Genus Hostuviroid [53 , 54] ) , a viroid of the same family , but classified to a different genus . Three weeks post infection upper leaves were collected and reduced viroid levels in DCL4i plants were also observed ( Fig 1B and S4 Fig ) . In an attempt to identify potential effects of DCL4 suppression in the replication of PSTVd we looked at ( - ) strand viroid RNAs , which are generally considered replication intermediates . Any over-accumulation or additional band of ( - ) strand viroid RNA , especially of the larger multiunit intermediates could indicate delay or other problems in specific steps of replication . Northern hybridization analysis of 3 weeks infected WT and DCL4i plants for the ( - ) strand RNA did not reveal significant differences in relative abundance of the bands corresponding to the ( - ) RNA species ( Fig 1C ) . Similar findings were observed for HSVd ( Fig 1D ) . An essential component of plants response to viruses is through the gene silencing mechanism . In order to investigate the role of gene silencing on PSTVd infectivity we used N . benthamiana plants suppressed for each and every Dicer and their combinations . Previously , we have shown the effect of individual DCL suppression on viroid accumulation [46] . The lines used were generated using RNAi-inducing hairpin constructs ( Fig 2A ) [46] . To avoid non specific effects due to the site of insertion of the T-DNA , two different lines were used for each DCL knock-down ( for DCL1: 1 . 9i , 1 . 13i , for DCL2: 2 . 11i , 2 . 41i , for DCL3: 3 . 1i , 3 . 10i , for DCL4: 4 . 9i , 4 . 16i ) . In addition , we have generated a plant line that carries a hairpin against DCL2 and DCL4 simultaneously ( DCL2/4i ) [46] . We have performed quantitative PCR ( qPCR ) in these lines and determined mRNA levels of each DCL ( Fig 2B ) . Each line successfully and specifically suppresses only the expected DCL . Homozygous DCLi plants where crossed to each other and F1 plants were used in this work , in order to avoid differences in the genetic background of the plants during infections . Relative levels of downregulation of each transcript in non infected plants were tested by qPCR and are presented in Fig 2C . DCL1 . 13 ( x ) 2 . 11i show a 28% decrease of DCL1 and 92 . 9% decrease of DCL2 transcripts . DCL1 . 13 ( x ) 3 . 10i has a downregulation of 77 . 4% of DCL1 mRNA and 71% of DCL3 . DCL1 . 13 ( x ) 4 . 9i plants 47 . 3% and 53 . 5% of DCL1 and DCL4 transcripts respectively . DCL2 . 11 ( x ) 3 . 10i plants display a 92 . 6% and 37% reduction of DCL2 and DCL3 transcripts respectively . DCL2/4i plants present 97 . 7% and 96 . 5% reduction of the cognate transcripts . DCL4 . 9 ( x ) 3 . 10i plants have 77 . 2% reduction of DCL3 and 83 . 8% reduction of DCL4 compared to WT plants . Finally , triple knock-down plants DCL3 . 10 ( x ) 2/4 . 5i have decreased levels of around 98% for transcripts DCL2 , DCL3 and DCL4 whereas DCL2/4 . 16 ( x ) 1 . 13i has a reduction of 76 . 2% , 98% and 93 . 2% for DCL1 , DCL2 and DCL4 respectively ( Fig 2C ) . It is to note that in a few cases other DCL proteins were mildly affected by the downregulation of two specific DCLs . Since no downregulation of the non targeted DCL were observed in the single DCLi lines , we argue that the observed effect is indirect . Each DCL is responsible for the generation of siRNAs of a specific size class . As a consequence , suppression of specific DCLs should lead to suppression of the cognate siRNA species . Such reduction would indicate the efficient suppression of the DCL function . To this end DCLi plants were infected with PSTVd and 3wpi vd-siRNAs were investigated . As shown in Fig 3A ( quantified results in S2 Table ) , PSTVd infected WT plants present three distinct vd-siRNAs classes of 21 , 22 and 24nt long , with the two first being dominant . In DCL2i plants 22nt vd-siRNAs are not detected and 24nt are increased . In addition , in DCL2 . 41i line , a line with more efficient suppression of DCL2 than line DCL2 . 11i ( Fig 2B ) , the increase of the 24nt population is even higher . This suggests that DCL3 products increase with the decrease of DCL2 indicating that DCL3 in WT conditions may be outcompeted for these substrates by DCL2 . In DCL3i plants , the 24nt class are dimishished . In DCL4i plants , 21nt vd-siRNAs are strongly reduced and the 22nt class increased compared to WT . This could be an indication that DCL1 is not or marginally involved in the production of 21nt vd-siRNAs , since their production is significantly reduced in these RNAi plants . In DCL1 . 13 ( x ) 2 . 11i or DCL2 . 41 ( x ) 1 . 13i F1 plants no 22nt band is detected . In DCL1 . 13 ( x ) 3 . 10i and DCL1 . 9 ( x ) 3 . 1i no 24nt vd-siRNAs are detected , and in addition , there was no visible effect on the 21nt class . This is in accordance with a marginal ( if any ) role of DCL1 in the generation of vd-siRNAs under these conditions . When both DCL4 and DCL3 are suppressed ( DCL4 . 9 ( x ) 3 . 10i and DCL4 . 16 ( x ) 3 . 1i F1 plants ) both cognate vd-siRNA classes are bellow detection level . In the triple mutant DCL3 ( x ) 2/4i , it seems that the maternal origin of DCL3 is important , since when DCL3i ( maternal ) is crossed to DCL2/4i ( paternal ) , only a faint band of 21nt is observed . In the reciprocal cross , when DCL2/4i is crossed to DCL3i , faint bands of 21nt and 24nt can be observed . Both cases differ from what is observed in DCL2/4i plants where only the 24nt class could be detected . Taken together , these results indicate that the produced crossed lines are efficiently and specifically downregulated for the cognate DCL ( s ) and this is mirrored by a specific alteration of the cognate vd-siRNA population . It should be noted that conclusions from this analysis cannot be drawn for DCL1 , since a ) the smallRNAs produced from the activity of this enzyme are of the same size as those of DCL4 , and b ) both DCL1i lines produced have a relatively low reduction of the targeted gene . This was not unexpected , since it was known from A . thaliana that strong dcl1 suppression leads to embryo lethality [55] . To investigate the interplay between the RNAi silencing machinery and PSTVd , we looked at the levels of principal components of RNAi following infection . We opted for a microarray analysis , in order to target all of the 35 different RNAi elements described for N . benthamiana [10] . To this end , we designed a custom genome-wide Agilent Gene Expression microarray ( see material and methods for details ) . For genes related to the RNAi machinery , a total of 10 different probes for each transcript were designed [10] . In order to achieve statistical accuracy , replicates of WT and PSTVd infected plants were analyzed . As shown in a volcano plot ( Fig 4A ) , no significant differences were found for any of the tested RNAi transcripts ( green spots ) . The obtained values are presented in details in S3 Table . qPCR experiments for the four DCL transcripts in the same samples verified the microarray results , as no significant differences were observed ( Fig 4B ) . Taken together , we have shown that PSTVd infection in our settings had no significant effect on the transcript levels of RNAi machinery . We previously showed that PSTVd is negatively affected upon DCL4 suppression . Here , we tested the effect of the combined suppression of DCL4 with each and every of the other three DCL enzymes on PSTVd infectivity . DCL1-DCL4 and DCL3-DCL4 knock-down plants infected with PSTVd for three weeks were analyzed by collecting RNA from young leaves and performing northern blots ( Fig 5A ) . Quantified results from this analysis are presented in Fig 5B . As shown , the combined suppression of DCL1-DCL4 or DCL3-DCL4 genes led to a 36% and a 52 . 2% decrease of PSTVd titer respectively ( Fig 5B ) . It is to note that in our previous work we have shown that simultaneous suppression of DCL2 and DCL4 also significantly reduced PSTVd levels [46] . These results show that DCL4 suppression has a negative impact on viroid titer even in combination with the suppression of any other individual DCL gene , demonstrating its important role in the PSTVd biological cycle . Next , we examined the effect of suppression of multiple DCLs , other than DCL4 , on PSTVd levels . As before , plants were infected by agroinfiltration with the PSTVdNB , and 3wpi young leaves were collected and examined in northern blots . Northern blots were quantified with appropriate software and results are presented in Fig 6 and S5 Fig . Infected plants DCL2 . 41 ( x ) 1 . 13i and DCL1 . 13 ( x ) 2 . 11i did not show any differences in PSTVd levels ( S5 Fig ) . Experiments with knock-down plants of both DCL1 and DCL3 as well as with plants with decreased levels of DCL1 , DCL2 and DCL4 were more complicated . Plants couldn’t be uniformly infected with PSTVd . Some plants showed increased levels and some could not be infected at all ( S5 Fig ) . As described earlier [46] , this phenomenon is also observed in DCL1i single knock-down plant lines and it is further discussed in the next section . As a result , experiments using DCL1i plants were considered as inconclusive . The DCL2 and DCL3 knock-down plants had a strong positive effect on viroid accumulation . This was the case for all three different F1 DCL2i-DCL3i crosses tested in this work . DCL2 . 11i ( x ) 3 . 10i showed on average a 2 fold increase of viroid levels , DCL2 . 11i ( x ) 3 . 1i an almost 1 . 4 fold increase and finally DCL3 . 10i ( x ) 2 . 41i a 1 . 9 fold increase ( Fig 6 ) . An important increase was also observed upon mechanical infection of PSTVd RNA ( S6 Fig ) . This effect was even more pronounced when , in addition to DCL2 and DCL3 , DCL4 was also suppressed . As shown in Fig 6Ad and 6B , a 4 and 3 . 6 fold increased PSTVd levels are observed in DCL3 . 10 ( x ) 2/4 . 5i and DCL2/4 . 5 ( x ) 3 . 10i F1 plants respectively . Taken together , these results show that it is the combination of DCL2 and DCL3 together that is needed to defend against PSTVd efficiently . It seems that when both DCL2 and DCL3 are knocked down , a role for DCL4 in anti-viroid response can also be attributed . This indicates that although DCL4 most probably comes first to cleave the viroid , this pathway is not the most efficient suppressor of the viroid . In contrast , it is the DCL2 and DCL3 pathways which are more efficient in antiviroid defense but are put in the shade by the hierarchically first DCL4 processing . A model , integrating these findings is presented in Fig 8 and is discussed in the Discussion section . In addition , we have found that these results are translated in differences observed in infected plant phenotypes . A significant ‘twisting’ of the upper leaves is observed in long time infected DCL3i lines ( Fig 7A ) , which was not observed in WT plants , contributing to our suggestion of an important involvement of DCL3 in viroid defense . Furthermore , differentiation of PSTVd levels is mirrored by the observed severeness of symptoms in the various DCLi F1 crosses . As shown in Fig 7B , DCL4 . 9i ( x ) 3 . 10i plants with decreased PSTVd titer , are less stunted than WT plants . At the opposite end , DCL3 . 10i ( x ) 2 . 11i , DCL2 . 11 ( x ) 3 . 10i , DCL3 . 10i ( x ) 2 . 41i and DCL3 . 10 ( x ) 2/4 . 5i F1 plants with increased PSTVd levels , show increased stunting and a ‘bushy’ effect , with increase of the branching and a decrease of the internode length , visible even at early weeks post infection ( Fig 7C ) . An interesting phenotype is also observed in DCL4 . 9i plants infected with HSVd . They show twisted shoots not observed for the other viroids used here ( Fig 7D ) . Taken together , these results show that the observed phenotypic effect of PSTVd infection follows closely viroid titers .
The complex relationship of viroids with the RNAi pathways has been puzzling researchers since the discovery of RNAi in 1998 . The partially dsRNA nature of their genome , in combination with the lack of any silencing suppressor proteins , suggested that viroids could be a good target for RNAi resistance . This was supported a few years later by the finding that indeed vd-siRNAs are abundant in infected plants [4 , 36 , 37] . However , even though viroids seem to have evolved in such way that they can escape this fate leading to infection , the way they manage to do so remains unclear [39–42] . Nevertheless , different studies have implicated RNAi components in viroid infection [45–47] . In a previous study , we have shown that , at least , DCL2 , DCL3 and DCL4 of N . benthamiana are involved in the production of cognate vd-siRNAs of specific size classes . In addition , we identified the importance of DCL4 , since when DCL4 is suppressed , PSTVd titer and symptoms follow [46] . This effect is in contrast to what is usually observed during viral infections , where DCL4 acts as a major antiviral protein [13 , 19 , 20] . Here , we aimed to further characterize this phenomenon and to understand the role of individual DCL proteins during viroid infection . Our results demonstrated that a different PSTVd strain as well as two different members of the Pospiviroidae family ( TASVd and HSVd ) were also affected by DCL4 suppression in a similar way to the one shown before , suggesting that the observed phenomenon could be a general feature of this viroid family . We estimated the effect of PSTVd infection on the expression levels of different RNAi components using a custom made gene-expression microarray followed by qPCR verification for the DCL genes . We did not find a significant effect of viroid infection on the expression of RNAi components . This is in contrast to what was shown previously for Citrus Exocortis Viroid ( CEVd ) where especially DCL4 levels increased as a result of CEVd infection [56] . This discrepancy may be due to differences in host species ( Solanum lycopersicum in [56] ) , the viroid used for the infection and/or the q-PCR normalization method , as only actin was used as a reference gene , a gene often affected by viral infections [57] . Further , we investigated effects of the simultaneous knock-down of more than one DCL protein , since functional redundancy between DCLs has been repeatedly reported [13 , 19 , 20 , 26–28] . DCLi N . benthamiana plants were crossed to each other in order to produce a set of plants suppressed for a different set of DCL proteins . Suppression through RNAi was effective even when all three DCL2 , DCL3 and DCL4 were targeted simultaneously . This suggests that even small amounts of siRNAs produced by the remaining DCLs are enough to be loaded into AGO complexes and efficiently suppress endogenous sequences . The effects of DCL4 suppression were found epistatic to that of any other individual DCL ( DCL1 , DCL2 and DCL3 ) suppression , since whenever DCL4 was suppressed , the viroid accumulation was lower than in WT plants . However , when DCL2-DCL3 or DCL2-DCL3-DCL4 were simultaneously suppressed , PSTVd titer was found significantly increased . We have previously shown that infectivity in single DCL2i or DCL3i lines is not significantly affected at 3wpi [46] . Taken together , our present and previous observations suggest that it is the combined action of DCL2 and DCL3 that seem to be important in the plant response to PSTVd infection . As far as we know , this is the first time that the combined effect of DCL2 and DCL3 against viroids or viruses has been observed . Analysis of viral infection for 3 ( + ) RNA viruses ( CMV , TCV and TRV ) in dcl2dcl3 mutant A . thaliana plants showed no significant effect of their combined knockout on viral accumulation [19 , 20] . However , there are some indications that upon CMV infection , DCL3 can act to amplify the production of the 21nt viral siRNAs ( vsRNAs ) when DCL4 is suppressed . This indicates that DCL3 can eventually enhance the antiviral silencing by operating upstream of DCL4 , although further elucidation of this effect is needed [58] . On the other hand , antiviral activity against DNA viruses such as Cauliflower mosaic virus ( Family: Caulimoiridae , Genus: Caulimovirus ) and Cabbage leaf curl virus ( Family: Geminiviridae , Genus: Begomovirus ) in A . thaliana relies on the action of all DCL proteins , although the activity of DCL3 is more pronounced , since an increased number of 24nt ( compared to the abundance of this siRNA class in RNA viruses ) is produced [13 , 59] . Additionally , an involvement of DCL1 has been proposed for CaMV and CaLCuV , which also differs from what it is observed for RNA viruses [13] . Both of these DNA viruses replicate in the nucleus as opposed to the majority of RNA viruses that replicate in the cytoplasm . Nevertheless , viroids differ from viruses . Pospiviroidae viroids replicate in the nucleus , thus it is tempting to speculate that the increased ‘need’ for DCL3 in host defense is due to this specific localization and partly resembles nuclear replicating viruses . This raises questions about where each DCL protein acts and why/how vd-siRNAs are mostly found in the cytoplasm [60] . We found strikingly contrasting effects of DCL2 and DCL4 in the plants antiviroid response . DCL2 is often described as acting in the shadow of DCL4 and is thought to have a role mainly in the absence of DCL4 to help in the restoration of its functions [19 , 20 , 61] . Both DCL2 and DCL4 have been known as important players in the antiviral response . Nevertheless , there have been increasing indications that DCL2 protein has a distinctive and possibly more effective role compared to DCL4 . It has been shown that even though both DCL2 and DCL4 are necessary for gene silencing , DCL4 mainly act in the production of primary siRNAs , whereas DCL2 in the production of secondary siRNAs [17 , 18] . The authors propose that this dissimilarity is due to affinity differences of the dsRNA by DCL4 compared to DCL2 protein [18] . In addition , a role of DCL2 in RNA decay has been proposed [62] . Collectively , these works suggest that the DCL4-mediated pathway can serve as a decoy to antagonize the more destructive DCL2-mediated pathway , protecting either endogenous mRNAs from undesirable clearance or viral RNAs from degradation [18 , 62] . In the present work we investigated vd-siRNA in infected WT and DCLi plants . In WT , DCL4 and DCL2 produced vd-siRNAs ( of 21 and 22nt class respectively ) , which are the main vd-siRNA classes produced upon viroid infection , followed by much smaller accumulation of 24nt DCL3 produced vd-siRNAs . Since deep sequencing studies have shown that vd-sRNAs of both polarities are found in more or less equal numbers [49 , 50] , it is likely that these vd-siRNAs are either produced during viroid replication acting on the ds-vdRNA ( as it has been proposed before [63] ) or are cleavage products of RDR-produced dsRNA substrates . The latter is more likely given the abundance of vd-siRNAs of all classes in infected cells . RDR6 seems to be an important candidate in this process , since its involvement has been shown before . However , the implication of another RDR protein , such as RDR2 or even RDR1 cannot be ruled out [45] . A strong increase of the 22nt class is observed in infected plants knocked down for DCL4 or DCL3-DCL4 , highlighting their possible importance correlated to DCL2 dicing activity , but also maybe more importantly to an anti-viroid AGO activity [30] . A milder yet important increase of the 24nt class is observed in DCL4i plants , suggesting a potential role for DCL3 in the absence of DCL4 . Furthermore , lines DCL2 . 11i and DCL2 . 41i , which differ in the level of DCL2 suppression , showed an increase in the 24nt class and a decrease in 21nt class , proportionally to DCL2 suppression . In addition , previous observations have shown that 24nt production is increased with the course of the infection , eventually becoming as abundant as 21 and 22nt vd-siRNA [64] . Taken together , the above evidence suggests an important role of DCL3 in anti-viroid response . The fate of the produced vd-siRNAs is probably affecting PSTVd levels . It has been shown that exogenous expression of A . thaliana AGO1 , AGO2 , AGO4 and AGO5 proteins in infected N . benthamiana plants decrease viroid levels . In addition , these AGOs bind 21 and 22nt vd-siRNAs . AGO4 and AGO5 additionally bind 24nt vd-siRNAs [47] . As described , a combination of DCL2-DCL3 knock-down leads to enhanced viroid infection whereas DCL4 reduction has the opposite effect . To integrate the results from this and other studies , we propose the following anti-viroid model ( presented in Fig 8 ) : In WT plants , PSTVd is targeted by DCL2 , DCL3 and DCL4 , producing mainly 21 and 22nt vd-siRNA , and a smaller portion of 24nt . The first two populations are preferentially loaded to AGO1 and AGO2 , whereas 24nt vd-siRNAs to AGO4 and AGO5 , driving plant anti-viroid defense probably through RDR proteins . The recent discovery of degradation products in PSTVd infected eggplants and to a less extent in infected S . lycopersicum and N . benthamiana supports this model [65] . The turnover is low compared to the very efficient replication rate and thus viroid infection is not significantly affected . Conversely , when DCL4 is suppressed , 22nt vd-siRNAs are loaded probably by AGO1/AGO2 and , together with the increased 24nt-mers loaded in AGO4 , they are responsible for a more efficient targeting of the PSTVd leading to a strong decrease of PSTVd levels . This is further supported by the observation that DCL2 products stimulate RDR6 synthesis of secondary siRNAs [18] . In conclusion , the DCL4 pathway seems to be the least efficient against Pospiviroids , since its activity leads to a less effective suppression of the viroid and infection is efficient . In a sense , DCL4 is ‘protecting’ the viroid from the more ‘devastating’ effect of DCL2 and DCL3 processing . For a long time , viroids ability to break plant resistance in spite of a functional silencing mechanism has been a conundrum . It has been suggested that vd-siRNA-mediated degradation was hindered due to viroids secondary structure [40] or through their localization to silencing free environments [66] . Although it is possible that viroid subcellular localization may aid viroid strategy in evading host defense , we believe that the present work highlights a novel important aspect in the survival strategy of viroids . Here we show that members of the Pospiviroidae family may have adapted in order to be primarily targeted and processed by DCL4 , rather than the more hostile combination of DCL2 and/or DCL3 . Even though they are processed by DCL4 , the viroid overcomes this cellular response to produce an efficient infection . Understanding co-evolution of viroid-plant mechanisms of survival remains an interesting challenge for next studies on viroid pathogenesis .
N . benthamiana plants which contain hairpins to decrease endogenous DCL1 , DCL2 , DCL3 and DCL4 transcripts were described in [46] . A plant that expressed a hairpin for both DCL2 and DCL4 has also been created ( DCL2/4 . 5i ) [46] . Experiments were conducted in F6 to F10 generation . Combinations were created by crosses , and all experiments conducted in crossed plants were in F1 generation . We have produced the following crosses: DCL1 . 13 ( x ) 2 . 11i , DCL2 . 41 ( x ) 1 . 13i , DCL1 . 13 ( x ) 3 . 10i , DCL3 . 10 ( x ) 1 . 13i , DCL1 . 13 ( x ) 3 . 1i , DCL1 . 9 ( x ) 3 . 1i DCL1 . 13 ( x ) 4 . 9i , DCL2 . 11 ( x ) 3 . 10i , DCL3 . 10 ( x ) 2 . 41i , DCL4 . 9 ( x ) 3 . 10i , DCL4 . 9 ( x ) 3 . 1i DCL3 . 10 ( x ) 2/4 . 5i , DCL2/4 . 5 ( x ) 3 . 10i DCL2/4 . 5 ( x ) 3 . 1i and DCL2/4 . 16 ( x ) 1 . 13i . Two different type of infections were performed . For infections by agroinfiltration , plants at the stage of 5 leaves were agroinfiltrated with either A . tumefaciens GV3101 strain carrying an infectious PSTVd dimer ( PSTVdNB-AJ634596 ) kindly provided by Dr . De Alba and Dr . Flores ( Institute for Cellular and Molecular Plant Biology—IBMCP ) or A . tumefasciens C58C1 strain containing plasmid pCdHSVd ( HSVdY09352 ) [54] . Samples were collected 3wpi . For mechanical infection using carborundum ( Prolabo , VWR ) , we either used infectious tissue from N . benthamiana containing TASVdKF484878 . 1 and PSTVdKF493732 . 1 , provided by the Institute for Agricultural and Fisheries Research—ILVO , Belgium [51] , or RNA . We have used 1μg of total RNA from PSTVdNB infected WT N . bentamiana plant ( S3 Fig ) . We have also used RNA from T3 in vitro transcription of plasmid EcoRI-pBKdNB provided by Dr . De Alba ( S3A and S6 Figs ) . Used RNA quantities as well as time of infection are indicated in each experiment . Young leaf samples were homogenized under liquid nitrogen and total RNA was extracted as previously described [46] . For large RNAs , five μg total RNA were separated in denaturing agarose gel ( 1 . 4% agarose , 0 . 7% formaldehyde ) and transferred to 0 . 45μm nylon membrane ( Whatman , GE healthcare ) . RNA ( - ) or ( + ) DIG labeled probes for PSTVd ( DIG RNA labelling mix , Roche Diagnostics ) were produced by either T7 transcription from a HindIII-pHa106 plasmid or by SP6 transcription from a EcoRI-pHa106 plasmid [67] . For the detection of HSVd viroid , DIG labeled in vitro transcription was produced either with T7 from Kpn1-pBdHSVd plasmid or with T3 from PstI-pBdHSVd . Hybridization was performed over night at 65°C and CDP-star ( Roche Diagnostics ) was used for the detection according to the manufacturer instructions . For small RNAs , 20μg of total RNAs were migrated into 17% polyacrylamide gel ( 38:2 ) and transferred to 0 . 2μm nylon membrane ( Whatman , GE healthcare ) . 100ng of a PSTVd PCR product produced by pHa106 plasmid with specific primers ( S4 Table ) was labeled with [α-32P]CTP using random priming reaction with Klenow ( Minotech ) . Hybridization was performed at 50°C as described before [46] . For TASVd , a PCR product with specific primers ( S4 Table ) was produced and used exactly as for PSTVd with hybridization temperature at 65°C . Leaves of plants at 3wpi were cut and placed on a 0 . 45μm nylon membrane ( Whatman , GE healthcare ) , with the upper side facing the membrane . A piece of Whatman paper was added at the other side of the leaf and , using a small rolling pin , leaves were pressed until the outline of the leaf was produced . The membrane was then used for DIG labeling as described above . 3μg of DNAseI-treated RNA were reverse transcribed with PrimeScript ( Takara ) using oligo-dT and random primers ( Invitrogen ) . Kapa SYBR Fast qPCR kit was used to perform qPCR ( Kapa Biosystems ) . All PCRs were carried out in a CFX CONNECTTM apparatus ( Biorad ) . Two reference genes ( L23 and FBOX ) were selected among different genes in order to have a p<0 . 05 using NormFinder and BestKeeper algorithm [68 , 69] . Analysis was performed using either qBASE or Pfaffl algorithm . Annealing temperatures as well as used primers are described in S4 Table . A custom Sureprint genome-wide G3 Gene Expression 4×180k microarray ( Agilent design ID 074128 ) was designed using the Agilent eArray platform ( Agilent Technologies ) based on the N . benthamiana genome annotation ( “Niben101” , 57140 transcripts , version from March 6 , 2015 , available at ftp://ftp . solgenomics . net/genomes/Nicotiana_benthamiana/annotation/Niben101/ ) and sequences of N . benthamiana RNA silencing ( RNAi ) genes reported by [10] ( 35 genes , available at http://sefapps02 . qut . edu . au/benWeb/subpages/downloads . php ) . For N . benthamiana RNAi genes ( 35 genes ) and N . benthamiana transcripts having a BLASTx hit ( E-value < E-10 ) with either A . thaliana RNAi protein sequences [9] or amino acid translations of N . benthamiana RNAi genes ( 407 transcripts in total ) , probe design aimed for four probes of 60 nt per gene-transcript with parameters set to “best probe methodology” and “3’bias” and six probes of 60 nt per gene-transcript with parameters set to “best probe distribution” and “without 3’ bias” . For the remainder of the N . benthamiana transcripts , probe design aimed for three probes of 60 nt per gene with parameters set to “best probe distribution” and “3’bias” . In the microarray design , we also included 3 probes ( “3’bias” , “best probe distribution” ) for the PSTVd genome sequence and 3 probes for its reverse complement . In total 173 , 491 probes were created and 98 . 5% of all N . benthamiana transcripts-genes ( 56310 sequences ) had at least three probes per transcript-gene , while only 263 transcripts had no probes . Finally , we also designed 8 probes per transcript ( “3’bias” , “best probe methodology” , ( S5 Table ) for 38 N . benthamiana housekeeping transcripts . These probes were randomly distributed in 9 copies per array and were used to measure intra-array reproducibility ( “replicate non-control probe group” ) . The array design was submitted to the National Center for Biotechnology Information ( NCBI ) under the Gene Expression Omnibus ( GEO ) -platform format ( GPL21946 ) . N . benthamiana WT plants infected or not with PSTVd for three weeks were used . Four biologically replicated RNA samples were obtained . Large RNAs were extracted as described earlier . Quantity and integrity was measured using an Agilent TapeStation system . RNA samples were labelled with cyanine dyes following the Low Input Quick Amp Labeling Kit ( Agilent Technologies ) , with 100 ng of total RNA as starting material . RNA samples from uninfected N . benthamiana plants were labeled with cy3 , while cy5-labelling was performed for RNA samples of PSTVd infected plants . Samples were hybridized to a custom-made Sureprint G3 4x180K array ( Agilent Technologies ( see above ) ) following the standard procedure of the Gene Expression Hybridization Kit ( Agilent Technologies ) . The following hybridization experiment was performed ( number of biological replicates is given between brackets ) : Cy5 labeled cRNA from WT plants infected with PSTVd versus Cy3 labeled RNA from WT plants ( 4 ) . After washing procedures ( Gene Expression Wash Buffer kit ( Agilent Technologies ) ) , the 4x180k slide was scanned by an Agilent high-resolution microarray scanner ( Agilent Technologies ) , raw data was extracted from the 4x180k slide using the GE2_107_Sep09 protocol of the Agilent Feature Extraction Software and subsequently transferred to limma for further processing and statistical analysis [70] . Based on the arrayQualityMetrics report [71] one array ( array 1_4 ) was considered as outlier , and data from this array was excluded from further analysis . Before creating the microarray ( MA ) files , data were processed by background correction using the ‘normexp’ method at offset 50 . Microarray data ( MA ) were then normalized within and between arrays by loess and Aquantile , respectively [72] . Using the normalized MA-object , differential expression was assessed by an empirical Bayes approach with cut-offs for the Benjamini–Hochberg FDR-corrected P-values and log2-converted FC [log2 ( FC ) ] at 0 . 05 and 1 respectively [73] . N . benthamiana gene expression data have been uploaded to the Gene Expression Omnibus with accession number GSE81923 . Artificial siRNA sequences were generated from the DCL4 . 9i hairpin sequence using a custom python script and a 21-nucleotide sliding window . These siRNA sequences ( S1 Table ) were used as query in a blastn search against the PSTVd genome with following blastn parameters ( word_size 7 , penalty -1 , gapopen 1 , gapextend 2 , evalue 1000 ) as in [74] . For the analysis of the northern blots , Quantity One 4 . 4 . 1 ( Biorad ) was used . Values were calculated and compared first to methylene blue values and then to the WT values . Statistical analysis was performed using GraphPad Prism 6 software ( GraphPad software Inc ) . | Viroids consist of a peculiar type of highly structured small circular RNAs , capable of infecting crop plants and ornamentals . They do not encode any protein , yet they manage to replicate , move through the plant and often cause severe symptoms . In order to achieve this , viroids hijack plant cellular machinery . In addition , they manage to overcome plant RNAi response , which is the major antiviral defense mechanism of plants . DCL proteins have a central role in the RNAi pathway . We have used Nicotiana benthamiana plants , an experimental host of some viroids , and produced plants suppressed for DCL proteins ( individually or in combination ) . By infecting DCL knockdown plants with Potato spindle tuber viroid we were able to identify which DCL proteins are mainly involved in the antiviroid response . We found that it is the combined activity of DCL2 and DCL3 pathways which most potently suppress viroid infectivity . In contrast , DCL4 , the main antiviral DCL , seemed to obscure the DCL2-DCL3 effect on viroid infectivity . This lead us to the hypothesis that viroids may have evolved to be primarily processed by DCL4 , as it seems to be the DCL protein with less detrimental effects on viroid infectivity . Our findings may aid understanding the complex interaction of viroids with the plant defense machinery . | [
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"el... | 2016 | Combined Activity of DCL2 and DCL3 Is Crucial in the Defense against Potato Spindle Tuber Viroid |
A number of incurable retinal diseases causing vision impairments derive from alterations in visual phototransduction . Unraveling the structural determinants of even monogenic retinal diseases would require network-centered approaches combined with atomistic simulations . The transducin G38D mutant associated with the Nougaret Congenital Night Blindness ( NCNB ) was thoroughly investigated by both mathematical modeling of visual phototransduction and atomistic simulations on the major targets of the mutational effect . Mathematical modeling , in line with electrophysiological recordings , indicates reduction of phosphodiesterase 6 ( PDE ) recognition and activation as the main determinants of the pathological phenotype . Sub-microsecond molecular dynamics ( MD ) simulations coupled with Functional Mode Analysis improve the resolution of information , showing that such impairment is likely due to disruption of the PDEγ binding cavity in transducin . Protein Structure Network analyses additionally suggest that the observed slight reduction of theRGS9-catalyzed GTPase activity of transducin depends on perturbed communication between RGS9 and GTP binding site . These findings provide insights into the structural fundamentals of abnormal functioning of visual phototransduction caused by a missense mutation in one component of the signaling network . This combination of network-centered modeling with atomistic simulations represents a paradigm for future studies aimed at thoroughly deciphering the structural determinants of genetic retinal diseases . Analogous approaches are suitable to unveil the mechanism of information transfer in any signaling network either in physiological or pathological conditions .
A number of incurable diseases in the visual system involve one or more components of the phototransduction signaling network ( Figure 1 ) . Visual phototransduction is the G protein-mediated process that generates a neuronal signal following light capture by visual pigments in photoreceptor cells ( rods and cones ) . A unique feature of rod cells , the vertebrate photoreceptors dedicated to dim light vision , is the capability to transduce signals from even single photons due to an extremely efficient amplification not paralleled by other signal transduction pathways [1] , [2] . The first event in scotopic vision is the absorption of a photon by rhodopsin ( R ) , the cornerstone of family A of the seven-transmembrane G protein coupled receptors ( GPCRs ) , which leads to the formation of the signaling active state ( R* ) [3] , [4] . The latter , in turn , catalyzes the exchange of bound GDP for GTP on the αβγ heterotrimeric G protein transducin ( Gt ) . The GTP-bound α subunit ( GαGTP ) dissociates from the βγ dimer thus stimulating the activation of phosphodiesterase 6 ( PDE ) , a tetramer made of two nearly identical α and β catalytic subunits and two identical γ subunits [5] . The binding of GαGTP to the γ subunit of PDE ( PDEγ ) releases its inhibitory constraint on the catalytic subunits , thus leading to the hydrolysis of guanosine 3′ , 5′-cyclic monophosphate ( cGMP ) , followed by a rapid closure of the cGMP-gated ionic channels in the outer membrane and a drop in the circulating current . The lowering in intracellular calcium concentration , associated with cell hyperpolarization ultimately signals the presence of light to the secondary neurons of retina . Signaling shutoff includes at least three calcium feedback mechanisms as well as the simultaneous deactivation of GαGTP and PDE . In this respect , the termination of PDE activation by GαGTP is achieved when the GTP-bound to Gα is hydrolyzed to GDP by the intrinsic GTPase activity of the protein . The latter process is significantly accelerated by a multiprotein complex containing the ninth member of the Regulators of G protein Signaling ( RGS ) family , hereafter indicated as RGS [6] . As a result of the GTPase Activating Protein ( GAP ) action of RGS , the GαGDP complex re-associates with the βγ dimer restoring the GαGDP-βγ heterotrimer ( i . e . Gt ) . Misfunctioning of any component of the phototransduction network causes more or less severe vision impairments . Such an example is the Nougaret form of dominant stationary night blindness ( Nougaret Congenital Night Blindness , NCNB ) caused by a missense mutation , G38D , found in the rod Gα of affected individuals [7]–[9] . Stationary night blindness is not associated with retinal degeneration and is characterized by the inability to see in the dark , whereas daytime vision is largely unaffected [7]–[9] . In vitro characterization showed that the aspartate substitution for G38 does not alter the interaction between Gα and Gβγ or activation of transducin by R* [9] . Furthermore , the mutant Gα is characterized by modestly reduced kcat value for the intrinsic ( ∼2 . 5-fold ) and RGS-catalyzed ( ∼5 fold ) GTP hydrolyses . In contrast , biochemical data showed that G38D is totally impaired in its ability to bind and activate PDE [9] , whereas suction electrode recordings revealed that homozygous GαGTPG38D−/− rods exhibit residual light responses , indicating that the mutation reduced but did not completely abolish effector function [8] . Functional consequences of substituting the homologous amino acid in other G proteins were found to inhibit GTPase activity and to prevent stimulation by GAP in Ras-p21 [10] , Gαi [11] , Gαz [12] , and Gαs [13] . Thus , a single-point mutation in Gα seems to elicit a multitude of effects not entirely clarified by in vitro and in vivo experiments and likely involving more than one component in the phototransduction signaling network . In this framework , to gain insights into the molecular bases of the NCNB disease , we integrated the information from in vitro/in vivo experiments with systems-based and atomistic modeling . The systems-based approach relied on a comprehensive quantitative model of phototransduction in rod cells that explicitly includes most of the molecular components of the cascade [14] ( Figure 1 ) . In this study , that model was extended to the NCNB pathological phenotype , thus highlighting those reactions and intermolecular interactions perturbed by the Gα mutation . The molecular systems involved in those reactions were subjected to atomistic Molecular Dynamics ( MD ) simulations and included: wild type and mutated GαGTP taken both in their isolated forms ( i . e . GαGTPWT and GαGTPG38D ) and in the ternary complex with RGS and PDEγ ( GαGTP-RGS-PDEγWT and GαGTP-RGS-PDEγG38D ) ( Figure 2 ) .
We presented a dynamical model of the phototransduction signaling network made up of ordinary differential equations , which describe the reactions and their kinetic parameters [14] . The working model used in this study includes also the dynamic scaffolding reactions between dark rhodopsin and Gt [15] . Herein , such model was further extended to describe the heterozygous ( GαGTPG38D+/− ) and homozygous ( GαGTPG38D−/− ) mutated conditions of GαGTPG38D . This was accomplished by introducing the mutated G protein as an explicit new molecule and adding all the relative reactions in the phototransduction cascade , which concerned the GαGTPWT+/+ status ( Table 1 , see Methods ) . The output of mathematical simulations ( i . e . change in photocurrent with respect to dark value , ΔJ ) was analyzed and compared with the photoresponses of rods from wild type and transgenic mice ( Figure 3A , 3B , and 3C ) . It is worth noting that , due to the significant difference in the species between in vitro ( i . e . mammals , Figure 3A , 3B , and 3C ) and in silico ( i . e . amphibian rods [14] , Figure 3D , 3E , and 3F ) experiments , the time scales of the photoresponses is different , thus allowing for semi-quantitative comparisons . The results obtained with our GαGTPWT+/+ model were in remarkable agreement with in vitro recordings on wild type cells ( Figure 3A and 3D ) . In order to fit the models onto the pathological NCNB condition , the rates of a number of reactions involving GαGTPG38D were systematically reduced by tuning the relative kinetic parameters in decreasing steps ( Table 1 , Figure 1 , see Methods ) . The reductions were combined into specific heterozygous and homozygous models . At the end , the best fit with electrophysiological recordings of the mutant cells was obtained by making changes in the following reactions: describing , respectively , a ) the binding of one molecule of GαGTP to one inactive PDE subunit , b ) activation of the GαGTP-PDE complex , and c ) binding of the RGS complex to a PDE tetramer with one active subunit . The relative changes in the parameters were 35000-fold reduction in kP1 and kP2 and 2-fold reduction in kRGS1 , with respect to the wild type value . Following such changes , heterozygous cells show a similar behavior to wild type cells under dim flash responses but elongated recovery under brighter flashes associated with slight loss in sensitivity ( i . e . a 40% brighter flash is required to generate a half-maximal response , Io; Figure 3G and Figure 5B in Moussaif et al . [8] and Table 2 ) . In contrast , homozygous cells show marked decrease in sensitivity to light ( 50-fold brighter Io , Table 2 ) and impaired response recoveries for all flash intensities . Same strengths of flashes delivered to the cells in simulations and electrophysiological recordings [8] result in quantitative differences concerning time scales and sensitivity of the photoresponses , likely due to the different species considered . The higher ΔJ elicited by the dimmest flash in computational experiments compared to electrophysiological recordings is exemplar in this respect ( Figure 3A and 3D ) . Differences between the cellular properties of mammals and amphibian rods include temperature and volume , which likely influence initial conditions and concentrations of the molecular species involved in signal transduction [5] . It is worth noting that , in the actual mathematical model , up to two GαGTP can bind , and in turn activate , either one of the catalytic subunits of PDE , hence leading to a 2∶1 stoichiometry . Nevertheless , as previously discussed [14] ( see also Table S4 therein ) , the 2∶1 GαGTP∶PDE complex is detectable only in the presence of light flashes with intensities in the order of 105 photons/µm2 and , even then , their presence is negligible . As a confirmation of those results , deep reductions of kP3 and kP4 ( regulating , respectively , the binding of the second molecule of GαGTP to GαGTP-PDE and the activation of both catalytic subunits of the PDE tetramer ) , in the background of any of the test models used in this study , did not elicit any change in the photoresponse ( Table 1 ) . We cannot , however , exclude that this was due to an inaccurate modeling of this part of the network , whose biochemical detail remains mainly unknown . For example , a finer treatment of the allosteric and regulatory mechanisms may be necessary to recover the role of the second PDE subunit [14] . This might also explain why , with the changes in kP1 , kP2 , and kRGS1 necessary to reproduce GαGTPG38D−/− photoresponses , loss in sensitivity and delay of the time to peak are more marked in simulated responses compared to in vitro ones ( 78- vs 47- and 2 . 4- vs 1 . 7-fold , respectively , see Table 2 ) . In line with the statements above , we relied on the fact that , in the present model , changes in kRGS1 and kRGS2 only affect the formation and activity of the 1∶1∶1 GαGTP-RGS-PDE complex . Noteworthy , as shown in Table 1 , kRGS2 is a rather coarse parameter , as it describes the RGS-catalyzed GTPase activity in both GαGTP-RGS-PDE and GαGTP-RGS-PDE-GαGTP complexes as well as disruption of these complexes . For this reason , we couldn't use the mathematical model to properly evaluate mutational effects on the GAP activity of RGS in the GαGTP-RGS-PDE complex . In summary , consistent with electrophysiological recordings [8] but not with earlier biochemical data [9] mathematical modeling highlights reduction of both PDE binding and activation as the major mutation-induced perturbations in the visual phototransduction signaling network . A marginal reduction in RGS binding helped as well in reproducing the electrophysiological phenotype . On these bases , insights into the structural determinants of such perturbations were searched by atomistic MD simulations targeting both wild type and mutated Gα in its isolated form and in ternary complex with both RGS and PDEγ . Atomistic simulations were firstly carried out on the isolated GαGTP in its wild type and mutated forms ( GαGTPWT and GαGTPG38D , respectively ) . The mutation site is the third position of the G box 1 ( i . e . G1:3 , see Figure 2 and its legend for description and visualization of the G protein regions as well as for explanation of the position-based numbering ) . Incidentally , the G boxes are five ultra-conserved regions of the Ras-like domain involved in nucleotide binding ( Figure 2 ) . Such mutated position is not involved in backbone-mediated H-bonding interactions with the nucleotide neither in the wild type nor in the mutant , ( Supplementary Figure 1A ( Figure S1A ) ) . The interaction pattern of the nucleotide remains almost unchanged in the two forms following MD simulations , as also indicated by the patterns of interaction energies between GTP and surrounding residues ( Figure S2 ) . Collectively , these data are consistent with the results of in vitro evidence that the mutation elicits a marginal effect on the intrinsic GTPase activity of the protein [8] , [9] . In contrast to lack of local structural effects , the G38D mutant turned out to affect the intrinsic dynamics of the protein . Indeed , the Cα-atom Root Mean Square Deviation ( RMSD ) of the mutant is higher than that of the wild type especially over the second half of MD simulation ( Figure S3 ) . As expected , Cα-atom fluctuations evaluated in terms of Root Mean Square Fluctuations ( RMSFs ) show peaks of flexibility in the loops connecting the elements of secondary structure , especially those in the α-helical domain . This effect is greater in the mutant than the wild type ( Figure 4 ) . In line with RMSFs , in the mutant form , selected portions of the protein show significant enhancements in their collective motions as inferred from the Principal Component Analysis ( PCA ) of the trajectories . These portions include linker1 , αB/αC loop , αC , αE , αF , inter-switch , and C-term of α3 ( Figure 4 ) . To investigate whether mutation-induced changes in intrinsic dynamics may affect Gα portions deputed to RGS and/or PDEγ recognition , we monitored the solvent accessibility of all the RGS and PDEγ recognition sites on Gα ( indicated , respectively , by orange and green stars in Figure 2 ) , finding more marked effects on the PDEγ sites , in particular Y254 ( in the α3/β5 loop ) . The latter is , indeed , exposed to the solvent in the wild type but buried in the mutant form where it is permanently involved in inter-helical interaction with F211 ( s2:11 ) , another PDEγ recognition site that becomes no longer available to PDEγ as well ( Figure 5 ) . In line with these observations , Functional Mode Analysis ( FMA , see Methods ) found that the Solvent Accessible Surface Area ( SASA ) of Y254 is correlated with the modes describing the essential subspace of Gα . Incidentally , the essential subspace ( ES ) is given by a variable number of eigenvectors whose associated eigenvalues account for 90% of the total variance of the Cα-atom displacements in a trajectory . The correlation is already present in the wild type but increases in the mutant form ( i . e . the correlation coefficients are 0 . 73 and 0 . 86 , respectively ) . Differences in functional modes between wild type and mutant amplify when considering only the first principal component ( PC1 ) ; in fact , the correlation remains still significant for the mutant ( i . e . 0 . 74 ) but it drops for the wild type ( i . e . 0 . 39 ) . Collectively , FMA is suggestive of a functional link between protein dynamics and structural environment of Y254 . Thus , mutation-induced burying of Y254 results in deformation of the swII/α3 cleft , which is the primary determinant in PDEγ binding . We also investigated mutational effects on the structural communication features of Gt by the Protein Structure Network ( PSN ) analysis , a product of graph theory applied to protein structures ( see Methods ) . The analysis searched for mutation-induced changes in network components ( e . g . nodes , hubs , links , shortest communication pathways , etc ) on the MD trajectories . The comparative analyses of the Protein Structure Graphs ( PSGs ) of wild type and mutated Gα revealed a slight reduction in number of nodes , hubs , and links in the G38D mutant compared to the wild type ( Table 3 ) . In contrast , the number of communication pathways and their average length increases in the mutant compared to the wild type . In line with this trend , the maximal , minimal , and average strengths reached by the totality of links in the paths tend to be higher in the mutant compared to the wild type . To infer a global and coarse view of mutation-induced changes in the communication pathways of Gα we drew global meta paths , i . e . assemblies of the most recurrent nodes and links in the pool of paths characterized by frequency ≥30% ( Figure 6A and 6B , see Methods ) . In this respect , whereas the wild type is characterized by nucleotide-mediated paths at the interface between Ras-like and α-helical domain , in the mutant form , inter-domain pathways are less frequent as also highlighted by the distribution of linked-node fragments ( Figures 6 and S4 ) . This inter-domain uncoupling may be in part related to the fact that , in the mutant , selected portions of the α-helical domain undergo increases in essential dynamics compared to the wild type ( Figure 4 ) . Noteworthy , this trend is also evident in the meta paths computed on the sub set of paths made by ≥50% of conserved amino acids ( Figure S5A and S5B ) . Differently from the wild type , in the mutant the most frequent nucleotide-involving pathways transverse essentially β1 and β3 rather than the swII/α3 interface , which is the primary PDEγ binding cleft ( Figure 6A and 6B ) . In summary , in spite of the lack of significant differences in the interaction pattern of GTP between wild type and mutated forms , GαGTPG38D is characterized by increased flexibility of the α-helical domain compared to wild type , which reflects on an apparent inter-domain uncoupling in terms of shortest communication pathways . In contrast , in the wild type , inter-domain pathways localized on the nucleotide binding site cover a significant part of the structural communication modes . Above all , the most significant effect of aspartate substitution for G38 is a structural perturbation in the swII/α3 cleft participating in the PDEγ binding site . A marker of such perturbations is the solvent accessibility of Y254 , which in the mutant becomes buried and no more available for PDEγ interaction , shielding also F211 ( s2:11 ) from effector binding . Atomistic MD simulations on monomeric GαGTP highlighted long-distance mutational effects on the Gα regions deputed to PDEγ recognition likely related to the reduced PDE binding and consequently activation inferred from both mathematical simulations and in vitro experiments [8] . In order to clarify ambiguities by in vitro experiments on the GAP activity of RGS towards the G38D mutant [8] , [9] , which couldn't be properly addressed by mathematical simulations , wild type and mutated Gα were simulated also in the context of the ternary complex with PDEγ and RGS . In this respect , MD simulations on the GαGTP-RGS-PDEγG38D ternary complex are justified by the fact that the mutated Gα holds a residual binding to PDEγ [8] . Comparing the dynamics and structural communication features of all the components of the ternary complex in the presence of either wild type or mutated Gα served to infer the effects of the G38D mutation on different structural aspects such as: a ) communication and interaction features of the nucleotide , b ) intrinsic dynamics of each component of the complex , and c ) inter-protein communication . In line with simulations on monomeric GαGTP , simulations on the ternary complex show that the interaction pattern of the nucleotide is substantially similar in wild type and mutated Gα ( Figure S1 and S2 ) , thus not providing any clue on mutational effects on the GAP activity of RGS . As for the intrinsic dynamics of the three proteins in the complex , differently from PDEγ , GαGTP and RGS are characterized by low mobility in terms of RMSDs , ( Figure S3 ) . The higher mobility of PDEγ is likely due to the poor intramolecular and intermolecular tertiary contacts made by such protein , which is a 42-amino acid fragment of a small subunit . In deep detail , as for GαGTP , the wild type and mutated forms do not differ significantly in terms of RMSDs or RMSFs; major differences concern only the swIII region , which fluctuates less in the mutant than in the wild type ( Figures S3 and S6 ) . In line with such behavior , the overlap between the ES of the two Gα forms in the context of the heterotrimer is quite high ( 0 . 90 ) , the essential motions of β2/β3 loop and swIII contributing to such differences ( Figure S6 ) . RGS shows low mobility as well , its intrinsic flexibility being comparable in the complexes with wild type and mutated Gα ( Figure S3 ) . In contrast , the intrinsic flexibility of PDEγ is higher in the complex with mutated GαGTP than in the complex with wild type GαGTP ( Figures S3 and S7 ) . This suggests that the pathogenic Gα mutation increases the intrinsic flexibility of PDEγ , which would imply increased instability of the PDEγ-GαGTP interface . According to the crystal structure of the heterotrimeric complex , RGS does not contribute directly to the active site by donating residues or through water-mediated interactions [16] . It is rather thought that RGS would increase the GTP hydrolysis rate by stabilization of the Gα switch regions in their transition state conformation and orientation of the critical Gα carbonyls used to position the nucleophilic water [16] , [17] . Thus , RGS action is likely due to inter-protein structural communication . On these bases , possible structural relations with the postulated mutation-induced reduction of the GAP activity of RGS were searched by the PSN analysis . Significant differences between the two simulated ternary complexes could be inferred from the analysis of the shortest communication pathways , which were almost halved in the mutated complex compared to the wild type ( Table 3 ) . Remarkably , more than 60% of the communication paths that characterize the wild type form hold the GTP-Q200 ( G3:5 ) -R:N364 fragment of linked nodes , which is completely absent in the mutant ( Figure S4 ) . The global meta path representation clearly shows that the most significant communication in the GαGTP-RGS-PDEγWT involves GTP , Q200 ( G3:5 ) , R:N364 , and E203 ( s2:3 ) ( i . e . the GTP-Q200 ( G3:5 ) -R:N364-E203 ( G3:8 ) meta fragment of linked nodes; Figure 6C and 6D ) . Remarkably , the GTP-Q200 ( G3:5 ) -R:N364 connection found in the wild type form is essential for the GAP activity of RGS [16] . Such connection is no longer present in the mutant . In line with path fragment distribution , the most representative nucleotide-mediated paths in the GαGTP-RGS-PDEγG38D complex are intra-Gα located ( Figure 6C , 6D , and S4 ) . These differences between wild type and mutant forms are strengthened by the meta paths computed on the sub set of paths made by ≥50% of conserved amino acids ( Figure S5 ) . Thus , nucleotide-mediated paths involving the RGS-Gα interface are few and characterized by the D38 ( G1:3 ) -Q200 ( G3:5 ) -R:N364 fragment of linked nodes ( Figure S4 ) . Another difference concerning the structural communication of wild type and mutated Gt is that , whereas for the wild type some ( 4% ) of the shortest pathways describe a communication between Gα and PDEγ , in the mutant form such communication could not be found , likely due to the increased flexibility of the effector subunit . In summary , atomistic simulations on the ternary complexes highlight a possible disturbing effect of the pathogenic mutation on the GAP activity of RGS . This would act , at least in part , by destabilizing the Q200 ( G3:5 ) -mediated communication between GTP and R:N364 . Finally , they strengthen the influence of the mutation on the G protein-effector interface , in line with electrophysiological recordings and mathematical simulations .
Mutations in any components of the visual phototransduction signaling network may cause more or less severe impairments in vision . Because of the complexity of such network , any alteration of one of the cascade components would lead to unpredictable and not easily determinable results . Thus , a monogenic disease such as NCNB , considered in this study , can result from perturbations not circumscribed to the mutated protein but involving also other members of the network . In this framework , modeling the effects of mutation by systems-based approaches serves to infer how the pathogenic signal propagates through the network and which molecular species are involved . When possible , the latter information is passed to atomistic simulations to gain insights into the structural determinants of the disease . In this study , we combined visual phototransduction modeling with atomistic simulations to thoroughly investigate the defect associated with the NCNB-causing G38D mutation of Gα . The mathematical model of visual phototransduction was able to reproduce the key features of the behaviors of heterozygous and homozygous rods typical of the NCNB disease . This could be possible upon reducing the constants governing: a ) the binding of GαGTP to PDE , b ) the activation of the catalytic activity of PDE , and c ) the binding of the RGS complex to a PDE tetramer with one active subunit . In fact , a strong reduction in PDE recognition and activation by GαGTP coupled to a two-fold reduction in the RGS binding constant was essential to reproduce the visual responses in Nougaret patients , while decreasing the intrinsic or RGS-catalyzed GTPase activities did not seem to have a significant effect . Thus , mathematical modeling emphasized the formation and activation of the GαGTP-PDE complex as the processes more significantly affected by the aspartate substitution for G38 in Gα , in line with electrophysiological recordings [8] . MD simulations on monomeric GαGTP suggest that such reduction in the PDEγ binding ability of mutated Gα is likely due to altered dynamics of the protein associated with changes in the architecture of the swII/α3 cavity , essential recognition point for PDEγ . A detrimental effect of the mutation on such cavity had been also postulated based upon crystallographic analyses [16] . We ended up independently with this conclusion by individuating also the main actors of this structural effect . In this framework , the Y254 position seems to be particularly sensible to the concerted motions of the protein triggered by the mutation . Indeed , deformation of the swII/α3 cavity results in the burying of Y254 in the α3/β5 loop , preventing both the Y254 itself and F211 ( s2:11 ) from being available to PDEγ . We also speculated that mutation-induced reductions of the catalytic activity of PDE may derive from the formation of an improperly assembled GαGTP-PDEγ complex . Along this line , also in the ternary complex with RGS and PDEγ the mutation exerts a long distance effect on the effector binding site resulting in increase in the intrinsic flexibility of PDEγ and lack of communication pathways at the Gα-PDEγ interface . Another clear effect of the Gα mutation is the incapacity to form a stable Q200 ( G3:5 ) -mediated communication between the nucleotide and N364 of RGS . Such communication is instead present in the wild type and is necessary for the GAP action of RGS [16] . In conclusion , the main structural effects of the G38D mutation turned out to be deformation of the primary effector binding site in monomeric Gα and enhanced flexibility of PDEγ in the ternary complex , which would destabilize the Gα-effector interface . Collectively these effects are connectable with the impaired effector recognition and activation shown by in vitro experiments . Finally , the pathogenic mutation of Gα seems to affect the communication between RGS and nucleotide essential for the GAP activity , thus suggesting that the observed slight reduction of the RGS-catalyzed GTPase activity is a matter of perturbed inter-protein communication . This study extends the dynamic model of visual phototransduction to the pathological NCNB phenotype and provides insights at the atomic level into the structural bases of the disease . This is an example of a thorough computational investigation employing different scales of description , an approach which should be pursued to unveil the structural determinants of genetic retinal diseases . Analogous approaches are suitable to infer the mechanisms of information transfer in any signaling network either in physiological or pathological conditions .
The mathematical model of rod phototransduction ( BioModels ID: BIOMD0000000326 ) , employed in the present work for numerical simulations of the phototransduction cascade includes 91 reactions , 71 molecular species , and 63 parameters and was previously developed and validated over a wide range of experimental data on normal and genetically modified rods [14] . The rate of change of the molecular species are monitored by calculating , at given time steps , their rates of production and consumption [14] . The original model was recently modified to account for the postulated R-Gt precoupling in the dark [15] , leading to the inclusion of the following reactions , which describe: a ) the dynamic formation and b ) dissociation of dark R-Gt complexes: The parameters were calculated as relative kinetic constants , following the relationships and taken from Surface Plasmon Resonance ( SPR ) experiments [15] . This led to a ≈20% of Gt to be dynamically precoupled to Rin the dark , consistent with published data [15] . In this study , we built a dynamic model able to describe the GαGTPG38D+/− and GαGTPG38D−/− mutated conditions of GαGTPG38D by introducing the mutated G protein as an explicit new molecule and adding all the relative reactions in the phototransduction cascade , which concerned the GαGTPWT+/+ status ( Table 1 ) . The concentration of the new species ( GαGTPG38D ) was obtained by using its ratio to the normal concentration of Gα in wild type cells taken from expression levels in transgenic mice for the mutation [8] . Therefore , the total level of Gα ( 25% of which is GαGTPG38D ) in heterozygous cells is the same as in wild type rods , while only 35% of the native levels are found in homozygous cells for the mutation . The levels of all the other proteins were kept unchanged and the mutation was assumed to have no effect on rhodopsin-GαGTPG38D binding [8] , [9] . In order to fit the models onto the pathological NCNB condition , the rates of a number of reactions involving GαGTPG38D were systematically reduced by tuning the relative kinetic parameters in decreasing steps ( Table 1 , Figure 1 ) . The reductions were combined into specific heterozygous and homozygous models . In detail , these reactions refer to: a ) intrinsic GTPase activity of GαGTP , b ) binding of GαGTP to PDE and resulting PDE activation , and c ) shut-off of the photoresponse by RGS binding and RGS-catalyzed GTP hydrolysis . These reactions are highlighted in Figure 1 and listed in Table 1 . In line with in vitro electrophysiological recordings on rods from transgenic mice , we monitored the following features of the photocurrent elicited by increasingly stronger flashes of light ( Figure 3 ) : a ) rate of the activation phase , b ) light sensitivity , and c ) speed of the recovery phase . In detail , a ) the rate of the activation phase was evaluated as the time needed to reach the maximum value of ΔJ after the delivery of the flash; b ) light sensitivity was taken as the normalized response amplitude as a function of flash strength ( Figure 3G and Figure 5B in Moussaif et al . [8] ) ; and c ) the speed of the recovery phase was evaluated as the time needed for the photocurrent to reestablish its dark value ( ΔJ = 0 ) after a flash . In some cases , the parameters had to be eventually set equal to 0 , while in other cases also more limited reductions led to modification of the output ( Table 1 ) . All the numerical simulations were carried out by means of Matlab , within the SBTOOLBOX2 framework [18] ( http://www . sbtoolbox2 . org/main . php ) as already described [14] . The following PDB structures were selected as inputs of MD simulations: GαGTPWT ( PDB code: 1TND [19] , residue range 27–342 ) , which is the GTP-bound form of Gα , and the GαGTP-RGS-PDEγWT ternary complex ( PDB code: 1FQJ [16] ) involving GαGTP ( amino acids from 28 to 344 ) , RGS ( i . e . the RGS domain of RGS9 , amino acids 286 to 418 ) and the 42-amino acid C-terminal fragment of PDEγ ( residues 46–87 ) . Input structure setup required a number of modifications in the original crystal structures . As for 1TND , the original GTPγS analogue was replaced by GTP , as recently reported [20] . As for 1FQJ , the original Gα was indeed a chimera identical to Gαt except for residues 216–294 which were replaced with the corresponding homologous region of Gαi1 ( residues 220–298 ) . The Gαi1 sequence was therefore mutated into the corresponding one in bovine Gαt . Moreover , the original GDP-AlF4− was replaced by GTP . All the simulated systems hold the Mg2+ ion together with the coordinating water molecules . The native G protein in the GαGTPWT and GαGTP-RGS-PDEγWT complexes was finally subjected to the substitution of aspartate for G38 , in order to produce the pathogenic mutant ( i . e . GαGTPG38D and GαGTP-RGS-PDEγG38D ) . In silico mutagenesis was performed by means of the Quanta software ( www . accelrys . com ) . MD simulations on the four systems , GαGTPWT , GαGTP-RGS-PDEγWT , GαGTPG38D and GαGTP-RGS-PDEγG38D , were carried out by using the GROMACS4 simulation package [21] with the AMBER03 all atoms force field [22] , [23] . The TIP3P water model was employed to describe the solvent . AMBER parameters to describe the GDP and GTP molecules were taken from the literature [24] . Depending on the dimensions of the systems , a variable number of Na+ and Cl− ions were placed at optimum electrostatic positions in order to neutralize the system . In detail , the systems included: 63740 total atoms for GαGTPWT ( 19512 water molecules , 48 Na+ and 38 Cl− ions ) ; 63743 total atoms for GαGTPG38D ( 19511 water molecules , 49 Na+ and 38 Cl− ions ) ; 81906 total atoms for GαGTP-RGS-PDEγWT ( 24609 water molecules , 60 Na+ and 50 Cl− ions ) ; 81882 total atoms for GαGTP-RGS-PDEγG38D ( 24599 water molecules , 61 Na+ and 50 Cl− ions ) . Periodic Boundary Conditions ( PBC ) were applied by using an octahedric box as a unit cell , imposing a minimum distance of 12 Å between the solute and the box boundaries . MD simulation setup is the same as the one recently employed to simulate a number of Ras GTPases [20] . All the input crystallographic structures were subjected to energy minimization keeping restricted the positions of the main chain atoms , the nucleotide , the Mg2+ cation and the coordinating water molecules . The systems were then equilibrated at 300 K for 4 ns of backbone restricted MD simulations . The Particle Mesh Ewald ( PME ) method was employed to compute the electrostatic interactions . Short range repulsive and attractive interactions were computed by using a Lennard-Jones potential with a cutoff of 10 Å . The LINCS algorithm [25] was used to constrain all bond lengths except those in water molecules , allowing for an integration time step of 2 fs through the leap-frog algorithm . The v-rescale thermostat [26] was employed to keep the system at a constant temperature of 300 K , by using a coupling constant ( τt ) of 0 . 1 ps . The pressure of the system was kept fixed at 1 atm , using the Berendsen weak coupling algorithm [27] with a coupling constant ( τp ) of 1 ps . The pre-equilibrated systems were then subjected to 100 ns of unrestrained isothermal-isobaric ( T = 300K , P = 1 atm ) MD simulations . MD trajectories were subjected to a variety of analyses aimed at inferring a ) the time series of a number of structural descriptors such as the SASA , b ) the intrinsic flexibility of the systems ( e . g . RMSD , RMSF , and Essential Dynamics ( ED ) or PCA ) , and c ) potential correlations between structural descriptors and essential motions ( i . e . FMA ) . As for ED , resting on the assumption that the major collective modes of fluctuation dominate the functional dynamics of a system , information on such global motions can be inferred from the atomic fluctuations by means of the PCA . The latter allows the decomposition of the atomic fluctuations into a set of principal components ( eigenvectors of the covariance matrix of positional fluctuations ) that describe the concerted motions of these atoms ( e . g . the Cα-atoms ) . The technique is based on the diagonalization of such covariance matrix producing a set of eigenvector and eigenvalue pairs in which the eigenvector and the eigenvalue describe , respectively , direction and amplitude of the concerted atomic motion ( a mode ) . The atomic components of an eigenvector provide a quantitative measure of the participation of each Cα-atom to the collective motion described by the corresponding eigenvector . The subspace spanned by the major modes of collective fluctuations is accordingly often referred to as “essential subspace ( ES ) ” . In the same framework , FMA is a technique to identify collective atomic motions related to a specific protein function . Given a large set of structures of one protein , for example from an MD trajectory , the method detects a mode that is maximally correlated to an arbitrary quantity of interest . Except for FMA , which was carried out by using the GROMACS package , all these MD analyses were performed by means of the Wordom software [28] . As for PCA , the covariance matrices were built on the Cα-atoms of the isolated MD trajectories . FMA [29] was carried out by using the Linear Mutual Information ( LMI ) estimator [30] . The structural descriptor correlated with the Principal Components ( PCs ) was the SASA calculated on selected Gα amino acids involved in Gα-PDEγ and Gα-RGS interactions . A number of PCs were probed . Non bonded interaction energies for the nucleotide were monitored every 20 ps along the trajectory with GROMACS4 . The structural communication ( i . e . PSGs and shortest communication paths ) in the four simulated systems was inferred by means of the graph-based approach proposed by Vishveshwara and coworkers [31] and defined as Protein Structure Network ( PSN ) , that was recently implemented in the Wordom software [28] . With this approach , the dynamics of the system is taken into account in terms of occurrence of network components along the trajectory and of correlated motions [32]–[34] . A graph is defined by a set of points ( nodes ) and connections ( edges ) between them . In a PSG , each amino acid is represented as a node and these nodes are connected by edges based on the strength of non-covalent interactions between nodes [31] . The strength of interaction between residues i and j ( Iij ) is evaluated as a percentage given by the following equation:where Iij is the percentage interaction between residues i and j; nij is the number of atom-atom pairs between the side chains of residues i and j within a distance cutoff ( 4 . 5 Å ) ; Ni and Nj are normalization factors for residue types i and j , which take into account the differences in size of the side chains of the residue types and their propensity to make the maximum number of contacts with other amino acid residues in protein structures . The normalization factors for the 20 amino acids were taken from the work by Kannan and Vishveshwara [35]; the normalization values for GTP ( derived from 3 heterotrimeric G proteins ) , Mg2+ ( based on 4 heterotrimeric G proteins to properly describe the coordination of such ion in the system under study ) and water ( based on 5 structures , comprising G proteins and rhodopsin ) were 361 . 3 , 23 . 8 and 27 . 0 , respectively . Thus , Iij are calculated for all nodes , excluding i ± n , where n is a given neighbor cutoff of 3 . An interaction strength cutoff Imin is then chosen and any residue pair ij for which Iij≥Imin is considered to be interacting and hence is connected in the PSG . As previously demonstrated [31] , the optimal Imin is the one at which the size of the largest cluster of nodes at Imin 0% halves . Incidentally a node cluster is a set of connected nodes in a graph . We approximated the Imin value to the second decimal place . The final Imin cutoffs were: 3 . 24% for GαGTPWT , 3 . 23% for GαGTPG38D , 3 . 52% for GαGTP-RGS-PDEγWT , and 3 . 58% for GαGTP-RGS-PDEγG38D . To build the PSG , only the edges present in at least 30% of the trajectory frames were used . Those nodes involved in at least four links are named as hubs . Possible shortest communication paths or optimal paths ( OPs ) in the different GαGTP binary complexes as well as between wild type and mutated GαGTP and the other two proteins in the GαGTP-RGS-PDEγ ternary complex were searched . All residue pairs except those at sequence distance ±5 were considered as path extremities ( i . e . the first and last amino acids in the path ) . In detail , the number of intra-Gα amino acid pairs was 49770 for the wild type and mutated forms of isolated GαGTP and 50090 for the wild type and mutated forms of GαGTP in the ternary complex with RGS and PDEγ . Finally , 56350 amino acid pairs were considered to search all possible communication paths between wild type and mutated GαGTP and the two proteins in the ternary complex . Vishveshwara and co-workers implemented also the search for suboptimal paths ( SOPs ) , alternate routes of communication , which can be computed by systematically removing all interactions of an OP node ( s ) , thus forcing the traversal of a less than optimal path [36] . Since our path searches , different from those by Vishveshwara and co-workers [36]–[38] , are not limited to a few selected node pairs but systematically consider almost all node pairs in a system , the additional search for SOPs would have been too costly in terms of computer time with the high risk to produce more noise than relevant information . For this reason our approach is dedicated exclusively to OPs . The search for the shortest path ( s ) between pairs of nodes as implemented in the PSN-path module of Wordom relies on the Dijkstra's algorithm [39] . They were searched by combining PSN data with cross-correlation of atomic motions calculated by using the LMI method . Following calculation of the PSG and of correlated motions ( by means of the LMI method [30] ) , for each frame , the procedure to search for the shortest path ( s ) between each residue pair consists of a ) searching for the shortest path ( s ) between each selected amino acid pair based upon the stable PSN connectivities , and b ) selecting the shortest path ( s ) that contains at least one residue correlated ( i . e . , with a LMI cross-correlation ≥0 . 3 ) with either one of the two extremities . All the shortest paths that pass the filter of correlated motions are subjected to a further filter based upon path frequency , i . e . number of frames containing the selected path divided by the total number of frames in the trajectory . The relative number of amino acids holding correlated motions with either one of the two extremities is quantified by the correlation score , i . e . the ratio between the number of correlated amino acids and the path length; the latter excludes the two extremities . Outcome of this stage is the total pool of paths for the system under study . Meta paths made of the most recurrent nodes and links in the path pool ( i . e . global meta paths ) are worth computing to infer a coarse/global picture of the structural communication in the considered system . In this study , meta paths were computed on the ensemble of paths with frequency ≥30% . For each link a recurrence score r is calculated using the following equation:where l is a given link present in the considered set of shortest paths , pij is the total number of shortest paths from node i to node j and pij ( l ) is the total number of shortest paths from node i to node j that include link l . Finally , only those links with a recurrence score ≥30% of the highest score are used in the meta path representation . | Incurable retinal diseases causing vision impairments may be due to spontaneous mutations in one component of the visual phototransduction signaling network . Such alterations include the transducin single point mutation G38D associated with the Nougaret Congenital Night Blindness ( NCNB ) . We combined a systems biology approach with atomistic simulations to gain insights into the structural fundamentals of the NCNB disease . Consistent with in vitro evidence , mathematical modeling suggests reduced effector recognition and activation as the main determinants of the pathological phenotype . Sub-microsecond molecular dynamics simulations improve the resolution of information , suggesting that such impairment is likely due to disruption of the effector binding cavity . Atomistic simulations also suggest that the observed slight reduction of the RGS9-catalyzed GTPase activity of transducin depends on perturbed inter-protein communication involving the nucleotide . The study highlighted manifold effects of a single point pathogenic mutation , thus paving the way for analogous studies towards a thorough understanding of the structural determinants of genetic retinal diseases . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2013 | Network and Atomistic Simulations Unveil the Structural Determinants of Mutations Linked to Retinal Diseases |
Chronic Chagas disease cardiomyopathy ( CCC ) is an inflammatory dilated cardiomyopathy with a worse prognosis than other cardiomyopathies . CCC occurs in 30 % of individuals infected with Trypanosoma cruzi , endemic in Latin America . Heart failure is associated with impaired energy metabolism , which may be correlated to contractile dysfunction . We thus analyzed the myocardial gene and protein expression , as well as activity , of key mitochondrial enzymes related to ATP production , in myocardial samples of end-stage CCC , idiopathic dilated ( IDC ) and ischemic ( IC ) cardiomyopathies . Myocardium homogenates from CCC ( N = 5 ) , IC ( N = 5 ) and IDC ( N = 5 ) patients , as well as from heart donors ( N = 5 ) were analyzed for protein and mRNA expression of mitochondrial creatine kinase ( CKMit ) and muscular creatine kinase ( CKM ) and ATP synthase subunits aplha and beta by immunoblotting and by real-time RT-PCR . Total myocardial CK activity was also assessed . Protein levels of CKM and CK activity were reduced in all three cardiomyopathy groups . However , total CK activity , as well as ATP synthase alpha chain protein levels , were significantly lower in CCC samples than IC and IDC samples . CCC myocardium displayed selective reduction of protein levels and activity of enzymes crucial for maintaining cytoplasmic ATP levels . The selective impairment of the CK system may be associated to the loss of inotropic reserve observed in CCC . Reduction of ATP synthase alpha levels is consistent with a decrease in myocardial ATP generation through oxidative phosphorylation . Together , these results suggest that the energetic deficit is more intense in the myocardium of CCC patients than in the other tested dilated cardiomyopathies .
Chagas disease is a significant cause of morbidity and mortality in Central and South America , affecting about 13 million people [1] . The disease is caused by infection with the intracellular protozoan parasite Trypanosoma cruzi . About 30 % of infected patients develop chronic Chagas disease cardiomyopathy ( CCC ) , an inflammatory cardiomyopathy that occurs decades after the initial infection . One-third of CCC patients further progress to a particularly aggressive , life-threatening dilated cardiomyopathy; CCC is a major indication of heart failure in Latin America [2] , [3] . Clinical progression , length of survival and overall prognosis are significantly worse in CCC patients when compared to patients with dilated cardiomyopathy of non-inflammatory etiology , like idiopathic dilated or ischemic cardiomyopathies ( IDC or IC , respectively ) [4]–[7] . Due to migration from endemic countries , an estimated 300 , 000 people with Chagas disease are living in the USA , where a significant number of cases of CCC are expected per year [8] . The pathogenesis of CCC is unclear , and multiple mechanisms have been proposed ( Reviewed in [9] ) . The most characteristic histopathological lesions in cardiac patients with CCC are consistent with inflammation and a myocardial remodeling process: T cell/macrophage-rich myocarditis , hypertrophy , and fibrosis with cardiomyocyte damage [10] , [11] . The local cytokine production profile shows a T1-type response , with interferon-gamma-induced chemokines [12]–[15] . As the currently licensed anti-T . cruzi drugs may not be effective in preventing the progression of heart lesions of CCC [16] , treatment is only supportive . In patients with refractory heart failure , the only available treatment is heart transplantation [17] . The absence of alternative treatment for CCC is a consequence of limited knowledge about the pathogenesis . Energy metabolism imbalances have been reported in dilated cardiomyopathies and heart failure [18] . Since the heart consumes more energy than any other organ , impairments in energy production could lead to a mechanical failure of the heart , and disturbances in electrical conduction [18] . Mitochondrial oxidative phosphorylation is essential for the production of energy for cardiac function . This system comprises the oxidative phosphorylation complex , which includes the electron transport chain ( complexes I to IV ) and the F1FO ATP synthase ( complex V ) . In aerobic tissues , most ATP is synthesized via the mitochondrial F1FO ATP synthase complex . Studies have shown that certain components of oxidative phosphorylation may be impaired in heart failure [18] . Patients with IDC or IC show a reduced myocardial activity of complex III when compared to controls [19] . In IDC patients , a decreased activity of myocardial cytochrome c oxidase ( complex IV ) was observed [20] . With the progression of dilated cardiomyopathy , higher levels of spatial and functional heterogeneity within mitochondrial populations are observed , indicative of mitochondrial damage [21] . It has been reported that mitochondrial damage leads to loss of mitochondrial function , impairing energy production and cell physiology , and to the enhancement of pathologic function , producing oxidative- , calcium- , apoptosis-mediated myocyte injury [22] . Creatine kinases ( CK ) are also key enzymes of energy metabolism , which connect mitochondrial ATP-producing and cytosolic ATP-consuming process , and are thus of central importance to the cellular energy homeostasis [23] . This system acts as an energy buffer , in which mitochondrial creatine kinase ( CKMit ) catalyzes the transfer of high energy phosphate bond from ATP to creatine to form phosphocreatine and ADP . Phosphocreatine , a molecule smaller than ATP , diffuses rapidly from the mitochondria to the myofibrils , where myofibrillar creatine kinase ( MM , MB , BB dimers , formed by CKM , the muscular isoform , and CKB , the brain isoform ) catalyzes ATP production from phosphocreatine , generating free creatine , which diffuses back into mitochondria [18] , [23] . Impaired ATP transfer and utilization may limit contractile function by means of a decrease in the average cytoplasmic ATP concentration [18] . Most of the components of the CK system are down-regulated in heart failure , with levels of creatine , phosphocreatine , CKMit and CKM all significantly reduced in animal models and in humans [24] , [25] . CK deficiency in isolated hearts may cause a decline of over 70 % in ATP delivery to myofibrils , leading to a blunted contractile reserve [26] . Proteomic profiling of myocardium from CCC patients revealed that 27 % of identified proteins belong to energy metabolism pathways [27] . Using gene expression profiling , our group found differential expression of a significant number of genes involved in oxidative phosphorylation and lipid catabolism in myocardial samples from CCC patients , but not in samples from patients with dilated cardiomyopathy , when compared to samples from subjects without cardiomyopathy [15] . Genetic profiling studies showed that hearts of T . cruzi-infected mice have shown a decreased expression of oxidative phosphorylation enzymes [28] . Likewise , biochemical and histochemical analysis revealed a reduced activity of the respiratory chain complexes in hearts of T . cruzi-infected mice [29] . Proteomic analysis of myocardial samples from acutely T . cruzi-infected Syrian hamsters showed an up-regulation of the energy metabolism proteins glutamate oxaloacetate transaminase 1 and pyruvate dehydrogenase β , that may be associated with a high ATP demand after T . cruzi infection [30] . Inflammatory cytokines , which are present in the CCC myocardium and induce local signaling [12]–[15] , have been reported to alter the energy metabolism . IFN-gamma was shown to inhibit the mitochondrial oxidative metabolism [31] and increase the rate of cardiac ATP depletion in cardiomyocytes [32] . Additionally , studies with cultured human skeletal muscle cells demonstrated that IFN-gamma treatment could inhibit CK activity [33] . Thus , the evidence is consistent with the hypothesis that the myocardium of patients with CCC could present an impaired energy metabolism . In order to test this hypothesis , we compared the protein and mRNA expression of CKM , CKMit , and the alpha and beta subunits of the catalytic F1 domain of ATP synthase complex ( ATPα and ATPβ , respectively ) in myocardial samples from CCC , with that of dilated cardiomyopathies of other etiologies , and healthy hearts from organ donors . We also measured total creatine kinase enzymatic activity in the same sample groups .
Myocardial samples were obtained from left ventricular-free wall heart tissue from end-stage heart failure patients at the moment of heart transplantation . Samples from 5 CCC ( at least 2 positive results in 3 independent anti-T . cruzi serology tests –ELISA immunoassay , indirect immunofluorescence assay and indirect hemagglutination test ) , 5 IDC ( dilated cardiomyopathy in the absence of ischemic disease , negative serology for Chagas disease ) and 5 coronary angiography-proven IC patients were collected ( Table 1 ) . Left ventricular free wall samples were also obtained from healthy hearts of organ donors , which were not used for transplantation for technical reasons . The protocol was approved by the Institutional Review Board of the University of São Paulo School of Medicine and written informed consent was obtained from the patients . Samples were cleared from pericardium and fat , quickly frozen in liquid nitrogen and stored at −70°C . Protein homogenates were obtained using lysing solution ( 1∶10 w/v ) containing 7 mol/L urea , 10 mmol/L Tris , 5 mmol/L magnesium acetate and 4 % CHAPS , pH 8 . 0 , by mechanical homogenization ( PowerGen , Fisher Scientific ) . For experiments measuring the creatine kinase enzyme activity , 20 mg of tissue was lysed in solution ( 1∶20 w/v ) containing 0 . 32 mol/L sucrose , 10 mmol/L HEPES and 1 mmol/L EDTA , pH 7 . 4 , by mechanical homogenization . The homogenate was then sonicated for three cycles of 10 s each to 10 Watts ( 60 Dismembrator Sonic , Fisher Scientific ) , centrifuged at 12 , 000 g for 30 min . Supernatants were collected and stored at −70°C . Protein quantification was performed with the Bradford method ( BioRad ) . The samples from myocardial tissue ( left ventricular free wall ) were fixed in buffered formalin solution ( pH 7 . 2 ) , embedded in paraffin , and cut into 5 µm sections . Sections were stained with hematoxylin-eosin ( H&E ) and picrosirius red . Extracts of myocardial samples containing 30 µg of protein were heated for 5 min at 95°C , and subjected to one-dimensional electrophoresis ( SDS-PAGE ) using 12 . 5 % polyacrylamide gel and the vertical electrophoresis system Ruby SE600 ( GE Healthcare ) . After electrophoresis , proteins were transferred from gel to a nitrocellulose membrane using the TE Semi-Dry Transfer Unit ( GE Healthcare ) . The nitrocellulose membranes were incubated with monoclonal antibodies to proteins involved in energy metabolism: anti- F1FO ATP synthase alpha ( ATPα ) and anti- F1FO ATP synthase beta ( ATPβ ) ( Molecular Probes ) , anti-mitochondrial creatine kinase ( CKMit ) and anti- muscle creatine kinase ( CKM ) ( Santa Cruz ) and polyclonal anti- glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) ( R & D Systems ) . Each membrane was subjected to incubation with compatible secondary antibodies conjugated with peroxidase , developed using ECL Plus Western Blotting Detection Reagents ( GE Healthcare ) and detection using X-ray equipment . Analysis of densitometry was performed using the program ImageQuant TL ( GE Healthcare ) . Total RNA from left ventricle samples was isolated using the RNeasy Fibrous tissue kit ( Quiagen ) . Contaminating DNA was removed by treatment with RNase-free DNase I . cDNA was obtained from 5 µg total RNA using Super-script II™ reverse transcriptase ( Invitrogen ) . mRNA expression was analyzed by real-time quantitative reverse transcriptase ( RT ) -PCR with SYBR Green I PCR Master Mix ( Applied Biosystems ) and 250 nM of sense and anti-sense primers using the ABI Prism 7500 Real Time PCR System ( Applied Biosystems ) . The following primers were designed using Primer Express software version 3 . 0 ( Applied Biosystems ) : GAPDH ( M33197 ) : ( F ) 5′-TGGTCTCCTCTGACTTCAACA-3′ , ( R ) 5′-AGCCAAATTCGTTGTCATACC-3′; ATPα ( NM_001001937 ) : ( F ) 5′-TCTTCAAAAGACTGGGACTGCTGA-3′ , ( R ) 5′-AAGACACGCCCAGTTTCTTCAAG-3′; ATPβ ( NM_001686 ) : ( F ) 5′-GCCCAGCATTTGGGTGAGA-3′ , ( R ) 5′-GATTGGTGCACCAGAATCCAGT-3′; CKM ( NM_001824 ) : ( F ) 5′-GCTCTCTGTGGAAGCTCTCAACA-3′ , ( R ) 5′-GATGAGCTGCTGCTGCTCCT-3′; CKMit ( NM_001825 ) : ( F ) 5′-TGACGAGGAGTCCTATGAGGTGTT-3′ , ( R ) 5′-AGATCCGTTGTGTGCTTCATCAC-3′ . After every PCR , an amplicon melting point curve was obtained . This yielded a single peak with the expected temperature provided by Primer Express software , confirming the specificity of the PCR . GAPDH mRNA expression was used for normalization . The amount of mRNA in the left ventricles samples was calculated using the 2-ΔCt method [34] . The enzymatic activity measurements of CK in the myocardial samples were performed using the CK-NAC kit ( Doles ) . Basically , this method is a kinetic system where CK catalyzes the transphosphorylation reaction of ADP to ATP . A series of coupled enzymatic reactions produce NADH in a concentration directly proportional to the enzymatic activity of CK in the sample . The analyses were performed using a UV/Vis U-2001 spectrophotometer ( Hitachi ) , monitoring the increase in absorbance of NADH per minute at the wavelength of 340 nm at 37°C , using a thermostatic bath ( MultiTemp III , GE Healthcare ) . The measurement of enzymatic activity is given in international units ( U ) ; one unit of CK is the amount of enzyme that oxidizes 1 µmol/L of NADH per minute . Values were normalized by the amount of protein present in the sample . All statistical analyses were performed with GraphPad Prism 4 . 0 software ( GraphPad Software ) . Descriptive statistics are given as average and standard deviation . The non-parametric Newman-Keuls test was used for comparison between the groups . P-values <0 . 05 were considered as statistically significant .
While myocardial sections from all 3 cardiomyopathy groups displayed cardiomyocyte hypertrophy and fibrosis upon histopatholological analysis , myocarditis associated with a predominant lymphocytic infiltration was only observed among CCC heart lesions ( Figure 1 , Table 1 ) . No significant differences were found in age , ejection fraction ( EF ) or left ventricular diastolic diameter ( LVDD ) among the three cardiomyopathy groups . Figures 2 A and B show the differential protein expression of the ATP-synthase , subunits alpha ( ATPα ) and beta ( ATPβ ) , respectively . Representative immunoblots are depicted in Figure S1 . The ATPα was 18 % less expressed in CCC myocardium when compared to myocardial samples from individuals without cardiomyopathies ( p<0 . 01 ) . In contrast , IC myocardium showed an increase of 25 % ( p<0 . 001 ) in ATPα when compared to control samples , while in IDC there was no significant reduction of ATPα levels ( 5 % , p = ns ) in comparison to the control group . In the comparison between cardiomyopathy groups , ATPα levels in CCC were 34 % lower than in IC myocardium ( p<0 . 01 ) ; and 13 % lower than those found in IDC myocardium ( p<0 . 05 ) . There was no significant decrease of ATPβ in CCC when compared to control samples ( 9 % , p = ns ) . However , we observed increased expression of ATPβ in IC and IDC myocardium when compared to the control group [32 % ( p<0 . 001 ) and 10 % ( p<0 . 05 ) , respectively] . In the comparison between cardiomyopathy groups , we found that CCC myocardium samples express significantly less ATPβ than IC or IDC myocardium [31 % ( p<0 . 001 ) and 17 % ( p<0 . 01 ) , respectively] . We have also detected significant protein expression differences among enzymes of the creatine kinase system . The expression of CKM ( Figure 2C ) was decreased in samples from patients with CCC ( 33 % , p<0 . 01 ) , IDC ( 23 % , p<0 . 05 ) and IC ( 24 % , p<0 . 05 ) when compared to the control group . Of note , in the comparison between cardiomyopathy groups , the average CKM expression was most decreased among CCC patients ( 13 % and 12 % reduction when compared to IDC and IC , respectively ) , although the difference was not statistically significant . The protein expression of CKMit ( Figure 2D ) was decreased in samples from patients with CCC and IDC , when compared to the control group ( 16 % and 4 % , respectively ) , but the differences failed to achieve statistical significance . In contrast , samples from patients with IC showed an increased expression of CKMit when compared to the control group ( 13 %; p = ns ) . In the comparison between cardiomyopathy groups , the protein levels of CKMit were decreased in samples from patients with CCC when compared to samples from patients with IC and IDC [26 % ( p<0 . 01 ) and 13 % ( p = ns ) , respectively] . We also analyzed the mRNA expression of the enzymes tested above , ATPα , ATPβ , CKM and CKMit . Figure 3 shows the values of relative quantification of mRNA expression of these enzymes . We found that mRNA expression of CKM was decreased in samples from patients with CCC and IDC when compared to samples from subjects without cardiomyopathy [78 % ( p<0 . 05 ) and 69 % ( p<0 . 05 ) ] . Also , the mRNA expression of CKMit was reduced in CCC samples ( 75 % , p<0 . 05 ) when compared to samples from subjects without cardiomyopathy . The average expression of mRNAs for all 4 enzymes was reduced in the 3 cardiomyopathies when compared to control samples , and samples from CCC patients showed the lowest expression levels . However , due to high interindividual variation of expression within each group , most of the comparisons were not statistically significant . Interestingly , mRNA levels of ATPα and ATPβ were not increased in samples from patients with IC in comparison to samples from control group , as seen in the analysis of protein expression . In the comparison between cardiomyopathy groups , none of the enzymes analyzed showed significant differences in mRNA expression for ATPα and ATPβ . In order to evaluate whether the differential protein expression of enzymes of the creatine kinase system observed above had an impact on myocardial enzyme activity , we compared total creatine kinase activity among groups ( Figure 4 ) . The creatine kinase enzyme activity was reduced in samples from patients with CCC ( 59 % , p<0 . 01 ) , IDC ( 35 % , p<0 . 01 ) and IC ( 31 % , p<0 . 01 ) when compared to samples from the control group ( Figure 4 ) . Of interest , creatine kinase enzyme activity was significantly reduced in myocardial samples from patients with CCC , when compared to IDC and IC patients [37 % ( p<0 . 05 ) and 41 % ( p<0 . 05 ) , respectively] .
In this paper , we observed a reduced expression of CKM , a key enzyme in myocardial energetic metabolism , in several dilated cardiomyopathies . Most importantly , we found that CCC myocardium shows significantly reduced levels of protein expression of ATP synthase alpha subunit and total creatine kinase enzyme activity , when compared to IDC or IC . We observed that the myocardial creatine kinase system shows impaired function in patients with all forms of cardiomyopathy . The reduced myocardial protein expression of CKM , observed in all cardiomyopathy groups , was reflected in the reduced total creatine kinase activity . This has been previously described for IDC and IC [35] . The reduced protein expression of CKM was probably due to transcriptional regulation at least in the cases of CCC and IDC , since CKM mRNA expression was also significantly reduced in samples from patients of such groups , when compared to samples from individuals without cardiomyopathy . Animals genetically deficient in CKM develop myocardial hypertrophy and left ventricular dilation [36] , as well as higher susceptibility to mitochondrial damage and cardiac disturbances in calcium homeostasis after ischemia and reperfusion [37] . Myocardial ATP flux through the CK system was shown to be reduced by 50 % in patients with heart failure [38] . Since the CK reaction is the prime source of the myocardial energy reserve , the deficit in ATP flux through CK may contribute to the pathogenesis of heart failure [39] . The finding that average CKM and CKMit levels from CCC samples were the lowest among all groups , and that total CK activity in CCC samples was significantly lower than that of the other cardiomyopathy groups , indicates that CCC patients may show a stronger functional impairment in the CK system than other etiologies of dilated cardiomyopathy . It is likely that the significantly reduced myocardial expression of CKMit , observed in comparison to IC , - and to a lesser degree also in comparison to control group - may have contributed to the reduction in the total CK activity observed in CCC samples . Regarding the discrepancy between the significantly reduced CKMit mRNA levels and the less prominent reduction of CKMit protein levels observed in CCC , it could be due to an increased stability of this protein . The loss of CK activity in isolated hearts has been reported to cause a decline in ATP delivery to myofibrils , leading to a blunted contractile reserve [26] . Reduced energy reserve via creatine kinase , as indicated by reduced phosphocreatine/ATP ratios , limits cardiac performance during metabolic stress conditions [40] . Significantly , CCC patients have been reported to display an impaired myocardial contractile response to dobutamine [41] . It is thus possible that this reduced contractile reserve is a consequence of the significant derangement in CK activity reported here in CCC myocardium . A correlation has been reported between decreased total CK activity and LV dysfunction [42] . However , samples from CCC patients studied here showed lower total CK activity than those of IDC or IC patients , despite the fact that LV dysfunction status was similar in CCC , IC and IDC patients . This may indicate that there are disease-specific factors that induce a stronger reduction in CK activity in CCC , when compared to non-inflammatory cardiomyopathies . While the creatine kinase system may buffer transient changes in ATP levels , the rate of oxidative ATP synthesis must be closely matched to the rate of consumption . Most myocardial ATP is generated through the mitochondrial oxidative phosphorylation ( complex I-V ) . In our study , we also found changes in the protein expression of ATPα and ATPβ ( belonging to the F1 subunit of ATP synthase complex - complex V ) . The finding that ATPα was only reduced in CCC myocardium , but not in IC and IDC , indicates that these patients could be at a greater impairment in cardiac ATP supply , as suggested by studies in animal models of heart failure [43] . However , in our study , patients with IC showed elevated protein levels of ATPα and ATPβ , and patients with IDC showed elevated levels of ATPβ when compared to the myocardium of subjects without cardiomyopathy . Significantly , a study showed an increase in mRNA of oxidative phosphorylation components in chronic ischemia due to severe atherosclerosis [44] . In vivo measurements in normal hearts subjected to adrenergic stress have shown that ATP production by oxidative phosphorylation increases with the demand , while ATP production by the CK system remains unchanged [39] . Authors thus suggest that the ratio of ATP production by the CK system to oxidative phosphorylation decreases upon demand; in addition , the ratio may be even lower in resting hearts from heart failure patients , due to a decreased CK flux [39] . Since a reduced CK flux may be one of the most prominent metabolic abnormalities in heart failure [39] , the findings of selectively reduced CK activity - and perhaps also oxidative phosphorylation activity - may suggest that energy production in CCC myocardium can be especially restricted in situations of increased demand . Inflammation associated to the significant lymphocytic infiltrate may play an important role in multiple steps of CCC pathogenesis . Inflammatory cytokines such as IFN-gamma and TNF-alpha , abundantly produced in the inflammatory milieu of CCC heart tissue , are known to induce gene expression changes in cardiomyocytes [12] , [45] , [46] , and may directly influence energy metabolism . It has been shown that in vitro treatment with IFN-gamma inhibited the oxidative metabolism [31] , and increased the rate of ATP depletion in cardiomyocytes [32] . Additionally , studies with cultured human skeletal muscle cells demonstrated that IFN-gamma treatment could inhibit the CK activity [33] . In summary , we reported that CK activity and ATPα levels are significantly reduced in CCC myocardium when compared to IDC and IC samples . If confirmed by studies with a higher number of samples , one could hypothesize that these findings could contribute to the contractile dysfunction , loss of inotropic reserve and worse outcome of CCC when compared to cardiomyopathies of non-inflammatory etiology . In vivo analysis of CK flux rate and ATP synthesis though oxidative phosphorylation may allow further validation of the present findings . | Chronic Chagas disease cardiomyopathy ( CCC ) affects millions in endemic areas and is presenting in growing numbers in the USA and European countries due to migration currents . Clinical progression , length of survival and overall prognosis are significantly worse in CCC patients when compared to patients with dilated cardiomyopathy of non-inflammatory etiology . Impairment of energy metabolism seems to play a role in heart failure due to cardiomyopathies . Herein , we have analyzed energy metabolism enzymes in myocardium samples of CCC patients comparing to other non-inflammatory cardiomyopathies . We found that myocardial tissue from CCC patients displays a significant reduction of both myocardial protein levels of ATP synthase alpha and creatine kinase enzyme activity , in comparison to control heart samples , as well as idiopathic dilated cardiomyopathy and ischemic cardiomyopathy . Our results suggest that CCC myocardium displays a selective energetic deficit , which may play a role in the reduced heart function observed in such patients . | [
"Abstract",
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] | [
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"bioenergetics",
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"... | 2011 | Selective Decrease of Components of the Creatine Kinase System and ATP Synthase Complex in Chronic Chagas Disease Cardiomyopathy |
For most organisms , chromosome segregation during meiosis relies on deliberate induction of DNA double-strand breaks ( DSBs ) and repair of a subset of these DSBs as inter-homolog crossovers ( COs ) . However , timing and levels of DSB formation must be tightly controlled to avoid jeopardizing genome integrity . Here we identify the DSB-2 protein , which is required for efficient DSB formation during C . elegans meiosis but is dispensable for later steps of meiotic recombination . DSB-2 localizes to chromatin during the time of DSB formation , and its disappearance coincides with a decline in RAD-51 foci marking early recombination intermediates and precedes appearance of COSA-1 foci marking CO-designated sites . These and other data suggest that DSB-2 and its paralog DSB-1 promote competence for DSB formation . Further , immunofluorescence analyses of wild-type gonads and various meiotic mutants reveal that association of DSB-2 with chromatin is coordinated with multiple distinct aspects of the meiotic program , including the phosphorylation state of nuclear envelope protein SUN-1 and dependence on RAD-50 to load the RAD-51 recombinase at DSB sites . Moreover , association of DSB-2 with chromatin is prolonged in mutants impaired for either DSB formation or formation of downstream CO intermediates . These and other data suggest that association of DSB-2 with chromatin is an indicator of competence for DSB formation , and that cells respond to a deficit of CO-competent recombination intermediates by prolonging the DSB-competent state . In the context of this model , we propose that formation of sufficient CO-competent intermediates engages a negative feedback response that leads to cessation of DSB formation as part of a major coordinated transition in meiotic prophase progression . The proposed negative feedback regulation of DSB formation simultaneously ( 1 ) ensures that sufficient DSBs are made to guarantee CO formation and ( 2 ) prevents excessive DSB levels that could have deleterious effects .
For most diploid organisms , the formation of haploid gametes relies on crossover ( CO ) recombination between homologous chromosomes for accurate chromosome segregation . Recombination is initiated during meiotic prophase by the programmed induction of DNA double strand breaks ( DSBs ) , catalyzed by the evolutionarily conserved topoisomerase-like protein Spo11 [1] . A subset of these DSBs are repaired by a specialized meiotic DSB repair pathway that uses the homolog as a recombination partner and generates intermediates that can be resolved as COs . This specialized repair is completed during the pachytene stage of meiotic prophase , in the context of meiosis-specific chromosome organization in which homologs are paired and connected along their axes by a structure known as the synaptonemal complex ( SC ) . By the last stage of meiotic prophase ( diakinesis ) , the SC has disassembled , and chromosomes have further condensed and reorganized to reveal CO-dependent structures called chiasmata , which connect homologous chromosomes and allow them to orient and segregate to opposite poles at the meiosis I division [2] . DSB formation must be tightly regulated to ensure successful meiosis: cells must both turn on DSB formation to achieve inter-homolog COs , but also turn off DSB formation to allow repair and subsequent chromosome re-organization in preparation for the meiotic divisions . Thus , DSB formation and repair must be coordinated with other aspects of meiotic chromosome dynamics . In addition , cells must make enough DSBs to guarantee one CO per chromosome pair , but too many DSBs could lead to unrepaired DNA damage and compromise genomic integrity . While Spo11 catalyzes DSB formation , little is known about how Spo11 activity is regulated and how the timing and number of DSBs are controlled . Several proteins besides Spo11 are required for meiotic DSB formation in various systems , although their mode ( s ) of action are not well understood [3] , [4] , [5] . The highly conserved Rad50/Mre11 complex is required for DSB formation in some systems but not in others , and even in an organism where it is normally required ( C . elegans ) , Spo11-dependent DSBs can form independently of Rad50/Mre11 in some contexts [6] , [7] . Further , many of the known DSB-promoting proteins are not well conserved at the sequence level , showing rapid divergence even among closely related species [4] . In C . elegans , the chromatin-associated proteins HIM-17 , XND-1 , and HIM-5 have been implicated in promoting normal levels and/or timing of DSB formation , particularly on the X chromosomes [8] , [9] , [10] . These proteins localize to chromatin throughout the germ line and are proposed to exert their effects by modulating the chromatin environment to affect accessibility of the DSB machinery . However , the localization of these proteins is not limited to the time of DSB formation , suggesting that other factors must control when the DSB machinery is active . In the current work , we identify the C . elegans DSB-2 protein ( encoded by dsb-2 , member of new gene class dsb for DNA double-strand break factor ) as a novel factor required specifically to promote the DSB step of meiotic recombination . We show that DSB-2 localizes to chromatin in meiotic prophase germ cells , and that the timing of its appearance and disappearance corresponds to the time window during which DSBs are formed . These and other data implicate DSB-2 in regulating the timing of competence for DSB formation by SPO-11 . Further , we find that the presence of DSB-2 on chromatin is regulated coordinately with multiple distinct aspects of the meiotic program , including specialized meiotic DSB repair features and the phosphorylation state of nuclear envelope protein SUN-1 . Thus , we propose that disappearance of DSB-2 reflects loss of competence for DSB formation , which occurs as part of a major coordinated transition in meiotic prophase progression . Moreover , our data suggest the existence of a regulatory network wherein germ cells can detect the presence or absence of downstream CO-eligible recombination intermediates . In the context of this model , successful formation of monitored intermediates would trigger removal of DSB-2 ( and other factors ) from chromatin and consequent shut-down of DSB formation , whereas a deficit of relevant intermediates would elicit a delay in DSB-2 removal ( and in other aspects of meiotic progression ) . We propose that the negative feedback property inherent in such a regulatory network provides a means to ensure that sufficient DSBs are made to guarantee CO formation , while at the same time protecting the chromosomes against formation of excessive levels of DSBs that could jeopardize genomic integrity .
The dsb-2 ( me96 ) allele was isolated following EMS mutagenesis in a screen for meiotic abnormalities visible in oocytes at diakinesis , the last stage of meiotic prophase ( see Materials and Methods ) . Whereas WT oocyte nuclei consistently exhibit 6 pairs of homologous chromosomes attached by chiasmata ( bivalents ) , oocyte nuclei in the dsb-2 ( me96 ) mutant exhibit a variable number of unattached ( achiasmate ) chromosomes ( univalents ) , indicating a defect in chiasma formation ( Figure 1A ) resulting from an underlying defect in CO formation ( below ) . The me96 mutation was mapped to a 126 kb interval on chromosome II , and RNAi against F26H11 . 6 ( a gene in the candidate interval ) phenocopied the me96 mutant ( Materials and Methods ) . Sequencing revealed a T-to-A transversion in F26H11 . 6 in the me96 mutant; as this mutation results in an early stop at codon 14 ( TTA = >TAA ) of F26H11 . 6 ( 280 codons total ) , me96 is presumed to be a null allele . A second , independently-isolated , dsb-2 allele ( me97 ) contains a premature stop at codon 168 in the same gene , further confirming the identity of F26H11 . 6 as dsb-2 . Homology searches revealed an F26H11 . 6 paralog in C . elegans ( F08G5 . 1 ) , and genes encoding orthologs of both proteins were found in other Caenorhabditis species ( briggsae , remanei and japonica ) but were not identified in other organisms ( Figures S1 and S2 ) . Based on independent analyses implicating both genes in meiotic double-strand break formation [11] , F08G5 . 1 and F26H11 . 6 were designated as dsb-1 and dsb-2 , respectively . Multiple sequence alignment of this highly diverged protein family shows two readily alignable regions corresponding to residues 1–103 and 195–251 of the C . elegans F26H11 . 6/DSB-2 protein , each containing several conserved sites ( Figure S1 ) . These two regions are connected by a variable segment that in each protein contains an S/T Q cluster domain [12] , a feature suggesting that these proteins are potential targets for phosphorylation by the ATM/ATR family of protein kinases . The number of achiasmate chromosomes detected in diakinesis-stage oocytes of dsb-2 mutant hermaphrodites increases with maternal age , indicating a worsening of phenotype over time ( Figure 1B ) . Whereas adult dsb-2 ( me96 ) hermaphrodites fixed one day after the L4 larval stage ( 24 hours post-L4 ) had an average of 8 . 5 DAPI-stained bodies at diakinesis ( reflecting a mixture of bivalents and univalents ) , 48 hour post-L4 hermaphrodites had an average of 11 . 1 DAPI bodies ( indicating that nearly all chromosome pairs lacked chiasmata ) . Further , as lack of chiasmata connecting homologs results in mis-segregation of chromosomes , both the frequency of inviable embryos ( reflecting autosomal aneuploidy ) and the frequency of males ( XO , reflecting X chromosome mis-segregation ) produced by dsb-2 ( me96 ) hermaphrodites likewise increased with maternal age ( Figure 1C ) : frequencies rose from 27% dead embryos and 6% males on day 1 of egg-laying to 89% dead embryos and 29% males on day 3 . Age dependence of the dsb-2 mutant phenotype was also observed for the dsb-2 ( me97 ) allele , using GFP::COSA-1 as a cytological marker of crossover ( CO ) sites ( Figure 1D ) . During wild-type meiosis , GFP::COSA-1 localizes to 6 foci per nucleus during the late pachytene and diplotene stages , marking the single CO/emerging chiasma on each homolog pair [13] . Whereas 6 GFP::COSA-1 foci were consistently observed in late pachytene nuclei of control worms regardless of maternal age , the number of GFP::COSA-1 foci was substantially reduced in dsb-2 ( me97 ) worms at 24 hours post-L4 and further declined by 48 hours post-L4 . The age effect in dsb-2 mutants is not caused by persistence of maternal gene product in the germ line , as it was observed in homozygous mutant worms derived from either heterozygous parents or homozygous mutant parents ( where no maternal product should be present ) . In addition , the age effect is evident at both standard ( 20°C ) , and elevated ( 25°C ) growth temperatures . Together , our data indicate that the function of DSB-2 is required throughout reproductive life to generate normal levels of COs and chiasmata , and becomes increasingly important for meiotic success in germ cell nuclei that enter the meiotic program at progressively later times . This implies that changes must occur as the worms age that render crossing over and chiasma formation increasingly sensitive to the loss of DSB-2 protein . Successful chiasma formation requires pairing of homologous chromosomes , assembly of the synaptonemal complex ( SC ) , and CO recombination between the homologs . Homolog pairing and SC assembly are not dependent on initiation or progression of recombination during C . elegans meiosis [14] , facilitating investigation of potential involvement of DSB-2 in these events . To this end , we conducted immunofluorescence analyses on germ lines dissected from dsb-2 worms at 48 hours post-L4 , when the CO/chiasma deficit is severe . Several lines of evidence indicate that the lack of chiasmata in dsb-2 mutants is due to a defect in the initiation of meiotic recombination . First , dsb-2 mutant worms are proficient for pairing of the X chromosomes , as immunofluorescence of pachytene nuclei showed a single focus of HIM-8 , a protein that binds a specific region of the X chromosome known as the pairing center [15] , [16] ( Figure 2A ) . Second , dsb-2 mutants are proficient for assembly of the SC , as immunostaining revealed proper loading of HIM-3 ( an SC lateral element component ) and SYP-1 ( an SC central region component ) [17] , [18] along the lengths of aligned homologs ( Figure 2B ) . Proficiency for pairing and synapsis suggests that dsb-2 mutants are deficient in the process of meiotic recombination per se . Meiotic recombination is initiated by formation of DNA double-strand breaks ( DSBs ) by the SPO-11 protein [14] , [19] , followed by processing of these DSBs to enable loading of the DNA-strand exchange protein RAD-51 , which can be detected as foci from zygotene to mid-pachytene stages in WT germlines [20] , [21] . dsb-2 germ lines display greatly reduced levels of RAD-51 foci , with most nuclei having no foci ( Figure 2C , D ) , suggesting either that fewer DSBs are made or that loading of RAD-51 is impaired . However , the dsb-2 mutant is proficient for loading of RAD-51 when DSBs are induced by gamma-irradiation , as seen by the presence of RAD-51 foci in germline nuclei fixed 1 hour post-irradiation ( Figure 2C ) . Furthermore , irradiation bypasses the requirement for DSB-2 and restores chiasma formation ( Figure 2E ) . It was previously shown that in C . elegans , providing DSBs by irradiation rescues chiasma formation in the spo-11 mutant , which lacks the enzyme responsible for making programmed DSBs [14] , [22] . The same effect was seen upon irradiation of dsb-2 mutant worms , demonstrating that the chiasma defect in dsb-2 worms is a result of a defect in SPO-11-induced DSB formation . In both dsb-2 worms and age-matched spo-11 worms , 1 kRad of irradiation resulted in efficient restoration of chiasmata in diakinesis-stage oocytes examined 18 hours post-irradiation ( Figure 2E ) . Thus DSB-2 is a novel protein required for robust meiotic DSB formation . Immunofluorescence experiments using an antibody against the DSB-2 protein ( Materials and Methods ) showed that DSB-2 localizes to chromatin in germ cell nuclei from meiotic entry to mid-pachytene ( Figure 3A ) . DSB-2 staining is first detected in the transition zone ( TZ; corresponds to leptotene and zygotene stages , when pairing and SC assembly occur ) , and is strongest overall in early pachytene , where it localizes to chromatin in an uneven pattern , showing a few bright patches per nucleus as well as fainter stretches/foci associated with most of the chromatin . Towards mid-pachytene , the bright patches diminish and the chromatin signal fades and then disappears from most nuclei . However , in a subset of nuclei in the mid/late pachytene region , DSB-2 staining becomes brighter , with bright stretches/foci along most of the chromatin; a few of these “outlier” brightly-staining nuclei are present in later pachytene and likely represent nuclei destined for apoptosis ( see Discussion ) . Apart from the outlier nuclei , the “DSB-2-positive” region of the germ line corresponds to nuclei at the stages in which DSB formation is presumed to occur [8] , [21] . Indeed , co-immunostaining experiments showed that RAD-51 foci ( marking DSB-dependent recombination intermediates ) appear in nuclei shortly after DSB-2 staining appears on chromatin upon meiotic entry , and the RAD-51 foci disappear shortly after DSB-2 is no longer present on chromatin in mid-pachytene nuclei ( Figure 3C ) . Previous work has demonstrated that germ cell nuclei at later stages of meiotic prophase are proficient to load RAD-51 when DSBs are introduced by irradiation [6] . Thus , the disappearance of RAD-51 foci in the endogenous case likely indicates that DSBs are no longer being formed and that existing DSBs have progressed to subsequent stages of repair . Indeed , we observe that COSA-1 foci marking designated CO intermediates appear in nuclei only after the removal of DSB-2 from chromatin ( Figure 3D ) . In addition , the “outlier” bright-staining DSB-2 nuclei in late pachytene contain high levels of RAD-51 foci and lack COSA-1 foci , suggesting these nuclei are arrested in their progression and may be triggering a checkpoint response . Thus , the close correspondence between the zone where DSB-2 localizes on chromatin and the zone where RAD-51 foci are detected is not only consistent with the demonstrated role for DSB-2 in promoting DSB formation , but further suggests that loss of DSB-2 coincides with loss of competence for DSB formation and progression to a subsequent stage of DSB repair . We used immunofluorescence analyses to investigate the relationships between DSB-2 and other meiotic factors that act at the DSB formation step . Figure 4 shows the relationship between DSB-2 and its paralog DSB-1 , which was independently implicated in DSB formation [11] . Nuclear localization of DSB-1 and DSB-2 is detected in the same region of the gonad , and their staining patterns on chromatin have a similar appearance ( Figure 4A ) . However , the relative intensity patterns of the two proteins differ during meiotic progression . Within the gonad , DSB-1 signal is detected on nuclei slightly before DSB-2 and has a stronger intensity early on , which then declines as nuclei progress through pachytene ( except for the outlier nuclei ) ; DSB-2 signal is weaker early on and peaks in intensity later than DSB-1 before eventually declining . Both proteins disappear from nuclei at the same time , and both localize to the same outlier nuclei . Within each nucleus , the intensity patterns on chromatin are also different , such that the DSB-1 and DSB-2 signals partially overlap but do not match each other ( Fig . 4A inset ) . Whereas DSB-2 localization is abolished in dsb-1 mutant germ lines [11] , some DSB-1 protein is present on chromatin in the dsb-2 mutant ( Figure 4B , C ) . DSB-1 staining in dsb-2 young adult germ lines ( 12 hours post-L4 ) appears comparable to age-matched wild-type controls despite that fact that RAD-51 foci are already substantially diminished by this stage; this indicates that the presence of DSB-1 on chromatin is not sufficient to promote efficient DSB formation in the absence of DSB-2 . Further , the association of residual DSB-1 protein in the dsb-2 mutant appears to change with age , as DSB-1 staining in older dsb-2 germ lines ( 48 hours post-L4 ) is typically fainter and declines and disappears sooner than in WT . Together these data suggest that DSB-2 may be required to augment the DSB-promoting activity of DSB-1 , possibly by affecting the nature of its association with chromatin , and that the reliance on DSB-2 for this augmentation becomes more acute with increasing age . We further showed that DSB-2 localizes to chromatin independently of DSB formation , indicating that DSB-2 localization is not a consequence of DSB formation . Specifically , in spo-11 mutants , which lack endogenous DSBs , DSB-2 is detected on chromosomes in transition zone and pachytene nuclei , and the overall appearance of the staining within nuclei is similar to that in WT nuclei . However , DSB-2 association with chromatin extends further into late pachytene , suggesting that endogenous DSB formation affects timing of DSB-2 removal ( Figure 5A; see below ) . Finally , we assessed DSB-2 localization in germ lines lacking HIM-17 , a THAP-domain containing protein that associates with germline chromatin and is required for normal levels of meiotic DSB formation [8] . In him-17 mutant germ lines , DSB-2 is detected on chromatin in nuclei from transition zone to late pachytene ( Figure 5B ) , but the DSB-2 signal has an altered appearance within the nuclei: the bright patches characteristic of DSB-2 localization in WT germ cells are not observed , and DSB-2 instead displays only the fainter , more uniform distribution ( Figure 5C ) . Thus , improper localization of DSB-2 may contribute to the observed defect in DSB formation in him-17 mutants . Taken together , these data suggest that association of DSB-2 and DSB-1 with chromatin is required to regulate competence for DSB formation by SPO-11 . The distribution of DSB-2 positive nuclei within the germ line is similar to that reported for nuclei exhibiting phosphorylation of serine-8 of nuclear envelope ( NE ) protein SUN-1[23] . SUN-1 is a part of a conserved protein complex that spans the NE and mediates attachment of the chromosomes to the cytoskeletal motility machinery [24] , [25] . Although the SUN-1 protein is present throughout the germ line , SUN-1 S8P is detected only in a subset of nuclei during meiotic prophase [23]: SUN-1 S8P appears abruptly at the onset of meiotic prophase , with TZ nuclei exhibiting both bright SUN-1 S8P patches , corresponding to the chromosome attachment points that mediate chromosome movement , and a diffuse SUN-1 S8P staining throughout the NE; in early pachytene , the patches dissipate ( except for one ) , but the diffuse NE staining persists , weakening until it disappears around mid-pachytene; however , a few outlier nuclei maintain SUN-1 S8P staining in later pachytene ( Figure 3A and B ) [26] . Co-staining experiments revealed that DSB-2 and SUN-1 S8P tend to be detected in the same nuclei ( Figure 3A ) . The relative intensity patterns are different , with SUN-1 S8P exhibiting a much stronger signal in the TZ , and showing generally weaker signal towards mid-pachytene when compared with DSB-2 ( Figure 3A ) . Most outlier nuclei are bright for both marks , but some are bright only for one of the marks and weak for the other . Nevertheless , the correlation is striking , suggesting that these two features ( presence of DSB-2 on chromatin and of SUN-1 S8P on the NE ) may be co-regulated . In support of this hypothesis , we found that DSB-2 localization depends on the CHK-2 protein kinase . CHK-2 was previously shown to be required for several early prophase events including DSB formation , homolog pairing and synapsis , reorganization of chromosomes within the nucleus , chromosome movement , and associated phosphorylation of SUN-1 [23] , [24] , [27] , [28] . We found that both DSB-2 staining and SUN-1 S8P ( in early meiotic prophase ) were severely reduced or absent in chk-2 mutant gonads ( Figure 6B ) , indicating that CHK-2 represents a common regulator of these two distinct features of the meiotic program . Although chromatin-associated DSB-2 staining was not observed by immunofluorescence , Western blot analysis indicated that the DSB-2 protein is expressed in the chk-2 mutant ( Figure 6B ) . Whereas localization of DSB-2 on chromatin and Ser-8 phosphorylation of SUN-1 at the NE in meiotic prophase nuclei tend to be correlated , they do not depend on each other . SUN-1 S8P immunostaining is present on meiotic prophase nuclei in dsb-2 mutant worms , and the zone of SUN-1 S8P-positive nuclei is extended into later pachytene ( Figure 6A , see below ) . Conversely , DSB-2 is able to load on chromatin in nuclei in sun-1 ( gk199 ) null mutant germ lines despite severe defects in germline organization and abnormal chromosome morphology ( data not shown ) . Thus , these two features appear to be independent downstream readouts of CHK-2 activity in meiosis . Together , our data suggest that CHK-2 coordinates the meiotic program by acting as a common upstream regulator of two parallel pathways , thereby linking competence for DSB formation ( mediated through DSB-2 ) with chromosome and NE dynamics ( mediated through SUN-1 S8P ) . The correlation between DSB-2 and SUN-1 S8P was also tested in him-19 mutants , which show an age-dependent pleiotropic phenotype that includes multiple defects ( in DSB formation , chromosome clustering and movement in TZ , pairing and synapsis ) that are hypothesized to result from mis-regulation of CHK-2 activity [29] . In 2-day old him-19 worms , SUN-1 S8P is missing from most of the TZ and early pachytene regions , but is present on a few scattered nuclei [23] that are also positive for DSB-2 ( Figure 6C ) , consistent with these two features being controlled by common factors including CHK-2 . The removal of DSB-2 and SUN-1 S8P at mid-pachytene during WT meiosis , concurrent with the timing of disappearance of RAD-51 foci , led us to hypothesize the existence of a coordinated regulatory mechanism that simultaneously shuts down competence for DSB formation and changes other properties of the nucleus as it enters another stage of meiotic progression . In spo-11 and him-17 mutants , the zone of DSB-2 and SUN-1 S8P marked nuclei was extended beyond what was seen in WT ( Figure 5A and B , Figure 7 ) ; extension of the SUN-1 S8P-positive zone in the spo-11 mutant was also reported by Woglar et al . [26] . In addition , in dsb-2 mutants , the zone of SUN-1 S8P staining was also prolonged ( Figures 6A , 7 ) . All of these mutants have defective DSB formation , and thus lack or have a deficit of downstream recombination intermediates and COs . We hypothesized that the deficit of appropriate recombination intermediates prolonged the zone of nuclei marked by DSB-2 and SUN-1 S8P . To test this hypothesis , we analyzed DSB-2 and SUN-1 S8P staining in several classes of meiotic mutants . We tested mutants lacking proteins involved in early steps of DSB processing and repair: the rad50 mutant , which lacks the RAD-50 protein that has been implicated in meiotic DSB formation , DSB resection and RAD51 loading [6] , [30]; the rad51 mutant , which lacks the RAD-51 recombinase that catalyzes strand exchange [20]; and the rad54 mutant , in which unloading of RAD-51 and progression of DSB repair are disrupted [31] . We found that in all of these mutants , DSB-2 and SUN-1 S8P staining are extended over most of the pachytene region ( which also tends to be smaller than in WT gonads ) ( Figures 8 , 7 ) . This prolonged staining in mutants defective in DSB formation , processing , and repair suggests that such mutants lack the signals that would normally trigger removal of DSB-2 and SUN-1 S8P . We next assessed zhp-3 , msh-5 , and cosa-1 mutants , which have a specific defect in CO formation . These mutants are proficient for homolog pairing and synapsis and can initiate and repair DSBs , but not as COs [13] , [21] , [22] , [32] . All of these mutants showed an extended zone of DSB-2 and SUN-1 S8P staining ( Figure 9 B , C , D ) , thus suggesting that lack of the CO-eligible recombination intermediates that depend on ZHP-3 , MSH-5 and COSA-1 will prolong DSB-2 localization to chromatin and phosphorylation of SUN-1 S8 . Finally , we tested whether meiosis-specific chromosome structures are required to mediate the persistence of DSB-2 and SUN-1 S8P when CO-eligible inter-homolog recombination intermediates are reduced or lacking . We first examined the syp-1 mutant , which loads chromosome axis proteins but lacks a key structural component of the central region of the synaptonemal complex , and thus cannot establish synapsis between homologs [18] . In this mutant , DSB-dependent RAD-51 foci form and persist at elevated levels before disappearing at the very end of pachytene , and COs do not form [18] , [21]; in addition , chromosome clustering , chromosome movement and SUN-1 phosphorylation are all greatly prolonged [18] , [26] , [28] , [33] . We found that DSB-2 and SUN-1 S8P staining were both extended to the end of the pachytene region in the syp-1 mutant ( Figure 9A ) . Thus , lack of SYP proteins leads to both lack of inter-homolog COs and prolonged DSB-2 and SUN-1 S8P staining . In contrast , lack of HORMA domain chromosome axis proteins HTP-1 or HTP-3 does not lead to extended DSB-2 or SUN-1 S8P staining in the respective mutant gonads , despite a lack or severe deficit of inter-homolog COs ( Figure 10 ) . htp-1 mutants are defective in pairing of autosomes and assemble SCs between nonhomologous chromosomes , and they exhibit reduced RAD-51 foci reflecting reduced DSB formation and/or altered kinetics of repair [34] , [35]; htp-3 mutants are defective in pairing and SC formation for all chromosomes and appear to lack DSBs [36] , [37] . We find that despite the deficit or lack of COs in the htp-1 and htp-3 mutants , the zone of DSB-2 and SUN-1 S8P-positive nuclei was not extended ( Figures 10 , 7 ) . This finding suggests that HTP-1 and HTP-3 , or features of axis organization that are dependent on these proteins , are needed for DSB-2 and SUN-1 S8P to persist when CO recombination intermediates are absent . In addition to acquiring and subsequently losing competence to form DSBs during meiotic prophase progression , C . elegans germ cells also switch on , then subsequently switch off , a specialized meiotic mode of DSB repair [6] , [13] , [38] , [39] . Whereas switching on this meiotic DSB repair mode enables formation of inter-homolog intermediates capable of yielding COs , switching off this repair mode is proposed to facilitate repair of any remaining DSBs in order to guarantee restoration of genome integrity prior to cell division . One notable feature of this specialized meiotic DSB repair mode is a requirement for RAD-50 to load RAD-51 on DSBs induced by gamma-irradiation: whereas essentially all germ cells in wild-type gonads rapidly acquire RAD-51 foci following gamma-irradiation , formation of irradiation-induced RAD-51 foci is strongly inhibited in a specific subset of rad-50 mutant germ cells , from meiotic prophase onset until after the transition to late pachytene [6] . Thus , dependence on RAD-50 for RAD-51 loading at DSBs provides a means to visualize germ cells in which the meiotic DSB repair mode is engaged . We used this feature to test the hypothesis that the presence of DSB-2 on chromatin correlates with engagement of the meiotic mode of DSB repair . By co-staining for DSB-2 and RAD-51 following irradiation of rad-50 mutant gonads , we found a striking correspondence between the nuclei in which DSB-2 was present on chromatin and the nuclei in which RAD-51 loading was inhibited ( Figure 11A ) . Further , we similarly observed strong correspondence between the presence of DSB-2 and inhibition of RAD-51 loading in htp-1; rad-50 double mutant gonads , in which both features are restricted to a smaller region of the germ line than in the rad-50 single mutant [6]; Figure 11B ) . Moreover , in both rad-50 and htp-1; rad-50 gonads , nuclei exhibited this inverse correlation between DSB-2 and RAD-51 staining even when neighboring nuclei were in a different mode . In the context of a model in which association of DSB-2 with chromatin is a marker for a DSB-competent state , these results suggest that competence for DSB formation and utilization of the meiotic DSB repair mode are coordinately turned on and shut off , and that coordination of these processes occurs at the level of individual nuclei .
In this work , we identify DSB-2 as a protein that is required for efficient meiotic DSB formation and that localizes to chromatin during the stages of meiotic prophase when DSBs are thought to form . DSB-2 localizes to chromatin independently of SPO-11 ( and thus of DSB formation ) and is restricted to the region of the gonad where RAD-51 foci mark processed DSBs ( from TZ to mid-pachytene ) . Further , the fact that exogenous DSBs induced by irradiation rescue the chiasma defect in dsb-2 mutant germ cells indicates that the downstream DNA processing and CO formation machinery are functional in the mutant . Moreover , the timing of disappearance of DSB-2 coincides with the cessation of DSB formation ( implied by the disappearance of RAD-51 foci ) , suggesting a model in which removal of DSB-2 ( and presumably other factors ) results in shutting down of DSB formation . Based on these data , we propose that DSB-2 regulates competence for SPO-11-dependent DSB formation during C . elegans meiosis . Several properties distinguish DSB-2 from other previously identified chromatin-associated proteins ( HIM-17 , XND-1 and HIM-5 ) that influence DSB formation in C . elegans . Whereas HIM-17 , XND-1 and HIM-5 proteins localize to chromatin in nuclei throughout the germ line [8] , [9] , [10] , the presence of DSB-2 on chromatin correlates with the timing of DSB formation . Further , while him-17 and xnd-1 mutants display pleiotropic phenotypes indicating that HIM-17 and XND-1 have additional roles regulating germ line proliferation and/or organization [9] , [40] , dsb-2 mutants are specifically defective in meiotic DSB formation . In addition , whereas XND-1 and HIM-5 affect DSB formation predominantly on the X chromosomes , DSB-2 is required for efficient DSB formation on all chromosomes . Together these data suggest that DSB-2 has a more direct role in promoting DSB formation than do HIM-17 , XND-1 or HIM-5 . We interpret the region of the germ line where nuclei are positive for DSB-2 localization to represent the zone in which nuclei are competent to undergo DSB formation . Consistent with this interpretation , in meiotic mutants in which the DSB-2-positive zone is extended ( and that are capable of making DSBs and loading RAD-51 ) , RAD-51 foci are higher in number and persist beyond mid-pachytene [20] , [21] , [31] , [32] . In principle , persistence of RAD-51 foci could be due to excess/prolonged DSB formation , delayed RAD-51 removal , or both . Thus , caution is warranted when using such mutants to estimate numbers of DSBs . We suggest that in mutants with an extended DSB-2 positive zone ( in which the DSB machinery is functional ) germ cells may continue to make additional DSBs for a prolonged period , whether or not they are ultimately competent to repair them . How might DSB-2 control DSB competence ? Given its broad yet uneven localization on chromatin , it might act by altering chromatin structure to create an environment that is permissive for the activity of SPO-11 and the DSB machinery . It might also act directly upon SPO-11 and the DSB machinery , by recruiting and/or activating it at certain locations depending upon the underlying chromatin structure . It is intriguing that DSB-2 localizes to a few bright patches/foci in addition to its broader chromatin staining . The fact that these bright patches are absent in him-17 mutants , which are defective in DSB formation , suggests that the patches may have functional significance . Immunofluorescence analyses of DSB-2 in both wild type and meiotic mutants were highly informative regarding how DSB formation is coordinated with multiple distinct aspects of the meiotic program . We found that presence of DSB-2 on chromosomes and the presence of SUN-1 S8P are highly correlated , despite the fact that neither feature is required for the other . Further , we identified CHK-2 as a common upstream regulator of these two features , and we suggest that CHK-2 links acquisition of competence for DSB formation ( promoted by DSB-2 ) with nuclear/chromosomal processes required for successful pairing and synapsis of homologous chromosomes ( mediated by SUN-1 at the NE ) . Moreover , the correlated removal of both DSB-2 and SUN-1 S8P at mid-pachytene , at the same time that RAD-51 foci disappear , further suggests the existence of coordinated regulatory mechanisms that shut down competence for DSB formation and change other properties of the nucleus as germ cells transition to a later stage of meiotic progression . As seen in multiple experimental systems , DSB formation is restricted to a specific time window in early prophase , indicating that cells must have a means to shut down the meiotic DSB machinery [3] . However , little is known about what controls this transition . Recent evidence from Drosphila , mice and budding yeast suggests that ATM , a protein kinase involved in DNA damage response , may play a role in limiting meiotic DSB formation [41] , [42] , [43] . It was suggested that ATM is activated by meiotic DSBs and inhibits further DSB formation at the local level by triggering a negative feedback loop . Based on the current work , we propose that additional negative feedback regulation operates at the nucleus-wide level to mediate shutdown of DSB formation during C . elegans meiosis . Our evidence that germ cells have the capacity to monitor and respond to the presence or absence of DSB-dependent CO-eligible recombination intermediates is based on the analysis of DSB-2 localization in various meiotic mutants . We found that DSB-2 persists in mutants with defects in DSB formation ( spo-11 , him-17 , rad-50 ) , in mutants with defects in early steps of DSB processing ( rad-50 , rad-51 , rad-54 ) , as well as in mutants that can make DSBs but repair them by pathways that do not yield inter-homolog COs ( zhp-3 , msh-5 , cosa-1 ) . Although we cannot exclude the possibility that different defects in these mutants elicit the same response , the parsimonious explanation is that DSB-2 persistence reflects a response to the common deficit shared by all of these mutants , i . e . , the inability to generate CO recombination intermediates . Thus , we infer that CO-eligible recombination intermediates are required for removal of DSB-2 with WT timing . We propose a model in which the appearance of CO-eligible recombination intermediates results in a signal ( or quenching of an inhibitory signal ) that is necessary to trigger the shutdown of DSB formation , in part by removal of DSB-2 ( Figure 12 ) . We suggest that this change occurs at the nucleus-wide level when cells sense that sufficient CO-eligible intermediates have been formed to guarantee one CO per chromosome pair . Once this requirement is met , cells are permitted to enter a different state of meiotic progression; if this condition is not met , cells experience a delay in this transition . This type of coupling can be viewed as analogous to checkpoint mechanisms that make cell cycle progression contingent upon fulfillment of a requirement to complete a monitored event . However , it is also appropriate to consider such a coupling as reflecting operation of a negative feedback circuit wherein the formation of threshold levels of a downstream product ( i . e . CO-eligible recombination intermediates ) feeds back to inhibit an earlier step in the pathway ( i . e . DSB formation ) . Thus , we envision a regulatory network governing DSB formation that involves negative feedback operating on ( at least ) two levels , one that inhibits DSB formation locally ( in a region where a DSB has already formed [41] , [42] , [43] ) , and one that inhibits DSB formation nucleus-wide once sufficient CO-eligible recombination intermediates are established . This regulatory network would ensure that sufficient DSBs are made to guarantee that every chromosome pair undergoes a CO [13] , [31] , [39] , while protecting against excessive DSB levels or local concentration of DSBs that could have deleterious effects . We further propose that multiple aspects of the meiotic recombination program undergo a coordinated transition that in wild type germ cells is marked by disappearance of DSB-2 and SUN-1 S8P ( Figure 12 ) . We proposed in a previous study that access to the homologous chromosome as a repair partner is shut down once sufficient CO-eligible recombination intermediates are formed [39] . We suggested that this transition occurs around mid-pachytene in WT germ lines , and we showed that inter-homolog access is prolonged in msh-5 mutants [39] . In light of the current results , an attractive possibility is that the appearance of sufficient CO-eligible recombination intermediates simultaneously signals both shut-down of DSB formation and shut down of inter-homolog access for DSB repair . Moreover , we found that another specialized aspect of the meiotic DSB repair program , namely the dependence on RAD-50 for rapid loading of RAD-51 on IR-induced DSBs , is restricted to nuclei positive for DSB-2 . This finding further strengthens the case that cessation of programmed DSB formation is coordinated with a major transition in the mode of DSB repair . It is notable that in mutants defective for HORMA domain axis proteins HTP-1 and HTP-3 , the DSB-2/SUN-1 S8P - positive zone is not extended despite the absence of CO-eligible recombination intermediates on most or all chromosomes . This finding raises the possibility that this family of proteins , which was previously implicated in the operation of checkpoint-like coupling mechanisms that coordinate early prophase chromosome movement , homolog recognition and SC assembly [34] , may also be required for operation of checkpoint-like mechanisms that make later events in meiotic progression contingent upon the formation of CO-eligible recombination intermediates . We speculate that the regulatory network that coordinates this meiotic transition ( i . e . the shutdown of DSB formation and accompanying changes ) likely involves the activities of one or more protein kinases . As the CHK-2 protein kinase is required to promote the acquisition of both DSB-2 and SUN-1 S8P , it is likely that the disappearance of DSB-2 and SUN-1 S8P requires inactivation of CHK-2 , suggesting that CHK-2 may be a key target of feedback regulation . Further , DSB-2 contains several potential phosphorylation sites both for CHK-2 and for the ATM/ATR protein kinases [12] , [44] . Future work will investigate the significance of these for DSB-2 function and regulation . The fact that DSB-2 and SUN-1 S8P are coordinately removed in wild-type meiosis ( and coordinately prolonged in mutants ) implies that the NE also responds to signaling from CO-eligible recombination intermediates . Our findings confirm and extend the recent report of Woglar et al . , who similarly showed that the SUN-1 phosphorylation is prolonged in spo-11 and rad-51 mutants and concluded that establishment of CO intermediates is necessary for exit from early pachytene ( as defined by loss of phospho-SUN-1 ) [26] . The change in SUN-1 phosphorylation status at this transition may be indicative of global changes in properties of the nucleus that occur as it enters a different stage of meiotic progression; e . g . , the fluidity of the nuclear membrane , which is modified upon entry into meiotic prophase [28] , may revert to a more constrained state similar to that of non-meiotic germ cells . Such a change would be analogous to the observed reversion to the non-meiotic mode of DSB repair that occurs at this same transition . While DSB-2 and SUN-1 S8P immunofluorescence signals become dimmer and disappear from most nuclei by the time they reach the mid-pachytene region of the germ line , a few “outlier” nuclei show bright DSB-2 and SUN-1 S8P staining later in the pachytene region . Sometimes the chromatin in these nuclei has a clustered organization reminiscent of zygotene or early pachytene stages , but in contrast to earlier nuclei , these outlier nuclei have brighter DSB-2 staining covering most of the chromatin as well as high levels of RAD-51 foci . This difference suggests that these nuclei are arrested in their progression and may have triggered a checkpoint response . This response could be due to failure to make appropriate CO-eligible recombination intermediates and/or to the presence of excess or persistent DNA breaks . These processes may be inter-related: if the failure to make CO-eligible recombination intermediates keeps DSB formation active , this could increase the chance of accumulating levels of DNA damage that challenge the capacity for repair . Accumulation of high levels of DSB-2 and SUN-1 S8P may indicate that these nuclei are triggering the recombination/DNA damage checkpoint and will be targeted for future apoptosis . While these outlier nuclei may be destined for apoptosis , however , they likely have not yet engaged the cell death program , as outlier nuclei are still detected in mutants lacking the pro-apoptotic factors CED-3 or CED-4 [11] , [26] . An intriguing aspect of the dsb-2 mutant phenotype is that the defect in meiotic recombination worsens with age . This implies that the DSB-1 protein retains some residual DSB-promoting activity in the absence of its paralog , but also indicates that the requirement for DSB-2 becomes more acute in older germ cells . Interestingly , CO distribution has also been found to differ between young and old WT C . elegans oocytes [45] . This suggests that meiotic recombination processes such as DSB formation and CO distribution are sensitive to changes in the germline environment that occur as worms age . However , the ability to achieve accurate and reliable meiosis in the context of a changing environment is advantageous for the reproductive success of the organism . The C . elegans reproductive system has substantial plasticity in this regard , as the duration of progression through meiotic prophase varies markedly with both sex and age and can be modulated dramatically in the female germ line by the availability of sperm [46] . The operation of feedback networks such as that demonstrated here provides a means to regulate and coordinate key events and transitions in a manner that buffers the system against a varying environment , thereby promoting reproductive success .
Strains were maintained at 20°C under standard conditions . Experiments were performed at 20°C unless otherwise noted . Strains used in this study: AV334 unc-119 III; ruIs32 [Ppie-1::GFP-his-11; unc-119 ( + ) ] III mnT12 ( IV;X ) AZ212 unc-119 III; ruIs32 [Ppie-1::GFP-his-11; unc-119 ( + ) ] III AV477 dsb-2 ( me96 ) II 4X outcrossed AV501 rol-1 ( e91 ) dsb-2 ( me96 ) II AV511 rol-1 ( e91 ) dsb-2 ( me96 ) unc-52 ( e998 ) II AV539 rol-1 ( e91 ) dsb-2 ( me96 ) /mnC1 [dpy-10 ( e128 ) unc-52 ( e444 ) ] II AV727 meIs8[unc-119 ( + ) pie-1promoter::gfp::cosa-1] II ;; ItIs37[unc-119 ( + ) pie-1::mcherry::histoneH2B]; ltIs38[pAA1;pie-1 promoter::GFP::PH::unc-119 ( + ) ] AV758 dsb-2 ( me97 ) meIs8[unc-119 ( + ) pie-1promoter::gfp::cosa-1] II ;; ItIs37[unc-119 ( + ) pie-1::mcherry::histoneH2B]; ltIs38[pAA1;pie-1 promoter::GFP::PH::unc-119 ( + ) ] AV630 meIs8[unc-119 ( + ) pie-1promoter::gfp::cosa-1] II AV645 spo-11 ( ok79 ) /nT1 IV; +/nT1[qIs51] V AV146 chk-2 ( me64 ) rol-9 ( sc148 ) /unc-57 ( e369 ) rol-9 ( sc148 ) V AV660 chk-2 ( me64 ) rol-9 ( sc148 ) /sC4 ( s2172 ) [dpy-21 ( e428 ) ] V VC292 +/nT1 IV; sun-1 ( gk199 ) /nT1 V VC255 +/nT1 IV , him-17 ( ok424 ) /nT1 V AV158 +/nT1 IV; rad-50 ( ok197 ) /nT1 [unc- ? ( n754 ) let- ? qIs50] V TG9 dpy-13 ( e184 ) rad-51 ( lg8701 ) IV/nT1[let- ? ( m435 ) ] ( IV;V ) VC531 rad-54 and tag-157 ( ok615 ) I/hT2[gli-4 ( e937 ) let ( 9782 ) qIs48] I; III AV449 zhp-3 ( me95 ) /hT2 [bli-4 ( e937 ) let- ? ( q782 ) qIs48] I AV603 msh-5 ( me23 ) /nT1 IV; +/nT1[qIs51] V AV596 cosa-1 ( tm3298 ) /qC1[qIs26] III AV307 +/nT1 IV; syp-1 ( me17 ) /nT1 V AV393 htp-1 ( gk174 ) IV/nT1[unc- ? ( n754 ) let- ? qIs50] ( IV;V ) TY4986 htp-3 ( y428 ) ccIs4251 I/hT2[bli-4 ( e937 ) let- ? ( q782 ) qIs48] ( I , III ) . AV473 +/nT1 IV; rad-50 ( ok197 ) /nT1[qIs51] V AV443 htp-1 ( gk174 ) /nT1[ unc- ? ( n754 ) let- ? qIs50] IV; rad-50 ( ok197 ) /nT1 [qIs51] V Bristol ( N2 ) wild type CB4856 Hawaiian wild type The dsb-2 ( me96 ) allele was isolated in a genetic screen for meiotic mutants exhibiting defects in chiasma formation or chromosome organization in diakinesis-stage oocytes , conducted in collaboration with M . Hayashi [47] . The AV334 strain used for this screen , which allows visualization of chromosomes using a germline-expressed GFP::histone H2B fusion protein , also contains a fusion of chromosomes IV and X . Parental ( P0 ) L4 hermaphrodites were treated with ethyl methanosulfonate ( EMS ) as in [48] and were plated individually . F1 progeny were picked to individual plates to produce progeny , and pools of F2 progeny worms from each F1 plate were mounted on multi-well slides in anesthetic ( 0 . 1% tricaine and 0 . 01% tetramisole in M9 buffer ) and their germ lines were visualized for meiotic defects . Two mutations affecting meiotic recombination , me95 and me96 , were identified based on the presence of univalents at diakinesis in a the subset of F2s ( from independent F1s ) and were recovered by plating of siblings; repeated outcrossing ( with N2 ) and selecting for the mutant phenotype removed the chromosome fusion as well as the GFP::H2B transgene . Mapping , complementation testing and sequencing revealed that me95 is an allele of zhp-3 , containing a C-to-T transition that results in a premature stop at codon 348 of the predicted 387 amino acid coding sequence of K02B12 . 8a . Mapping of the me96 mutation ( below ) indicated that it identified a new component of the meiotic machinery . Initial SNP mapping based on the methods of [49] and [50] placed me96 near genetic map position 20 on the right side of chromosome II . To select for informative COs near this region , rol-1 me96 unc-52 worms were crossed with CB4856 males , and Rol non-Unc F2 progeny were selected and genotyped for SNP pkP2117 at genetic map position 17 . 9 to select for COs occurring between this marker and unc-52 ( genetic map position 23 ) . Informative recombinants were assessed for me96 phenotype and typed using additional SNP markers in the region , narrowing down the position of me96 to a 165 kb interval ( between SNP markers uCE2-2315 and uCE2-2332 ) comprising 36 candidate genes . RNAi of candidate genes was performed using bacterial clones from the RNAi feeding library [51] , [52] as in [53] . Worms used were AZ212 worms , which contain GFP::histone , as this genotype was shown to be more sensitive to RNAi [54] . RNAi against candidate gene F26H11 . 6 ( at 15°C , but not at 20°C ) phenocopied the me96 mutation , eliciting a mixture of bivalents and univalents at diakinesis . Sequencing identified a T-to-A transversion generating an early stop at codon 14 ( TTA = >TAA ) of the predicted F26H11 . 6 coding sequence ( 280 codons total ) in the me96 mutant . A second dsb-2 allele ( me97 ) was isolated independently in a screen for mutants with altered numbers of GFP::COSA-1 foci ( which mark CO sites in late pachytene ) ; me97 fails to complement me96 and contains a premature stop at codon 168 , further confirming the identity of F26H11 . 6 as the dsb-2 gene . Except where otherwise noted , all analyses were conducted using the me96 allele . L4 hermaphrodites were picked to individual plates , allowed to lay eggs , and transferred to fresh plates every 24 hours for three days . Hermaphrodites start laying eggs after they transition from L4 to adult , and lay most eggs in the first three days . Inviable embryos that do not hatch are indicative of autosomal mis-segregation , while male progeny indicate X-chromosome mis-segregation . Eggs from eight dsb-2 ( me96 ) ; rol-1 ( e91 ) hermaphrodites ( where rol-1 is a marker with no meiotic defects ) were counted . The number of eggs counted for each day is: 573 ( day 1 ) , 879 ( day 2 ) and 488 ( day 3 ) , with an average of 243 eggs per hermaphrodite over the three day interval . Numbers of DNA bodies present in diakinesis oocytes were assessed in intact adult hermaphrodites of indicated age , fixed in ethanol and stained with 4′ , 6-diamidino-2-phenylindole ( DAPI ) as in [40] . This method underestimates the frequency of achiasmate chromosomes , as some univalents lie too close to each other to be resolved unambiguously . Numbers of nuclei scored were: 88 and 106 for dsb-2 worms , 1 day and 2 day post L4 respectively; 43 and 57 for WT worms , 1 day and 2 day post L4 respectively . Numbers of GFP::COSA-1 foci in late pachytene nuclei of live anesthetized worms ( 0 . 1% tricaine and 0 . 01% tetramisole in M9 buffer ) were quantified by taking 3D image stacks on a DeltaVision microscope . GFP foci were counted in the last five rows of pachytene nuclei; only nuclei completely contained within the stack were scored , and nuclei with features indicative of apoptosis ( compact and bright mCherry::histoneH2B signal ) were excluded . 24 h control data were from fixed immunofluorescence images [13] . Numbers of nuclei scored: dsb-2 24 h , n = 127; dsb-2 48 h , n = 101; WT 24 h , n = 76; WT 48 h , n = 78 . ( Note: Whereas most nuclei in a mutant that lacks DSBs and COs have zero COSA-1 foci , 20% have one or two foci presumably reflecting non-specific aggregation of CO proteins when a suitable substrate is absent [13] . Thus , a subset of dsb-2 nuclei with one or two COSA-1 foci may similarly lack COs , especially at 48 h post L4 where nuclei with zero foci are frequent . ) Immunofluorescence was conducted as in [55] with minor modifications . Unless otherwise noted , all experiments were performed at 40–48 hours post L4 . Worms were cut at the vulva to dissect the gonads ( in egg buffer with 0 . 1% Tween-20 ) and fixed with 1% paraformaldehyde ( in egg buffer ) for 5 minutes . Slides ( Superfost Plus ) were covered with a coverslip and frozen in liquid nitrogen . The coverslip was removed , and slides were immersed in cold ( −20°C ) methanol for 1 minute . Slides were washed three times for 8–10 minutes in phosphate-buffered saline containing 0 . 1% Tween-20 ( PBST ) and then blocked for one hour with 0 . 5% bovine serum albumin ( BSA ) diluted in PBST . Primary antibody solution was added ( 50 µl ) on top of the dissected gonads and covered with a parafilm square . Slides were incubated overnight in a humid chamber at room temperature , then washed three times for 8–10 minutes in PBST . Secondary antibody solution was added ( 50 µl ) and slides were incubated with parafilm cover for 2 hours at room temperature in the dark . Slides were washed three times with PBST and incubated for 5 minutes with 2 µg/ml DAPI solution in the dark , followed by two more washes . Slides were mounted with Vectashield and the coverslip was sealed with nail polish . The following primary antibodies were used at the indicated dilutions in PBST with 0 . 5% BSA: guinea pig anti-HIM-8 ( 1∶500 ) [16] , rabbit anti-HIM-3 ( 1∶200 ) [17] , guinea pig anti-SYP-1 ( 1∶200 ) [18] , rabbit anti-RAD-51 ( 1∶500 ) [21] , guinea pig anti-SUN1 S8P ( 1∶1000 ) [23] , rabbit anti-DSB-2 ( 1∶5000 ) , rat anti-RAD-51 ( 1∶250 ) , guinea pig anti-DSB-1 ( 1∶500 ) [11] . An affinity-purified rabbit polyclonal antibody against DSB-2 was generated by SDIX ( Newark , DE ) using the C-terminal 100 amino acids of F26H11 . 6 as the immunogen . Specificity of the antibody was demonstrated both by the lack of chromatin staining in immunofluorescence analysis of dsb-2 mutant gonads ( Figure 6A ) and by Western blot analysis ( Figure 6B ) . Rat anti-RAD-51 antibody was generated using a His-tagged fusion protein expressed from plasmid pET28a containing the entire RAD-51S coding sequence [56]; immunizations and bleeds were performed by SDIX . Rat anti -RAD-51 was affinity purified against membrane-bound protein as described in [57] with the following modifications: nitrocellulose membrane was blocked in 5% milk in 1×TBST; and , eluates containing rat anti -RAD-51 were further purified by dialysis with 12–14 kDa dialysis tubing ( Spectrum ) in 1×TBST for 1 hour and overnight at 4°C . Specificity was demonstrated by showing that rat anti-RAD-51 foci colocalize with rabbit anti-RAD-51 foci [21] by immunofluorescence and that these recombination-dependent foci are eliminated in spo-11 ( me44 ) gonads . All secondary antibodies were Alexa Fluor goat from Invitrogen used at 1∶200 dilution in PBST with 0 . 5% BSA . Immunofluorescence images were acquired using the DeltaVision microscopy system ( Applied Precision ) and deconvolved using softWoRx software . Images shown are maximum-intensity projections of Z-stacks acquired at 0 . 3 µm intervals . For each wild-type germ line evaluated , RAD-51 foci were quantified in 8 contiguous rows of pachytene nuclei from the region where foci were most abundant . The average distance ( in rows of nuclei ) between the position of this peak and the end of the transition zone was calculated for wild-type germ lines , and this distance was used to define the corresponding regions to be scored in dsb-2 ( me96 ) mutant germ lines ( in which the abundance of RAD-51 foci was low throughout ) . Quantitation was carried out on deconvolved 3D image stacks using SoftWoRx software; only nuclei that were completely contained with in the image stack were scored . Occasional atypical nuclei with condensed , bright DAPI signals were excluded . Numbers of nuclei scored: WT , n = 335; dsb-2 , n = 196 . For each genotype , sixty adult worms ( 24 hours post-L4 ) were picked into M9+0 . 05% Tween 20 , washed gently three times , then lysed by resuspension in 2× Laemmli Sample Buffer ( Bio-Rad ) . Gel electrophoresis was performed on a 4–15% Criteriot TGX gradient gel ( Bio-Rad ) , followed by transfer of proteins to a PVDF membrane . Blots were probed with rabbit anti-DSB-2 ( 1∶1000 in 5% milk ) for 2 hours , followed by HRP-conjugated secondary antibody and detection by ECL . Worms were exposed to 1 kRad ( 10Gy ) of gamma-irradiation using a Cs-137 source . The 1 kRad dose was chosen based on its sufficiency to restore chiasmata to 95% of chromosome pairs in affected nuclei of the spo-11 ( ok79 ) mutant [6] . Worms were irradiated at 36 hours post L4 , and the number of DNA bodies at diakinesis was assessed in worms fixed at 18 hours post-irradiation , for both dsb-2 and age-matched spo-11 mutants . The dsb-2 worms also carried the rol-1 marker , which does not affect meiosis . This assay tends to underestimate the incidence of achiasmate chromosomes , as some lie too close together to be resolved . Numbers of nuclei scored were: 71 and 76 for dsb-2 worms , untreated and irradiated respectively; 76 and 45 for spo-11 worms , untreated and irradiated respectively . Worms were exposed to 5 kRad ( 50Gy ) of gamma-irradiation using a Cs-137 source . Formation of RAD-51 foci was assessed by immunofluorescence in gonads dissected and fixed 1 hour after irradiation . Germ lines from rad-50 and htp-1; rad-50 mutants were irradiated at 24 hours post-L4 , and stained with DAPI , RAD-51 antibody and DSB-2 antibody . Germ lines from dsb-2 mutants were irradiated at 48 hours post-L4 and stained with DAPI and RAD-51 antibody . | Formation of haploid gametes during meiosis relies on deliberate induction of DNA double-strand breaks ( DSBs ) , followed by repair of a subset of DSBs as crossovers between homologous chromosomes . Crossovers form the basis of connections that enable homologs to segregate toward opposite spindle poles at meiosis I , thereby reducing ploidy . Thus , germ cells must generate enough DSBs to guarantee a crossover for every chromosome pair while avoiding an excessive number of DSBs that might endanger their genomes . Here , we provide insight into how this crucial balance is achieved . We identify C . elegans DSB-2 as a key regulator of DSB formation , and we propose that its association with chromatin is an indicator of DSB competence . Disappearance of DSB-2 is part of a coordinated transition affecting multiple distinct aspects of the meiotic program , and failure to form crossover-eligible recombination intermediates elicits a delay in DSB-2 removal and other transition events . Our data are consistent with a model in which meiotic DSB formation is governed by a negative feedback network wherein cells detect the presence of downstream crossover intermediates and respond by shutting down DSB formation , thereby ensuring that sufficient DSBs are made to guarantee crossovers while simultaneously minimizing the threat to genomic integrity . | [
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"biology"
] | 2013 | The C. elegans DSB-2 Protein Reveals a Regulatory Network that Controls Competence for Meiotic DSB Formation and Promotes Crossover Assurance |
Massangam health district ( HD ) , in the West Region of Cameroon , has received ivermectin mass drug administration ( MDA ) for 20 years , however there is evidence of continued high transmission of Onchocerca volvulus . In order to better understand the transmission dynamics in the HD and inform intervention strategies there is a need to delineate the boundaries of the suspected area of high transmission within the wider transmission zone . Parasitological and entomological surveys were conducted to map out the breeding sites of Simulium damnosum and evaluate the prevalence of onchocerciasis in neighbouring communities , including Makouopsap sentinel community . Potential rapids were prospected for identification of S . damnosum larvae and black flies collected to determine infectivity rates . Adults were assessed for the presence of O . volvulus microfilariae through a skin snip biopsy and examined for the presence of nodules . Anti Ov-16 antibodies were tested for in children . Four perennial breeding sites were identified on the Rivers Mbam and Nja . Large number of flies were collected along the River Mbam , especially in the rainy season , with up to 955 flies per day , suggesting this river is a perennial source of black flies . A total of 0 . 8% of parous flies were infective across the study area . Parasitological studies provided evidence of high rates of infection in the sentinel community and three neighbouring communities , with 37 . 1% of adults microfilariae positive in Makouopsap . High Ov-16 seropositivity in children also provided evidence of recent on-going transmission . In comparison , communities sampled further away from the sentinel community and neighbouring breeding sites were much closer to reaching onchocerciasis elimination targets . This study provides evidence of a particular geographic area of high transmission in an approximate 12 km range around the sentinel community of Makouopsap and the neighbouring breeding sites on the River Nja . To eliminate onchocerciasis by 2025 , there is a need to explore alternative intervention strategies in this area of high transmission .
Onchocerciasis also known as “river blindness” is a tropical disease caused by the filarial nematode Onchocerca volvulus . It is transmitted to humans through the bite of an infected black fly of the genus Simulium , which mainly breed in fast flowing rivers and streams . The female worms produce microfilariae ( mf ) that migrate out of the nodules and circulate in the skin [1] causing clinical manifestations , including forms of dermatitis . The mfs can also enter the eyes , leading to inflammatory lesions ( keratitis , chorio-retinitis ) , optic nerve atrophy and blindness [2] . Ivermectin is a safe and potent microfilaricide that has been used widely in onchocerciasis endemic areas [3] . Evidence shows that annual or semi-annual distribution of ivermectin to affected communities can eliminate transmission of O . volvulus in Africa , especially in meso and lower hyper-endemic areas [4–7] . As a result the World Health Organisation ( WHO ) has declared onchocerciasis as a target for elimination by 2025 [8] . Rapid Epidemiological Mapping of Onchocerciasis ( REMO ) surveys conducted in Cameroon in the early 1990s showed that onchocerciasis was a country-wide public health problem with an estimated 12 million people at risk [9 , 10] . The Ministry of Health ( MoH ) started the community-directed treatment with ivermectin ( CDTI ) strategy in the West Region in 1996 , initially with the support of The Carter Center , Lions Clubs International Foundation ( LCIF ) and the African Programme for Onchocerciasis Control ( APOC ) and then Sightsavers [11 , 12] . Impact evaluations conducted in the region in 2011 , found that only 3 out of 11 health districts ( HDs ) were close to the elimination targets despite 16 years of uninterrupted MDA [12] . In particular , there were still very high levels of infection ( humans and flies ) in selected sentinel sites in Massangam and Foumbot HDs . In Massangam , 0 . 18% of flies were infective and 59 . 6% of individuals had mf , whilst in Foumbot , 0 . 19% of flies were infective and 41 . 9% and 12 . 7% of individuals had mf from the two sentinel sites [12] . In 2014 , a study by Sightsavers in collaboration with the MoH investigated the potential local and micro-level determinants that could be facilitating on-going transmission in these two HDs , including poor ivermectin coverage [11] . In recent years , the MoH have reported high annual ivermectin coverage including over 80% therapeutic and 100% geographic coverage across Massangam district since 2007 . Senyonjo et al . aimed to validate the 2014 ivermectin coverage reported by community drug distributors ( CDDs ) through an independent treatment coverage survey and understand barriers to compliance using a mixed methods approach . The authors concluded compliance to ivermectin was relatively good ( 71 . 2% , 95%CI: 61 . 7–79 . 2% ) although a little under the 80% coverage target as set by WHO . However , compliance was an issue in certain population groups due to both programme delivery issues and individual determinants , which need to be addressed . With this in mind , the authors concluded that due to very high infection rates , especially in the sentinel community of Makouopsap , Massangam HD , that improving compliance alone would not be enough to interrupt transmission [11] . A change in intervention strategy would be required to achieve elimination by 2025 . In order to inform strategic programmatic changes , further studies were required to better understand the transmission dynamics in the area . The study described in this paper aimed to delineate the boundaries of the area of high transmission , firstly through mapping of the breeding sites of S . damnosum and assessment of key entomological and parasitological parameters at purposefully selected sites , within the flight range of the vector from the breeding sites identified . Parasitological and entomological surveys were conducted in 2015 and 2016 . It should be noted that the study was not aiming to delineate the wider transmission zone but understand the boundaries of any area of high transmission within the zone , in order to inform changes in programmatic interventions .
This study was approved by the Comité National d’éthique de la Recherche pour la Santé Humaine in Cameroon ( approval number 2014/12/519/CE/CNERSH/SP ) . The administrative authorization to collect the data was obtained from the MoH ( approval number 631–3715 ) . The aim and objectives of the studies were explained to all potential participants or caregivers for those aged less than 21 years old and written informed consent was obtained and recorded . All operators of the aspirators were covered to minimise skin exposure to Simulium bites . The West Region has an estimated population of about 1 . 7 million people living across 20 HDs . It is among the most mountainous zones of the country with peaks reaching up to 3000 metres above sea level . These physical features create many perennial fast-running rivers that support breeding of black flies throughout the year . In the west and east of the region there are thick forests , whilst the central area transitions from a forest to savannah woodland [12] . The study was conducted in the Massangam HD in the West Region of Cameroon . Massangam HD , with a population of approximately 40 , 000 [11] , is situated in the extreme east of the region . There are two main rivers in the area , the Noun and the Mbam rivers which lie to the south-west and the east of Massangam respectively . The HD is a watershed from which many tributaries of the River Mbam flow , the main one being the River Nja and a second the River Kim . The heavy rainy season lasts from August to November and the dry season from December to March . This is then followed by a small rainy season from April to June and a short dry season in July . The study used a variety of methods , which included both parasitological cross-sectional studies and entomological surveys . Figure maps were created using ArcGIS software ( ESRI 2011 . ArcGIS Desktop: Release 10 . Redlands , CA: Environmental Systems Research Institute ) .
A total of seven breeding sites were identified in the study area , these included one on the River Mbam , four on the River Nja and two on the River Kim ( Fig 2 ) . Two additional breeding sites were identified on the River Noun but due to their distance from the sentinel community , it is believed they are unlikely to support transmission in this area . Of the three main rapids of interest on the River Mbam , only one was found to have S . damnosum larvae . No larvae were found in the potential breeding site to the north of the study area , however larvae were found in one of the two rapids to the south . Due to the volume of water these sites could not be re-prospected during the rainy season . Of the eight rapids on the River Nja , three were productive in the dry season ( two in close proximity to the sentinel community of Makouopsap ) and one a distance upstream , an additional breeding site was also identified upstream ( from Makouopsap ) in the rainy season . No further S . damnosum larvae were found in any of the other tributaries prospected . In the dry season , a total of 4 , 598 female S . damnosum were collected from the two different catching points , over a nine day period in July . Out of this number , 3 , 162 were dissected and 20 . 0% ( 633 ) were found to be parous . A total of 1 . 3% of parous flies were infected ( n = 8 , 0 . 3% of all flies ) while 0 . 3% ( n = 2 ) were infective , each of them having two infective larvae in the head . The majority ( 95% ) of the black flies were caught from the site near the River Mbam . During the rainy season , a total 15 , 240 flies were collected from nine different sites . Using landing rates as a proxy for biting rates , the highest rates were at Gah with 1 , 209 flies/day and on the shores of the River Mbam ( Camp SIC ) with a total of 955 flies/day . Makouopsap ( sentinel community ) had a landing rate of 352 flies/day . A total of 30% ( range 0–50% ) of all flies were parous , with the highest proportion of parous flies from the collection points near the Rivers Kim and the Noun . In all , 1 . 0% of all parous flies were found to be infected and 0 . 8% infective . Again the highest proportion of infective flies were at the catching points by the Rivers Kim and Noun , however , there was also a significant proportion of infective flies identified in Makouopsap . Overall , there was about 2 . 5 L3H ( third stage infective larva found in the head ) per infective fly ( 23 L3H/1000 parous females ) , with the highest proportion in Makouopsap ( Table 1 ) . Using morphological characteristics , it was concluded that the vector for onchocerciasis in the area belongs to the forest cytospecies and could be S . squamosum sensu stricto ( ss ) ( a member of the S . squamosum sub complex ) as previously identified from the area by D . Boakye .
The parasitological and entomological findings suggest there is an area of perennial high transmission in Massangam HD , encompassing four communities of Makouopsap , Mankakoun and Njingouet/Njinja and facilitated by S . damnosum breeding sites on the rivers Nja and Mbam . The entomological assessments showed that infective flies were identified throughout both the dry and rainy seasons , with the sites being more productive during the rainy season . Similar results were reported by Katabarwa and colleagues who showed an increase in biting rates in Makouopsap from January to June [12] . In the rainy season , a total of 0 . 8% of parous flies were infective , higher than the figures ( 0 . 18% ) reported by Katabarwa and colleagues [12] and higher than elimination thresholds of 0 . 1% [20] . Prospection of the River Mbam , especially in the rainy season was restricted by accessibility issues and high water levels , compounded by a recent release of volumes of water from the two retention dams ( Magba and Bamendjing ) to regulate hydroelectricity supplies . However , the vector collection sites chosen near the River Mbam in the rainy season were very productive and the flies had relatively high rates of infection , especially at the site near Camp SIC and downstream at Gah ( highest biting rate per day recorded ) . The large number of flies caught at sites along the river shores even in the dry season , imply the River Mbam is a main perennial source of biting S . damnosum especially between Mankare and Makouopsap down to Gah ( about 30km south of the sentinel community , Makouopsap ) . Within this section of the Mbam there are several rapids , of which S . damnosum larvae were positively identified in one , although suspected in the other rapids due to high vector landing rates in near-by catching points . Even when the water levels are low , Simulium flies can still find isolated substrates in the rapids to enable them to reproduce . The parasitological assessments confirm the sentinel community of Makouopsap is still endemic , despite 20 years of treatment with ivermectin . Overall , nodule and mf prevalence have decreased since the 2011 study by Katabarwa et al that reported 43 . 4% of individuals over 20 years had nodules and 59 . 6% mf as compared to 19 . 1% ( 40% in adults ) and 37 . 1% in this study . Another study by Kamga et al conducted in Makouopsap in May 2015 , reported a slightly higher CMFL of 0 . 9 ( mf/ss ) in Makouopsap as compared to 0 . 57 ( mf/ss ) in this study [22] . These are positive findings suggesting some progress is being made in reducing O . volvulus prevalence , intensity and transmission but that the study area is still off track to reach current elimination targets . There was a high prevalence of antibodies to Ov-16 in children in the sentinel community with 59 . 0% testing positive . The neighbouring communities of Mankakoun ( 32 . 4% ) and Njingouet/Njinja ( 6 . 2% ) also have high seropositivity rates in children , well above the WHO elimination threshold ( 0 . 1% ) [20] . In fact , only two of the nine communities in the study reached the elimination criteria , although the seropositivity rates in the remaining communities were all below 3% . Although the Ov-16 assay is only a measure of exposure to O . volvulus , as it was conducted in children born after MDA began and positive cases were found in very young children , this does provide evidence of recent and on-going transmission especially around the sentinel community of Makouopsap and the neighbouring breeding sites on the River Nja . A predominant source of income in these communities is farming and many adults take their children with them to the fields , especially during the holidays and infection in children in these communities could be a result of these practices . The data from the remaining sampled communities ( outside this area of high transmission ) suggest they are closer to reaching elimination thresholds . The communities of Mansouen 1 and 2 , Matam and Mankimbouot ( Fig 2 ) likely have no in situ transmission with no or very low levels of antibodies detected in children . Machatoum and Mankoumbi , which are nearer the River Mbam may experience brief periods of invasion by migrant black flies during peak biting season and therefore some seasonal transmission may be occurring . The higher levels of infection in males is interesting and similar associations have been reported elsewhere [23] . The difference is potentially a result of gender roles which put males at more risk of black fly biting [24] , such as fishing practices or herdsmanship known to be important livelihood practices in this area . Although differences in gender and compliance to ivermectin could also be a factor , previous research in this area found no difference in overall ivermectin compliance after the 2014 MDA round by sex . In fact , the authors further concluded that systematic non-compliers ( never taken the drug ) were more likely to be female and this was linked to the fears that ivermectin could affect fertility or interrupt menstruation [11] . A study by Kamga et al ( 2017 ) also found little evidence between the geometric mean of mf among carriers and treatment adherence in the West Region of Cameroon [22] . A further consideration is the potential source of black flies flying in from the south from the productive breeding sites on the River Noun . It is also an area where flies may be feeding on the cattle that were observed transiting through this area as they move north . However , the distance between the identified breeding sites on the River Noun and the sentinel community of Makouopsap , approximately 40km , suggests that these breeding sites may have little influence on the area of high transmission in Massangam HD . There are a number of methodological issues that need to be taken into account when interpreting results of this study and in future similar research . First , the entomological evidence suggests that there is on-going transmission of O . volvulus in Massangam HD . However , PCR using O . volvulus specific PCR primers were not employed and therefore there is a possibility that some of the parasites found in the flies could be O . ochengi , as found elsewhere in Cameroon [25] , especially as there are substantial population of cattle in the area , the reservoir for this species of Onchocerca . Current WHO guidelines only recognize the use of PCR and species-specific primers in order to determine O . volvulus infection rates in flies [18] . However , although PCR was not used in this study , opening up the possibility of misinterpretation of the true infection levels in flies , the high proportion of individuals infected with O . volvulus do suggest that the majority of S . damnosum are truly infected with O . volvulus . The study would be strengthened if flies could have been collected over a longer period of time , to collect a larger number of flies and to be able to determine monthly and annual biting rates . There are also difficulties in using morphological characteristics only to identify the exact species of adult S . damnosum . However , based on previous cytotaxonomic identification of larvae collected in these rivers , the suggestion is that the species are likely forest flies belonging to S . squamosum . It would be preferable to confirm the species and infectivity using DNA analysis , although there are no validated molecular tests for species identification of S . damnosum complexes at present . Further , a combination of modified Esperanza traps and human bait collections were used . During this study we were not able to obtain the commercial skin-lure ( BG-Lure attractant , Biogents AG , Regensburg , Germany ) . In the dry season it was not possible for us to buy the Tangle TrapTM insect trap coating paste ( Contech , Victoria , BC , Canada ) and although another type of glue ( TanglefootTM , Grand Rapids , Michigan , USA ) was tried , it acted as a repellent . The lack of standardisation of traps and human collections used makes it more difficult to compare data across time periods . There are issues with using nodule prevalence as a parameter of infection , due to the poor specificity of the indicator [26] , although the combination of parasitological data collected during this study confirms that onchocerciasis is still prevalent in Massangam district . There were issues of high rates of refusal for skin snip biopsies in children in the study communities , which resulted in a change of inclusion criteria after the start of the data collection . The low response rate in children for skin snip biopsies were offset by the higher response rate in the use of the Ov-16 assay , which was more useful in determining historical transmission dynamics in the area . The skin snips were also not weighed , which would have provided a better indication of CMFL , although efforts were made to standardize the skin biopsy taken so the variability in weight should not have been large . To conclude , both parasitological and entomological findings confirm an ongoing transmission of O . volvulus in the Massangam HD despite 20 years of annual mass distribution of ivermectin . The study was able to refine the boundaries of an area of high perennial transmission ( approximately 12km ) within the wider transmission zone , which was around the sentinel site of Makouopsap and facilitated by productive breeding sites on the River Nja and River Mbam . The evidence from past studies suggests that this area of high transmission is not a failure to treat ie a result of poor coverage or compliance [11] but is more likely a failure of the treatment strategy . Although ivermectin is effective as a microfilaricide , recuperation of fertility and repopulation of the skin with mf begins three to four months after the drug is ingested [27] . In an area of perennial transmission such as identified in Massangam HD , this mf repopulation in the skin , combined with the abundance of vectors to pick and transmit the O . volvulus parasite , indicate annual ivermectin alone is likely not enough to interrupt transmission . Alternative strategies such as semi-annual ivermectin delivery or use of a more effective microfilaricide such as moxidectin could be beneficial in ensuring suppression of mf levels in the population for longer time periods [28] . However , as there is evidence of recent transmission in the area and the lack of a macrofilaricidal effect of these drugs , even with the introduction of these interventions , 2025 elimination targets would unlikely be met . We therefore advocate for a multi-faceted approach aimed at targeting the remaining significant reservoir of infection . Such an alternative intervention would comprise of a test and treat strategy with a known macrofilaricide such as doxycycline , targeting communities in the area of high transmission , combined with associated ground larviciding of relevant S . damnosum breeding sites . Doxycycline is known to target the O . volvulus Wolbachia endosymbiont resulting in the increased sterility and death of the adult worm [29] . Ground larviciding is a complementary approach that will target the life cycle of the black fly vector and an approach used to great success in West Africa during the Onchocerciasis Control Programme ( OCP ) days [30 , 31] . In an effort to ensure low mf prevalence in communities surrounding this area of high transmission and help reduce the potential of re-introduction of infection into the area , we also suggest increasing ivermectin distribution from annual to semi-annual delivery . Sustaining high treatment coverage will be very important and it will be necessary to tailor efforts to improve ivermectin compliance , especially in certain groups that consistently do not take the drug e . g nomadic groups that travel in and out of the area . Interventions should also be co-ordinated with on-going elimination efforts in the Central Region that have very high force of infection and neighbour Massangam district [12] . | The global goal is to eliminate onchocerciasis by 2025 . However , not all areas are on track to do so , as is the case for Massangam health district ( HD ) in the West Region of Cameroon . A new strategy is required in order to accelerate efforts to reach elimination targets . This study aimed to better understand the transmission dynamics of Onchocerca volvulus in Massangam HD through the identification of black fly ( Simulium damnosum ) breeding sites , parasitological and entomological studies . The study was able to define an approximate 12 km area of high transmission encompassing four communities with very high levels of infection and evidence of on-going transmission in humans . This area is facilitated by perennial productive black fly breeding sites in the neighbouring rivers of the Nja and Mbam . Multi-faceted interventions are required to target the reservoir of infection facilitating transmission . We suggest the need for a new test and treat strategy in the area of high transmission ( with a drug that kills the adult O . volvulus worm living in humans ) and larviciding of the breeding sites of the black fly . In areas surrounding the area , increase distribution of ivermectin from annual to twice yearly distribution , whilst also using innovative strategies to improve compliance . | [
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"diptera",... | 2018 | On-going transmission of human onchocerciasis in the Massangam health district in the West Region of Cameroon: Better understanding transmission dynamics to inform changes in programmatic interventions |
Wolbachia are common endosymbionts of terrestrial arthropods , and are also found in nematodes: the animal-parasitic filaria , and the plant-parasite Radopholus similis . Lateral transfer of Wolbachia DNA to the host genome is common . We generated a draft genome sequence for the strongyloidean nematode parasite Dictyocaulus viviparus , the cattle lungworm . In the assembly , we identified nearly 1 Mb of sequence with similarity to Wolbachia . The fragments were unlikely to derive from a live Wolbachia infection: most were short , and the genes were disabled through inactivating mutations . Many fragments were co-assembled with definitively nematode-derived sequence . We found limited evidence of expression of the Wolbachia-derived genes . The D . viviparus Wolbachia genes were most similar to filarial strains and strains from the host-promiscuous clade F . We conclude that D . viviparus was infected by Wolbachia in the past , and that clade F-like symbionts may have been the source of filarial Wolbachia infections .
Wolbachia are alphaproteobacterial , intracellular symbionts of many non-vertebrate animal species , related to rickettsia-like intracellular pathogens such as Anaplasma and Ehrlichia [1] . Wolbachia was first detected as a cytoplasmic genetic element causing mating type incompatibilities in Culex pipiens mosquitoes , and subsequently has been found to infect many insect species [2] . In insects , most Wolbachia can be classified as reproductive parasites , as they manipulate their hosts' reproduction to promote their own transmission [3] . This is achieved by induction of mating type incompatibilities , induction of parthenogenesis in females of haplo-diploid species , and killing or feminisation of genetic males . In some insects , Wolbachia infections are apparently “asymptomatic” , in that no reproductive bias has been detected . There is evidence that Wolbachia infection can be beneficial to hosts , particularly in protection from other infectious organisms [4] . Importantly , in most insect systems tested the symbiosis is not essential to the hosts , which can be cured by antibiotic treatment . Wolbachia strains have been classified into a number of groups using molecular phylogenetic analyses of a small number of marker loci [5] , [6] . Insect Wolbachia largely derive from clade A and B . Outside Insecta , arthropod Wolbachia infections have been identified in terrestrial Collembola ( Hexapoda ) , Isopoda ( Crustacea ) , Chelicerata and Myriapoda , and also in marine Amphipoda and Cirripeda ( Crustacea ) . Most non-insect arthropod infections also involve Wolbachia placed in clades A or B . A minority of arthropod infections involves Wolbachia placed in distinct lineages ( clades E through N ) [5] , [7] . In clade A and B symbionts , transmission appears to be essentially vertical ( mother to offspring ) in ecological time , but phylogenetic analysis reveals that lateral transfer between hosts has been common on longer timescales . Wolbachia infections have also been identified in nematodes , notably in the animal parasites of the Onchocercidae . These filarial parasites utilise arthropod vectors ( dipterans and chelicerates ) in transitioning between their definitive vertebrate hosts , but the Wolbachia they carry are not closely related to those of the vector arthropods . The majority of Wolbachia from onchocercid nematodes are placed in two distinct but related clades , C and D [6] , [8] . The biology of the interaction between filarial nematodes and their C and D Wolbachia is strikingly different [9] . There is no evidence of reproductive manipulation . Transmission is vertical , as in other Wolbachia , but , unlike the arthropod symbionts , in species with infections all members carry the symbionts , and the phylogeny of hosts and symbionts show remarkable congruence . Treatment with antibiotics both kills onchocercid nematode Wolbachia , and also affects the viability of the nematodes , suggesting a strongly mutualistic , possibly essential interaction [10] , [11] . The interaction is not essential on a phylogenetic timescale , as nested within the Wolbachia-infected onchocercids are species that have lost their infections [12] . The biological bases for the mutualism is a topic of significant research interest , and may include manipulation of embryogenesis , metabolic provisioning and modulation of host immune responses [9] , [13]–[16] . Not all nematode Wolbachia are placed in clades C and D [17] . Clade F Wolbachia have a distinct host profile compared to the other clades , as they have been found in both onchocercid nematodes ( Mansonella , Madathamugadia and Cercopithifilaria species ) [18] , and arthropods ( hexapods and chelicerates ) . The Wolbachia symbiont from the nematode Dipetalonema gracile is the sole representative of clade J , but is closely related to clade C Wolbachia [19] . A Wolbachia infection has been described in Radopholous similis , a tylenchid plant parasitic nematode distantly related to the Onchocercidae [20] . This symbiont has been placed in a new clade I . The biological role ( s ) of these nematode Wolbachia have yet to be defined . Wolbachia have been sought in other nematode species , both parasitic and free-living . These searches , carried out using Wolbachia-specific PCR amplification of marker genes , have generally proved negative in individuals sampled across the diversity of Nematoda other than Onchocercidae [21] . In the many ongoing nematode genome and transcriptome projects , Wolbachia-derived sequence has only been described from onchocercid nematodes and R . similis . However , there are two overlapping expressed sequence tags from Ancylostoma caninum ( also a member of Strongyloidea ) that have high similarity to Wolbachia genes [22] , but these have not been verified as derived from a Wolbachia symbiont in this species . ( The relationships of the nematode taxa discussed are illustrated in Figure 1 [18] , [23] , [24] . ) Lateral transfer of Wolbachia genome fragments into the host nuclear genome has been detected in arthropods and nematodes that carry live infections [25] , [26] . Inserted fragments range from what is likely the whole bacterial genome inserted into an azuki beetle chromosome , to short fragments at the limit of specific detection . These fragments have excited much debate , particularly concerning the Onchocercidae , where it has been hypothesised that they may represent functional gene transfers into the nematode genome and thus play significant roles in host biology [27]–[30] . However most Wolbachia insertions have accumulated many substitutions and insertion-deletion events compared to their functional homologues in extant bacterial genomes . In this they most resemble nuclear insertions of mitochondrial DNA , which are ‘dead on arrival’ and evolve neutrally in the host chromosome [25] . Interestingly , the onchocercid nematodes Onchocerca flexuosa [31] , Acanthocheilonema viteae [11] , [32] and Loa loa [12] lack Wolbachia despite their placement within the group of Wolbachia-containing species . This suggests that they have lost their live Wolbachia infections . Fragments of Wolbachia-like sequence have been detected in the nuclear genome in these species [31] , [33] . Wolbachia nuclear transfers , or nuwts , in nematodes that currently lack live Wolbachia infection can be thought of molecular fossils of the previous symbiosis history of the host . Just as fossil skeletal remains can reveal the past distribution of larger biota , and viral insertions reveal the history of host infection [34] , , nuwts can reveal past symbioses , and their divergence from current Wolbachia genomes can be used to estimate the date of the symbiosis . We are engaged in a phylum-wide survey of genomes within the Nematoda [36] . As part of our analytic procedures we routinely screen raw genomic DNA data for contamination with environmental , commensal and host DNAs with a pipeline that uses read coverage , contig GC% and sequence identity to known protein sequences [37] . This serves to identify , and ease removal of , contaminating genomes , which in turn improves target genome assembly and aids independent assembly of symbiont genomes where present . Here we present an analysis of genome sequence data from the strongyloidean nematode Dictyocaulus viviparus , the bovine lungworm , which reveals molecular fossils of an ancient Wolbachia symbiosis in this economically important species , which is only distantly related to the previously known nematode hosts ( Figure 1 ) .
We generated a draft genome for the strongyloidean nematode D . viviparus based on a single adult male specimen provided from a cow slaughtered at an abattoir in Ngaoundéré , Cameroon . The D . viviparus genome was assembled using Velvet from 16 gigabases of cleaned data from 165 million , 100-base , paired-end reads from a 500 base pair ( bp ) insert library sequenced on an Illumina HiSeq2500 instrument . The draft assembly spanned 169 . 4 megabases ( Mb ) ( Table 1 ) . In terms of contiguity , the draft was of moderate quality with an N50 ( length of contig at which 50% of the genome is in contigs of this size or larger ) of 22 kilobases ( kb ) , and N90 of 5 kb . There were 17 , 715 contigs above 500 bp . The assembly had a GC content of 34 . 5% and estimated read coverage of ∼80 fold ( Figure 2A ) . The mitochondrial contigs from the assembly had >99 . 5% identity to the published mitochondrial genome of D . viviparus . The size of this draft assembly is within the range of published genome sizes from species of the same suborder ( Rhabtitina ) , which range from 80 Mb ( Heterorhabditis bacteriophora [38] ) to 320 Mb ( Haemochus contortus [38] ) ( Table 2 ) . Given that we used a single library , and had no long-range mapping data , it is likely that this genome size estimate is lower than the true genome as near-identical repeats will have been collapsed or left unassembled . We assessed the completeness of the draft assembly using the Core Eukaryotic Genes Mapping Approach ( CEGMA [39] ) , and identified 90% complete and 93% partial genes . A previous Roche 454 transcriptome assembly for D . viviparus [40] was used to assess the assembly's completeness in terms of representation of known D . viviparus transcripts . Retaining matches where over 70% of the transcript were mapped to the same genome contig , 87% of transcripts were present in the assembly . Many additional transcripts were split between contigs . Using a MAKER2-Augustus pipeline [41] , [42] , we predicted 14 , 306 protein-coding genes , with a median length of 834 bp , median exon length of 168 bp , and a median of 7 exons per gene . We compared this predicted gene set for D . viviparus to those of Caenorhabditis elegans [43] , H . bacteriophora [38] and H . contortus [44] , [45] using orthoMCL [46] . A majority ( 75% ) of the predicted D . viviparus proteins clustered with proteins from these rhabditine nematodes ( Figure 2 ) . The only species which had a low proportion of proteins clustered was H . bacteriophora ( ∼40% ) , an observation that has been noted previously [38] . Thus , while the goal of our study was not to produce a high-quality reference genome for D . viviparus , the draft assembly and annotation produced are still of reasonable quality ( Table 2 ) . A majority of known D . viviparus genes are present , similarity to related nematode species is high , and most of the genes appear to be present and in full length . The genome assembly and a dedicated BADGER genome exploration environment [47] are available from http://dictyocaulus . nematod . es . As part of our standard quality control processes , we generated a taxon-annotated GC-coverage plot ( TAGC plot ) [37] , with the goal of identifying any non-nematode ( either bovine host or environmental bacterial ) contamination ( Figure 3 A ) . This process allows identification of contaminants by their presence as contigs with differing GC content or estimated read coverage compared to that of assured target genome contigs [48] . The taxonomic annotation , using the NCBI BLAST+ suite , serves to assign contaminant contigs to their possible species of origin . This process identified a total of 193 contigs , spanning 1 Mb , that had best matches to Wolbachia ( Figure 3 B ) . The Wolbachia-like contigs had a GC content very close to the mode for the nematode genome , but they had a wide range of estimated coverages , from approximately equal to the majority of nematode-derived contigs to 3–4 fold higher Figure 3 C ) . Unusually , the Wolbachia-like contigs were not better assembled than the nuclear genome . The lower complexity of the alphaproteobacterial genome usually results in more contiguous assembly , even at low coverage . The putative Wolbachia from D . viviparus ( wDv ) contigs were compared to the complete genomes of Wolbachia from Brugia malayi ( wBm ) [9] and O . ochengi ( wOo ) [16] . The average identity of the BLAST hits was 84 . 5% ±3 . 2% to both of the other Wolbachia genomes , indicating similar evolutionary distance from these two taxa ( Figure 3 D ) . The matches were distributed across the genomes of other Wolbachia ( Figure 3 B ) . The Wolbachia-like fragments were uploaded to the RAST server [49] for direct annotation , and 1580 coding sequences were predicted , almost double than found in previous nematode Wolbachia genomes ( http://rast . nmpdr . org/ ? page=JobDetails&job=112231; Table 3 ) . This elevated number largely resulted from frameshifts and stop codons in the middle of genes , which fragmented the open reading frames , and overall only 567 different Wolbachia genes ( of a usual 800 to 1500 ) were identified . We also screened the contigs that had Wolbachia matches for other informative similarities , and identified 29 that contained both nematode and Wolbachia matches ( examples are illustrated in Figure 3 E ) . We explored both read coverage and read-pair sanity across these 29 contigs using Tablet [50] to validate the co-assembly of nematode and Wolbachia-like segments , as de Bruijn graph assemblers can create chimaeric contigs . We found the contigs to be valid , contiguous regions of the genome . Even in cases such as scaffold00357 ( Figure 3 E ) where the nuclear and Wolbachia components had distinct read coverages , manual inspection of the presumed Wolbachia-nuclear junctions revealed no issues of inconsistent read pairing or inferred insert length . Segments with much higher coverage than the nuclear genome may be derived from collapse of dispersed repeat copies of the Wolbachia fragment . From these analyses we conclude that the Wolbachia-like fragments are not from an unsuspected live Wolbachia infection of D . viviparus , but are rather neutrally-evolving insertions of Wolbachia genome fragments into the nematode nuclear genome , and are relics of an ancient symbiosis , now lost . We have called the fragmented Wolbachia wDv , though , obviously , we have no evidence of an extant wDv organism ( and in fact regard it as being extinct ) . As a preliminary assessment of whether the insertions are restricted to some populations of D . viviparus ( and thus that the symbiosis may have been recent and only in part of the species ) , or are more widespread ( and thus likely to derive from more ancient symbiosis ) , we screened an independent D . viviparus isolate for presence of Wolbachia gene fragments . We performed directed PCR and Sanger sequencing of Wolbachia gene fragments from a D . viviparus isolate maintained at the Moredun Institute , Edinburgh , isolated in Scotland in 2005 . Both ftsZ and 16S rRNA fragments were amplified from this strain , and , when sequenced , were closely similar to the whole genome assembly-derived fragments , but differed by several substitutions ( Figure 4 A , B ) . Comparison of the nuclear small subunit ribosomal RNA sequence from the assembly to those from Dictyocaulus species affirmed the species identification ( Figure 4 C ) . We also screened the previous D . viviparus transcriptome assembly [40] for Wolbachia-like fragments and identified six transcribed fragments ( Table 4 ) that were likely to be derived from Wolbachia , confirming presence of symbiont gene fragments in a third isolate . These transcribed Wolbachia-like fragments might offer evidence for functional integration of the remnants of the wDv genome into the nuclear genome . We thus investigated each fragment for possible function . In four of five fragments deriving from protein-coding genes there were frameshifts and in-frame stop codons . None of the transcribed fragments showed evidence of splicing . One transcript , where the Wolbachia-like sequence was in the likely 3′ UTR of a nematode gene ( a homologue of C . elegans FRM-1 ) , showed standard spliceosomal introns in the nematode-gene-like part , but the Wolbachia fragment itself was not spliced . Four of the transcript fragments were very short ( 500–600 bases , approximately one 454 read length ) . To identify the relationships of wDv , sequences from the Wolbachia-like contigs were added to a five-gene supermatrix ( including 16S rDNA , groEL , ftsZ , dnaA and coxA loci ) used previously for phylogenetic analyses of Wolbachia [18] . This matrix does not include data from all 14 recognised Wolbachia clades , as sequencing in most has been limited . wDv fragments corresponding to these genes were identified using BLAST and aligned with MUSCLE . We were not able to identify a dnaA gene in the D . viviparus assembly . We added to the alignment data from wOo and available sequences from the Wolbachia from Radopholus similis ( wRs ) . Both RAxML , MrBayes and PhyloBayes analyses suggested that wDv belongs to clade F , with strong branch support ( Figure 5 ) . The long terminal branch of wDv compared to other Wolbachia in the same clade is likely to be a consequence of the accumulation of mutations in the wDv regions due to their insertion and subsequent neutral evolution in the nematode genome . wOo was placed robustly within clade C as expected . Placement of wRs was less definite as it clustered as a sister taxon to clade D , but on a long branch with low support . We were unable to recover the published phylogeny [20] with wRs arising basally to other Wolbachia , even when the matrix was analysed with wDv excluded ( data not shown ) , and thus this previous finding may be a methodological artifact . One genomic feature that distinguishes clade C and D Wolbachia is the absence of WO phage . WO phage are active temperate bacteriophage that are present in the sequenced clade A and B genomes , and that may mediate genetic transfer of key symbiosis genes between strains [51] . Using the 1363 protein sequences derived from WO phage available in the NCBI/ENA/DDBJ databases we identified 15 scaffolds in the D . viviparus genome that contained significant ( BLAST E-values less than 1e-20 ) to WO phage proteins . These matches ( Table 5 ) were to a wide range of WO phage genes , including capsid proteins , portal proteins , secretion system components , recombinases and others . In this genomic feature , wDv resembled A and B Wolbachia more than it did C and D .
D . viviparus is the first nematode from the Rhabditina ( the group that includes C . elegans and the important animal-parasitic Strongyloidea ) that has been shown to have a relationship with Wolbachia . However , the Wolbachia sequences identified in the draft genome sequence do not appear to derive from a living organism , but rather show features suggestive of being ancient laterally transferred fragments of the genome of a clade F-like Wolbachia , which is now extinct . The insertions were not unique to the individual Cameroon nematode sampled , but were identified in another D . viviparus ( from Scotland ) . Published and unpublished transcriptome data for D . viviparus include a very low level of fragments that mapped to Wolbachia-like regions of our assembly . We suggest that the lateral transfers may be found in all D . viviparus , and that it will be exciting to survey additional Dictyocaulinae and related families within Strongyloidea for evidence of ( palaeo- ) symbiosis , and to better date the origin of the laterally-transferred fragments . Lateral transfers of Wolbachia DNA into the host nucleus , nuwts , have been identified previously in filarial nematodes and arthropods [26] , [52] , [53] . The evidence for the D . viviparus Wolbachia-like sequences being ancient lateral transfers include their fragmentation , their interspersion with nematode sequence in robustly-assembled contigs , and their having inactivating mutations . Read coverage of the Wolbachia-like fragments varied greatly . If all the fragments derived from the genome of a live infection , it would be expected that they would have very similar coverage , as seen in other Wolbachia infected nematodes [37] , [54] . Fragments with very high read coverage are likely to be repeats ( within the nematode genome ) . While about 1 Mb of contigs had matches to Wolbachia , these did not constitute a complete genome . Only ∼60% of the expected Wolbachia gene content was present ( for example the dnaA gene was missing ) and many genes and gene fragments were duplicated . Genome fragmentation and gene inactivation is suggestive of a long period of residence in the D . viviparus nuclear genome [25] . Do these Wolbachia-like but nuclear-encoded sequences have a current expressed function in D . viviparus ? The majority of the potential protein-coding genes in the Wolbachia-like fragments contain insertions , deletions , frameshift mutations or nonsense codons compared to their homologues from living Wolbachia genomes . We identified only six Wolbachia-like transcript fragments in 61 , 134 transcripts assembled from 3 million D . viviparus transcriptome sequences [40] . Four of the transcript fragments were very short , about one 454 read length , and one Wolbachia-like match was in the 3′ untranslated region of a bona fide nematode gene . Four of five fragments from protein-coding genes had frameshift and in-frame stop codon mutations , while the 16S rRNA fragment had a large deletion compared to 16S from living Wolbachia . On these bases it is unlikely these Wolbachia-derived sequences play roles in D . viviparus biology . This discovery suggests that all three suborders of the nematode order Rhabditida ( Rhabditina , Tylenchina and Spirurina ) have members whose genomes and biology have been shaped by symbioses with Wolbachia . In the well-studied clade C and D Wolbachia the relationship has features of mutualism [14] . The Wolbachia observed in R . similis is apparently live , as bacterial cells can be seen within host cells by microscopy [20] , but there are currently no data on the nature of the symbiosis: its genome sequence is awaited with interest . In D . viviparus we have no positive evidence for live infection . Our analyses placed both wDv and wRs close to clade F Wolbachia , and showed that clades C , D and F form a group distinct from clades A and B . From these and previous [18] analyses Clade F appears more “promiscuous” in its host relationships ( its known hosts include both nematodes and arthropods ) . The symbiont biology of clade F is not well known: in Cimex , the clade F symbiont may be essential for fertility and nymphal development [55] but symbiont-host interactions remain unexplored elsewhere . We note that the presence of Wolbachia ( albeit now extinct ) in D . viviparus , a nematode that does not use an arthropod intermediate vector host , suggests that a simple model of nematode acquisition of Wolbachia from their vector arthropods is less likely . Clade F-like Wolbachia emerge as a credible source of the clade C and D Wolbachia of filarial nematode species . The wDv genome was likely to have contained WO phage [51] , a mobile element present in clade A and B genomes but strikingly absent from clade C and D genomes . In this scenario , the genomic fossils of Wolbachia found in D . viviparus are evidence of infection of an F-like Wolbachia in a dictyocauline ancestor . We identified insertions in independent isolates of the parasite suggesting that the association was not limited to one subpopulation of D . viviparus . We note that there are Wolbachia-like sequences in transcriptome data from A . caninum , another strongyloidean nematode , and thus it is possible that Wolbachia infections may have been widespread in this group . While reports of Wolbachia in the strongyloidean Angiostrongylus have been discounted [56] , [57] , we are excited by the possibility that other palaeosymbioses , now extinct , may be revealed in forthcoming genome projects across the Nematoda and Metazoa . Finally , we provide a first draft assembly and annotation of the important nematode parasite D . viviparus . The identification of our specimen as D . viviparus is based on close identity of sequenced loci and the complete mitochondrial genome between our specimen and previously published D . viviparus data . As the specimen was destroyed during DNA extraction we no longer have a voucher for the individual . We note that there are very few records of D . viviparus in sub-Saharan Africa , and it is typically described as a temperate species [58] . A very large abattoir survey in the Democratic Republic of Congo found only 3 infected carcasses from 571 examined , and all of these were from cattle reared above 1 , 500 m ( Ngaoundéré is at 1 , 200 m ) [59] . The genome and annotation can be used as a springboard for further analysis both investigating the Wolbachia-nematode interaction and also potential gene identification for drug and vaccine development .
A single Dictyocaulus viviparus male was isolated from Bos indicus ( an individual of the local Gudali breed ) in Ngaoundéré abattoir , Adamawa Region in Cameroon by David Ekale and Vincent Tanya during the ongoing Enhancing Protective Immunity Against Filariasis EU-Africa programme . The nematode was frozen at −80°C and shipped to Liverpool , UK , where DNA was extracted using the DNeasy Blood & Tissue Kit ( Qiagen ) . Genomic sequencing was carried out by the Edinburgh Genomics Facility , using Illumina TruSeq library preparation reagents and a HiSeq 2500 instrument . A single 300 bp insert library was constructed , and 100 base paired-end data generated . Raw data have been submitted to the International Nucleotide Sequence Database Consortium under the project accession PRJEB5116 ( study ERP004482 ) . All software tools used ( including versioning and command line options used ) are summarised in Table 6 . The quality of Illumina reads was checked with FASTQC [60] . Raw reads were quality trimmed ( base quality of 20 ) , and paired reads were discarded if either pair was below 51 bases using fastq-mcf [61] . The trimmed reads were digitally normalised to ∼20X coverage with khmer [62] . A draft assembly was generated using the normalised reads with Velvet [63] and gaps within scaffolds were filled using GapFiller [64] . Scaffold coverage was obtained by mapping all the reads back to the assembly using the clc-bio toolkit ( CLC-Bio Ltd ) . Taxon-annotated GC%-coverage plots ( TAGC plots ) [37] were used to identify potential bovine and other contamination . Bovine contamination , which was minimal , was removed . A MAKER2-Augustus annotation pipeline was used to predict protein-coding genes from the genome [41] . The MAKER2 program combines multiple ab initio and evidence-based gene predictors and predicts the most likely gene model . MAKER2 was run in a SGE cluster using the SNAP ab initio gene finder trained by CEGMA [39] output models , GeneMark-ES ab initio finder , D . viviparus transcripts and SwissProt proteins . We used the MAKER2 predictions to train Augustus [42] and create a gene finder profile for D . viviparus . Using the gene finder profile , the assembled transcriptome [40] and available expressed sequence tag data [65] , Augustus was used alone to predict the final gene set , which was used for downstream analysis . Protein sets from selected nematode species , downloaded from Wormbase [66] , were clustered using orthoMCL [46] . The Dictycaulus viviparus draft assembly was broken into 500 bp fragments and each fragment was compared to Brugia malayi and Onchocerca ochengi Wolbachia endosymbiont genomes using BLAST+ [67] . Similarity hits with lengths above 100 bases were considered for downstream analysis . Contigs with Wolbachia-like sequences were annotated using the RAST server , which provided both gene finding and gene functional annotation . Junction fragments between putative Wolbachia insertions and D . viviparus nuclear genomic DNA were identified using BLAST+ . Putative phage WO fragments were identified through tBLASTn comparison of the 1353 phage WO proteins available in NCBI nr to the D . viviparus assembly , using an E-value cutoff of 1e-20 . The phylogenetic relationships of Wolbachia from D . viviparus were assessed by identifying orthologues of 16S rDNA , groEL , ftsZ , dnaA , and coxA genes , and aligning these to orthologues from other Wolbachia . The five-gene supermatrix was analysed using RAxML [68] , MrBayes [69] and PhyloBayes [70] ( see Table 6 for specific parameters used ) . Trees were visualised in iTol [71] and FigTree [72] . D . viviparus genomic DNA from the Moredun , Scotland , isolate was provided by Prof . Jacqui matthews , Moredun Institute [73] . The Moredun strain has no known connection with Cameroon . Caenorhabditis elegans ( free-living rhabditid nematode , which does not carry Wolbachia ) and Litomosoides sigmodontis ( a filarial nematode that carries a clade D Wolbachia [11] ) genomic DNAs were used as negative and positive controls , respectively . PCR primers designed to amplify Wolbachia 16S , Wolbachia ftsZ [8] , nematode nuclear small subunit rRNA ( nSSU ) [74] and mitochondrial cytochrome oxidase I ( cox1 ) [75] were used in PCR with Phusion enzyme ( NEB ) to identify similar fragments in each nematode genomic DNA . A list of primers used and PCR conditions are given in Table 7 . Positive PCR fragments were directly sequenced in both directions using BigDye v3 reagents in the Edinburgh Genomics facility . D . viviparus Roche 454 transcriptome data ( Bioproject PRJNA20439 ) were downloaded from ENA and screened using BLAST for sequences corresponding to the Wolbachia insertions in our assembly . | Bovine lungworms are economically important nematode parasites of cattle . We have sequenced the genome of the bovine lungworm to provide information for drug and vaccine discovery . Within the lungworm genome we found extensive evidence of an ancient association between the lungworm and a bacterium called Wolbachia . The lungworm Wolbachia is now a “fossil” in the genome , but tells of an ancient infection . Association between lungworms , and related nematode worms , and Wolbachia was not known previously . We have used the lungworm Wolbachia sequence to explore the history of nematode-Wolbachia interactions , particularly the jumping of these symbionts between arthropods and nematodes . | [
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"mol... | 2014 | Palaeosymbiosis Revealed by Genomic Fossils of Wolbachia in a Strongyloidean Nematode |
The HLA-C gene appears to have evolved in higher primates to serve as a dominant source of ligands for the KIR2D family of inhibitory MHC class I receptors . The expression of NK cell-intrinsic MHC class I has been shown to regulate the murine Ly49 family of MHC class I receptors due to the interaction of these receptors with NK cell MHC in cis . However , cis interactions have not been demonstrated for the human KIR and HLA proteins . We report the discovery of an elaborate NK cell-specific system regulating HLA-C expression , indicating an important role for HLA-C in the development and function of NK cells . A large array of alternative transcripts with differences in intron/exon content are generated from an upstream NK-specific HLA-C promoter , and exon content varies between HLA-C alleles due to SNPs in splice donor/acceptor sites . Skipping of the first coding exon of HLA-C generates a subset of untranslatable mRNAs , and the proportion of untranslatable HLA-C mRNA decreases as NK cells mature , correlating with increased protein expression by mature NK cells . Polymorphism in a key Ets-binding site of the NK promoter has generated HLA-C alleles that lack significant promoter activity , resulting in reduced HLA-C expression and increased functional activity . The NK-intrinsic regulation of HLA-C thus represents a novel mechanism controlling the lytic activity of NK cells during development .
Natural Killer ( NK ) cells use two major receptor systems to detect alterations in the expression of MHC class I on potential target cells: the CD94:NKG2A receptor recognizing non-classical HLA-E , and the MHC class I receptors represented by Ly49 in the mouse and KIR in humans [1] . The recognition of HLA-E by NKG2A is dependent on the presentation of the MHC class I leader peptide , and thus surveys cells for the presence or absence of MHC class I expression in general . In contrast , each Ly49 or KIR is specific for a subset of MHC class I molecules , providing a more precise detection of alterations in the expression of individual MHC class I genes . Several studies have demonstrated a switch from NKG2A expression to Ly49/KIR expression as NK cells mature [2–4] . The measurement of HLA expression levels by mass spectroscopy of peripheral blood lymphocytes revealed that HLA-A/B/C levels are at least 25 times higher than that of HLA-E [5] , suggesting that the level of inhibitory signaling by MHC class I receptors may increase as NK cells mature and switch from NKG2A recognition of HLA-E to KIR-mediated HLA binding . The education of NK cells by MHC class I is currently an area of intensive research [6–8] . The interaction of inhibitory MHC class I receptors with their ligands has been shown to augment NK cell potential , leading to higher lytic activity and cytokine secretion . The dynamic nature of NK cell education has been revealed by transfer of NK cells into a novel MHC environment , leading to a change in their responsiveness [9–11] . A recent study of human NK cell education has indicated a role for NK cell-intrinsic expression of HLA in the tuning of NK cell activity , as silencing of HLA expression in primary NK cells reduced their function [12] . The role of the human HLA-C gene in NK cell education is of particular interest , as it appears to have developed primarily as a ligand for the KIR2D family of receptors [13 , 14] . Whereas only small subsets of HLA-A and HLA-B alleles possess KIR ligands , all HLA-C alleles are recognized . Furthermore , HLA-A or HLA-B cell surface expression levels are 13–18 times higher than HLA-C [5] , consistent with a primary role of HLA-C in tuning NK cell responsiveness rather than presenting antigen to T cells . Evolutionary selection for an optimal level of KIR:HLA interaction is implied by the observed allelic variation of KIR cell surface expression levels and differences in ligand affinity of KIR alleles for HLA molecules [15] . Recent studies have also revealed variability in the level of cell surface expression of HLA-C alleles , indicating that variation in ligand levels may also be involved in the tuning of NK responsiveness [16 , 17] . In order to gain insight into the mechanisms underlying allele-specific differences in HLA-C expression , we conducted a detailed analysis of polymorphisms in predicted transcription factor ( TF ) binding sites in the 1 . 5 kb region upstream of the HLA-C coding region . Several TF sites were identified that possessed a disruptive single nucleotide polymorphism ( SNP ) associated with reduced promoter activity [18] . However , a SNP that disrupted a consensus Ets-binding site located approximately 1 . 3 kb upstream of the HLA-C start codon was not associated with altered promoter activity in the panel of cell lines studied . The detailed analysis of this region described in the current study reveals that the upstream Ets site is contained within an NK-specific promoter that produces translatable full-length HLA-C transcripts . The presence of these transcripts is associated with a higher level of HLA-C expression on NK cells . Disruption of the Ets site by a SNP in the HLA-C*02/*05/*07/*08 alleles results in the loss of NK-specific transcripts and decreased HLA-C expression . The analysis of NK cells from individuals homozygous for the Ets-disrupting SNP revealed that the loss of NK-specific HLA-C transcripts is associated with higher functional activity . The presence of NK-specific control elements in the HLA-C gene supports a central role for this gene in the development and regulation of NK cells .
A detailed analysis of the HLA-C gene region located ~1300 bp upstream of the start codon using the UCSC Genome Browser ( http://genome . ucsc . edu/ ) , revealed the presence of two spliced transcripts ( GenBank numbers: DA932871 , DA955942 ) that initiated 14 and 22 bp downstream of the polymorphic Ets element , suggesting the presence of a promoter in this region ( Fig 1A ) . Both of these transcripts were obtained from a human spleen oligo-capped EST library , indicating that they represent true transcription start sites ( TSS ) . The putative promoter region upstream of these TSS contains predicted AP1 , SP1 , and Ets elements that have previously been associated with the promoters of genes expressed by NK cells [19] ( Fig 1A ) , suggesting that an NK-specific promotor may be present . Furthermore , a single nucleotide polymorphism in the Ets site of four HLA-C alleles ( C*02 , C*05 , C*07 , C*08 ) is predicted to abrogate Ets binding to this site and might affect promoter activity . The homologous region of the HLA-A gene has several nucleotide differences that disrupt the AP1-binding site , and the SP1/Ets element is replaced by binding sites for XBP1 and RORα ( Fig 1A ) , indicating a distinct function for this region in the HLA-A gene . Transcripts from this region of HLA-A were observed in a macrophage EST library ( GenBank numbers: BP306201 , BP300425 , BP297407 ) , suggesting that a macrophage-specific promoter is present in this region of the HLA-A gene . This observation is consistent with the evolution of this region from supporting an antigen presentation function of HLA-A in macrophages to a role for HLA-C in NK cell function . A panel of human tissue RNAs was tested for the presence of transcripts initiating from the putative upstream HLA-C promoter . Fig 1B shows the results of RT-PCR using a forward primer downstream of the observed initiation sites , but preceding a consensus splice donor site , and a reverse primer in exon 1 . The strongest signal was found in spleen RNA , with weaker signals found in bone marrow , lung , and uterus , suggestive of NK-specific transcription . Only a very faint band is present in thymus , excluding T cells as a significant source of transcripts . Comparison of the level of PCR products generated from purified peripheral blood NK cell ( pNK ) cDNA with bone marrow , spleen , thymus , and purified monocyte cDNA , demonstrated that NK cells are the principal source of HLA-C upstream transcripts ( Fig 1C ) . We will henceforth refer to the novel upstream HLA-C promoter as the NK-promoter ( NK-Pro ) . Interestingly , different patterns of amplified bands were observed in the tissues that produced NK-Pro transcripts ( Fig 1B and 1C ) . Sequencing of the RT-PCR products from purified peripheral blood NK cells , spleen , and the YT human NK cell line revealed a large repertoire of alternatively spliced mRNAs ( Fig 2 ) . The highly variable nature of NK-Pro transcripts and the presence of a large 5´-UTR region containing competing initiation codons could provide an additional mechanism of modulating HLA-C protein expression , or it could reflect an enhancer/repressor function for the -1300 element , rather than a promoter capable of producing translatable HLA-C mRNAs . In order to assess the translatability of NK-Pro transcripts , full-length HLA-C cDNAs were generated from bone marrow , spleen , and peripheral blood NK cells from multiple donors possessing a variety of HLA-C alleles . Sequencing of the full-length products revealed additional alternative splicing events that could impact the translation of HLA-C ( Fig 3 ) . The most abundant bone marrow transcript retained intron 1 , and was therefore untranslatable . Splice forms lacking exon 1 were observed in spleen and NK cell cDNA . Exon 1 contains the leader sequence of HLA-C . However , exon 2 contains an in-frame start codon , suggesting that an intracellular HLA-C molecule could be made . Fig 4A summarizes all of the exons observed . HLA-C transcripts initiating at the upstream promoter contain 1–3 additional non-coding exons that have been named -1a1-7 , -1b1-6 , and -1c1-4 , with subscripts indicating differing exon sizes due to the use of alternative splice acceptors or donors for each exon . In addition , the size of the first HLA-C coding exon varies due to the presence of 7 alternative splice acceptors that can be used , so we have also named exon 1 isoforms as 11−7 . Interestingly , it appears that there has been selection for distinct exon variants in certain alleles , such as the observation of the -1b2 exon only in the HLA-C*06 and C*12 alleles , which is likely due to the presence of a SNP in these two alleles that generates a stronger splice donor consensus ( G|GT versus A|GT in other alleles ) . The -1b3 and -1b4 exons are only found in cDNAs originating from the HLA-C*01 or *04 alleles , since the key G nucleotide of the consensus GT splice donor is only present in these alleles . Only the HLA-C*01 , C*03 , C*04 , and C*14 alleles can generate exon 12 , due to a G to A nucleotide substitution that creates a splice acceptor site . The complex splicing patterns observed in HLA-C distal transcripts are not seen in HLA-A , which produces only one distal transcript containing two invariant untranslated 5´ exons ( GenBank BP306201 ) , indicating that modulation of the 5´-UTR structure has evolved specifically in the HLA-C gene . Notably , there appears to be tissue-specific differences in exon usage of HLA-C NK-Pro transcripts ( Figs 3 and 4B ) . Many of the NK-Pro transcripts in bone marrow retained intron 1 , whereas a large 1 . 3 kb first exon ( -1a7 ) was only observed in spleen , and most of the transcripts in peripheral blood NK cells contained a small 81 bp exon 1 ( 11 ) and no intron retention was observed . The highly variable and tissue-specific splicing patterns observed for HLA-C NK-Pro transcripts suggests that HLA-C expression levels in NK cells from various tissues could be distinct . In order to directly address the translatability of the alternatively spliced HLA-C mRNAs , full-length cDNAs were cloned into the pEF6 mammalian expression vector , transfected into the JAR trophoblast cell line that lacks HLA-C expression , and HLA-C protein levels were assayed by Western blot ( Fig 4C and 4D ) . To evaluate the effect of 5´-UTRs of differing size on expression , a series of four constructs were tested , ranging from the full 1 . 3 kb UTR , to the minimal UTR generated by transcription from the proximal promoter . Fig 4C shows that the level of HLA-C protein produced decreased with increasing UTR size , suggesting that the variable splice forms with differing 5´-UTR sequences could “tune” the levels of HLA-C protein . Fig 4D demonstrates that exon 1 is required for HLA-C expression . None of the splice forms lacking exon 1 produced detectable HLA-C protein , indicating that the skipping of this exon results in an inefficiently translated mRNA or an unstable protein product . Furthermore , removal of exons -1a and -1b from a full-length NK-Pro transcript containing exon 1 , so that the cDNA started at exon -1c , substantially decreased protein levels , suggesting that the mRNA secondary structure of the 5´-UTR of NK-Pro transcripts may prevent an ATG start codon in exon -1c from competing with the downstream HLA-C start codon . In addition , the cDNA containing the full 1 . 3 kb 5´-UTR was translatable ( exon -1a7 , Fig 4C ) , even though it contained multiple alternative 5´-ATG codons , further supporting a role for RNA secondary structure . The significant differences in HLA-C mRNA splicing in different tissues and at distinct stages of NK differentiation imply that changes in spliceosome function may play an important role in NK cell maturation or function . The subset of untranslatable NK-Pro transcripts generated by skipping of exon 1 could potentially represent a mechanism to control HLA-C expression levels during NK development . In order to address the possibility of differential HLA-C mRNA splicing during NK development , HLA-C levels and the splicing patterns of NK-Pro transcripts were analyzed in NK subsets representing different stages of NK cell differentiation . Fig 5A shows the HLA-C expression levels in peripheral blood NK subsets from 7 individuals . There is a clear increase in HLA-C expression on the more mature CD56dim NK cells relative to the less differentiated CD56bright population in all subjects tested . As CD56dim NK cells differentiate further , they acquire KIRs and CD57 [4] . This late-stage differentiation was also accompanied by an increase in HLA-C expression ( Fig 5A and 5B ) . Analysis of educated NK cells ( NK cells that express KIR molecules capable of recognizing self ) revealed higher levels of HLA-C on educated NK cells as compared to non-educated or KIR-ve NK cells ( Fig 5C ) , indicating that high HLA-C expression occurs in highly functional , mature NK cells . FACS analysis of lymphocytes from various human tissues showed that increased expression of HLA-C is found on CD56dim NK cells from multiple tissues , and the relative increase in HLA-C levels on KIR-expressing NK cells from tissues is similar to that seen in peripheral blood NK cells ( Fig 5D ) . Furthermore , the level of HLA-C expression varies widely between tissues , which may reflect tissue-specific splicing ( Fig 3 ) or differences in the activity of NK-Pro . In order to determine if differential splicing of the NK-Pro transcript is associated with the changes in HLA-C expression observed , RT-PCR of NK-Pro transcripts was performed on RNA isolated from sorted CD56bright versus CD56dim peripheral blood NK cells ( Fig 5E ) or CD56dim/CD57-negative versus CD56dim/CD57-positive NK cells ( Fig 5F ) . Fig 5E shows that the fraction of HLA-C NK-Pro mRNAs that contain exon 1 increases as cells progress from CD56bright to CD56dim NK . Fig 5F reveals an increase in the number of splice variants containing exon 1 in CD57-positive NK as compared to CD57-negative NK . Taken together , these results are consistent with a model whereby skipping of exon 1 results in the generation of non-productive transcripts , and represents a mechanism that prevents NK-Pro transcripts from generating increased HLA-C levels in immature NK cells . The comparison of splicing isoforms observed in Fig 5E and 5F also demonstrates the high degree of variability in splice forms observed with different HLA-C alleles . The donor analyzed in Fig 5E is homozygous for the HLA-C*06 allele , and a relatively simple splicing pattern is observed . In contrast , Fig 5F represents NK cells from a HLA-C*15/C*16 heterozygote , and a much greater number of distinct splice forms are observed , none of which are in common with the HLA-C*06 isoforms . In order to confirm the functionality of the predicted TF-binding sites in the NK-specific promoter and the effect of the Ets site SNP , an electromobility-shift assay ( EMSA ) was performed with oligonucleotides spanning the predicted AP1 , SP1 , and Ets TF-binding sites shown in Fig 1A . The AP1 site bound c-Fos and JunB proteins present in YT nuclear extract ( Fig 6A ) . The combined SP1:Ets site bound both SP1 and the Ets family member Elf-1 . The SNP in the NK-Pro Ets site was predicted to disrupt binding of Ets family members to this site , and EMSA with a probe containing the disruptive SNP demonstrated that the altered site had greatly diminished binding to both Elf-1 and SP1 , most notably in the YT human NK cell line , indicating a cooperative interaction between these TFs ( Fig 6B ) . A cooperative interaction between Ets family members and SP1 has been observed in many promoters [20 , 21] , and tandem SP1/Ets sites have been identified in many genes , including the CD16 promoter [19] . The strong reduction in TF binding observed as a result of the A to G substitution in the Ets site ( Fig 6B ) , suggests that transcriptional activity should be affected by this SNP found in the HLA-C*02/*05/*07/*08 alleles . The central role of an Ets site capable of binding Elf in conferring NK cell/T cell-specific promoter activity has been observed previously for the MUNC4D gene [22] , and the in vitro analysis of NK-Pro indicated that an intact Ets site was required for TF binding to the SP1/Ets site . Therefore , we predicted a significant loss of NK-Pro transcription in HLA-C alleles with a disrupted Ets site . RT-PCR of the HLA-C NK-Pro transcript using RNA isolated from purified peripheral blood NK cells obtained from donors selected for the presence of alleles containing an intact or disrupted NK-Pro Ets site revealed that an intact Ets site was associated with the production of high levels of NK-specific transcripts ( Fig 7A ) . Individuals that were homozygous for any combination of the HLA-C alleles that lacked an intact Ets-binding site ( HLA-C*02/05/07/08 ) , had greatly reduced/absent HLA-C NK-Pro transcripts . Interestingly , in some of the individuals that lacked HLA-C NK-Pro transcripts , homologous transcripts derived from the HLA-B*08 gene were detected , suggesting the presence of NK-Pro activity in some HLA-B alleles . A Blast search of GenBank revealed that the HLA-B*14 allele contained an intact Ets site . However , HLA-B*08 and all other HLA-B alleles in GenBank contained the same SNP in the Ets site found in HLA-C*02/05/07/08 . Interestingly , three of the donors shown in Fig 7A that did not produce any detectable transcripts had the HLA-B*14 allele , indicating that the Ets site is not associated with the production of distal transcripts in the HLA-B gene . This suggests that there are multiple nucleotide changes relative to HLA-B that are associated with NK-specific activity in the HLA-C gene in addition to the Ets site . Fig 7B shows a FACS analysis of HLA-C expression by T , B , and NK cell subsets performed on peripheral blood from the same individuals tested for NK-Pro transcripts by RT-PCR . The presence of an intact Ets site in NK-Pro was associated with significantly higher expression of HLA-C by NK cells . A weaker effect of the Ets SNP was observed in T cells , indicating some activity of this element in T cells . However , there was no difference between intact versus disrupted Ets alleles with regard to HLA-C expression on B cells . It therefore appears that the -1300 element evolved in order to generate higher levels of HLA-C expression on NK cells . The functional effect of enhanced HLA-C expression by NK cells could be manifested in numerous ways . High HLA-C levels could protect NK cells from fratricide mediated by other NK [23] , create a higher threshold for NK activation due to cis recognition , or it could lead to increased NK function as predicted by the observation that reduction of NK cell HLA expression can reduce NK activity [12] . The functional consequences of high versus low HLA-C expression on NK cells was studied by comparing CD107a expression triggered by interaction of NK cells from high or low HLA-C expressing donors with 721 . 221 target cells . All donors possessed the KIR2DL3 , KIR2DL1 , and KIR3DL1 genes present on the KIR-A haplotype to ensure that receptors for both HLA-C1/C2 and HLA-Bw4 were present . Both Bw4 and Bw6-homozygous individuals were tested in the assay in order to control for possible KIR3DL1/HLA-B effects . After a 5-hour incubation with target cells , NK cells from individuals with high HLA-C expression had significantly lower CD107a expression in the CD56dim subset than donors with a disrupted HLA-C NK promoter and lower HLA-C expression ( Fig 7C ) . The increased degranulation response in individuals with reduced HLA-C expression due to the absence of upstream transcripts implies an important role for cis expression of HLA-C in determining the functional activity of NK cells .
The presence of an NK-specific promoter coupled with an elaborate alternative splicing mechanism to control the translatability of HLA-C mRNA implies an important role for endogenous NK cell HLA-C protein in the development and/or function of NK cells . The increased NK activity we have observed in individuals unable to upregulate HLA-C levels on mature NK cells suggests that endogenous HLA-C plays a role in the tuning of NK cell function . These results are consistent with the evolution of the HLA-C gene to function as a ligand for the KIR family of MHC class I receptors expressed by human NK cells . The change in RNA splicing patterns and the increase in HLA-C expression levels as NK cells mature provides additional evidence for a role of HLA-C in controlling human NK cell function . It is remarkable that alternative RNA splicing generates both on/off ( intron retention and exon skipping ) and rheostat-like ( variation in 5´-UTR length ) control of HLA-C expression in NK cells , consistent with endogenous HLA-C levels playing an important role in NK function or differentiation . There is a considerable body of evidence demonstrating an association of increased NK cell activity with the presence of KIR that recognize self HLA ( NK education ) . However , this correlation has been attributed to the recognition of HLA on target cells . MHC class I on the NK cell surface has been shown to control NK activity in mouse NK cells , and this is explained by the ability of Ly49 to recognize class I MHC in cis due to the presence of a flexible stalk in the Ly49 proteins . Although cis interactions with MHC have been demonstrated for the Ly49 receptors [24] , and cis interaction is required for murine NK cell licensing [25] , there has been no direct evidence of cis interaction of KIR with HLA . Since KIR do not have a flexible stalk , it is believed that cis interaction of KIR with HLA on the cell surface does not occur . It may be possible , however , for inhibitory signaling to occur in endosomes if both KIR and HLA are present . Vesicles containing target cell HLA-C have been observed in KIR2DL1-expressing NK cells , indicating acquisition and internalization of ligand by KIR [26] . It is therefore possible that high levels of endogenous NK cell HLA-C may contribute to inhibitory signaling in these endosomes . The results presented here , together with the previous observation that modulation of HLA expression in NK cells suppresses their activity [12] , strongly suggests that KIR:HLA interactions are occurring in human NK cells . The maturation of NK cells from a CD56bright to a CD56dim phenotype is associated with the acquisition of lytic activity [27] . HLA-C levels increase in CD56dim NK cells as well as mature CD57-positive NK cells , and this upregulation is associated with higher levels of translatable NK-Pro HLA-C transcripts due to increased inclusion of exon 1 . Therefore , it appears that HLA-C levels increase when the NK cell acquires lytic activity , or alternatively the acquisition of lytic activity is triggered by increased HLA-C levels . At first glance , the increased functional activity of NK cells with reduced HLA-C due to the lack of NK-Pro transcripts seems to be at odds with the observed increase in HLA-C levels on NK cells as they become more mature and functionally active . However , these results are consistent with a model wherein the upregulation of HLA-C in mature NK cells occurs to regulate their function rather than having a direct effect on education . KIR gene expression is activated by a stochastic mechanism in developing NK cells , and there is sequential receptor acquisition until a sufficient inhibitory signal is achieved , which results in a fully functional , educated NK cell [28] . The observed increase in HLA-C expression that occurs with increasing numbers of expressed KIR genes in an NK cell suggests that increased HLA-C levels are a result of NK cell education , rather than driving it . In order for upregulated levels of NK cell HLA-C to play a role in NK education , it would have to occur in the uneducated NK population . Furthermore , it has been shown that differing levels of HLA-C have no effect on the licensing/education of NK cells [29] . The higher levels of HLA-C observed on educated versus uneducated NK cells , further supports upregulation of HLA-C on NK cells as a product of NK education rather than a cause . Fig 8 shows a schematic detailing the changes in NK-Pro mRNA structure and HLA-C expression as NK cells differentiate . The immature CD56bright , NKG2A+ve , KIR-ve cells have low levels of HLA-C produced primarily from proximal promoter transcripts , since NK-Pro cDNAs in immature cells are largely untranslatable . At the intermediate stage of differentiation , KIR expression is stochastically activated in CD56dim cells , and the presence of a ligand for the expressed KIR produces an educated NK cell . This leads to upregulated HLA-C in the mature NK cell due to an increase in the level of translatable HLA-C NK-Pro mRNAs . Increased levels of HLA-C in the mature NK cell are predicted to control lytic activity due to cis inhibitory signaling . The tissue-specific differences in mRNA splicing of the NK-Pro transcript and the differences in HLA-C protein expression on NK cells from various tissues that we have observed could represent a tuning mechanism that regulates the responsiveness of NK cells in certain tissues . It will therefore be of interest to examine if the observed differences in HLA-C levels on NK cells from different tissues correlates with their ability to recognize and lyse targets with reduced HLA-C expression . The increased activity of NK cells with low HLA-C expression due to the presence of an inactivating SNP in NK-Pro suggests that the threshold of NK cell activation is lower , which might also produce a state of decreased lytic potential over time due to more frequent degranulation . Conversely , an increased accumulation of granules , and thus lytic potential would be predicted to occur in NK cells with a high level of HLA-C . Testing the serial-killing activity of high versus low HLA-C expressing NK cells could address this possibility . Allele-specific differences in 5´-UTR exon content also imply selection for an optimal level of HLA-C expression in NK cells , perhaps in conjunction with allelic differences in KIR-binding affinity , in order to achieve an appropriate level of inhibitory signaling . A detailed examination of NK cell HLA-C levels in individuals that are homozygous for HLA-C alleles with distinct splicing patterns and comparing expression levels with the affinity of the allele for the corresponding KIR would be required to investigate this . The existence of HLA-C alleles of both C1 ( C*07/C*08 ) and C2 ( C*02/C*05 ) supratypes with an inactivated NK-specific promoter suggests that there may be circumstances wherein the absence of upregulated expression of NK cell HLA-C would be beneficial . It will be of interest to determine whether there are any associations of NK-Pro deficient HLA-C alleles with clinical outcomes in infectious disease or bone marrow transplantation .
HeLa , EL-4 , MOLT-4 , and the JAR/BeWo human trophoblast cell lines were obtained from ATCC ( Manassas , VA , USA ) and grown in the recommended media . YT cells were cultured in RPMI 1640 media containing 10% fetal bovine serum , 100 U/ml penicillin , 100 U/ml streptomycin , sodium pyruvate and L-glutamine . Healthy volunteers were recruited through the NCI-Frederick Research Donor Program ( http://ncifrederick . cancer . gov/programs/science/rdp/default . aspx ) . The KIR and HLA genotype of each donor was determined as previously described [30] . NK cells were separated from the peripheral blood of healthy donors by Histopaque ( Sigma-Aldrich , St Louis , MO , USA ) gradient centrifugation using the RosetteSep Human NK Cell Enrichment Cocktail ( STEMCELL Technologies , Vancouver , BC , Canada ) . Human tissue samples were obtained from surgically removed tissue ( liver , spleen , endometrium , decidua , tonsil ) at the Karolinska University Hospital , Stockholm , Sweden . Written and oral informed consent was obtained from all patients , and the study was approved by the Regional Ethics Review Board , Stockholm , Sweden ( approval numbers: 2017-1659-32 , 2013/2285-31/3 , 2006/229-31/3 , 2013/1324-31/2 , 2017-649-31/1 ) . Lymphocytes from liver , decidua , and endometrium were isolated using enzymatic digestion as previously described [31] . Tonsil and spleen were mechanically dissociated using scalpels followed by filtration . Mononuclear cells were obtained by density centrifugation using Histopaque ( Sigma-Aldrich ) . Total RNA from 20 human tissues ( Human total RNA master panel II ) was obtained from Clontech ( Mountain View , CA , USA ) . Total RNA from purified NK cells or the YT cell line was isolated from 1–5 x 106 cells with the RNeasy kit ( Qiagen , Valencia , CA , USA ) . A cDNA synthesis reaction was carried out using Random Hexamer primer , Taqman Reverse Transcription Reagents kit ( Applied Biosystems , Foster City , CA , USA ) according to the manufacturer’s instructions . A forward primer in exon -1a of the NK-Pro transcript ( 5´-AGAAGGGCTGGAGAAGCAGGAG-3´ ) was used together with an exon 13 reverse primer upstream of the major HLA-C TSS ( 5´-GGACTGCGGAGACGCTGATTGG-3´ ) for the initial detection of NK-Pro transcripts . Additional HLA-C specific exon-1a forward ( 5´-GGGATGAGAGGGGCAGASAG-3´ ) and exon 2 reverse ( 5´-GTGCCTGGCGCTTGTASTTC-3´ ) primers were used to confirm the NK-specificity of HLA-C transcripts ( Fig 1C ) . For full-length transcripts , an alternative 3´ primer located immediately following the HLA-C stop codon was used ( 5´-GTCCCACACAGGCAGCTGTCTC-3´ ) . To assay the level of exon 1 skipping , an exon 2 reverse primer was used ( 5´-GAACTGCGTGTCGTCCACGTAG-3´ ) . PCR products were cloned into the pCR2 . 1-topo vector ( Invitrogen , Carlsbad , CA , USA ) and sequenced . The sequences of the alternatively spliced NK-Pro transcripts have been deposited in GenBank , and can be found under accession numbers MF536989-MF536999 for the peripheral blood NK cell cDNAs , and accession numbers MF563479-MF563493 for the spleen and bone marrow cDNAs . Full-length HLA-C cDNAs with varying 5´-UTR lengths were cloned into the pEF6/V5-His TOPO-TA vector ( Thermo Fisher Scientific , Waltham , MA , USA ) and verified by sequencing . 1 ug of each construct was transfected into the human JAR trophoblast cell line using HilyMax Transfection Reagent ( Dojindo Molecular Technologies Inc . , Rockville , MD , USA ) . Cells were harvested with Nonidet-P40 ( NP-40 ) lysis buffer ( 1% NP-40 , 50 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl ) supplemented with complete mini protease inhibitor cocktail tablets ( Roche Diagnostics , Indianapolis , IN , USA ) , and protein concentrations were determined using a Nanodrop 2000 spectrophotometer ( Thermo Fisher Scientific ) . Equal amounts of total protein were separated using sodium dodecyl sulfate-PAGE ( SDS-PAGE ) on 4–12% Tris-Glycine gels ( Invitrogen ) and transferred to Immobilon-P membrane ( Sigma-Aldrich ) . Membranes were blocked for one hour in 5% milk solution in PBST ( Phosphate Buffered Saline , pH 7 . 4 , 0 . 1% Tween 20 ) and subsequently probed for 1 . 5 hours with an anti-HLA-C antibody ( Abcam , Cambridge , MA , USA; ab126722 ) diluted 1:5000 in 5% milk PBST solution . Blots were washed 4 times for 5 minutes each with PBST and then were incubated for 20 minutes with an anti-rabbit HRP-linked IgG antibody ( Cell Signaling Technology , Danvers , MA , USA ) diluted 1:20 , 000 in 5% milk PBST solution . Blots were washed 4 times for 5 minutes with PBST and proteins were visualized using Amersham ECL Western blotting detection reagents ( GE Healthcare , Pittsburgh , PA , USA ) . Normalization for protein loading was accomplished by first stripping blots with SDS stripping buffer for 10 minutes and then washing 3 times with PBS , pH7 . 4 for 5 minutes . Blots were then probed for 1 hour with a monoclonal anti-β-Actin antibody ( Sigma-Aldrich A2228 ) at a concentration of 1 μg/ml followed by an anti-mouse HRP linked IgG antibody ( Cell Signaling Technology ) for 20 minutes diluted 1:20 , 000 in 5% milk PBST . Levels of β-Actin were visualized using Amersham ECL Western detection ( GE healthcare ) . Nuclear extracts were prepared from cell lines using the CellLytic NuCLEAR extraction kit ( Sigma-Aldrich ) . Protein concentration was measured with a Bio-Rad protein assay , and samples were stored at −70°C until use . Double-stranded DNA oligonucleotide probes containing either the AP1 site ( 5´-GACACGACCTGAGTCACATTAGC-3´ ) or the the combined SP1/Ets site ( 5´-GACATGGGCAGGAAGTGAGGGAC-3´ ) were synthesized ( IDT , Newark , NJ , USA ) . Probes were labeled with α-[32P]deoxycytidine triphosphate ( 3000 Ci/mmol; PerkinElmer , Waltham , MA , USA ) by fill-in using the Klenow fragment of DNA polymerase I ( Invitrogen ) . 32P-labeled double-stranded oligonucleotides were purified using mini Quick Spin Oligo Columns ( Roche ) . DNA–protein binding reactions were performed in a 10-μl mixture containing 5 μg nuclear protein and 1 μg poly[dI-dC] ( Sigma-Aldrich ) in 4% glycerol , 1 mM MgCl2 , 0 . 5 mM EDTA , 0 . 5 mM DTT , 50 mM NaCl , 10 mM Tris-HCl ( pH 7 . 5 ) . Nuclear extracts were incubated with 1 μl 32P-labeled oligonucleotide probe ( 10 , 000 cpm ) at room temperature for 20 min and then loaded on a 5% polyacrylamide gel ( 37:5:1 ) . Electrophoresis was performed in 0 . 5x TBE for 2 h at 130 V , and the gel was visualized by autoradiography . Lymphocytes isolated from whole blood via Ficoll-Paque Plus gradient centrifugation ( GE Healthcare ) from 16 healthy volunteers were stained extracellularly with antibodies to determine expression of HLA-C on various subsets . The antibodies used were: CD3 [clone UCHT1] BV605 , CD19 [clone HIB19] PE-Cy7 , CD56 [clone HCD56] BV711 ( BioLegend , San Diego , CA , USA ) ; CD4 [clone SK3] PE-Cy7 , CD8 [clone RPA-T8] APC ( BD Biosciences , Franklin Lakes , NJ , USA ) ; HLA-C [clone DT9] conjugated to AlexaFluor488 . Data from the samples collected on a BD LSRFortessa flow cytometer were analyzed using FlowJo v10 . 1 . Mononuclear cells from additional blood donors and from indicated tissues were analyzed by flow cytometry at the Karolinska Institutet , Stockholm , Sweden . The following additional antibodies were used: CD3 [clone UCHT1] PE-Cy5 ( Beckman Coulter , Brea , CA , USA ) ; CD19 BV510 ( Biolegend ) ; NKG2A [clone Z199] BB515 , CD56 [clone NCAM16 . 2] BUV737 , CD57 [clone NK-1] BV605 , HLA-C [clone DT9] PE , KIR2DL2/3/S2 [clone CH-L] BUV395 ( BD Biosciences ) ; KIR2DL1/S1 [clone EB6] PC-5 . 5 ( Beckman Coulter ) ; KIR3DL1 [clone DX9] BV421 ( Biolegend ) ; KIR2DS4 [clone REA860] PE-Vio615 ( Miltenyi Biotec , Cambridge , MA , USA ) . Live/Dead marker Aqua ( Thermo Fisher Scientific ) was also used . Samples were acquired on a 5 laser BD LSRFortessa flow cytometer and analyzed using FlowJo v10 . 1 . The frequency of degranulated human NK cells was quantitated by flow cytometric detection of cell surface CD107a . Purified donor human NK cells ( 105 ) were co-cultured with 721 . 221 tumor cells in a round bottom 96-well plate at an effector target ratio of 1:1 in RPMI 1640 supplemented with 10% FBS , 10 mM Hepes , 2 mM L-glutamine , 0 . 1 mM nonessential amino acids , 1 mM sodium pyruvate , Penicillin 100 U/ml , Streptomycin 100 ug/ml , and 100 IU/ml recombinant human interleukin 2 ( Tecin; Teceleukin , obtained from the Biological Resources Branch of the National Cancer Institute ) . Anti-human CD107a PE or BV421 ( Biolegend ) was added at 3 ul to 200 ul of the co-culture and the mixture was gently mixed by pipetting and then spun down at 50 g for 5 minutes and incubated for 1 h at 37°C in 5% CO2 after which Golgi-Stop ( BD Biosciences ) was added and cells were incubated for an additional 4 h at 37°C in 5% CO2 . Cells were washed with sorter buffer ( HBSS containing 0 . 5% BSA , 1mM EDTA , 25mM Hepes , 0 . 05% sodium azide ) and Fc receptors were blocked with human FcR Blocking Reagent ( Miltenyi Biotec ) for 5 minutes and then then stained with anti-human CD56 PE or APC ( clone HCD56 , Biolegend ) for 10 minutes . Stained cells were washed twice with sorter buffer and fixed with BD Cytofix ( BD Biosciences ) for 20 minutes . Cells were washed twice with sorter buffer and flow cytometric analysis was performed on a LSRFortessa instrument ( BD Biosciences ) . Degranulated NK cells were analyzed using FlowJo software by gating on single cells ( FSC-H x FSC-A ) in the lymphocyte gate and then the frequency of CD56dim CD107a+ cells was quantitated . | It has been proposed that the human HLA-C gene evolved in higher primates to serve as a ligand for the KIR family of inhibitory receptors for MHC class I that are expressed by natural killer ( NK ) cells and regulate their activity . NK cell potential is determined by the level of MHC class I on surrounding cells and on the NK cell itself . We have uncovered a highly complex system regulating HLA-C expression in NK cells . A NK-specific promoter produces a large array of differentially-spliced transcripts that vary in their ability to be translated into HLA-C protein . As NK cells differentiate and become more cytotoxic , the level of HLA-C expression increases , and this correlates with an increased abundance of translatable HLA-C mRNAs . A subset of HLA-C alleles have a promoter polymorphism that abrogates its activity , resulting in NK cells that are unable to upregulate HLA-C levels , and consequently , possess increased functional activity . Overall , our findings provide insight into the mechanisms of NK cell development , as well as a method to identify individuals with high NK activity , that may provide superior outcomes in hematopoietic stem cell transfer . | [
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"... | 2018 | Identification of an elaborate NK-specific system regulating HLA-C expression |
Endosomal sorting complex required for transport ( ESCRT ) machinery supports the efficient budding of Marburg virus ( MARV ) and many other enveloped viruses . Interaction between components of the ESCRT machinery and viral proteins is predominantly mediated by short tetrapeptide motifs , known as late domains . MARV contains late domain motifs in the matrix protein VP40 and in the genome-encapsidating nucleoprotein ( NP ) . The PSAP late domain motif of NP recruits the ESCRT-I protein tumor susceptibility gene 101 ( Tsg101 ) . Here , we generated a recombinant MARV encoding NP with a mutated PSAP late domain ( rMARVPSAPmut ) . rMARVPSAPmut was attenuated by up to one log compared with recombinant wild-type MARV ( rMARVwt ) , formed smaller plaques and exhibited delayed virus release . Nucleocapsids in rMARVPSAPmut-infected cells were more densely packed inside viral inclusions and more abundant in the cytoplasm than in rMARVwt-infected cells . A similar phenotype was detected when MARV-infected cells were depleted of Tsg101 . Live-cell imaging analyses revealed that Tsg101 accumulated in inclusions of rMARVwt-infected cells and was co-transported together with nucleocapsids . In contrast , rMARVPSAPmut nucleocapsids did not display co-localization with Tsg101 , had significantly shorter transport trajectories , and migration close to the plasma membrane was severely impaired , resulting in reduced recruitment into filopodia , the major budding sites of MARV . We further show that the Tsg101 interacting protein IQGAP1 , an actin cytoskeleton regulator , was recruited into inclusions and to individual nucleocapsids together with Tsg101 . Moreover , IQGAP1 was detected in a contrail-like structure at the rear end of migrating nucleocapsids . Down regulation of IQGAP1 impaired release of MARV . These results indicate that the PSAP motif in NP , which enables binding to Tsg101 , is important for the efficient actin-dependent transport of nucleocapsids to the sites of budding . Thus , the interaction between NP and Tsg101 supports several steps of MARV assembly before virus fission .
Tsg101 is a component of the endosomal sorting complex required for transport ( ESCRT ) machinery that mediates biogenesis of multi-vesicular bodies , specifically the formation and scission of the intraluminal vesicles , and is thus essential for the degradation and recycling of plasma membrane resident receptors [1] . In addition , ESCRT has been shown to function in the late steps of cytokinesis and to support the efficient budding of several enveloped viruses at the plasma membrane [2] , [3] . For many viruses , Tsg101 serves as the central player for mediating the interaction between viral matrix proteins and the ESCRT machinery [4] , [5] . This interaction is mediated by a tetrapeptide motif , PT/SAP , referred to as late domain because its mutation impairs viral release at a late stage of budding [4] , [6] . The late domain phenotype is best characterized in retroviral infections in which viral particles are arrested in the budding process upon mutation of the PTAP motif in Gag and remain connected to the infected cell by only a thin membrane stalk [2] . Recently , Tsg101 has been reported to interact with Rab11 interacting effectors , the class II Rab11-FIPs , suggesting a functional link between transport pathways and the ESCRT machinery [7] . Additionally , Tsg101 interacts with regulators of cytoskeleton dynamics , such as IQGAP and ROCK1 [8] , and is essential for translocating the tyrosine kinase Src to cellular protrusions [9] . Together , these results indicate a role for Tsg101 in intracellular transport processes . Tsg101 expression and functions are highly regulated by an intrinsic autoregulatory mechanism and by ubiquitination involving several distinct ubiquitin ligases [10]–[14] . A cargo-dependent degradation of Tsg101 as a feedback mechanism for modulating endosomal sorting has also been described [15] . Marburg virus ( MARV ) , a filovirus , causes severe hemorrhagic fever in humans and non-human primates , with mortality rates of up to 90% [16] . Despite advances in experimental vaccine approaches , no vaccine or antiviral treatment against filoviral hemorrhagic fever are available for human use [17] . The filamentous MARV particles , which are approximately 900 nm in length and 90 nm in diameter , are composed of seven proteins . Five viral proteins are associated with the nucleocapsid . The nucleoprotein ( NP ) encapsidates the non-segmented negative-strand RNA genome and forms long helical nucleocapsids together with the viral proteins VP35 , L , VP30 and VP24 [18] , [19] . The nucleocapsid is surrounded by a layer of the matrix protein VP40 , which is associated with the viral envelope , where homotrimers of the surface glycoprotein ( GP ) are inserted [18] , [20] . Virogenesis starts with the appearance of inclusions in the cytosol , which represent accumulations of MARV nucleocapsids [21] , [22] . For Ebola virus , the other member of the Filoviridae family , the inclusions have recently been shown to represent sites of viral replication [23] . Ultrastructural analysis has revealed that the inclusions contain nucleocapsids of variable electron density . Nucleocapsids of higher electron density are transported to the plasma membrane , where they become associated with VP40 and enveloped by budding through the GP-enriched plasma membrane [22] , [24]–[26] . Viral budding primarily occurs at the sides or tips of filopodia [22] , [27] , [28] . The intracellular transport of nucleocapsids is driven by the polymerization of actin [26] . The efficient release of VLPs induced by VP40 , the key player in MARV assembly and budding , requires the support of the ESCRT machinery that is recruited to VP40 by the late domain motif PPPY [29] , [30] . Interestingly , the co-expression of NP strongly enhanced the release of VP40-induced VLPs [29] , [30] . Our previous data showed that a PSAP late domain in the C-terminus of NP was responsible for this enhancing effect by recruiting Tsg101 to the plasma membrane [31] . Here , we investigated these results in the context of MARV infection using reverse genetics . Our study revealed that recombinant MARV with a mutated PSAP late domain motif in NP ( rMARVPSAPmut ) could not recruit Tsg101 to the nucleocapsids , was attenuated in growth and impaired in cell-to-cell spread . By using several experimental approaches , we found that rMARVPSAPmut displayed a novel late domain phenotype with an altered morphology of the viral inclusions and an accumulation of nucleocapsids in the cytoplasm , at the plasma membrane and in the process of envelopment . In addition , nucleocapsids in rMARVPSAPmut–infected cells had an altered pattern of movement most likely due to reduced recruitment of IQGAP1 , a regulator of actin dynamics . Together , these data indicate the involvement of the NP late domain at several pre-fission steps during MARV assembly .
Tsg101 interacts with the MARV NP through a C-terminal PSAP late domain . The mutation of this domain impairs binding of Tsg101 and simultaneously abolishes the positive impact of NP on the release of VLPs induced by the MARV matrix protein VP40 [31] . To further analyze the function of Tsg101 during MARV infection , we down-regulated Tsg101 expression in MARV-infected cells using small interfering RNA ( siRNA ) technology . Tsg101-specific siRNA reduced the levels of Tsg101 expression to 30% compared with control siRNA ( Fig . 1A ) . Western blot analysis of the cell lysates detected two prominent forms of Tsg101 at 46 and 65 kDa , respectively ( Fig . 1B , lane 1 ) . Transfection of Tsg101-specific siRNA reduced the levels of both forms of Tsg101 , and Tsg101 incorporation into viral particles was reduced to undetectable levels ( Fig . 1B , lanes 3 and 4 ) . Because Tsg101 ( 46 kDa ) can be multi-monoubiquitinated , it was presumed that the 65-kDa form represented ubiquitinated Tsg101 [10] , [12]–[14] . To confirm this presumption , Flag-tagged Tsg101 and HA-tagged ubiquitin ( HA-Ub ) were co-expressed in HEK293 cells , and the cell lysates were used for immunoprecipitation with anti-HA agarose . Western blot analysis of the cell lysates using an anti-Tsg101 antibody mainly revealed the expected 46-kDa form of Tsg101 ( Fig . 1C , lanes 1 and 2 , upper panel ) . Immunoprecipitation of HA-tagged ubiquitinated cellular proteins and staining of the precipitates with anti-Tsg101 antibody revealed Tsg101-specific proteins with a major signal at approximately 65 kDa , corresponding to multi-ubiquitinated Tsg101 ( Fig . 1C , lane 2 , lower panel ) . Although the intracellular amounts of viral proteins remained unaffected by Tsg101 knockdown ( Fig . 1B , lane 2 ) , the release of viral proteins was reduced to 32%±18 . 6% for NP and 44%±10 . 4% for VP40 ( n = 3; Fig . 1B , lane 4 ) . Corresponding with this impaired release of viral proteins , the viral titers in the supernatants of Tsg101-depleted cells were 3- to 4-fold lower than in the control cells ( Fig . 1D ) . Additionally , Tsg101 depletion resulted in the accumulation of nucleocapsids in the cytoplasm . Although 15% of the infected and Tsg101-depleted cells showed intracytoplasmic accumulation of nucleocapsids , only 1 . 5% of the infected cells treated with the control siRNA showed a similar phenotype ( Fig . 1E–F , white arrows , and lower panel right ) . Together , these results support the hypothesis that Tsg101 is needed for the efficient release of MARV nucleocapsids , and correspondingly , a lack of Tsg101 leads to the intracellular accumulation of nucleocapsids . Because Tsg101 is a multifunctional protein that is involved in several cellular pathways , its down-regulation may have multiple effects on viral replication that are not directly related to its interaction with MARV proteins [32] . We therefore specifically inhibited the interaction of Tsg101 with NP by mutating the C-terminal PSAP motif of NP in the MARV genome using reverse genetics [33] . Our previous studies revealed that this mutation significantly inhibited the interaction between NP and Tsg101 [31] . The mutated virus ( rMARVPSAPmut ) was rescued , and its phenotype was analyzed ( Fig . 2 ) . The rMARVPSAPmut growth kinetics at a low MOI ( 0 . 01 ) were reduced , and the measured TCID50 titers were between one and two logs lower than for rMARVwt ( Fig . 2A ) . These differences were reflected in the amount of viral protein in the supernatant of infected cells . At all tested time-points post-infection ( p . i . ) , the rMARVPSAPmut-infected cells released less VP40 and NP than the rMARVwt-infected cells , whereas the cellular amounts of viral proteins remained similar , indicating that rMARVPSAPmut and rMARVwt had similar RNA synthesis and translation rates ( Fig . 2B , left ) . These results were in line with previous results showing that the PSAP mutation does not affect NP activity in transcribing/replicating a MARV-specific minigenome [31] . A delay in the release of rMARVPSAPmut particles was also observed at a higher MOI ( 0 . 1 ) . At three days p . i . , cytopathic effects were less pronounced in the cells infected with rMARVPSAPmut than with rMARVwt ( Fig . 2C–D ) . We then analyzed the supernatants of rMARVPSAPmut-infected cells and found that although the release of VP40 was comparable with rMARVwt , the NP levels were severely reduced ( Fig . 2E ) . This result suggests that the budding activity of VP40 is most likely not affected by mutation of the PSAP motif in NP . In contrast , NP incorporation into particles is reduced , resulting in the release of less infectious particles ( Fig . 2E ) . Together , these results suggested a defect in the release of infectious rMARVPSAPmut particles . This idea was supported by the analysis of plaque sizes formed by the different viruses . Plaques produced by rMARVPSAPmut were 5-fold smaller than those produced by rMARVwt ( Fig . 3A–B ) . A comparison of the released viral particles by electron microscopy showed no morphological difference between rMARVPSAPmut and rMARVwt virions ( Fig . 3C ) . Our previous study revealed that Tsg101 was incorporated into purified MARV particles [31] . To verify that the mutation of the PSAP late domain in NP specifically impaired the interaction with Tsg101 , we analyzed whether rMARVPSAPmut particles remained able to incorporate Tsg101 . Lysates from infected cells and released virus particles were subjected to Western blot analyses . In the lysates from rMARVPSAPmut- and rMARVwt-infected cells , both the non-ubiquitinated ( 46 kDa ) and multi-ubiquitinated ( 65 kDa ) forms of Tsg101 were detected . Immunoprecipitation of infected cells transfected with Tsg101-Flag and HA-Ub confirmed that the 65-kDa form of Tsg101 represents multi-ubiquitinated Tsg101 ( Fig . S1 , lane 3 ) . Both forms of Tsg101 were incorporated into rMARVwt particles ( Fig . 4A , lane 1 ) . In contrast , rMARVPSAPmut particles mainly incorporated the ubiquitinated form of Tsg101 ( Fig . 4A , lane 2 ) . To confirm that ubiquitinated Tsg101 is indeed incorporated into virus particles , rMARVwt- and rMARVPSAPmut-infected cells were transfected with HA-Ub , and the released virus particles were purified from culture supernatants and analyzed for the presence of Tsg101 , HA-Ub and NP . Reactivity with an HA-specific antibody indicated that the 65-kDa Tsg101 form was ubiquitinated ( Fig . 4B , anti-HA ) . Ubiquitinated Tsg101 was incorporated at low levels in both rMARVwt and rMARVPSAPmut particles . In contrast , non-ubiquitinated Tsg101 was the prominent form in rMARVwt particles and nearly undetectable in rMARVPSAPmut particles ( Fig . 4B , anti-Tsg101 ) . In conclusion , these data indicate that the PSAP late domain of MARV NP specifically recruits non-ubiquitinated Tsg101 into viral particles . We then investigated whether the reduced amount of Tsg101 in rMARVPSAPmut particles altered virus infectivity . rMARVPSAPmut and rMARVwt particles , each at a concentration of 6×102 TCID50/µl , were subjected to Western blot analysis . Fig . 4C shows that the equal infectious doses of the two viruses corresponded with the same amount of viral proteins , indicating that the infectivity-to-particle ratio between the two viruses was similar . This was confirmed by infection of Huh-7 cells with rMARVwt and rMARVPSAPmut suspensions , which were normalized to equal amounts of NP ( Fig . 4D ) . Infected cells were fixed at 17 h p . i . , which corresponds to the duration of one viral replication cycle , and the cells were subsequently immuno-stained with an anti-MARV polyclonal antibody . The number of infected cells was higher for rMARVPSAPmut compared with rMARVwt ( 1 . 7±0 . 4-fold more rMARVPSAPmut-infected cells , n = 4 ) , indicating that the rMARVPSAPmut was not less infectious than the wild-type MARV ( Fig . 4D ) . In summary , these data show that the presence of Tsg101 in viral particles does not influence viral infectivity , suggesting that the attenuation of rMARVPSAPmut is likely caused by reduced release of viral particles . We next analyzed which step in the transport and release of nucleocapsids was impaired by the missing interaction with Tsg101 . Because depletion of Tsg101 led to an accumulation of cytoplasmic MARV nucleocapsids ( Fig . 1E ) , we tested whether nucleocapsids in rMARVPSAPmut-infected cells exhibited a similar phenotype ( Fig . 5 ) . Indeed , indirect immunofluorescence analysis revealed an accumulation of elongated filamentous structures , mostly in the periphery of rMARVPSAPmut-infected cells ( Fig . 5A , white arrows ) . Based on their NP content and dimensions , these structures have recently been identified as nucleocapsids [26] . Cells infected with rMARVwt did not exhibit this accumulation of nucleocapsids ( Fig . 5B ) . In addition , although inclusions in rMARVwt-infected cells were pleomorphic ( Fig . 5D ) , inclusions in rMARVPSAPmut-infected cells were more round and compact and frequently exhibited a bright NP signal at their rim ( Fig . 5C ) . Using electron microscopy , we further analyzed how mutation of the PSAP motif affected the morphology of viral inclusions . These analyses confirmed that inclusions in rMARVwt-infected cells primarily appeared as a disperse pleomorphic viroplasm in which nucleocapsids were packed with variable density , similar to the MARV inclusions described previously ( Fig . 6A–B ) [21] , [22] , [34] . In contrast , the majority of viral inclusions in rMARVPSAPmut-infected cells had a compact and spherical appearance , and they always contained densely packed nucleocapsids ( Fig . 6C–D ) . Using the stereological morphometry of electron micrographs , we quantitatively determined the volume density of nucleocapsids . Packing of nucleocapsids was 1 . 7-fold higher in inclusions from rMARVPSAPmut-infected cells than in those from rMARVwt-infected cells ( 75%±6% and 44%±8% , respectively , Fig . 6E ) . Electron-dense nucleocapsids were detected 3 . 3-fold more frequently in inclusions from rMARVPSAPmut-infected cells compared with rMARVwt-infected cells ( 8 . 6±5 and 2 . 6±2 per 2 . 5 µm2 , respectively , Fig . 6F ) . These results confirmed the immunofluorescence analyses and indicated that the missing interaction between Tsg101 and NP modifies the morphodynamics of viral inclusions . We next assessed the localization of nucleocapsids outside viral inclusions in thin sections of infected cells . In rMARVwt-infected cells , mature nucleocapsids were mainly detected in released virions ( Fig . 7A–B ) . In contrast , in rMARVPSAPmut infected cells nucleocapsids were frequently detected in the cytosol , close to the cell surface and in protruding buds ( Fig . 7C–D ) . Because the association of cytoplasmic nucleocapsids and budding viruses with membranes can be assessed unequivocally for only full-length nucleocapsids , we quantified the nucleocapsid distributions in tomograms reconstructed from thick sections of rMARVwt- and rMARVPSAPmut-infected cells ( Fig . 7E–J ) . Intracellular nucleocapsids ( outside the inclusions ) were detected at three different locations: ( i ) cytoplasmic , without any connection to the plasma membrane ( white arrow in 7A and 7D ) ; ( ii ) plasma-membrane-bound nucleocapsids attached on one side to the plasma membrane ( light-blue arrows in 7C ) ; ( iii ) protruding buds with partially enveloped nucleocapsids ( blue arrow in 7D ) . We differentiated those intracellular nucleocapsids from nucleocapsids that were completely enveloped but still had a thin connection to the plasma membrane ( grey arrows in 7C–D ) . The latter phenotype had been referred to as late domain phenotype in previous publications [2] . Finally , we detected nucleocapsids in the extracellular space corresponding to free virus ( i . e . , nucleocapsid inside released viral particles; black arrows in 7A and 7B ) . The relative amount of intracellular nucleocapsids was significantly higher in cells infected with rMARVPSAPmut than in cells infected with rMARVwt ( Fig . 7I–J ) . Interestingly , the percentage of fully protruded buds was not different for both viruses , which argued against a classical fission defect of rMARVPSAPmut [2] . Together , the ultrastructural data are in line with the immunofluorescence analyses ( Fig . 5A–B ) indicating that the missing interaction with Tsg101 retards nucleocapsids in the cytoplasm and/or slows down envelopment of the nucleocapsids at the plasma membrane rather than influencing the final fission step . The release of MARV nucleocapsids occurs by budding at the side or tips of filopodia , which is difficult to detect in tomograms of semi-thick sections [18] , [27] , [28] . Localization of nucleocapsids in filopodia was therefore analyzed by electron microscopy of whole-mounted cells ( Fig . 8 ) . Using this method , most of the budding rMARVwt nucleocapsids were found in association with filopodia ( 80% , Fig . 8A ) . In contrast , only 32% of budding nucleocapsids in rMARVPSAPmut-infected cells were detected in association with filopodia . The majority of nucleocapsids bud at the planar cell surface ( 68% ) . Western Blot analysis of virus infected cells confirmed prominent accumulation of NP in MARVPSAPmut-infected cells at 19 h p . i . ( Fig . 8B ) . We then analyzed the trajectories of migrating nucleocapsids by live-cell imaging . Cells were infected either with rMARVPSAPmut or rMARVwt and then transfected with a plasmid encoding the nucleocapsid-associated protein , VP30 , fused to the green fluorescent protein , GFP ( VP30-GFP ) . Fluorescent VP30-GFP was previously shown to be efficiently associated with nucleocapsids to allow monitoring of nucleocapsid transport in real time by live-cell imaging [26] . The transport speed of the rMARVPSAPmut nucleocapsids was not significantly different from that of the rMARVwt nucleocapsids [304±99 ( n = 34 ) and 313±116 nm/s ( n = 39 ) for rMARVwt and rMARVPSAPmut , respectively] . However , the pattern of nucleocapsid transport was altered . rMARVwt nucleocapsids displayed directed movement from the cell center to the cell margins and along the plasma membrane over long distances in line with previous observations [16 . 2±4 . 2 µm ( n = 50 ) , Fig . 8D] [26] . In contrast , the observed trajectories for nucleocapsids in rMARVPSAPmut-infected cells were considerably shorter [7 . 9±4 . 95 µm ( n = 50 ) ] , and many nucleocapsids located beneath the plasma membrane exhibited an immobile phenotype ( Fig . 8C , white asterisk middle panel , and movie S1 ) . To further analyze the role of Tsg101 in the transport of nucleocapsids , a protein complementation assay was used to visualize functional Tsg101 in living cells [35] , [36] . Fusion proteins of Venus-1 and Tsg101 and Venus-2 and Tsg101 were generated ( Venus1-Tsg101 and Venus2-Tsg101 ) that show immunofluorescence signals only upon interaction of the two Venus fragments which is enabled when the fusion partner Tsg101 interacts with itself . The two Tsg101-Venus constructs were co-expressed in cells infected with either rMARVwt or rMARVPSAPmut and the intracellular localization of fluorescent oligomerized Tsg101-Venus1/2 was analyzed by confocal laser scanning microscopy ( CLSM ) . Tsg101-Venus1/2 was localized in the inclusion bodies of rMARVwt-infected cells , whereas rMARVPSAPmut inclusions did not accumulate Tsg101-Venus1/2 ( Fig . 8E ) . In addition , Tsg101-Venus1/2 was co-localized with individual rMARVwt nucleocapsids outside the inclusions , which was not observed in rMARVPSAPmut-infected cells ( Fig . 8E , white arrows ) . We then investigated the behavior of Tsg101-Venus1/2 using live-cell imaging of rMARVVP30RFP-infected cells . During the observation period , Tsg101-Venus1/2-positive granular structures were increasingly accumulated within the viral inclusions ( Fig . S2A and movie S2 ) . In addition , co-transport of Tsg101-Venus1/2 and nucleocapsids was observed ( movie S3 ) . The maximal projection of the acquired sequences showed an overlay of the nucleocapsid signal with the Tsg101-Venus1/2 signal ( Fig . S2B ) . Collectively , these data demonstrated a co-transport of Tsg101 with nucleocapsids and suggest that PSAP-dependent recruitment of Tsg101 by NP improves the transport of MARV nucleocapsids . We have shown previously that transport of MARV nucleocapsids is dependent on the activity of the actin polymerization [26] . We were therefore looking for candidates that could link nucleocapsids via Tsg101 to the actin cytoskeleton . One likely candidate was the cellular factor IQ motif containing GTPase-activating-like protein ( IQGAP1 ) , which is involved in cytoskeletal dynamics and had been shown to interact with Tsg101 [8] . Control cells expressing fluorescent IQGAP1-YFP only displayed actin network-like distribution with some vesicular structures ( Fig . 9A , upper panel right ) . The mCherry-Tsg101 showed a dot-like and vesicular appearance as observed with Tsg101-Venus1/2 ( Fig . 9A , upper panel left ) . To analyze whether IQGAP1 was recruited by Tsg101 into NP-inclusions , we expressed IQGAP1-YFP and mCherry-Tsg101 together with NPwt or NPPSAPmut and VP40 and performed an immunofluorescence analysis . In cells expressing NPwt together with IQGAP1-YFP and mCherry-Tsg101 , the three proteins co-localized in NP-inclusions ( Fig . 9A , white arrow middle panel ) . When mCherry-Tsg101 and IQGAP1-YFP were co-expressed with NPPSAPmut , neither mCherry-Tsg101 nor IQGAP1-YFP were localized to the inclusions . However , mCherry-Tsg101 and IQGAP1-YFP were detected co-localized outside of NP-inclusions in vesicular-like structures that were devoid of NP ( Fig . 9A , white arrowhead lower panel ) . To confirm this observation , mCherry-Tsg101 and IQGAP1-YFP were co-transfected into rMARVwt and rMARVPSAPmut infected cells and analyzed by CLSM . In cells infected with rMARVwt we found co-localization of mCherry-Tsg101 and IQGAP1-YFP in inclusions ( Fig . 9B , white arrow in the merge panel ) and with individual nucleocapsids ( Fig . 9B , white arrow in the lower panel ) . In cells infected with the rMARVPSAPmut we detected mCherry-Tsg101 and IQGAP1-YFP neither in NP-inclusions nor in association with individual nucleocapsids . ( Fig . 9B , right row ) . We then asked whether siRNA-mediated down-regulation of IQGAP1 impaired MARV propagation . Western blot analysis showed IQGAP1 depletion in cells treated with IQGAP1-specific siRNA ( Fig . 9C ) . Depletion of IQGAP1 resulted in a reduced release of MARV NP into the culture supernatant of the infected cells ( Fig . 9C , right panel ) . It was then analyzed whether virus titers in the supernatant of the infected cells were also affected by the down regulation of IQGAP1 . TCID50 analysis showed that virus titers dropped by 2–3 fold upon IQGAP1 depletion ( Fig . 9D ) . In contrast , down regulation of IQGAP1 had no effect on the release of rMARVPSAPmut . This suggested that IQGAP1 acts through Tsg101 , which is bound via the PSAP motif in NP to the nucleocapsid ( Fig . 9B ) . The lower titer of rMARVPSAPmut in comparison with rMARVwt ( Fig . 9D ) corresponded to data presented in Fig . 2 . In immunofluorescence analysis , MARV-infected IQGAP1-depleted cells were characterized by accumulations of nucleocapsids in the cell periphery similar as observed with Tsg101 depletion or rMARVPSAPmut infection ( Fig . 9E , see white arrows ) . Finally , we investigated by live cell microscopy whether IQGAP1 was co-transported with MARV nucleocapsids . We transfected MARVVP30GFP -infected cells with IQGAP1-YFP and monitored the trajectories of individual nucleocapsids . We observed that IQGAP1 signals appeared like comet tails at the rear end of the rocketing nucleocapsids ( movie S4 , see along the white line ) . Altogether these results indicated that the PSAP motif of NP recruits Tsg101 and IQGAP1 to support efficient transport of nucleocapsids to the budding sites .
In this study , we explored the role of Tsg101 in the assembly process of MARV and showed that the PSAP-mediated interaction of NP with Tsg101 influences several intermediate steps of MARV assembly before viral fission . This phenotype is different from retroviruses , in which mutation of the PS/TAP motif in retroviral Gag domains results in defects of viral fission [2] , [3] . Using electron tomography , it was possible to show that the number of intracellular nucleocapsids was enhanced in the rMARVPSAPmut-infected cells . In contrast , the number of completely enveloped particles was similar between the rMARVPSAPmut- and rMARVwt-infected cells . This suggested that the NP interaction with Tsg101 influences mainly pre-fission steps . The observed phenotype is clearly different from the “classical” late domain phenotype , which is characterized by the accumulation of completely enveloped viral buds connected to the cell by only a thin neck [37]–[41] . We previously showed that the ESCRT machinery influences the budding function of VP40 , which recruits Nedd4-like ubiquitin ligases through a PPPY late domain motif [29] . Possibly , the recruitment of ESCRT by VP40 is sufficient to mediate the final fission step , whereas the interaction between NP and Tsg101 is necessary to support pre-fission steps , being mainly responsible for the transport of nucleocapsids into filopodia . Mutating the late domain motif PSAP in NP has previously been shown to impair its binding to Tsg101 and the release of MARV-specific VLPs [31] . Here , we demonstrate that rMARVPSAPmut , whose mutated NP is no longer able to bind Tsg101 , displayed altered inclusion morphology and an increased accumulation of mature nucleocapsids inside inclusions and in the cytosol ( Fig . 10 , point 1 ) . We hypothesized that this phenotype was the result of the missing interaction between NP and Tsg101 , which reduced nucleocapsid transport efficiency . This idea was confirmed by live cell imaging data showing that Tsg101 was co-transported with wild type MARV nucleocapsids which was not observed in rMARVPSAPmut–infected cells . Recently published data showed that the intracellular actin-driven transport of MARV nucleocapsids proceeds in two steps: ( i ) nucleocapsids migrate from inclusions to the plasma membrane at a rate of 200 nm/s , and ( ii ) in the periphery , nucleocapsids are transported along the plasma membrane and inside filopodia at a rate of 100 nm/s [26] . Our data indicate that the speed of nucleocapsid transport from inclusions to the plasma membrane remains unaltered in rMARVPSAPmut-infected cells . However , the lengths of the trajectories of individual nucleocapsids were significantly shortened when the NP interaction with Tsg101 was disrupted . More importantly , the transport of nucleocapsids in the periphery of rMARVPSAPmut-infected cells was strongly impaired by the missing interaction between NP and Tsg101 ( Fig . 10 , point 2 ) . This result was confirmed by EM studies . Using a whole-cell-mount method , we found that nucleocapsids in rMARVPSAPmut-infected cells were not efficiently recruited into filopodia , which represent the preferential budding sites for MARV ( Fig . 8A and Fig . 10 , point 3 ) [27] , [28] . Recent studies indicated that the entry of nucleocapsids into filopodia depends on interaction with VP40 , which takes place close to the plasma membrane [26] , [42] . Because the interaction of NPPSAPmut and VP40 is intact [31] , the severely hampered movement of nucleocapsids in the periphery of rMARVPSAPmut-infected cells is likely caused by the transport defect of nucleocapsids . The budding of viruses from filopodia is believed to contribute to more efficient cell-to-cell spread [43]–[45] . Indeed , the reduced delivery of nucleocapsids to filopodia may be responsible for the significantly impaired spread of rMARVPSAPmut which mainly budded from the planar cell surface ( Fig . 3A and Fig . 10 , point 4 ) . Although the function of Tsg101 in supporting the formation of intraluminal vesicles at multivesicular bodies and during cytokinesis has been well studied , it is unclear how Tsg101 may function to support intracellular transport of nucleocapsids , as reported in this study . However , several publications suggest a potential link between Tsg101 and cytoskeleton activity . Tsg101 has been reported to interact with regulators of cytoskeleton dynamics , such as IQGAP and ROCK1 [8] , and Tsg101 was essential for translocating the Src tyrosine kinase to cellular protrusions [9] . Together with small GTPases Rac1 and Cdc42 , IQGAP1 controls dynamics of actin filament polymerization by different actions including actin bundling , barbed-end capping and binding actin branching as well as nucleating proteins [46] , [47] . It has been shown that IQGAP1 supports egress of Moloney murine leukemia virus and classical swine fever virus [48] , [49] . Recently it was published that IQGAP1-depletion of cells resulted in reduced release of EBOV VP40-induced VLPs [50] . We detected here recruitment of IQGAP1 into MARV inclusions and to individual nucleocapsids in dependence of the PSAP motif in the nucleoprotein . We also observed reduced virus release upon IQGAP1 depletion in infected cells . Based on these data in combination with the finding that the actin cytoskeleton is the dominant driver of MARV nucleocapsid transport [26] , we hypothesize that Tsg101 mediates the interaction between viral nucleocapsids and IQGAP1 via the PSAP motif in NP . IQGAP1 then links Tsg101 to actin and simultaneously coordinates actin dynamics . Future studies are needed to test this hypothesis and elucidate how IQGAP1 and other cellular and/or viral factors mechanistically coordinate the intracellular transport of MARV nucleocapsids . This question is of particular interest because small molecule inhibitors interfering with the interaction of viral late domains with Tsg101 have recently been described as potential new antivirals [51] , [52] . Mutation of the PSAP motif in NP severely impaired the incorporation of Tsg101 into viral particles but left the incorporation of multi-ubiquitinated Tsg101 intact . This phenomenon is currently not understood . The function of multi-ubiquitinated Tsg101 is still debated; for example , multi-ubiquitinated Tsg101 has been suggested to regulate endosomal trafficking by regulating the amount of active intracellular Tsg101 [12] , [13] . Ubiquitinated Tsg101 has been reported to form insoluble complexes , which is in line with the observation that during siRNA down-regulation of Tsg101 , the ubiquitinated form can be detected , whereas the non-ubiquitinated form is below the detection limit ( Fig . 1B , [12] ) . Urata and colleagues reported in a previous study that Tsg101 can interact with VP40 in a PPPY-dependent manner and suggested later an indirect interaction with the involvement of an additional cellular factor [30] , [53] . This interaction may be responsible for the recruitment of ubiquitinated Tsg101 into budding particles possibly via the arrestin-related proteins as suggested by Rauch and Martin-Serano [54] . Although the PSAP motif is a known binding motif for Tsg101 [55] , , it cannot be completely ruled out that mutation of the PSAP motif in MARV NP inhibited the interaction with other cellular proteins that can bind similar motifs [57] . However , down-regulation of Tsg101 in the MARV-infected cells led to a phenotype similar to that observed in the rMARVPSAPmut-infected cells ( Fig . 1E ) . In summary , our results indicate that the efficient transport and envelopment of nucleocapsids depend on the PSAP motif in NP which recruits Tsg101 and in turn IQGAP1 . The mutation of the PSAP motif in the recombinant rMARVPSAPmut resulted in an intracellular accumulation of nucleocapsids caused by defective transport , which attenuated the efficient viral spread to neighboring cells .
Human embryonic kidney cells ( HEK293; bought from the American Type Culture Collection ( ATCC ) , Manassas , USA ) , human hepatoma cells ( Huh-7; kindly provided by R . Bartenschlager , Heidelberg , Germany ) and African green monkey kidney cells ( Vero E6; from ATCC ) were maintained in Dulbecco's modified Eagle medium ( DMEM ) supplemented with 10% fetal calf serum , L-glutamine and penicillin-streptomycin solution ( Gibco , Karlsruhe , Germany ) . The Musoke strain of MARV was propagated on Vero E6 cells grown in DMEM supplemented with 3% fetal calf serum , L-glutamine and penicillin-streptomycin solution . Infections with MARV were performed under BSL-4 conditions at the Institute of Virology , Philipps University Marburg . Mouse monoclonal antibodies were used for the detection of MARV NP and VP40 in Western Blot . A goat anti-MARV serum was used for the detection of viral plaques in the immunoplaque assay . Mouse monoclonal antibodies anti-Tsg101 ( GTX , San Antonio , USA ) , anti-IQGAP1 ( Upstate , Biotechnology , Lake Placid , USA ) , anti-tubulin ( Sigma-Aldrich , Deisenhofen , Germany ) , and rabbit anti-HA antibody ( Rockland Immunochemicals , Gilbertsville , PA , USA ) were used according to supplier's instructions . Biotinylated anti-Flag antibody M2 was purchased from Sigma ( Sigma-Aldrich Biochemie GmbH , Hamburg , Germany ) . Secondary antibodies conjugated to horseradish peroxidase ( HRP ) were from Dako ( Glostrup , Denmark ) . HRP-conjugated streptavidin was from GE Healthcare Bio-Science , Pittsburg , USA . Goat anti-mouse conjugated with Alexa Fluor 680- and IRDye 800-conjugated goat anti-rabbit secondary antibodies ( Life Technologies GmbH , Darmstadt , Germany ) were used for detection of proteins by the Li-Cor Odyssey imaging system according to supplier's instructions . For immunofluorescence analysis polyclonal guinea pig anti-NP sera was used . Secondary antibodies conjugated to fluorescein isothiocyanate ( FITC ) ( Dianova , Hamburg , Germany ) , or Alexa 594 or Marina blue ( Life Technologies GmbH , Darmstadt , Germany ) were used for immunofluorescence analysis . Cloning of full-length MARV cDNA clones was performed by amplifying non-coding 3′- and 5′-ends of the MARV genome from the MARV-specific minigenome [19] . Genomic viral RNA was used as RT-PCR template for coding and intragenic regions . The anti-genomic sequence of MARV Musoke ( accession number: NC 001608 ) was cloned in three parts [Fragment 1 ( FR1 ) : T7 leader-NP-VP35-VP40-GP-; FR2: GP-VP30-VP24-L-; FR3: L-trailer-ribozyme ) ] flanked by unique restriction sites into three individual pBlueScript plasmids . Assembly of the full length plasmid containing the whole anti-genome of MARV Musoke was performed by standard ligation of the three DNA fragments into a minimal pBlueScript vector under the control of the T7 polymerase promoter [19] . To distinguish between recombinant ( rMARVwt ) and wild type MARV , silent mutations at position 6498 ( C>T ) and 7524 ( A>G ) were introduced resulting in the deletion of a KpnI restriction site and insertion of a SacII restriction site , respectively . Multi-site-directed mutagenesis was used to introduced mutations into the NP gene in FR1 resulting in amino acid substitution of the PSAP motif into AAAA and the PTAP motif into ATAA . Introduced mutations were confirmed by sequencing . In addition to the introduced mutations , two additional silent mutations at the nucleotide positions 7092 ( G>A ) in the GP gene and 15317 ( G>A ) in the L gene were detected in rMARVwt . For the construction of the plasmid pCAGGS-VP30-GFP coding for the VP30-GFP fusion protein , the GFP ORF was cloned in frame to the 3′ end of the VP30 gene using homolog recombination and primer-extension PCR . For dual-color live cell imaging a recombinant MARV coding the VP30RFP fusion protein was constructed ( rMARVVP30RFP ) . The VP30RFP ORF was in addition inserted into an artificial AvrII restriction site between the VP35 and VP40 genes as described by Schudt et al . [26] . Sequencing of the viral RNA from rMARVVP30RFP revealed no addition mutations to the indicated silent mutations in rMARVwt . Detailed cloning strategies as well as primer sequences are available upon request . Hemagglutinin-tagged ubiquitin ( HA-Ub ) was expressed from pCAGGS expression vector . The expression plasmid for IQGAP1 C-terminally fused to YFP ( IQGAP1-YFP ) was kindly provided by George S . Bloom [58] . Tsg101 N-terminally fused to mCherry ( mCherry-Tsg101 ) was created by Quan Lu and provided by addgene ( ID 38318 ) [59] . Vero and Huh-7 cells were mixed in a proportion of 1∶1 and grown in 6-well plates to 50% confluence . Transfection with support and full-length plasmids was performed as published earlier [60] . Culture supernatants were blind passaged on fresh sub-confluent Vero E6 cells 7 days post transfection ( p . tr . ) and cells monitored for cytopathic effect ( CPE ) development . Virus rescue was confirmed by Western Blot analysis of culture supernatants using MARV specific antibodies and by RNA isolation and sequencing . Sequencing of the viral RNA from rMARVPSAPmut revealed in addition to the above indicated silent mutations in rMARVwt additional mutations . An amino acid exchange V396 to L ( nucleotide exchange at position 1289 G>T ) in NP and at nucleotide position 2837 A to G mutation in the transcriptional stop codon of NP was found . Databank analyses revealed that aa 396 in NP of MARV strains Ravn and DRC99 is represented by Alanine indicating that this amino acid is not highly conserved . Our further studies revealed no changes in the proportion of NP to VP35 at protein level in lysates of rMARVPSAPmut- in comparison to rMARVwt–infected cells indicating that the mutation in the transcriptional stop codon of NP was not relevant within the scope of this study . In the L gene an insertion of three additional adenosines ( nucleotide 12071-3 ) was detected which results in an insertion of a Lysine at the highly variable N-terminal region of L . Analyses of viral transcription and replication in cells infected with recombinant mutant and rMARVwt revealed no differences . This result suggested that the insertion of the Lysine did not alter the ability of L to support viral transcription and replication . All experiments performed with recombinant mutant virus ( rMARVPSAPmut ) used recombinant wild type virus ( rMARVwt ) as control . Vero E6 cells were inoculated in 12-well plates with two-fold serial dilutions of rMARVwt or rMARVPSAPmut and 1 h post infection ( p . i . ) overlaid with 1 . 2% Avicel in MEM with 4% FCS . At day 4 p . i . the overlay was removed and cells were fixed with 4% paraformaldehyde ( PFA ) in DMEM . The next day the plates were completely covered with fresh 4% PFA and removed from the BSL-4 laboratory . Immunostaining was conducted 24 h later using goat anti-MARV specific sera and a secondary HRP-conjugated donkey anti-goat antibody . Immunoplaques were visualized after incubation with TrueBlue peroxidase substrate containing 0 . 03% hydrogen peroxide . The plates were dried and pictures of plaques captured utilizing a Nikon TS-100 microscope with a digital sight DS-SMC camera . Relative plaque areas were determined with Leica LAS AF software . Vero E6 cells were cultured in 96-well plates to 50% confluence and infected with 10-fold serial dilutions ( eight replicates ) of supernatants from infected cells . At 10 to 14 days p . i . , when the CPE had stabilized , cells were analyzed by light microscopy . The TCID50/ml titers were calculated using the Spearman-Kärber method [61] . For immunopreciputation of ubiquitinated Tsg101 a Flag-tagged Tsg101 was used as described earlier ( Dolnik et al . , 2010 ) . Cells were transfected with Tsg101-Flag and HA-Ub expression plasmids and 48 h p . tr . lysed in Co-IP buffer ( 20 mM Tris-HCl , pH 7 . 5 , 100 mM sodium chloride , 0 . 4% ( w/v ) deoxycholic acid , 1 . 0% Triton X-100 , 0 . 5% ( w/v ) NP-40 , 5 mM EDTA and 2% BSA ) for 20 min at 4°C . Cell debris were removed by centrifugation at 14 . 000 rpm for 10 min . Lysates were incubated by with anti-HA agarose or anti-Flag agarose ( Sigma-Aldrich ) for 3 h at 4°C . Precipitates were washed 3 times with Co-IP buffer without Triton X-100 , resuspended in samples buffer boiled and subjected to SDS-PAGE and Western blotting . Immunofluorescence analysis was performed as described previously [31] . Images were taken either on Zeiss Axiophot upright fluorescence microscope using a Spot inside B/W QE digital camera ( Visitron Systems , Puchheim , Germany ) and VisiView image acquisition software , or on Leica SP5 confocal laser scanning microscope . For live cell imaging , Huh-7 cells were seeded into 35-mm μ-dishes ( Ibidi , Munich ) 24 h prior to infection . Cells were infected in Opti-MEM without phenol red ( Life Technologies ) for 1 h , then inoculum was removed and cells were transfected with plasmids encoding VP30-GFP or Venus1-Tsg101 and Venus2-Tsg101 . Live cell time-lapse experiments were recorded with a Leica DMI6000B using a 63× oil objective equipped with a remote control device to operate the microscope from outside the BSL-4 facility . Pictures and movie sequences were processed with Leica LAS AF software . SDS-PAGE and Western Blot analysis were performed as described previously [62] and the intensity of bands was quantified using the Chemicon system and ImageLab software from Biorad . Dual protein detection was performed with Li-Cor Odyssey imaging system using fluorescent conjugated secondary antibodies as indicated in the antibodies section . Huh-7 cells were infected with MARV at a MOI of 1 and subsequently transfected with Tsg101-specific or IQGAP1-specific siRNA ( Qiagen , Hs-TSG101-7 , final concentration 20 nM; Hs-IQGAP1-3 , final concentration 50 nM ) or a control siRNA ( Qiagen; control non-sil . siRNA ) using Hiperfect transfection reagent . Second transfection was performed at 18 h p . i . and cells and supernatants were harvested at 48 h p . i . Tsg101 knockdown in cells was confirmed by Western Blot using Tsg101-specific antibody . Virus particles were pelleted from supernatants by ultracentrifugation and analyzed for Tsg101 incorporation by Western Blot . Protein levels were quantified using Image Lab software from Bio-Rad Laboratories . Virus titers in the supernatants were determined by TCID50 titration . Huh-7 and Vero E6 cells were grown on Thermanox cover slips , infected with rMARVwt or rMARVPSAPmut and fixed on the cover slips at 26 h p . i . , then cells were processed for electron microscopy as described previously [18] , [28] . Ultrathin ( 65 nm ) and semi thick ( 300 nm ) sections of the cell monolayers were cut nearly parallel to the plane of the cover slip with a Leica Ultracut UCT microtome ( Leica Microsystems , Wetzlar , Germany ) . Thin sections were examined and imaged using a Zeiss EM10 TEM operated at 80 kV and a 1K×1K side mounted Gatan DualVision CCD camera . Electron tomography was carried out essentially as described elsewhere [28] . 10 nm gold fiducials were adsorbed to both surfaces of 300 nm thick sections on Formvar-coated grids and sections were post-stained with Reynold's lead citrate . Tilt series were recorded on a FEI Tecnai G2 F30 microscope , operated at 300 kV , using SerialEM software and a 4K×4K FEI Eagle CCD camera , at binned pixel sizes of 1 . 5 nm to 2 . 54 nm on the specimen level over a −60° to 60° tilt range ( increment 1° ) and at a nominal defocus of −1 µm [63] . Tomograms were reconstructed using the IMOD software package ( version 4 . 1 . 4 ) [64] . 3D surface renderings were done with the AMIRA Visualisation Package ( version 5 . 4 . 0 , Visage Imaging , Berlin , Germany ) . Electron microscopy of whole mounted cells was performed as described previously [27] . Ultrathin sections of infected cells were stained with uranyl acetate and lead citrate and images were acquired on a Zeiss 109 electron microscope with 1K×1K side mounted CCD camera ( Tröndle , Moorenweis , Germany ) . Stereological method described by Weibel et al . [65] was used to determine the volume density of nucleocapsid structures ( VVnc ) inside viral inclusions . Briefly , the test system consisting of regular dots was randomly placed on electron micrograph of viral inclusion . Dots lying on preformed nucleocapsid ( NNC ) structures in inclusions ( NIC ) and dots lying outside these nucleocapsid structures but still inside inclusions ( NCyto ) were counted separately . The ratios of NNC/NIC and NCyto/NIC determined the relative volume ( or volume density ) of nucleocapsids and cytosol within inclusions . Nucleocapsid localisation outside viral inclusions was assessed and quantified in 3D tomograms from 300 nm thick sections of rMARVwt- and rMARVPSAPmut-infected Vero E6 and Huh-7 cells . The length of all nucleocapsid structures within tomograms was measured using the AMIRA Visualisation Package ( Visage Imaging , Berlin , Germany ) , and only full-length nucleocapsids that were completely represented in the tomograms were included in the quantification [18] , [28] . Human Tsg101 was visualized in live cells using yellow fluorescent protein ( YFP ) based on protein fragment complementation assay . Briefly , fragments 1 ( amino acids 1–158 ) and 2 ( amino acids 159–239 ) of the YFP derivative Venus were generated by PCR using Venus1-GCN4-leucine zipper or Venus2-GCN4-leucine zipper constructs as templates ( kindly provided by S . W . Michnick , University of Montreal , Canada ) and subcloned into pCAGGS expression vector . Tsg101 was fused at its N terminus with either Venus fragment 1 or Venus fragment 2 resulting in the Venus-Tsg101 fusion chimeras designated Venus1-Tsg101 and Venus2-Tsg101 . In addition , a 10-amino-acid flexible linker consisting of ( Gly-Gly-Gly-Gly-Ser ) ×2 was inserted between the Venus fragments and the open reading frame of Tsg101 . The presented data for each experiment represent the mean value and standard deviation of at least three independent experiments . The statistical significance was determined using Student's t test . Asterisks indicate statistically significant differences ( *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 ) . | Marburg virus ( MARV ) is endemic in central Africa and causes hemorrhagic fever in humans and non-human primates , with high lethality . Presumably , the disease severity primarily depends on the response of host-cell factors interacting with viral proteins . We generated a recombinant MARV encoding an NP with a mutated PSAP late domain motif , which has previously been shown to mediate interaction with the cellular ESCRT protein Tsg101 . We found that the PSAP-mediated interaction with Tsg101 was important at several steps of MARV assembly before viral fission . First , the egress of mature rMARVPSAPmut nucleocapsids from viral inclusions was inhibited . Second , actin-driven transport of rMARVPSAPmut nucleocapsids was impaired , displaying significantly shortened trajectories and reduced movement in the cell periphery . Third , rMARVPSAPmut nucleocapsids accumulated in cell periphery , and the number of filopodia-associated nucleocapsids decreased , indicating that rMARVPSAPmut nucleocapsids were defective to enter filopodia , the major budding sites of MARV . These defects resulted in the attenuated growth of rMARVPSAPmut . Interestingly , IQGAP1 , an actin cytoskeleton regulator which interacts with Tsg101 , was also recruited to nucleocapsids in dependence of the PSAP late domain . Thus , the interaction of NP with Tsg101 not only impacts viral budding at the plasma membrane but also nucleocapsid transport through the cytoplasm . | [
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] | 2014 | Interaction with Tsg101 Is Necessary for the Efficient Transport and Release of Nucleocapsids in Marburg Virus-Infected Cells |
Mutation bias in prokaryotes varies from extreme adenine and thymine ( AT ) in obligatory endosymbiotic or parasitic bacteria to extreme guanine and cytosine ( GC ) , for instance in actinobacteria . GC mutation bias deeply influences the folding stability of proteins , making proteins on the average less hydrophobic and therefore less stable with respect to unfolding but also less susceptible to misfolding and aggregation . We study a model where proteins evolve subject to selection for folding stability under given mutation bias , population size , and neutrality . We find a non-neutral regime where , for any given population size , there is an optimal mutation bias that maximizes fitness . Interestingly , this optimal GC usage is small for small populations , large for intermediate populations and around 50% for large populations . This result is robust with respect to the definition of the fitness function and to the protein structures studied . Our model suggests that small populations evolving with small GC usage eventually accumulate a significant selective advantage over populations evolving without this bias . This provides a possible explanation to the observation that most species adopting obligatory intracellular lifestyles with a consequent reduction of effective population size shifted their mutation spectrum towards AT . The model also predicts that large GC usage is optimal for intermediate population size . To test these predictions we estimated the effective population sizes of bacterial species using the optimal codon usage coefficients computed by dos Reis et al . and the synonymous to non-synonymous substitution ratio computed by Daubin and Moran . We found that the population sizes estimated in these ways are significantly smaller for species with small and large GC usage compared to species with no bias , which supports our prediction .
The quantitative modeling of molecular evolution is of key importance for reconstructing evolutionary histories , as well as for understanding how the properties of natural macromolecules are influenced by their evolution . Already for a long time population size has been recognized as a crucial factor that influences both the evolutionary process and the stability that macromolecules can attain . On the other hand , even if mutation bias in prokaryotes varies from extreme GC rich to extreme AT rich , its influence on the evolutionary process , the stability of evolving macromolecule , and on the fitness of the population has received much less attention . Here , we simulate an evolutionary model that combines population size , GC mutation bias , and protein folding stability , and we show the deep interplay between these variables . Kimura's neutral model [1] , [2] is still one of the most influential models of molecular evolution . This model considers all viable macromolecules as equally fit and all the others as nonviable . Within this neutral model , the functional properties of the evolving macromolecules , in particular their folding stability , are independent of population size and , by entropy arguments , they are expected to coincide with the minimal properties compatible with viable molecules [3] . If mutations with small fitness effects are included in the model , population size becomes a key variable of the evolutionary process , since slightly deleterious mutations are more likely to be fixed in small populations [4]–[6] . This study has been pioneered by Ohta , who showed that population size can provide a possible explanation for empirical observations such as the generation time effect [7] , [8] . Obligate intracellular lifestyle , such as that of endosymbiotic or parasitic bacteria , implies a strong reduction in effective population size due to bottlenecks upon transmission from one host to another . Inspired by Ohta's theory , computational studies have compared bacterial species displaying an obligate intracellular lifestyle with their free living relatives , suggesting that the genes of intracellular bacteria evolve faster as a result of relaxed selection [9] ( but Itoh et al . [10] give a different interpretation ) and that their structural RNAs [11] and their proteins [12] are less stable than the orthologous macromolecules of free living bacteria . Evolution experiments with virus and bacteria confirm the influence of small population size , demonstrating fitness loss in populations evolving under repeated bottlenecks [13] , [14] , and show that such a loss can be partly compensated by over-expressing chaperones that assist protein folding [15] . These findings support the idea that fitness is reduced in small populations as a consequence of the reduction of protein folding stability . Recent theoretical work has shown that , in the appropriate limits , the statistical properties of population genetics are formally equivalent to a statistical mechanical system , so that there is an exact analogy between the reduction of fitness for small populations and the increase of entropy for large temperature [16] , [17] . In the present study , we will exploit this correspondence to get analytic insight into non-neutral evolution . Another key evolutionary variable , which however has received little attention , is the nucleotide spectrum . In prokaryotic genomes , it varies from extreme adenine plus thymine ( AT ) content in obligatory intracellular bacteria to extreme guanine plus cytosine ( GC ) content , for instance in actinobacteria . These differences in GC content are prevalently thought to be due to mutation bias [18] , [19] . They are strongest at the third codon position , where GC content barely affects the amino acid composition of the protein , but also influence the coding positions [20] , [21] . Due to the structure of the genetic code , a mutation bias favoring thymine at the nucleotide level favors the incorporation of hydrophobic amino acids in the translated protein [12] , [22] . Hydrophobicity is a key property for protein folding [23] . Proteins that are too hydrophylic tend to be naturally unfolded , whereas proteins that are too hydrophobic tend to misfold and aggregate [24] . This qualitative trade-off between unfolding and misfolding was confirmed by a computational study of the properties of homologous proteins in the proteomes of several bacterial species , using a model of protein folding stability that correlates well with experimentally measured unfolding stabilities [12] . In previous work , two of us and colleagues investigated the relationship between unfolding stability , misfolding stability and mutation bias using a protein evolution model with a realistic genotype ( DNA sequence ) to phenotype ( folding stability ) mapping in a neutral fitness landscape in which all proteins with stabilities above thresholds have the same fitness . We found that the mutation bias modulates the trade-off between the two kinds of stability , making proteins evolving under AT mutation bias more stable against unfolding but less stable against misfolding [25] . Interestingly , the two aspects discussed above , small population size and mutation bias towards AT , are strongly correlated in nature . In fact , most bacterial and eukaryotic lineages that adopted an intracellular lifestyle , with consequent reduction of their effective population size , also shifted their mutation spectrum towards AT [26] , as indicated by the strong correlation between reduced genome size , which is a signature of intracellularity , and the AT bias [9] , [12] . In this work , we investigate the association between population size and mutation bias , studying its consequences through a model that takes into account all of the relevant features of protein evolution discussed above: folding stability with respect to both unfolding and misfolding , population size , mutation bias , and neutrality , i . e . the relationship between folding stability and fitness .
We adopt the Moran model [27] , which describes an evolving haploid population with individuals that reproduce asexually and stochastically under mutation and selection . The model can be easily extended to diploid populations . We assume here that the product of population size times mutation rate is small , , so that the population is monomorphic , i . e . the time scale for appearance of a new mutant in the population is large and at most one single mutant genotype is competing with the wild-type for fixation each time . This assumption is justified for small and intermediate populations when considering an individual protein coding gene , but not an entire genome ( see Discussion ) . However , for large populations the assumption is violated even for an individual gene , and we can not apply the model to this case . In this monomorphic limit , the probability that a mutation arising as a single individual is fixed in the whole population can be exactly computed as [27] ( 1 ) where is the exponential growth rate of the phenotype associated to sequence , which will be called fitness in the following . This analytic result enormously simplifies the numeric study of the system allowing the systematic exploration of its parameter space . In our simulations , we randomly generate a mutated sequence , evaluate its fitness with respect to the wild type , and accept the new mutation according to the above probability . We model mutations at the DNA level through the HKY process [28] , whose only parameters are the equilibrium frequencies of the four bases in the absence of selection , and the transition/transversion ratio , whose influence is very weak and which we set to [8] . In order to reduce the number of parameters , we assume that Chargaff's second parity rule holds , so that and . Thus , the mutation model only depends on the GC usage , . GC usage different from determines a mutation bias towards AT or towards GC , therefore we sometimes refer to the GC usage variable as the mutation bias . In our model , the GC usage variable very strongly correlates with the GC content of the evolving gene in the stationary state of the evolutionary dynamics . The same correlation is thought to exist between the GC content of bacterial genomes , in particular at third codon position , and the GC usage of the mutations arising in bacterial replication . Therefore , we will compare the variable GC usage in our model with the variable GC content at third codon position in bacterial genomes . We can analytically predict how the population size and the neutrality exponent influence stability and fitness by exploiting the formal analogy between population genetics and statistical mechanics demonstrated by Berg and coworkers [16] and by Sella and Hirsh [17] . These authors noticed that , in the monomorphic limit mentioned above and that we assume throughout this work , the Moran process , as well as other evolutionary processes studied in population genetics , tends to a stationary distribution of the form . This distribution is equivalent to a Boltzmann distribution where population size plays the role of inverse temperature and the logarithm of fitness , plays the role of minus energy . This result implies that the probability to find a protein with stability values and in the stationary state of an evolving population is proportional to multiplied by a factor that depends on the mutation process . The bias arising in the mutation process was treated as a “chemical potentia” by Sella and Hirsh [17] or as a mutational entropy by Berg et al . [16] . These two formalisms are qualitatively equivalent . We find the name mutational entropy more intuitive , and we will use it in the following . We define the probability to find stability parameters and under mutation alone , and we introduce the quantity , which we call the mutational entropy compatible with stabilities and under the given mutation process ( notice that strictly speaking is not an entropy , however we find this name intuitive for indicating the mutational force that opposes protein stability ) . As discussed above , the mutational entropy depends on the GC usage , which can favor one kind of stability with respect to the other . Taking all this into account , the stationary distribution of stability that results from mutation and selection is ( 3 ) The logarithm of the above probability can be interpreted as minus an evolutionary free energy divided by temperature , and it is given by ( 4 ) where is called the additive fitness [17] . The distribution Eq . ( 3 ) is peaked around the values and that maximize the exponent , i . e . minimize the evolutionary free energy . The equations that define these most likely values read ( 5 ) where . We call the above the maximum-likelihood ( ML ) equations . Notice that the maximum likelihood values and depend on the parameters , and . We can study this dependence analytically , assuming that Eq . ( 3 ) is narrowly peaked around these values , so that averages can be calculated as and . This approximation is justified by the fact that the mutational entropy is expected to be proportional to protein length , which is of the order of , and the selective term is proportional to population size , which is also large , so that the exponent is large and the distribution very narrow . The condition that has a maximum at requires that its Hessian matrix , consisting of its second derivatives , is negative definite , ( 6 ) This Hessian is the sum of the Hessian of , which is negative by construction , as it is easy to verify , and the Hessian of , which is the logarithm of a probability . We assume that the mutational entropy has a single maximum at stabilities , so that its Hessian is negative . The values that represent the most likely values of and in the absence of selection depend on . By definition of , is always negative , which is not a viable stability ( ) . However , our numerical results show that is positive for small GC usage , corresponding to hydrophobic sequences . The mutational entropy decreases for and for , which implies that the corresponding derivatives are negative , as required for the existence of the solution of the ML equations . We can go beyond the maximum-likelihood approximation writing the exponent at second order as , which is equivalent to approximating the distribution Eq . ( 3 ) as a Gaussian with covariance matrix . Therefore , negativity of the Hessian matrix is equivalent to requiring the covariance matrix to be positive . All simulations presented here are based on the native structure of some natural protein . When not otherwise stated , we exemplify our numerical results using the protein lysozyme , PDB id . 31zt . In all cases , the starting sequence is the sequence in the PDB . Results are collected after fitness has converged to its stationary value , discarding the first accepted substitutions , which are enough for equilibration , as it can be seen in Fig . 2 in the Text S1 . As an illustration of the stationary states of the evolutionary dynamics , we represent in Fig . 2 the mean stability values and obtained using the fitness function with for different population sizes from to and GC usage from to . The distributions , Eq . ( 3 ) , are narrowly peaked around the plotted points . Sets of points with the same GC usage are joined with solid lines , and sets of points with the same are joined with dashed line . The data are superimposed to a heat map that shows the value of fitness in colour code . We can see from the figure that both stabilities grow with . On the other hand , grows and decreases with , so that and are negatively correlated for fixed population size . For , tends to a finite value when tends to zero ( corresponding to very small ) , i . e . the most likely value of in the absence of selection is and , for such small GC usage , there is very weak selective pressure on unfolding . One can see from the plot that the GC usage at which and are equal increases with population size , which implies that the selective pressure on increases more than the selective pressure on for increasing population size . In the large population limit both and tend to finite values independent of GC . We estimated from our numerical results that and , so that for large populations it is always . Fitness clearly increases with . The variation of fitness with is weaker , but one can nevertheless notice it from the plot . This variation translates into the fact that , for fixed fitness function and population size , there is an optimal usage such that fitness is maximal , as predicted in Eq . ( 7 ) . The existence of this optimal mutation bias is demonstrated in Fig . 3 , where we plot the fitness of populations with constant and as a function of their usage . For each set of parameters , we obtained the optimal GC usage by cubic interpolation , as exemplified in Fig . 3 , and plotted it versus . We found that is small for very small populations , large for intermediate populations , and the bias is almost absent ( ) for very large populations ( see Fig . 4 ) . We obtained qualitatively similar results as long as the neutrality exponent is not too large or too small ( in that case , the fitness landscape becomes almost neutral ) . The population size at which the optimal GC usage is highest increases with decreasing for small , while the opposite holds for large . Our numerical results are consistent with the optimal GC usage becoming less dependent on in the infinite population limit , see Fig . 3 in the Text S1 . Eq . ( 4 ) implies that a trait that confers a selective advantage can only be fixed against the entropic effect of random mutations when the difference in the selection coefficients is larger than . We therefore verified whether the difference of selective coefficients between populations adopting different GC usages is large enough so that the optimal one would be eventually selected . We found that decreases with population size , but more slowly than , so that increases with , see Fig . 4 in the Text S1 . This implies that two populations evolving with different mutation bias ( the optimal one and another one ) attain a fitness difference large enough so that the optimal GC usage can be selected . We tested that our results do not change qualitatively when different protein structures are used in the simulation . To this end , we computed the relationship between the optimal GC usage and population size at neutrality exponent for five proteins of different length and secondary structure ( see Methods ) . All curves , plotted in Fig . 5 , have the same shape , although they are shifted in the vertical direction in a way that suggests that shorter proteins are characterized by larger optimal GC usage ( but more proteins are needed to confirm this trend ) . We then combined the five curves . We assumed that a genome composed of these five proteins is evolving with very low mutation rate , so that at most one protein is mutated at each step , consistent with the assumption . The global fitness of the organism was obtained through two different ansatz that yielded qualitatively similar results , either as the minimum of the fitness of all proteins , or as the product of the fitnesses , , assuming absence of epistatic interactions . From these we then obtained the optimal GC by cubic interpolation . This is represented in Fig . 5 , bottom plot for . One can see that the qualitative behavior of the individual curves is preserved . We expect therefore that this qualitative behavior would be maintained for a large number of proteins as well . To further test the robustness of our results we changed the neutral thresholds and up to 20% , examining nine combinations of thresholds for neutrality exponent . The results are shown in Fig 6 . One can see that the qualitative behavior is unchanged . As expected , when becomes more tolerant the optimal GC usage decreases , and the contrary happens when becomes more strict . Finally , we verified that the results are robust with respect to the energy parameters used . For such a test , we adopted the contact interaction energies determined by Godzik , Kolinsky and Skolnick ( GKS ) [41] . These parameters have correlation with the BVK parameters adopted in the present study , so that their differences are not small . We determined a new parameter for conformation entropy by demanding the folding free energies computed with the two sets of energy parameters to coincide on the average . As one can see from the dotted curve in Fig . 7 , the qualitative behavior is the same for the two parameter-sets , but the optimal GC usage for GKS parameters is lower than for BVK parameters . This is due to the fact that , for our test protein lysozyme , GKS energy parameters produce a very low normalized energy gap instead of with BVK parameters , which means that the native conformation is closer in energy to random conformations when GKS parameters are used . Consequently , is very small ( we recall that is proportional to the value of for the native sequence ) and the selective pressure on misfolding is very weak . We then increased this selective pressure by setting instead of . The resulting curve can be seen in Fig . 7 as a dashed curve . One finds that the maximum GC usage is now much larger , reaching . Finally , we show in Fig . 8 the optimal GC usage versus the neutrality exponent for small ( ) , intermediate ( ) and large ( ) populations . For small populations the optimal GC usage increases with the neutrality exponent , from very small values to . For intermediate and large populations the optimal GC usage has a maximum and then it decreases . The maximum value of increases with population size , and it is reached at smaller neutrality exponent for intermediate populations ( at ) than for large populations ( at ) . We then tested the mean-field prediction that the stability coefficient has a maximum and the sequence entropy has a minimum as a function of neutrality exponent . As expected , maximum stability and minimum entropy occur at the same value of , see Fig . 5 in the Text S1 . The results that we have presented suggest that mutation bias towards AT or GC favor protein folding stability for very small and intermediate population sizes , respectively , while very large populations are advantaged in the absence of bias ( ) . As it will be discussed below , this suggests that species evolving with mutation bias , either towards AT or GC , will have smaller population size than species with no bias . This prediction is consistent with the fact that almost all bacterial species with intracellular lifestyles , implying a reduction of effective population size through bottlenecks , shifted their mutation spectrum to AT , which resulted in small genomic GC content . On the other hand , among bacteria with large GC content some are facultative pathogens , such as Mycobacterium tuberculosis , and some live symbiotically in plant nodules , but there is no general tendency allowing for the deduction of their population size from their lifestyles . Therefore , to test our prediction , we tried to directly estimate their effective population size . The effective population size depends on the breeding structure and the natural history of a population , and in particular it is influenced by the bottlenecks that the population may undergo if a few individuals periodically colonize new environments . Therefore , the effective population size cannot be measured experimentally , but is estimated by fitting some observed population feature to its expected value under evolution in a population with given . Optimal codon usage was used several years ago to estimate the effective population size of Escherichia coli [42] . A recent work supports the existence of a correlation between effective population size and synonymous codon usage [43] , and the availability of many complete genomes makes it possible to analyze codon usage on a large scale . Codon usage and mutation bias are intimately correlated . It is commonly believed that the mutation bias , rather than selection for optimal codon usage , ultimately influences the global GC content of a genome [18] , [19] . The definition of the optimal codon usage on which the results that we use here are based considers the excess frequency of preferred codons with respect to the frequency expected under mutation alone , and is therefore not expected to depend on the mutation bias in a trivial way . Dos Reis el al . [44] have recently estimated the optimal codon usage in a large number of prokaryotic species . We use their data rather than the analogous data obtained by Sharp et al . [45] , since Dos Reis et al . evaluated the optimal codon usage on the entire genome , whereas Sharp et al . concentrated their attention only on ribosomal genes , which can be a biased sample . Fig . 9 shows the average optimal codon usage versus the average GC content at the third codon position , which is not affected by the selection on the amino acid sequence and is expected to be very strongly correlated with the mutation bias . We distinguished species with small ( ) , intermediate ( to ) and large ( ) GC content . Species with intermediate GC content turned out to have significantly larger optimal codon usage , which suggests that they have larger effective population size . The scatter plot and the histogram of the GC content are shown in Fig . 7 and 8 ) in the Text S1 . Error bars in the plot represent the standard error of the mean , and show that the mean values are significantly different . However , data prior to the mean are rather broadly distributed , with standard deviations equal to ( , ( ) and ( ) . As a second estimate of effective population size , we considered the ratio between non-synonymous and synonymous substitutions , which is thought to represent the strength of negative selection [8] . We examined values of computed for pairs of entire genomes , recently published by Daubin and Moran [46] . From their table , we eliminated two pairs of genomes for which the evolutionary divergence , estimated through , was very small ( ) , corresponding to Bordetella pertussis/parapertussis and two strains of Xylella fastidiosa , since it is known that the amino acid substitution rate is significantly higher at small time separation [47]–[49] and in fact these two pairs of genomes showed the two largest values of . We also eliminated two pairs for which the two compared species had genomic GC content in different bins: two strains of Prochlorococcus marinus having GC = 36% and 51% , and the pair Synechocystis/Synechococcus having GC = 48% and GC = 65% , respectively . We divided the remaining 19 pairs in 3 bins of low , mean and high GC content and averaged their . Results , shown in Fig . 9 , clearly show that species evolving with no bias are characterized by lower , hence larger effective population size , in agreement with the analysis of the optimal codon usage and with the prediction of our model . Finally , we reanalysed our data on protein folding stabilities computationally estimated for orthologous proteins in different prokaryotic genomes [12] . Unfolding and misfolding stabilities are negatively correlated , as predicted by our model ( see Fig . 10 ) . We found that most of the organisms evolving with mutation bias have proteins whose misfolding stability is lower than what could be expected based on their unfolding stability , see Fig . 11 . This further supports the idea that these species are characterized by reduced effective population sizes .
We studied here a mathematical model of protein evolution where the genotype to phenotype mapping is determined by the stability of the mutated protein against unfolding and misfolding , predicted using a protein folding model that correlates well with experimental measures . As observed in previous work , the two kinds of stability respond in an opposite way to changes in the GC usage of the mutation process . This fact produces a trade-off between the two kinds of stability , and an interesting phenomenology arises from the impossibility to find a mutation process that optimizes both stabilities at the same time , a concept that in the physical literature has received the name of frustration . We considered three key evolutionary parameters: the effective population size , the neutrality exponent , which determines how protein stability influences fitness , and the GC usage that expresses the mutation bias . Despite its importance in shaping the folding properties of proteins , the latter has been rarely considered in evolutionary models . Here we show that , in the non-neutral regime , mutation bias has a very interesting interplay with population size . We suggest that this can explain why some microbial species adopted extreme mutation bias . At high neutrality exponent , all proteins with stability above the neutral threshold provide the same fitness and evolution is only able to attain the lowest allowed stabilities [3] , almost independent of population size . Consistently , our analytic and numerical results indicate that the neutrality exponent has a non-monotonic influence on protein stability , which reaches a maximum at intermediate for given population size . The increase of in our model has its biological counterpart in the increase of the expression level of chaperones , which make proteins more tolerant to stability losses . Therefore , the decrease of stability for increasing predicted by our model would correspond in the real world to the decrease of protein stability when the chaperone expression is increased . This outcome appears rather plausible . However , given the cost of synthesizing chaperones , in real evolution it is to be expected that the increase of the expression level of chaperones is a consequence of the loss of protein stability , as observed in intracellular bacteria with reduced population size , rather than the other way round . In the neutral regime the GC usage influences the amino acid composition and consequently the folding properties , favoring proteins more stable with respect to misfolding but less stable with respect to unfolding , without modifying the fitness . In contrast , in the non-neutral regime fitness is a continuous function of stability and the outcome of evolution depends non-trivially on mutation in the sense that for fixed population size there is an optimal mutation bias at which fitness and stability are maximal . This is an unexpected result , which implies that mutation and selection are effectively entangled , and that the mutation spectrum constrains the maximum stability and fitness that an evolving population can attain . The possibility that the mutation rate is optimized as a response to evolutionary forces [50] has received considerable attention in experiments ( see Ref . [51] for a recent work ) and modelling ( see for instance Refs . [52] , [53] ) . The main forces influencing mutation rate evolution have been identified as the population size [50] , the ruggedness of the fitness landscape [54] and the average negative effect of a mutation [55] . Recently , a theoretical work has established a relation between mutation rate , maximal genome size and thermodynamic response of proteins to point mutations , showing that populations go extinct via lethal mutagenesis when their mutation rate exceeds a few mutations per genome per replication [56] . Simulations of this model confirmed the predicted behaviour , showing that the limiting number of mutations is approximately seven for RNA viruses and about four for DNA-based organisms , with some weak dependence on the number of genes in the organism and the organism's natural death rate [57] . This model predicts that species with high mutation rates tend to have less stable proteins compared to species with low mutation rates . Therefore , the notion that the mutation process can influence protein stability , and that the optimal mutation process is influenced by properties of the selection process is not new , but the extension of this concept to the evolution of the mutation bias is novel to our knowledge . Quite interestingly , small populations attain higher fitness with AT bias , intermediate populations get an advantage with GC usage , and very large populations attain higher fitness with almost absent bias . This result establishes a deep interplay between population size and mutation bias . The ML equations show that the optimal GC usage depends on how the number of stable sequences decreases with the stability values , i . e . it is an effect of probability in sequence space . For very small population size and stabilities the optimal mutation bias is attained at small GC usage , which makes folding easier . At higher stabilities ( intermediate population size ) the optimal GC usage increases , therewith improving the stability against misfolding at the optimal GC . Approaching the maximal stabilities the optimal GC usage decreases again towards the value , which means absence of bias in the mutation process . As a speculative remark , we note that it was not obvious that our model would predict as the optimal GC usage for very large populations . In this limit the absolute maximum fitness is reached . We have shown numerically ( see Text S1 ) that the optimal GC usage in the infinite population limit is little dependent on the parameters of the fitness function , and , as long as the selective pressure affects mostly , so that in this limit mainly depends on the contact energy parameters and on the genetic code . This conjecture is consistent with our data . Nevertheless , a systematic test requires cumbersome simulations that we did not perform here . We obtained a different result when using the GKS contact energy parameters , which yielded for in the very large population limit . However , we notice that these parameters also produced a very small normalized energy gap , which suggests that they might be less suitable for this kind of study . The model that we adopt here is based on the assumption that the population is genetically homogeneous , i . e . the product of population size times mutation rate is small . This allows us to analytically compute the fixation probability of a new mutation through Eq . ( 1 ) instead of explicitly simulating population dynamics . This approximation is considered valid if measures the mutation rate of a single protein , in particular if population size is small . However , the high mutation rates of RNA viruses may violate this assumption even for a single protein , and in this case several works [58] , [59] have shown that the load due to nonviable mutations significantly modifies the evolutionary process even in the case of a neutral fitness landscape , leading to the evolution of mutational robustness and enhanced folding stability [60]–[62] . This situation can be studied analytically in the framework of the quasi-species theory [63] . We did not consider this theory here , because it assumes that the population size is infinite and therefore it prevents to study the effect of finite populations that is the main focus of the present work . If we considered a whole evolving genome instead of a single protein , the approximation of very small mutation rate would not be justified , since genomic mutation rates are in a range of to mutations per genome per generation for DNA-based microbes , including viruses , bacteria , and eukaryotes [55] . In this context , a new interesting effect has to be considered , namely the hitch-hiking effect , which consists in the fixation of mildly disfavoured alleles driven by a positively selected allele present in the same chromosome . However , since treating the hitch-hiking effect would make both the analytic and the numeric study much more complicated , we leave it as a subsequent step . Our model depends on several assumptions and parameters . As evolutionary model , we adopted the Moran process , one of the best studied population genetic models . The theoretical work by Sella and Hirsh [17] shows that other evolutionary processes , such as for instance the Wright-Fisher process , would yield the same qualitative results . The mutation process was modelled using a single parameter , the GC usage . While this parametrization might appear too simplified , it has the merit to focus on a variable whose relevance has been pointed out by a large number of experimental studies , statistical analysis and models . The ingredients of our model that seem more debatable are the form of the fitness function and its parameters , and . To test the robustness of our results , we simulated different functional forms of the fitness function , using exponential functions of stability instead of power laws or letting the fitness depend only on the minimum between the two stabilities and . In all cases , we found the same qualitative results: There is an optimal mutation bias at which the fitness is maximal , such that for very small populations the optimal bias is towards AT , and for intermediate populations the optimal bias is towards GC . We then studied in detail the fitness function Eq . ( 2 ) . Changing the neutrality exponent does not modify the qualitative results as long as the combination of and is in the non-neutral regime . Experiments on the evolution of small populations [13] , [14] and computational studies of protein folding stability [12] suggest that stability does depend on population size for populations subject to repeated bottlenecks , so that for such populations it is justified to assume that the non-neutral regime is the relevant evolutionary regime . We also varied the neutral thresholds and by more than 20% , finding that they do not change the qualitative behavior , although they have a quantitative influence on the optimal GC usage . We observed more important quantitative changes when we changed the contact energy parameters , but even in this case the gross qualitative features of the versus relationship remain valid . The result that the mutation bias directly influences the fitness that a population can attain in its evolution suggests the intriguing possibility that there may be a feedback between mutation and selection such that a particular mutation bias favors optimal protein folding stability , and selection may favor the replication machinery yielding this optimal mutation bias . Nevertheless , the selective advantage of evolving with the optimal GC usage is only apparent after a sufficiently large number of substitutions in protein coding genes . A mutant for GC usage would have a very low selective advantage during the first generations , and therefore its fixation would be a matter of almost neutral genetic drift . After the mutant is fixed , however , our model predicts that the population evolving with optimal bias will accumulate a sufficiently high selective advantage to take over populations with a less favourable GC usage when they , or their hosts in the important case of endosymbiotic bacteria , come to compete . Therefore , we expect this meta-population selection to almost deterministically favour the selection of the strain with optimal GC usage in contrast to the almost neutral fixation of a mutant with optimal GC usage within a single population . Thus the optimal mutation bias can facilitate the selection of more stable proteins and , on a longer time scale , selection at the meta-population level may favor the replication machinery that is most suitable to protein stability . The population sizes at which we find the maximum of in our model are of the order of a few hundreds individuals for . These values appear very small compared with real bacterial populations , even if they tend to grow rapidly for very high or very low neutrality exponent . We may reconcile our model with biology if we notice that the effective population size is not the same as the total number of individuals of a species . Berg [42] showed that , if a small number of individuals often colonize new habitats with colonization probability almost independent of the founders fitness , the effective population size is given by the number of generations between two colonization events . This is a very small number for obligatory endosymbiotic and parasitic bacteria , and it may also be small for facultative parasites or symbionts , and even for the paradigm of a free living bacterium such as Escherichia coli for which Berg [42] estimated an effective population of individuals . The meta-population structure of bacterial species raises the question of whether the molecular evolution properties of a species such as the codon usage bias and the ratio are primarily determined by the effective size of a local population or by the global size of the meta-population . This is an important question that requires modelling the meta-population dynamics and the different levels of selection that are relevant for it . Our opinion is that both population sizes influence the evolutionary dynamics , and that , despite the losses of stability of small local populations can be in part compensated at the meta-population level , the influence on evolution of the local population size remains important even taking into account these corrections , so that observables such as codon usage bias and strongly reflect the local structure of the population . The distribution of GC content observed in bacterial genomes is remarkably broad . We assume here , as it is widely believed , that these differences in the GC content are mainly determined by different mutation pressures [18] , [19] . The third codon position , where a shift from A to G and from C to T does not change the coded amino acid in most cases , is thought to strongly reflect the mutation bias . However , the GC content at third codon position is strongly correlated with the GC content at first and second codon position [20] , [21] , and through this correlation , the mutation bias influences the properties of the protein sequence , most notably its hydrophobicity [12] , [22] . This is surprising , since hydrophobicity is considered the main determinant of folding stability [23] , and it is expected to be finely tuned since the protein has to avoid unfolding on one hand , and misfolding and aggregation on the other hand ( of course this balance is very different for membrane proteins , which are not considered here ) . One possible interpretation is that , due to the trade-off between unfolding and misfolding , the hydrophobicity is to some extent neutral so that it is possible to modify it without significantly affecting the global fitness of the protein . Our results suggest a different interpretation: There may be an optimal range of hydrophobicity , but this range may be different for different values of protein stability . So proteins with low stability , as those found in small populations , may tend to be more hydrophobic than proteins with high stability as those found in large populations , hence leading to a preference for a lower GC usage in their evolution . Our model predicts that species with large population size will tend to evolve without mutation bias ( GC usage equal to ) , whereas species with small and intermediate populations will tend to present such a bias , either towards AT or towards GC . This prediction is in qualitative agreement with two independent estimates of effective population size based on optimal codon usage and on the ratio between non-synonymous and synonymous substitutions represented in Fig . 9 , and with a computational comparison of unfolding and misfolding stabilities in orthologous bacterial proteins , see Fig . 11 . Of course bacterial genomes are rather complex , and we do not expect the mechanism proposed here to explain their GC content as the result of a single factor , population size . Another important factor influencing the GC content has been identified in a previous statistical study , which demonstrated that aerobiosis is an important determinant of GC rich genomes [64] . This interesting result is not in contradiction with our model , since many bacteria with small GC content tend to have an intracellular lifestyle , which in turn can make them anaerobic and at the same time reduce their effective population size . As mentioned above , the proposed relationship between low GC content and small population size is consistent with the known fact that most bacterial species that adopted an intracellular lifestyle shifted their mutation spectrum towards AT with respect to their free living relatives [26] . This AT bias is , in most cases , the consequence of the loss of repair genes . For instance , three out of the four sequenced species of Buchnera lost the gene mutH , which in Escherichia coli is responsible of repairing the replication errors produced by methylation of cytosine that causes C to T mutation [65] . Moran proposed that this loss of repair genes and the consequent mutation bias is a selectively nearly neutral event in the evolution of endosymbionts [9] . Nevertheless , the results presented here suggest that this shift has important consequences on the folding properties of the whole proteome . In fact , a strong AT bias , together with reduced population size , is expected to produce severe misfolding problems , as indicated by the low predicted misfolding stability of proteins of intracellular bacteria with respect to orthologous ones in free living bacteria [12] , and by the observed positive selection and over-expression of molecular chaperones in endosymbiotic bacteria [66] , which is an expensive but effective strategy to reduce misfolding problems . Interestingly , it has been found that the fitness observed in an experimental population subject to frequent bottlenecks can be in part recovered by over-expressing chaperones [15] . Nevertheless , AT bias also enhances stability with respect to unfolding , and the results presented here suggest that its influence on fitness is globally positive for small populations . The relationship between small population size and GC richness is even less expected . Only a few out of several prokaryotic species having high GC content are obligatory intracellular bacteria , such as for instance Mycobacterium leprae , and some are facultative pathogens or plants associated symbionts . Our results suggest the intriguing possibility that they tend to have small population size , although larger than for obligatory endosymbionts . To test this prediction , we estimated the population size using optimal codon usage [44] , which has often been used to estimate population sizes . There are several caveats: The selective advantage of optimal codon usage strongly varies from one gene to another , and from one species to another . However , it is expected that the average codon usage bias estimated on the whole genome is correlated with population size . The optimal codon usage is computed subtracting the average mutation background , therefore it should not be trivially influenced by mutation bias . We found significantly reduced selection for optimal codon usage in bacteria evolving with large mutation bias compared to those with moderate or no bias , supporting our prediction that the former are characterized by smaller effective population size . Furthermore , we tested the relationship between GC content and effective population size estimating the latter through the ratio between non-synonymous to synonymous substitutions computed by Daubin and Moran [46] for entire bacterial genomes . This analysis presents important caveats . For instance , the non-synonymous substitution rate has been shown to depend on the time separation between two species [47]–[49] . We tackled this point by eliminating values of estimated at short timescales , which are known to be strongly overestimated . Given the above , it is remarkable that the qualitative picture provided by this measure qualitatively coincides with the one obtained analysing optimal codon usage . Both measures strongly support the prediction of our model that species with are characterized by larger effective population size . Nevertheless , among species presenting large mutation bias , those with bias towards GC are estimated through the measure to have smaller effective population than those with bias towards AT , which is in contrast with our prediction . This point is worth further investigation taking into account more carefully the time dependency of the estimate [48] . Of course , there exist several exceptions to these predictions , as there are several other factors , some already identified [64] , [67] and others still unknown , that influence the differences in GC content of prokaryotic species . One remarkable exception to the association between intracellularity and low GC content is the genome of the endosymbiotic bacterium Hodgkinia cicadicola , very recently sequenced by Moran's group [68] . This genome is extremely reduced ( 144 kb ) , as generally observed for endosymbiotic bacteria , but it shows GC content of 58% , which came as a big surprise since it is probably the most serious exception to the association between genome size and GC content . This genome also challenges the association between endosymbiotic bacteria and AT bias . It has been suggested that Hodgkinia belongs to the Rhizobiales division of alpha proteobacteria , characterized by high GC content . Interestingly , the genetic code of Hodgkinia underwent a modification such that UGA codes for Tryptophan instead of Stop . This modification is expected to ease the evolution of proteins that are stable with respect to misfolding . Consistently with this expectation , we found that the optimal GC usage for small populations slightly increases when this alternative genetic code is used in simulations , but this effect is too small to reconcile the GC content of Hodgkinia with its expected small effective population size ( data not shown ) . Further research is needed to identify the origin of the GC content in this genome that lacks any repair gene [68] . Nevertheless , the association between intracellular lifestyle and AT bias , despite not being deterministic as demonstrated by this counterexample , is still strongly significant . A second exception is represented by Prochlorococcus marinus , a very abundant species of small marine cyanobacteria [69] , [70] . It is expected that this species has a very large population size , which is in agreement with a recent estimate of its ratio [46] . 11 out of 13 fully sequenced strains of this cyanobacterium present low GC content , in the range between 30 to 38 percent , apparently contradicting the association between large population size and lack of mutation bias . However , the two remaining strains have GC content of 50% , as expected according to our model , and one of these was used to estimate the small ratio that supports the large population size . Prochlorococcus has a complex meta-population structure in which the strains with 50% GC content , characterized by large genomes , appear to act as gene reservoirs . These strains are also characterized by a larger cell size than other Prochlorococcus strains , which the authors describe as “a feature that may have led to their lower isolation recovery due to the filtration step most often used to separate Prochlorococcus from Synechococcus . Hence , there are probably more LL-adapted Prochlorococcus strains with cell and genome sizes similar to those of Synechococcus thriving deep in the euphotic zone . This is apparently confirmed by the dominance of this ecotype at the base of the euphotic zone in the Atlantic Ocean , as revealed by quantitative PCR data” [70] . These strains with large genomes and without mutation bias are found at considerable depth in the ocean and thus at low oxygen pressure . There seems to be a positive association between ocean depth and GC content for Prochlorococcus strains , thus a negative association between oxygen pressure and GC content , opposite to the observed general association between oxygen and GC content [64] . Comparative analysis of the sequenced Prochlorococcus strains will be necessary to test the hypothesis that there is an association between the GC content and the population size of these strains . Consistent with this possible association , it was found that in the MED4 strain , characterized by the smallest GC content among all Prochlorococcus strains , translational selection does not shape the codon usage variation among the genes in this organism [71] . We have shown here that the AT mutation bias can increase the fitness associated with essential proteins if the population size is very small . The same happens with GC mutation bias for intermediate population . These results suggest that the mutation bias is not selectively neutral , but it may be the preferred outcome for the evolution of small populations . We found a deep interplay between the estimated effective population size and the GC content that is consistent with the predictions of our model . Of course this association is not deterministic , since many other factors influence the GC content . However , the influence of population size is an intriguing one that we believe is worth further investigation . Thus , we hope that this proposal will be subject to experimental test in the future .
As in our previous work , the unfolding free energy of a protein with sequence and contact matrix if the minimal interatomic distance between residues and is below , 0 otherwise , is defined as ( 9 ) where is the contact interaction matrix determined in [72] , was determined fitting Eq . ( 9 ) to a set of experimentally measured unfolding free energy ( UB , unpublished ) and is protein length . Although rather simple , this model is accurate enough to allow quantitative predictions of the folding free energy of small proteins that fold with two-state thermodynamics ( the correlation coefficient between experimental and predicted free energy is over a representative test set of 20 proteins , UB , unpublished result ) and of the stability effect of mutations ( correlation coefficient over a set of 195 mutations , UB , unpublished result ) . This is comparable to state-of-the-art programs such as Fold-X [73] . However , the computational simplicity of the model makes it affordable to use it for simulating very long evolutionary trajectories with a large number of parameters , which would not be possible using other tools . The normalized energy gap measures how alternative compact conformations are higher in energy than the native , and it is defined using the random energy model [74] , [75] as ( 10 ) with , , , , and and are the mean and standard deviation of the interaction energy of both native and non-native contacts in sequence . We studied five proteins with different size and secondary structures: Phosphocarrier protein of E . Coli ( 85 amino acids , PDB id . 1opd ) , Lysozyme of G . Gallus ( 129 amino acids , PDB id . 3lzt ) , ATP synthase epsilon chain of E . Coli ( 135 amino acids , PDB id . 1aqt ) , Triose Phosphate Isomerase of E . Coli ( 255 amino acids , PDB id . 1tre ) and Tryptophan Synthase alpha chain of S . Typhimurium ( 260 amino acids , PDB id . 1a50 ) . When not otherwise stated , we exemplify our results with the structure of the protein lysozyme . Mutations are modelled through the HKY process [28] , in which the mutation rate from nucleotide to , , is if is a transition , if it is a transversion . The transition/transversion ratio is fixed at . The microscopic rate is assumed to be very small and it does not affect the results . We further assume and ( Chargaff second parity rule ) , so that the only parameter of the mutation model is the stationary GC content , , which we call GC usage . Simulations were performed starting from the native sequence , which was changed through random mutations subject to the acceptance probability Eq . ( 1 ) computed using the estimated folding stabilities . We checked that simulations converged in all cases after a number of accepted substitutions not larger than a few times the protein length , and discarded the first steps of the trajectory for collecting statistics . The simulations were run until accepted substitutions were collected , which makes it rather cumbersome to simulate large populations for which the acceptance rate is small . For each set of parameters we run 10 independent simulations in order to evaluate the statistical error . At every step , we randomly draw one mutating DNA site with probability dependent on the nucleotide that occupies it , , and we draw a new nucleotide with probability proportional to . The mutation is then translated to the amino acid sequence , whose stability is computed through Eq . ( 9 ) and ( 10 ) from which we obtain fitness through Eq . ( 2 ) . The fitness is compared to the one of the current wild type sequence and the mutation is accepted with probability given by Eq . ( 1 ) . For fixed and the equilibrium fitness is simulated for 9 GC usages from to and the results are fitted to a cubic function , from which we obtain the optimal at the point where the first derivative vanishes . If is monotonically increasing or decreasing the maximum ( minimum ) is chosen . To estimate the error , we estimated from 10 independent simulations , and we computed mean and standard error of the mean . | The Guanine plus Cytosine ( GC ) content of bacterial genomes varies from 20% to 80% . This variation is attributed to the mutation bias produced by replication and repair machinaries . However , the evolutionary forces that act on these very different machinaries have remained elusive . It is known that the GC content of genes strongly influences the resulting proteins' hydrophobicity , which is the main determinant of folding stability . This may lead to expectation that the GC content is strongly selected at its optimal value , since proteins that are too hydrophylic face unfolding problems and proteins that are too hydrophobic face misfolding and aggregation problems . In this work , using a realistic model of genotype ( DNA sequence ) to phenotype ( protein folding stability ) to fitness mapping and a standard population genetics model , we find that the optimal GC usage depends on population size . In particular , very small populations prefer small GC usage , intermediate populations prefer large GC usage , and large populations prefer no bias . Our results may explain why most intracellular bacteria , evolving with small effective populations , tend to adopt small GC usage . To test this hypothesis , we estimated the effective population size of several bacterial species , finding that those that evolve with 50% GC usage are characterized by significantly larger populations , although several exceptions exist . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"computational",
"biology/evolutionary",
"modeling",
"molecular",
"biology/molecular",
"evolution",
"evolutionary",
"biology/evolutionary",
"and",
"comparative",
"genetics",
"biophysics/protein",
"folding"
] | 2010 | Mutation Bias Favors Protein Folding Stability in the Evolution of Small Populations |
Eumycetoma is a morbid chronic granulomatous subcutaneous fungal disease . Despite high environmental exposure to this fungus in certain regions of the world , only few develop eumycetoma for yet unknown reasons . Animal studies suggest that co-infections skewing the immune system to a Th2-type response enhance eumycetoma susceptibility . Since chronic schistosomiasis results in a strong Th2-type response and since endemic areas for eumycetoma and schistosomiasis do regionally overlap , we performed a serological case-control study to identify an association between eumycetoma and schistosomiasis . Compared to endemic controls , eumycetoma patients were significantly more often sero-positive for schistosomiasis ( p = 0 . 03; odds ratio 3 . 2 , 95% CI 1 . 18–8 . 46 ) , but not for toxoplasmosis , an infection inducing a Th1-type response ( p = 0 . 6; odds ratio 1 . 5 , 95% CI 0 . 58–3 . 83 ) . Here , we show that schistosomiasis is correlated to susceptibility for a fungal disease for the first time .
Eumycetoma is a chronic granulomatous subcutaneous infectious disease endemic in many tropical and sub-tropical regions in the so-called mycetoma belt between 30°N and 15°S of the equator [1] . Sudan is a country with the highest country-wide prevalence of eumycetoma ( Figure 1 ) . In a recent survey conducted by the Mycetoma Research Centre , it appeared that in the endemic villages in the Gezira area of Sudan 2% of the population has eumycetoma ( Prof . A . Fahal , personal communication ) [2] , [3] . Although mycetoma can be caused by a variety of bacterial and fungal micro-organisms , most mycetoma cases in Sudan ( ca . 70% ) are caused by the fungus Madurella mycetomatis ( eumycetoma ) [4] . Based on antibody measurements in earlier studies it was noted that although most people living in endemic areas in the Sudan have developed antibodies against M . mycetomatis , and thus have been exposed to M . mycetomatis , only few of them actually developed eumycetoma [5] , [6] . To date , it is unknown why some people are predisposed to develop eumycetoma . Multiple explanations can be considered for the scanty susceptibility to eumycetoma . Firstly , genetic differences in the pathogen might exist that could lead to pathogenic and non-pathogenic variants of M . mycetomatis . Secondly , genetic polymorphisms in the host involved in sex hormone synthesis and neutrophil function have already been associated with eumycetoma , indicating that the genetic make-up of the host is a crucial factor in susceptibility to eumycetoma development [7]–[9] . Furthermore , a combination of specific genetic requirements and capabilities in both the pathogen and the host could lead to an even more sporadic development of eumycetoma . Thirdly , temporal conditions influencing the host immune response , such as co-infections , nutritional status , use of antibiotics and/or immune suppression or skewing may also play a role in susceptibility to M . mycetomatis . This possibility is supported by the observation that M . mycetomatis could only induce eumycetoma in animals in the presence of an adjuvant predisposing towards a Th2-response [10] but not a Th1-response [11] , [12] . Skewing of the immune response is highly affected by invasive pathogens [13] , and therefore , co-infections could play a critical role in eumycetoma [14] . In this respect , infections inducing a strong and long-lasting Th2-type of immune response could favour the development of eumycetoma disease most . Schistosomiasis seems to meet such requirements for the following reasons . Firstly , schistosomiasis induces a long-lasting Th2-type immune response that is strong enough to even convert an already established Th1-response [15] , [16] . Secondly , in endemic countries schistosomiasis is often a chronic life-long disease . Even when patients are regularly treated for schistosomiasis , their continuous exposure to the parasite during fresh water contacts and the lack of the development of immunity against schistosomes will rapidly result in a re-infection with a persistent Th2-response . Based upon the above mentioned observations , and the fact that we recently have shown that eumycetoma patients have increased concentrations of circulating IL-10 [7] , we hypothesize that schistosomiasis , which induces a Th2-type response with elevated levels of IL-10 , might increase the susceptibility to eumycetoma , whereas toxoplasmosis which induces a Th1-type response [16] , [17] , should not be associated with eumycetoma .
A total of 84 serum samples was taken from 53 eumycetoma patients and 31 controls , matched for age and gender , in the endemic areas of Sudan between 2001 and 2008 ( Table 1 ) . Serum samples were stored at −80°C until assay . The patients' demographic characteristics were recorded and that included gender , duration of disease , lesion size and site of infection . Eumycetoma was confirmed by culture and molecular identification based on sequencing the Internal Transcribed Spacer [18] . Written informed consent was obtained from all participants and ethical clearance was obtained from Soba University Hospital Ethical Committee , Khartoum , Sudan . Specific IgG antibodies against Toxoplasma gondii were determined with the commercially available Toxo IgG II assay on the automated Liaison serology platform according to the manufacturer's protocol ( Diasorin , Saluggia , Italy ) . Antibody levels against Schistosoma species were determined as described before by a combination of a commercial indirect hemagglutination test with Schistosoma mansoni adult worm antigens ( IHA; Fumouze Laboratories , Levallois-Perret Cedex , France ) and an enzyme-linked immunosorbent assay with homemade S . mansoni Soluble Egg Antigens ( SEA ) [19] . The IHA was considered positive when the titre was ≥1∶80 and the SEA ELISA was considered positive when the Optical Density ( O . D . ) at 492 nm was ≥0 . 15 . For optimal specificity , Schistosoma spp . serology was only considered positive when a positive result was obtained in both the IHA and SEA-ELISA tests . Antibody levels against Madurella mycetomatis Translationally Controlled Tumour Protein ( TCTP ) were measured with Luminex Technology as described before [5] . Difference in positive and negative serology for schistosomiasis and toxoplasmosis between eumycetoma patients and endemic controls were calculated with the Chi square test ( GraphPad Instat 3 . 00 ) by determining both the two-sided p-value and the Odds Ratio using Yates correction . The 95% confidence interval of the Odds Ratio was calculated using the approximation of Woolf . The Mann-Whitney U test was used to compare differences between IgG levels raised against the MmTCTP antigen in the study populations ( GraphPad Instat 3 . 00 ) . The Kruskal-Wallis test ( SPSS Inc 17 ) was used to test if concentrations of antibodies against Schistosoma spp . differed significantly between patients with larger lesions compared to patients with smaller lesions , by including size ( small , moderate , large ) as the grouping variable . A value of p<0 . 05 was considered significant .
Between 2001 and 2008 , 53 patients and 31 endemic controls were included in the study . Most patients were male and had eumycetoma of the foot ( Table 1 ) . All patients and controls came from the same area mainly from Central Sudan as indicated in Figure 1 . As shown in Figure 2 , eumycetoma patients were significantly more often sero-positive for Schistosoma infections as compared to endemic controls ( Chi square , p = 0 . 03 ) . In other words , an association exists between the infections caused by Madurella mycetomatis and Schistosoma spp . since the odds ratio for co-occurring schistosomiasis in eumycetoma patients is 3 . 2 ( 95% Confidence Interval 1 . 18–8 . 46 ) . In contrast , eumycetoma patients were not significantly more often sero-positive for Toxoplasma gondii infections ( Figure 2 , Chi square , p = 0 . 5 ) thus the risk for developing eumycetoma does not seem to be increased in case of concurrent toxoplasmosis ( Odds ratio 1 . 5 , 95% Confidence Interval: 0 . 60–3 . 75 ) . No correlation was found between sero-positivity for schistosomiasis and sero-positivity for toxoplasmosis ( Chi square , p = 0 . 1 , Odds ratio 0 . 44 , 95% Confidence Interval: 0 . 14–1 . 32 ) . Positive serology for either schistosomiasis or toxoplasmosis was not correlated to the size of the eumycetoma lesion ( Chi square , p = 0 . 8 and p = 0 . 2 , respectively , data not shown ) . Since schistosomiasis is known to reduce the humoral immune response against co-infecting pathogens [20] , [21] , we investigated the antibody response against M . mycetomatis antigen TCTP . The antibody levels against TCTP did not differ between the eumycetoma patients with positive schistosomiasis serology and those with negative schistosomiasis serology , nor did they differ between matched endemic controls with positive schistosomiasis serology and healthy endemic controls with negative schistosomiasis serology ( Figure 3 ) . This suggests that schistosomiasis does not influence the humoral immune response against the TCTP antigen of M . mycetomatis .
Schistosomiasis is a chronic disease with an estimated 200 million people infected in subtropical countries [22] . Therefore , almost all schistosomiasis patients will subsequently be infected by one or more additional pathogens . Although a prior infection with schistosomes often has an effect on the subsequent infection by a virus , bacterium , protozoan or other helminth , schistosomiasis can cause both an increase or a decrease in the severity of the subsequent infection for yet unknown reasons ( reviewed in Abruzzi and Fried [23] . Decreased subsequent disease severity was observed for co-infections with Helicobacter pylori , Fasciola hepatica , Echinostoma and with Plasmodium in case of S . haematobium schistosomiasis [23] . In addition , a worsened outcome of a subsequent co-infection has been described for HIV ( reduced viral clearance ) [24] , Leishmania donovani [25] , Toxoplasma gondii , Entamoeba histolytica infections and Plasmodium in case of S . mansoni schistosomiasis [23] , [26] . The effect of subsequent fungal infections has not been addressed yet . Since eumycetoma infections can only be established in animals with adjuvants inducing a strong Th2-type immune response , we hypothesized that co-infections inducing a Th2-type immune response would predispose to eumycetoma disease . This study compared eumycetoma patients with matched endemic controls without eumycetoma for co-infections with Schistosoma spp . . As a control , Toxoplasma gondii infections were monitored since this infection is also endemic in Sudan and results in a Th1-type of immune response in immune-competent hosts [17] . Although we only studied a limited number of people , which might not represent the full population of the study area , we did find an overall sero-prevalence of schistosomiasis in our study population of 51% , which is consistent with the earlier reported variable sero-prevalences for schistosomiasis in Sudan in the New Halfa and Um Zukra villages in the Gezira and Kassala regions ( Figure 1 ) ( 16% and 70% , respectively ) [27]–[29] . Large variations in prevalence of schistosomiasis even occur among villages in close proximity and depend on multiple factors , such as the environmental conditions for the intermediate snail host and the hygiene and bathing habits of the inhabitants [22] . The overall sero-prevalence of Toxoplasma gondii in our study population was 42% , which was exactly the same as found by Abdel-Hameed et al . in Gezira in 1991 [30] . This study now showed that eumycetoma patients were significantly more often sero-positive for schistosomiasis when compared to matched , endemic controls . The correlation is strengthened by the fact that antibody levels against Toxoplasma , another prevalent infection in Sudan , did not correlate with eumycetoma disease . Furthermore no correlation was found between seropositivity of schistosomiasis and toxoplasmosis . The main underlying cause may be the strong Th2-response induced by schistosomiasis and the high expression of interleukin-10 ( IL-10 ) by Th2 cells [16] , [31] . IL-10 is capable of inhibiting synthesis of pro-inflammatory cytokines . Another anti-inflammatory trait of IL-10 is its potent ability to suppress the antigen-presentation capacity of antigen presenting cells . Increased IL-10 cytokine levels have also been detected in mycetoma lesions [7] , [32] as well as in animal models of actinomycetoma caused by Nocardia brasiliensis [33] . Moreover , IL-10 levels were significantly elevated in serum of M . mycetomatis eumycetoma patients in Sudan [7] , suggesting that IL-10 concentrations also play an important role in development or maintenance of eumycetoma . In conclusion , even among this relatively small number of patients , eumycetoma was significantly associated with schistosomiasis and not with toxoplasmosis . Since this correlation is only based on serological data , animal studies are currently performed to investigate the precise role of schistosomiasis and Th2-predisposition in the development of fungal eumycetoma . | Eumycetoma is a mutilating fungal disease of mainly the foot and is found in ( sub ) tropical regions such as Sudan . At the moment it is not understood why some people develop eumycetoma and others not . In the regions were eumycetoma is prevalent many other infections are also found . These infections could alter the immune system which makes people more or less susceptible in obtaining another infection . One of the infections with such an effect is Schistosomiasis . In Africa , eumycetoma is found in regions were schistosomiasis is prevalent . In this study we show that eumycetoma patients more often have antibodies against Schistosoma species , than healthy controls from the same region . In contrast , eumycetoma patients did not have more often antibodies against Toxoplasma species . This might implicate that schistosomiasis predisposes eumycetoma development . If schistosomiasis indeed predisposes eumycetoma development , eradicating Schistosoma in a population could also lower the number of eumycetoma cases in that area , which in the end could lead to intervention strategies not only for schistosomiasis but also for eumycetoma . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"schistosomiasis",
"mycetoma",
"neglected",
"tropical",
"diseases",
"fungal",
"diseases",
"toxoplasmosis"
] | 2013 | Association of Eumycetoma and Schistosomiasis |
Schistosomes , parasitic flatworms that cause the neglected tropical disease schistosomiasis , have been considered to have an entirely carbohydrate based metabolism , with glycolysis playing a dominant role in the adult parasites . However , we have discovered a close link between mitochondrial oxygen consumption by female schistosomes and their ability to produce eggs . We show that oxygen consumption rates ( OCR ) and egg production are significantly diminished by pharmacologic inhibition of carnitine palmitoyl transferase 1 ( CPT1 ) , which catalyzes a rate limiting step in fatty acid β-oxidation ( FAO ) and by genetic loss of function of acyl CoA synthetase , which complexes with CPT1 and activates long chain FA for use in FAO , and of acyl CoA dehydrogenase , which catalyzes the first step in FAO within mitochondria . Declines in OCR and egg production correlate with changes in a network of lipid droplets within cells in a specialized reproductive organ , the vitellarium . Our data point to the importance of regulated lipid stores and FAO for the compartmentalized process of egg production in schistosomes .
Infection with helminth parasites of the genus Schistosoma causes chronic and debilitating disease in over 200 million people worldwide [1] , [2] . Adult S . mansoni worms live within the portal vasculature , producing eggs ( 200–300/day/female ) that are intended to pass into the intestinal lumen for release into the environment to allow transmission of the infection [3] . However , many eggs are carried by the blood flow to the liver , where they become trapped in sinusoids and elicit strong Th2 cell mediated immunopathology , which is the cause of disease manifestations [3] . Since egg production is key for both transmission and pathogenesis , studying reproductive biology in schistosomes could lead to new methods for preventing or treating disease [4] . Adult schistosomes exhibit sexual dimorphism , a trait that is unusual among parasitic trematodes , and display a fascinating codependency: the female resides in a groove ( the gynecophoric canal ) on the ventral side of the male and is dependent on ongoing physical pairing , but not sperm transfer [5] , for proper sexual development [5]–[11] . Virgin adult female schistosomes , from female-only infections , are developmentally stunted compared to fecund females from mixed-sex infections and are unable to lay eggs [11] , [12] . Furthermore , egg-laying females that are physically separated from their partners and surgically implanted into a host in the absence of male worms cease egg production and regress reproductively to an immature state . Interestingly , regression is reversible because normal reproductive activity is resumed when separated females are re-paired with males [11] , [13] , [14] . Regression is largely the result of involution of the vitellarium , a proliferative tissue that occupies the posterior two thirds of the female and produces cells that surround the ovum and provide proteins for eggshell formation and nutrients for the developing embryo [12] . There have been numerous suggestions that male parasites promote female maturation by “providing” nutrients [15] . The fact that starvation in planaria ( free living flatworms ) can lead to reversible tissue involution [16] is consistent with the possibility that loss of vitelline cells is the end result of nutritional deprivation in female parasites . Glucose is considered to be the key macronutrient required by adult schistosomes to meet their bioenergetics needs [17] , [18] , but there is a lack of clarity in the literature regarding the relative extent to which Warburg metabolism ( the homolactic fermentation of glucose in the presence of oxygen ) versus mitochondrial oxidative phosphorylation ( OXPHOS ) are important in these organisms [17] , [19] , [20] . Nevertheless , fecund adult females gradually stop ovipositing in vitro even when glucose and oxygen are not limiting [21] , and under anaerobic conditions egg production ceases immediately despite the fact that the worms remain viable for extended periods [17] . These findings led us to consider the possibility that worms are able to survive using Warburg metabolism , but require substrates other than glucose for oxidative metabolic pathways critical for egg production . Despite the general view that there is no appreciable lipid catabolism in helminth parasites [18] , the genes encoding the enzymes of the β-oxidation pathway , through which fatty acids ( FA ) are catabolized into the TCA cycle , are conserved in schistosomes [22] . Moreover , greater than 40% of the lipid in adult schistosomes is in the form of triacylglyceride ( TG ) , usually considered an energy store for β-oxidation [23] , and FA are able to promote egg production and egg viability in vitro [24] . We therefore decided to ask whether adult female schistosomes use FA oxidation ( FAO ) for egg production .
The β-oxidation pathway allows FA to be used as fuel for the TCA cycle , which in turn generates substrates for the electron transport chain to make ATP via OXPHOS . To examine whether this process occurs in adult female schistosomes , we used extracellular flux analysis to compare mitochondrial oxygen consumption rates ( OCR , [25] ) in individual fecund and virgin female schistosomes immediately ex vivo ( Fig . S1 ) . OCR in female schistosomes declined in the presence of oligomycin , and antimycin-A plus rotenone ( Fig . 1A ) , indicating that it is largely a function of mitochondrial OXPHOS ( Fig . S1 ) . Baseline OCR ( Fig . 1A , B ) , and mitochondrial spare respiratory capacity ( SRC , Fig . 1A , C ) [26] , were significantly higher in fecund vs . virgin females ( P<0 . 01 ) . SRC is the difference between OCR at basal state and after addition of FCCP ( Fig . S1 ) , and reflects the extra mitochondrial capacity available to produce energy under conditions of increased work or stress and is an important determinant of long-term cellular survival and function [26] , [27] . Since the sizes of fecund and virgin adult females differ [21] , the SRC measurement also provides an internally controlled indication that there are significant qualitative differences in mitochondrial respiration between fecund and virgin worms . Previous work showed that female schistosomes require oxygen to produce eggs [17] . To assess whether these findings reflect a dependence on OXPHOS , we cultured fecund female worms for 24 h in oligomycin , antimycin A or rotenone , all of which inhibit mitochondrial OCR ( Fig . 1 ) , and measured egg production and worm viability; these inhibitors had a significant ( p<0 . 01 in each case ) negative effect on egg production ( Fig . 2A ) , but little adverse effect on worm viability over this time period ( Fig . 2B ) . Moreover , when the inhibitors were washed out after 24 h , egg production resumed at normal levels over the ensuing 24 h ( Fig . 2C ) . These data indicate that female worms can survive independently of mitochondrial respiration , but absolutely require this process in order to produce eggs . Carnitine palmitoyl transferase 1 ( Cpt1 ) catalyzes the initial rate limiting step in FAO in which FA are transferred from the cytosol into the mitochondria [28] . To determine whether OXPHOS depends on FAO we incubated fecund female worms with the Cpt1 inhibitor etomoxir [29] , [30] immediately ex vivo and measured OCR . We found that etomoxir caused a significant decline in basal OCR ( Fig . 3A; S2 ) , without affecting basal extracellular acidification , an indicator of glycolysis ( data not shown ) . Since OXPHOS is essential for egg production ( Fig . 2 ) , we reasoned that if FAO is a significant source of substrates for the TCA cycle and therefore for OXPHOS , then inhibition of FAO should have a deleterious effect on egg output . To examine this we recovered fecund female worms from infected mice and measured the effect of etomoxir on egg production over 24 h in culture . Under these conditions , etomoxir completely suppressed egg production ( Fig . 3B ) , although worms remained viable . These data implicate FAO in egg production by female schistosomes . The understanding of how FA are utilized by cells is evolving rapidly . The current view is that FA are converted into TG and stored in cytoplasmic lipid droplets , from which they are released in a regulated fashion by lipolysis [31] to be used as energy substrates in FAO , or as ligands for nuclear receptors . It has been reported that schistosomes possess considerable TG stores when recovered from mice [23] , but the function and location of these stores remains enigmatic [22] . To examine this we stained female worms immediately ex vivo with Oil-Red-O , which binds to neutral TG and was recently authenticated as a true lipid stain in the free-living helminth Caenorhabditis elegans [32] . The results were striking , revealing that fecund female parasites possess an extensive lipid droplet network . This network was evident microscopically , and by measuring extracted dye spectrophotometrically ( Fig . 3C ) . In contrast , virgin females had significantly lower lipid reserves ( Fig . 3C ) . Moreover , the intensity of Oil-Red-O staining declined markedly over time as fecund worms were maintained in tissue culture for 3 or 13 days ( Fig . 3C ) . Previous reports have commented on the presence of lipid droplets within mature ( Stage 4 ) vitelline cells [13] . Although we do note have proof that all of the droplets that we have visualized using Oil-Red-O staining and confocal microscopy are within the vitellarium , their location is anatomically consistent with the majority of them being associated with this organ . The lack of Oil-Red-O staining in virgin worms , and in fecund females after culture , is consistent with the failure of the vitellarial lineage to produce Stage 4 cells under these conditions [10] , [13] . The decline in lipid reserves in vitro is of interest since it occurs with a similar kinetic to the decline in egg production by cultured worms [21] . We reasoned that this could reflect a causal link between lipid droplet exhaustion and the cessation of FAO under these conditions . To explore this , we used real time flux analysis to measure FAO activity and mitochondrial OCR in fecund females immediately ex vivo and in vitro . We found that cumulative levels of palmitate oxidation and basal OCR declined significantly in vitro ( Fig . 3D and 3E ) , and that as anticipated this was paralleled , between days 3 and 13 , by a significant decline in egg production ( Fig . 3F ) . To formally examine whether there is a link between FAO and lipid droplet depletion in vitro , we recovered fecund females from infected hosts and cultured them with etomoxir for 24 h and used Oil-Red-O staining to quantify lipid droplets . We found that etomoxir significantly inhibited depletion of lipid reserves in these worms ( Fig . 3G ) . FA liberated from lipid droplets by lipolysis are activated and shuttled into mitochondria for FAO by acyl-CoA synthetase ( ACSL ) [33] , [34] . We reasoned that if FA are essential for OXPHOS and egg production , then loss of function of ACSL should affect both of these parameters by preventing the use of FA resulting from lipolysis . We examined this using chemical inhibitors and RNAi . First , we tested the effect of the fungal metabolite Triacsin C , which is a potent inhibitor of most mammalian ACSLs [35] , [36] . We recovered fecund female parasites from their hosts and immediately assessed the effect of Triacsin C on basal OCR and egg production . We found that Triacsin C inhibited OCR ( Fig . 4A ) and blocked egg production entirely ( Fig . 4B ) . We used RNAi to substantiate the importance of ACSL in OXPHOS and egg production . Immediately after explantation , and prior to assessing OCR and egg production , fecund females were electroporated with siRNAs against SmACSL , or control siRNAs , [37] . Using this approach , SmACSL expression was significantly attenuated within 72 h ( Fig . S2A ) . Concomitant with reduced expression of SmACSL there were significant declines in OCR ( Fig . 4C ) and egg production ( Fig . 4D ) . Moreover , SmACSL-siRNA resulted in greater retention of lipid reserves over 3 days in culture ( Fig . 4E ) , which was also apparent to some extent in Triascin-C treated worms ( Fig . S2B ) . The initial step in mitochondrial β-oxidation is catalyzed by acyl-CoA dehydrogenase ( ACAD ) . We targeted schistosome ACAD using siRNAs; this approach resulted in reduced ACAD mRNA , ( Fig . S3C ) , decreased mitochondrial OCR ( Fig . 4F ) , and decreased egg production ( Fig . 4G ) . Furthermore , the stimulation of FAO by added palmitate was significantly impaired by this siRNA treatment ( Fig . 4H ) . Taken together , these data support a role for the mobilization of lipid droplet reserves for FAO in female schistosomes , and the use of this pathway to support egg production . Schistosomes cannot synthesize their own FA [22] , but they can take up lipids and convert them into TG [23] , [38] . Therefore we propose that in vivo , TG in lipid droplets are continuously catabolized for FAO and replenished through the uptake of FA from the environment . We hypothesize that FAO is essential for the differentiation and/or survival of Stage 4 vitelline cells . In this model , the reduced OCR and SRC of virgin vs . fecund females are due to the absence of mature vitellocytes that normally are committed to FAO and OXPHOS . Our data indicate that , in vitro , lipid stores are used but not replenished , thereby accounting for the loss of Oil-Red-O staining and declines in OCR as TG reserves are depleted in cultured parasites . Our data fit with the view that reproductive maturation and regression are closely linked to nutritional status in female schistosomes [15] , and point to FA as a key nutritional requirement for this process . How male parasites help females to acquire FA remains to be determined . Schistosomes eat blood , and it has been proposed that male worms physically assist females in this process . However , we have been unable to show any positive effect in the assays described herein of adding red blood cells to cultures of schistosomes , regardless of whether males are present or not ( data not shown ) . Since glucose is an essential nutrient for schistosomes ( Krautz-Peterson et al . 2010 , and data not shown ) , it is possible that virgin females are subsisting largely on glucose absorbed directly from the blood through tegumental surface transporters [39] , [40] . A plausible explanation for the observation that females cease egg production in vitro , even when male worms are present , is that certain FA present in vivo are missing in the media that have been routinely used to culture schistosomes . Possibilities include short chain FA , which are present in high concentrations in portal blood , and which interestingly are depleted in plasma samples from schistosome-infected mice [41] , [42] , and stearic acid , which when complexed with bovine serum albumin is able to replace fetal calf serum in a defined medium that is able to support short term egg production by cultured schistosomes [24] . It has been assumed that FAO does not occur in schistosomes , and that glucose is the key substrate for energy generation . However , the data presented here indicate that schistosomes use FAO specifically for the compartmentalized process of egg production . A role for FAO in schistosome egg production is consistent with the important roles of FA in reproduction in insects and mammals [43] , [44] . It will be important to identify the FA that support egg production and to understand the specific mechanism by which male schistosomes assist females in acquiring these nutrients . Unraveling the metabolic requirements for reproduction in schistosomes may enable development of enhanced tissue culture systems that will support continuous egg production in vitro . This , in turn , would greatly facilitate the application of emerging tools for transgenesis in these important parasites [45] . Moreover , it is conceivable that a greater understanding of the metabolic processes that support schistosome egg production may offer new opportunities to simultaneously prevent transmission and disease development .
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 . The protocol was approved by the Institutional Animal Care and Use Committee of Washington University School of Medicine ( Animal Welfare Assurance Number: A-3381-01 ) . Seven to eight wk old adult Schistosoma mansoni ( NMRI strain ) were recovered from infected C57BL/6 female mice ( Jackson Laboratory ) . Parasites were cultured in RPMI containing 10% fetal calf serum ( FCS ) ( both from GIBCO ) , 2% antibiotic/antimycotic , 1% HEPES , 10 mM glucose , 2 mM L-glutamine and 1 mM sodium pyruvate ( all from Sigma ) at 37°C in 95%air/5%CO2 . Medium was replaced every 3 days . Eggs produced every 24 h were counted using a microscope . Real-time measurements of OCR and extracellular acidification were made using an XF-24 Extracellular Flux Analyzer ( Seahorse Bioscience ) . Worms were plated in XF-24 Islet Capture Microplates ( one worm per well ) and analyzed in non-buffered RPMI 1640 , 25 mM glucose , 10% FCS , 100 U/mL penicillin/streptomycin , 2 mM L-glutamine and 1 mM sodium pyruvate under basal conditions or in the presence of oligomycin ( 3 µM ) , fluoro-carbonyl cyanide phenylhydrazone ( FCCP , 4 . 5 µM ) , rotenone ( 0 . 3 µM ) , antimycin A ( 3 µM ) , etomoxir ( 200 µM ) ( all from Sigma ) or Triacsin C ( 10 µM , Enzo Life Sciences ) . For FAO assay , real-time oxidation rates of palmitate in worms was assessed by extracellular flux analysis as described above . Basal OCR rates were measured prior to 2 h treatment with palmitate ( 200 µM ) with fatty acid free bovine serum albumin ( BSA ) , or with fatty acid free BSA ( 0 . 17 µM ) alone ( Seahorse Bioscience ) . siRNA targeting acyl-CoA synthetase ( ACSL; GI: 256090263 and GI: 238666949 ) and acyl-CoA dehydrogenase ( ACAD; GI: 353231171 and GI: 256070604 ) were designed and synthesized by Ambion , Applied Biosystems ( Silencer Select Custom Designed siRNA; http://www5 . appliedbiosystems . com/tools/sirna/ ) . siACSL: sense- GCAUACAGAUGGAAGUUUAtt; antisense-UAAACUUCCAUCUGUAUGCat . siACAD: sense-GGAAUCAAAUGAUAUCUUAtt; antisense-UAAGAUAUCAUUUGAUUCCat . Silencer Negative control siRNA#1 , which is not matched to any sequence in the parasite genome , was also provided by the manufacturer and used as a control . siRNA ( 10 µM ) was delivered by electroporation [46] . Parasites were fixed in 4% paraformaldehyde ( Electron Microscopy Sciences ) diluted in PBSTx ( PBS , 0 . 3% Triton X-100 ) for 1 h , [47] , dehydrated in 60% isopropanol for 15 min , stained with Oil-Red-O ( Sigma ) overnight [48] , washed in PBSTx 4 times and stained with phallotoxin-Alex Fluor 488 ( Invitrogen ) and Hoechst at 4°C for 1 h prior to imaging using a Leica SP5 LSCM confocal microscope and a PL APO CS 20× NA = 0 . 70 objective in the format of 2048×2048 . To quantify Oil-Red-O staining , dye was eluted in 100% isopropanol for 30 min and absorbance of the eluate vs . 100% isopropanol at 490 nm was measured [48] . The significance of observed differences was assessed using Student's t-test . | Schistosomes are parasitic worms that are the cause of the Neglected Tropical Disease schistosomiasis . Female schistosomes mated with males produce eggs , which either pass out of the host's body for transmission of the infection , or become trapped in host tissues , where they induce inflammation that contributes to disease symptoms . It has been assumed that egg production is a bioenergetically-demanding process fuelled by glucose metabolism . However , we have discovered that egg production is blocked by inhibition of fatty acid oxidation ( FAO ) , the process through which FA are utilized within mitochondria to fuel the tricarboxylic acid cycle and thereby produce substrates for ATP synthesis through oxidative phosphorylation . Consistent with a role for FAO in egg production , fecund females have extensive fat stores , in the form of lipid droplets , whereas virgin adult females have little or no fat reserves . Moreover , fecund females placed into tissue culture exhaust their fat reserves and cease to be able to produce eggs . Since schistosomes cannot produce their own FA , our data point to the acquisition of FA from the host as a key process necessary for egg production . Our findings point to the importance of regulated lipid stores and FAO for egg production by schistosomes . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"biology",
"microbiology",
"host-pathogen",
"interaction",
"parasitology"
] | 2012 | Fatty Acid Oxidation Is Essential for Egg Production by the Parasitic Flatworm Schistosoma mansoni |
Outbreaks of cutaneous leishmaniasis are relatively common among soldiers involved in nocturnal activities in tropical forests . We investigated the population dynamics of sand flies in a military training camp located in a remnant of Atlantic rainforest in northeastern Brazil , where outbreaks of cutaneous leishmaniasis have sporadically been described . From July 2012 to July 2014 , light traps were monthly placed in 10 collection sites , being nine sites located near the forest edge and one near a sheep and goat stable . Light traps operated from 5:00 pm to 6:00 am , during four consecutive nights . Leishmania infection in sand flies was assessed using a fast real-time PCR assay . Cases of cutaneous leishmaniasis among soldiers were also investigated . In total , 24 , 606 sand flies belonging to 25 species were identified . Males ( n = 12 , 683 ) predominated over females ( n = 11 , 923 ) . Sand flies were present during all months , being more numerous in March ( n = 1 , 691 ) and April 2013 ( n = 3 , 324 ) . Lutzomyia choti ( 72 . 9% ) was the most abundant species , followed by Lutzomyia longispina ( 13 . 8% ) , Lutzomyia complexa ( 5 . 3% ) , representing together >90% of the sand flies collected . Forty cases of cutaneous leishmaniasis were recorded among soldiers from January 2012 to December 2014 . Leishmania isolates were obtained from eight patients and were all characterized as Leishmania braziliensis . Soldiers and anyone overnighting in Atlantic rainforest remnants should adopt preventative measures such as the use of repellents on bare skin or clothes and insecticide-treated tents .
Cutaneous leishmaniasis is a neglected tropical disease highly prevalent in Central and South America . Indeed , Brazil , Costa Rica and Peru are among the 10 countries accounting for 70–75% of the global estimated cutaneous leishmaniasis incidence [1] . Only in Brazil , 554 , 475 cases of cutaneous leishmaniasis were notified to public health authorities between 1988 and 2007 , with an average incidence of 17 . 3 cases per 100 , 000 inhabitants [1] . The disease is widespread in this country occurring mainly in rural areas and forest environments , affecting mainly individuals older than 10 years and males [1 , 2] . While widespread in Brazil , cutaneous leishmaniasis is more prevalent in the Amazon forest and Atlantic forest regions [1] . The Amazon forest biome covers 48% of the Brazilian territory ( 4 , 245 , 024 km2 ) , but the Atlantic forest biome has been over explored since the arrival of the first Portuguese settlers and in the past centuries its original cover has been reduced to almost nothing . Today , only 2–5% of its original area is considered to be in its original state ( http://www . fao . org/3/a-i3825e/i3825e6 . pdf ) . Indeed , the Atlantic forest region presently encompasses an area of 1 , 129 , 760 km2 , extending along the Atlantic coast of Brazil , from Rio Grande do Norte ( in the north ) to Rio Grande do Sul ( in the south ) . The largest cities ( e . g . , Rio de Janeiro and São Paulo ) and industries in the country are located in the Atlantic forest region , which houses 70% of the Brazilians and accounts for about 80% of its gross domestic product . Nonetheless , the Atlantic forest biome is still home to about 2 , 000 species of animals and 20 , 000 species of plants; a biological diversity similar to that found in the Amazon region ( http://www . nature . org/ourinitiatives/regions/southamerica/brazil/placesweprotect/atlantic-forest . xml ) . Cutaneous leishmaniasis in the Atlantic forest region usually affects rural and forest workers that live near or literally within forest fragments , as well as people developing nocturnal activities in these environments . For instance , several cases of cutaneous leishmaniasis have been reported among soldiers after periods of nocturnal training activities in remnants of Atlantic forest in northeastern Brazil [3–6] . Indeed , the presence of proven and putative sand fly vectors of parasites such as Leishmania braziliensis , the most widespread causative agent of cutaneous leishmaniasis in the Americas , is acknowledged in these areas [3 , 7–11] . Nonetheless , our knowledge on the ecology of sand flies ( Phlebotomine ) and its relationship with cutaneous leishmaniasis incidence in the Atlantic forest region is still fragmentary . In this study , we investigated the sand fly fauna in a military training camp located in a remnant of Atlantic rainforest in northeastern Brazil , where outbreaks of cutaneous leishmaniasis have been sporadically described . Our hypothesis was that the temporal dynamics of sand flies was correlated with climate variables and the incidence of cutaneous leishmaniasis in this region .
This study used secondary data ( i . e . , anonymized data that has previously been collected in the course of normal care ) on human cutaneous leishmaniasis obtained from Reference Service on Leishmaniasis of the Aggeu Magalhães Institute , Oswaldo Cruz Foundation ( Fiocruz ) . No ethical approval was required . This study was carried out in the Campo de Instrução Militar Marechal Newton Cavalcanti ( CIMNC ) , located in the Atlantic forest zone of Pernambuco State . This military training camp comprises an area of 7 , 324 hectares ( Fig 1 ) , distributed in five municipalities: Araçoiaba , Paulista , Igarassu , Paudalho and Tracunhaém . The camp possesses has a central pavilion of command , two villages with 16 adjoining houses , a school , a chapel , 14 houses portholes , eight sheds used for any troops under training and six areas for military training and exercises . The climate of this region is rainy tropical type with dry summer . The vegetation is represented by the Atlantic rainforest distributed into two main types: open ombrophilous forest and seasonal semidecidual . Outbreaks of cutaneous leishmaniasis are sporadically reported in this region [3–5] . Sand fly collections were carried out monthly , for four consecutive nights , from July 2012 to July 2014 in 10 pre-selected collection sites ( CS1-CS10 ) , totaling 96 actual nights and 960 total trap-nights ( Table 1 ) . In each site , a CDC-type light trap was placed ca . 1 . 5 m above the ground , operating from 5:00 pm to 6:00 am . Collection sites were typically near animal holes , trunks and roots of large trees , usually in shady and humid environments . Collection sites were also near the places where the soldiers use to camp overnight during training periods . The only exception was CS10 , which was a sheep and goat stable , located near the military camp headquarter . Collected sand flies were separated from other insects and placed in labeled vials containing 70% ethanol until processing for morphological identification [12] . For females , the head ( containing the cibarium ) and the last three abdominal segments ( containing the spermathecae ) were used for morphological identification , whereas the thorax and the remaining part of the abdomen were used for DNA extraction and PCR testing . DNA extraction was performed from 1 , 003 females ( grouped in 108 pools ) belonging to four species: Lu . choti ( 688 females grouped in 71 pools ) , Lu . sordellii ( 197 females grouped in 24 pools ) , Lu . complexa/wellcomei ( 113 females grouped in 12 pools ) and Lu . amazonensis ( one pool of five females ) . Each pool contained 5–10 specimens of the same species and separated according to date and place of collection . DNA extraction was performed using the Chelex 100 resin as described elsewhere [13] . Extracted DNA samples were kept frozen at –20°C until testing . Real-time PCR testing for detecting Leishmania kinetoplast DNA was performed using the primers LEISH-1 ( 5’-AACTTTTCTGGTCCTCCGGGTAG-3’ ) and LEISH-2 ( 5’-ACCCCCAGTTTCCCGCC-3’ ) and the TaqMan probe FAM-5’-AAAAATGGGTGCAGAAAT-3’-non-fluorescent quencher-MGB , as described elsewhere [14] . All real-time PCR assays were run on a QuantStudio 5 Real-Time PCR machine ( Applied Biosystems ) and contained a standard curve of 10-fold serial dilutions ( 1 ng , 100 pg , 10 pg , 1 pg , 100 fg , 10 fg and 1 fg per reaction mixture ) from DNA of L . infantum and a no-template control ( DNA-free water ) . The real-time PCR results were analyzed using QuantStudio Design and Analysis Software v1 . 4 ( Applied Biosystems ) . DNA was also extracted from human patients using QIAamp DNA mini kit ( Qiagen ) , according to the manufacturer’s recommendations . Conventional PCR for the detection of Leishmania ( Viannia ) DNA in human samples was performed as described elsewhere [15] . PCR products ( 10 μl ) were digested with HaeIII ( 1 μl ) ( New England BioLabs ) for 1 h , at 37°C and then electrophoresed in a 3% high-fidelity agarose gel ( Invitrogen ) . Bands were stained with ethidium bromide . Positive controls ( amplified standard DNA from both L . infantum and L . braziliensis ) were included in each analysis . The species of Leishmania detected in each sand fly pool was determined by comparing the banding profile with the ones obtained with positive controls . A 100 bp ladder ( Invitrogen ) was used as molecular weight . Secondary data on human cases of cutaneous leishmaniasis were obtained from the Reference Service on Leishmaniasis , Aggeu Magalhães Institute , Oswaldo Cruz Foundation , Recife , Brazil . All cases whose patients were soldiers involved in training activities in the study area during 2012–2014 were included in this study . In brief , these patients presented skin lesions suggestive of cutaneous leishmaniasis and were referred to a local military hospital in Recife . At the hospital , the physician in charge determined the diagnosis , initially based on clinic-epidemiological data , and then by leishmanin skin test ( Montenegro skin test ) and skin-scraping cytology , as recommended by Ministry of Health of Brazil [16] . Skin samples from these patients were seeded in tubes containing biphasic Novy-MacNeal-Nicolle culture medium at 26°C . Isolates obtained were identified using multilocus enzyme electrophoresis [17] at the Oswaldo Cruz Institute ( Rio de Janeiro , Brazil ) and using monoclonal antibodies [18] at the Evandro Chagas Institute ( Belém do Pará , Brazil ) . Geographical coordinates and altitude of each patient’s house were recorded using a Garmin eTrex Venture HC GPS ( Garmin International Ltd , US ) . Meteorological data ( i . e . , relative humidity , monthly average temperature , and precipitation ) for the whole period of study were obtained from Instituto de Tecnologia de Pernambuco ( ITEP ) ( meteorological station: 82900 ) . The saturation deficit ( SD ) was calculated as follows: SD = ( 1 − RH/100 ) × 4 . 9463 × e0 . 0621 × T , where RH is relative humidity and T is temperature . The correlation between climatic variables and the number of sand flies collected monthly or daily was done using Spearman’s ( rs ) or Pearson’s ( r ) correlation coefficients , as appropriate . Normality of data was assessed using Lilliefors . The number of sand flies of each species collected according to collection sites or months was compared using Kruskal-Wallis . The significance level was set at P < 0 . 05 . Statistical analyses were performed using BioEstat , version 5 . 3 [19] . The diversity indices ( Species richness , Shannon’s diversity index and Pielou’s equitability index ) and abundance were calculated using PAST , version 2 . 16 [20] . The standardized index of species abundance ( SISA ) was as described elsewhere [21] .
A total of 24 , 606 sand flies ( Table 2 ) , being 12 , 683 males and 11 , 923 females ( overall sex ratio close to unity ) , belonging to 25 species ( i . e . , Lutzomyia choti , Lutzomyia longispina , Lutzomyia complexa , Lutzomyia sordellii , Lutzomyia amazonensis , Lutzomyia walkeri , Lutzomyia wellcomei , Lutzomyia quinquefer , Lutzomyia evandroi , Lutzomyia barrettoi barrettoi , Lutzomyia ayrozai , Lutzomyia capixaba , Lutzomyia naftalekatzi , Lutzomyia claustrei , Lutzomyia schreiberi , Lutzomyia umbratilis , Lutzomyia whitmani , Lutzomyia brasiliensis , Lutzomyia viannamartinsi , Lutzomyia shannoni complex , Lutzomyia yuilli pajoti , Lutzomyia aragaoi , Lutzomyia furcata , Lutzomyia migonei and Lutzomyia oswaldoi ) were identified . Lutzomyia choti , Lu . longispina , Lu . complexa and Lu . sordellii were the most frequently collected species , representing together 95 . 8% of the total collections . Yet , Lu . barrettoi barrettoi , Lu . ayrozai , Lu . capixaba , Lu . naftalekatzi , Lu . claustrei , Lu . schreiberi , Lu . umbratilis , Lu . whitmani , Lu . brasiliensis , Lu . viannamartinsi , Lu . shannoni complex , Lu . yuilli pajoti , Lu . aragaoi , Lu . furcata , Lu . migonei and Lu . oswaldoi were rarely collected , representing together 1 . 1% of the total . The species richness was higher in CS2 ( 20 spp . ) , followed by CS1 ( 17 spp . ) , CS3 ( 17 spp . ) , CS8 ( 16 spp . ) and CS9 ( 16 spp . ) , respectively . The highest Shannon’s diversity and Pielou’s equitability indexes were found in CS9 and CS6 ( Table 3 ) . Overall , no significant difference was found in the number of specimens from each species in relation to the site of collection ( H = 11 . 874; df = 9 , P = 0 . 221 ) . Overall , the sand fly population studied herein presented a unimodal temporal distribution pattern , peaking in the first semester of each year . The number of sand flies collected monthly during the whole study period ranged from 271 to 3 , 324 , peaking in March ( 1 , 691 ) and April ( 3 , 324 ) 2013 and April ( 1 , 512 ) , May ( 1 , 217 ) and June ( 1 , 619 ) 2014 ( Fig 2 ) . No significant variation was found in the number of specimens from each species in relation to the month of collection ( H = 14 . 404; df = 24 , P = 0 . 937 ) . No correlation was found between the monthly number of sand flies collected and climate variables ( rs = 0 . 29 , P = 0 . 167 , for temperature; rs = 0 . 14 , P = 0 . 519 , for precipitation; rs = –0 . 05 , P = 0 . 829 , for relative humidity; and rs = 0 . 11 , P = 0 . 608 , for saturation deficit ) . However , comparing daily data , the number of sand flies collected was significantly correlated with temperature ( rs = 0 . 29 , P = 0 . 003 ) , but not with precipitation ( rs = –0 . 185 , P = 0 . 066 ) , relative humidity ( rs = –0 . 153 , P = 0 . 128 ) and saturation deficit ( rs = 0 . 181 , P = 0 . 072 ) . Considering only the most abundant species ( i . e . , Lu . choti ) , the daily number of specimens collected was significantly correlated with temperature ( rs = 0 . 44 , P < 0 . 001 ) , precipitation ( rs = –0 . 242 , P = 0 . 015 ) , relative humidity ( rs = –0 . 25 , P = 0 . 012 ) and saturation deficit ( rs = 0 . 293 , P = 0 . 003 ) ( Fig 3 ) . Forty male soldiers ( age range , 19–22 years ) were diagnosed with cutaneous leishmaniasis in a regional military hospital from January 2012 to December 2014 . Most of the soldiers were involved in nocturnal training activities in the study area in September 2013 ( n = 7 ) , October 2013 ( n = 16 ) , and September 2014 ( n = 11 ) . Among the remaining soldiers , two were training in June 2013 , two in July 2013 , one in September 2011 and one in October 2011 . One of the soldiers that were training in September 2013 was also in the forest in June 2013 . Most of them were diagnosed with cutaneous leishmaniasis in November ( n = 22 ) , December ( n = 10 ) or October ( n = 6 ) ; the remaining two cases were diagnosed with cutaneous leishmaniasis in January 2012 . All suspected cases but three were confirmed by one or more diagnostic test: 100% ( 30/30; eight refused to do the test ) were positive at the leishmanin skin test , 31 . 6% ( 12/38 ) at cytology , 28 . 9% ( 11/38 ) at PCR , and 21 . 1% ( 8/38 ) at culture . All eight isolates obtained from those patients were all characterized as L . braziliensis . Patients presented localized ulcers on legs , forearm , hands and neck . In most of the cases ( 84 . 2% ) , the diagnosis was made 1–2 months after the training period in the forest . All patients were successfully treated with n-methyl-glucamine antimoniate ( Glucantime ) , except one whose lesion healed spontaneously without treatment . Out of 1 , 003 females tested , six pools ( 10 females each ) of Lu . choti were positive at real-time PCR , which gives an overall minimum infection rate of 0 . 6% . Considering only Lu . choti , the minimum infection rate was 0 . 87% ( 6/688 ) . Three pools ( i . e . , F968 , F651 , and F1002 ) presented a HaeIII restriction profile identical to L . braziliensis ( Fig 4 ) . The three remaining positive pools did not show any pattern probably due to the very low amount of DNA detected by real-time PCR .
A high diversity of sand flies was recorded in the studied Atlantic forest remnant studied herein . Our results , together with data available in the literature [11 , 22 , 23] , suggest that most sand fly species found in Atlantic forest remnants are still more adapted to the forest than to human-modified environments; i . e . , only 11 out 25 species identified in the study were collected in the animal stable , and typically in low numbers . Indeed , in some areas where cutaneous leishmaniasis by L . braziliensis is endemic , some vector species appear to be almost exclusively found in the forest interior or forest edge , rather than in the peridomicile [11 , 22 , 23] . Nonetheless , it is acknowledged that some vector species like Lu . whitmani are well adapted to human-dwellings [11] . This is in agreement with data obtained in the current study , where Lu . whitmani was found in very low numbers ( 15 specimens in the whole study period ) and mostly in the horse stable ( 66 . 7% of the specimens collected ) . The sand fly population studied herein presented a well-defined unimodal temporal distribution pattern , peaking in the first semester of each year . The highest peak was recorded in April 2013 , with over 3 , 324 sand flies collected . This finding is congruent with a preliminary 1-year study conducted in 2003 in the same area [8] . These results clearly indicate that this sand fly population displays a relatively stable temporal distribution pattern , throughout the years . In turn , this suggests that the studied Atlantic forest remnant has not been much modified during the last 10 years or that possible modification that may have occurred during this period did not influence the sand fly population , neither negatively nor positively . Still regarding the population dynamics , it is important to state that the heavy rains observed during some capture nights may have reduced the efficiency of our light traps during these nights . While obvious , this is not usually considered in the analysis of studies on sand fly population dynamics . It is crucial to consider this aspect because an apparent decline in the population may not be a real decline but merely an artifactual reduction of collection due to heavy rains or strong winds occurring during trapping nights . For instance , the reduced number of sand flies collected during some months ( e . g . , June and July 2013 ) may be a result of heavy rains recorded in these months . So , the common assertion that sand fly populations present a peak after rainy periods may be an artifact in some Brazilian regions . Indeed , it is expected that in drier areas sand flies ( e . g . , Lu . longipalpis ) may be more active after rainy periods , when the relative humidity becomes higher [24] . However , in Atlantic forest remnants , where environmental conditions are optimal to sand flies throughout the year , population peaks after rainy periods may be actually due to adults that resume their activity after periods of unfavorable weather conditions rather than due to the emergence of new adults . What do adult sand flies do when it rains is uncertain . Small fliers appear to be more robust than we think to in-flight perturbations [25] . Mosquitoes , which are larger than sand flies , have a strong exoskeleton and low mass that make them impervious to falling drops [25] . Water resistant hairs cover the wings and the whole body of sand flies and this may also protect them from falling raindrops . Lutzomyia choti was the most abundant species and its daily number was positively correlated with temperature and saturation deficit , which is in line with a recent investigation conducted in low-density residential rural area , with mixed forest/agricultural exploitation [11] . This sand fly species was the only one found infected by Leishmania in the present study , with an estimated minimum infection rate of 0 . 87% . Real-time PCR-positive females were collected in January ( one pool ) , February ( two pools ) , April ( one pool ) and June ( two pools ) . Whether by coincidence or not , all infected pools where collected in the first semester of the year , in parallel to the main sand fly population peak recorded in this study . Lutzomyia choti is one of the most common sand fly species in Atlantic forest remnants in Brazil [8 , 10 , 11] . It is also willing to feed on humans [9] . Altogether , these findings may indicate that Lu . choti may be an important vector of L . braziliensis in remnants of Atlantic forest in Brazil . The period of the year when there is highest risk of Leishmania spp . transmission is always an issue of debate . In fact , one may say that the risk is higher when the sand fly population peaks up; higher biting rates . Other may argue that the highest risk would be later , when the vector population drops down and gets older; lower biting rates but higher infection rates in sand flies . Indeed , after emergence from the pupae , adult females need to take a meal to become infected , mature the infection in its gut to be able to transmit the parasites to a susceptible host . Taking into account our data on sand fly temporal distribution pattern and Leishmania infections in both sand flies and soldiers , we may speculate that the risk of cutaneous leishmaniasis may be permanent , but probably higher in the second semester . Nonetheless , this is a hypothesis that needs further investigations to be confirmed . Cases of cutaneous leishmaniasis have been sporadically detected in the study area [3–5] . These cases are usually associated to soldiers or other military personnel that were involved in nocturnal activities in the forested environment . Indeed , the risk of infection by L . braziliensis in Atlantic forest remnants is eminent , as sand fly vectors [7–11] and reservoirs ( e . g . , small rodents ) of this parasite are abundant in this biome [26–28] . It means that anyone overnighting in Atlantic forest remnants should seriously consider the adoption of protective measures , such as the use of repellents on bare skin or clothes and insecticide-treated tents . This should help reducing the risk of cutaneous leishmaniasis , especially among individuals like soldiers and forest workers that cannot avoid the contact with forested environments during the night . The Atlantic forest biome has been reduced to almost nothing of its original land cover [29] . Nonetheless , this biome is still home to a great diversity of animals and plants . Among animals living in Atlantic forest remnants , there are arthropods that may act as vectors and small mammals that may act as reservoirs of disease agents ( e . g . , Leishmania spp . and Rickettsia spp . ) . It has been demonstrated that habitat fragmentation and biodiversity loss can increase the risk of pathogen transmission to humans through the so-called dilution effect [30–32] . It is yet to be investigated whether the almost complete destruction of the Atlantic forest biome has played a role on the epidemiology of cutaneous leishmaniasis in Brazil .
This study demonstrates that the temporal dynamics of sand flies is correlated to some extent to climate variables , with some species contrasts . The finding of Lu . choti females infected with L . braziliensis , along with its known anthropophyly and high abundance in Atlantic forest fragments in Brazil , highlight the need for further studies to assess the vector competence of this sand fly for transmitting L . braziliensis under experimental conditions . Finally , people overnighting in Atlantic rainforest remnants should adopt preventative measures such as the use of repellents on bare skin or clothes and insecticide-treated tents to reduce their exposure to sand flies and other potential disease vectors . | Outbreaks of cutaneous leishmaniasis are relatively common among soldiers involved in nocturnal activities in tropical forests . However , there is limited information on the relationship between sand fly population dynamics and cases of cutaneous leishmaniasis in Atlantic forest remnants . In this study , we investigated the population dynamics of sand flies in a military training camp located in a remnant of Atlantic rainforest in northeastern Brazil , where outbreaks of cutaneous leishmaniasis have sporadically been described . In total , 24 , 606 sand flies belonging to 25 species were identified . Sand flies were present during all months , being more numerous in March and April 2013 . Lutzomyia choti was the most abundant species and three pools of females belonging to this species were found to be positive for Leishmania braziliensis DNA . Our results suggest that the risk of cutaneous leishmaniasis by Leishmania braziliensis in Atlantic rainforest remnants is permanent and thus not dictated by sand fly population peaks . People overnighting in Atlantic rainforest remnants should adopt preventative measures such as the use of repellents on bare skin or clothes and insecticide-treated tents . | [
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"diseases... | 2017 | Sand fly population dynamics and cutaneous leishmaniasis among soldiers in an Atlantic forest remnant in northeastern Brazil |
Recombination rate and linkage disequilibrium , the latter a function of population genomic processes , are the critical parameters for mapping by linkage and association , and their patterns in Caenorhabditis elegans are poorly understood . We performed high-density SNP genotyping on a large panel of recombinant inbred advanced intercross lines ( RIAILs ) of C . elegans to characterize the landscape of recombination and , on a panel of wild strains , to characterize population genomic patterns . We confirmed that C . elegans autosomes exhibit discrete domains of nearly constant recombination rate , and we show , for the first time , that the pattern holds for the X chromosome as well . The terminal domains of each chromosome , spanning about 7% of the genome , exhibit effectively no recombination . The RIAILs exhibit a 5 . 3-fold expansion of the genetic map . With median marker spacing of 61 kb , they are a powerful resource for mapping quantitative trait loci in C . elegans . Among 125 wild isolates , we identified only 41 distinct haplotypes . The patterns of genotypic similarity suggest that some presumed wild strains are laboratory contaminants . The Hawaiian strain , CB4856 , exhibits genetic isolation from the remainder of the global population , whose members exhibit ample evidence of intercrossing and recombining . The population effective recombination rate , estimated from the pattern of linkage disequilibrium , is correlated with the estimated meiotic recombination rate , but its magnitude implies that the effective rate of outcrossing is extremely low , corroborating reports of selection against recombinant genotypes . Despite the low population , effective recombination rate and extensive linkage disequilibrium among chromosomes , which are techniques that account for background levels of genomic similarity , permit association mapping in wild C . elegans strains .
The allelic variants that underlie heritable phenotypic variation are distributed along chromosomes . Their distribution is shaped by the machinery of meiosis within individuals and by mutation , selection , and drift among them . To discover the genetic basis of complex traits , and to understand the evolutionary dynamics that shape this genetic architecture , we must characterize empirical patterns of linkage and linkage disequilibrium . We have undertaken this task in the nematode C . elegans . Mapping of thousands of mutants to the genome and molecular studies of meiotic machinery have provided a view of the large-scale landscape of the C . elegans recombination map . The chromosomes exhibit nearly complete crossover interference [1] , such that each chromosome experiences one crossover per meiosis and has a genetic length of 50 cM [2] . Accumulated data from thousands of two- and three-point mapping crosses and small-scale SNP-based analyses have demonstrated a general pattern of large , nearly constant-rate domains on the autosomes , with high recombination in chromosome arms and low recombination in chromosome centers . Despite strong global regulation of crossover number , many details remain unclear , including the locations of the domain boundaries , the occurrence of fine-scale variation within domains , and the existence of domain structure on the X chromosome . Moreover , evidence for the genetic control of crossover number and position [1]–[4] leaves open the possibility that segregating variants may influence recombination patterns in experimental crosses of natural isolates . Because recombination patterns have been studied only on broad scales in individual crosses , involving fewer than two dozen markers per chromosome , dense characterization of a massive cross promises to clarify the recombinational landscape . C . elegans is one of the most exhaustively studied of all species with respect to developmental , behavioral , and physiological genomics , but studies of its population biology have lagged . Although natural genetic variation has been a source of alleles for genetic analysis in C . elegans since long before the system became a model [5] , the widely accepted notion that worms exhibit little variation has discouraged investigations of their diversity . The difficulty of collecting C . elegans from the wild has compounded the problem . Nevertheless , recent work has revealed abundant heritable phenotypic variation among wild C . elegans strains [6]–[20] and has begun to reveal the ecological context for this species [16] , [17] , [21]–[25] . C . elegans geneticists have exploited this variation to map quantitative trait loci [26]–[37] , and in a handful of cases to identify the causal mutations underlying phenotypic variation ( in genes npr-1 , mab-23 , tra-3 , zeel-1 , plg-1 , and scd-2 [10] , [30] , [38]–[43] ) . In parallel , studies of variation at molecular markers have begun to provide an account of the distribution of genetic variation within and among localities and across genomic regions [6] , [7] , [23] , [24] , [40] , [41] , [43]–[60] . These studies have shown that the species exhibits substantially lower levels of polymorphism and higher levels of linkage disequilibrium than other model systems , even those , like Arabidopsis thaliana , that share with C . elegans a primarily selfing mating system . The empirical pattern of linkage disequilibrium may result as much from selection against recombinant genotypes as from attributes of population biology such as population size and outcrossing rate [24] , [61] . A genome-wide assessment of linkage disequilibrium is required to determine whether natural isolates of C . elegans will be useful for mapping loci by association . We generated and genetically characterized a recombinant inbred advanced intercross population to gain insights into the recombination map in C . elegans , and we characterized a large panel of wild strains to characterize linkage disequilibrium . The data on recombination in the lab and in the wild reveal the role of population genomic processes in shaping genotypic diversity in C . elegans , and they lay the groundwork for rapid discovery of the genes underlying phenotypic variation .
We genotyped 1454 nuclear SNP markers in 236 recombinant inbred advanced intercross lines ( RIAILs ) . These lines represent the terminal generation of a 20-generation pedigree founded by reciprocal crosses between the laboratory wild type strain N2 ( Bristol ) and the Hawaiian isolate CB4856 . The pedigree includes ten generations of intercrossing ( random pair mating with equal contributions of each pair to succeeding generations [62] ) followed by 10 generations of selfing . The SNP markers span 98 . 6% of the physical length of the chromosomes ( Table S1 ) . The median spacing is 61 , 160 bp , and 80% of intervals are shorter than 100 kb . Only 35 marker intervals ( 2 . 4% ) are greater than 200 kb . The RIAILs contain 3 , 629 breakpoints in 772 marker intervals; some breakpoints may be identical by descent because of the shared ancestry during the intercrossing phase of RIAIL construction . An estimate of the mapping resolution of the panel , based on the distances between intervals containing breakpoints , yields a median bin size of 98 kb . Because larger bins contain more of the genome than smaller bins , the expected size of a bin in which a uniformly distributed QTL will fall is 225 kb . The RIAILs exhibit a genetic map length of 1588 cM , a 5 . 3-fold expansion of the 300 cM F2 genetic map . The realized expansion is 93% of the expected 5 . 7-fold map expansion , a difference attributable , at least in part , to the action of selection during the construction of the lines . Although selection and drift may alter the relationship between recombination fraction and meiotic recombination rate [63] , [64] , the observed recombination fractions are qualitatively informative about global patterns of recombination rate variation across C . elegans chromosomes . The genetic maps for the six C . elegans chromosomes are similar to one another and exhibit five distinct domains: two tips with effectively zero recombination , two high recombination arms , and a low recombination center , consistent with the pattern observed in classical two- and three-point mapping crosses [65] . These domains are evident in Marey maps [66] , which show genetic position as a function of physical position ( Figure 1; Table 1 ) . As the recombination rate within each domain is relatively constant , we used a segmented linear regression to identify the boundaries between the domains . The central domain of each autosome occupies roughly half the chromosome's length , despite the very different lengths of the chromosomes ( Table 1 ) . For example , the center of chromosome V is 10 . 7 Mb , 51% of the chromosome length , while the center of chromosome III is 6 . 6 Mb , 48% of that chromosome's length . Because all the centers have very similar rates of recombination per base pair ( Table 1 ) , their different physical lengths mean that the amount of recombination in each center ( its genetic length ) varies with total chromosome length . The constraint of one breakpoint per chromosome then requires that the amount of recombination in the arms of each chromosome varies inversely with chromosome length; shorter chromosomes have a larger fraction of their recombination events in their arms , and the physical sizes of the arms explain much of the variation among arms in recombination rates ( r2 = 0 . 51 , p = 0 . 009 ) . Nevertheless , the arms are heterogeneous in relative and absolute length and recombination rate , and the central domains are not perfectly centered on the chromosomes , consistent with the finding of Barnes et al . [65] . Most notably , the left arm of chromosome IV has a relative recombination rate more than twice that of the right arm , though they differ in size by only 15% ( Figure 1; Table 1 ) . Inspection of the Marey maps suggests that there may be additional rate variation within the defined domains . To determine whether such variation is expected in the case of constant-rate domains , we simulated chromosomes along the RIAIL pedigree with discrete , constant-rate recombination domains , and we recorded the simulated genotypes at the same marker intervals as our actual genotype data . The simulated chromosomes exhibit patterns of variation within the discrete rate domains qualitatively similar to the observed data , preventing us from placing confidence in the fine-scale patterns in the data ( Figure 2A ) . Nevertheless , the fine-scale variation observed in our data is largely concordant with that present in genetic maps derived from independent two- and three-point mapping crosses with classic visible markers ( Figure S1 ) , compiled in WormBase [67] . The general concordance between our map , derived from meioses at 25°C , and the WormBase map , which comes from crosses performed at various temperatures but primarily at 20°C , does not support the notion that the distribution of crossovers is strongly temperature dependent [68] . In our data , each chromosome has one very sharp center-arm boundary and one that is less sharp , and boundaries exhibit the identical pattern in the classical maps . In five of the six chromosomes , the less-sharp boundary is on the side of the chromosome that holds the pairing center [69] ( Figure 1 ) . The exception is chromosome III . We find two points of disagreement between our results and previous discussion of recombination maps in C . elegans . First , the X chromosome clearly possesses domain structure similar to that of the autosomes ( Figure 1 ) , contrary to inferences from sparser data . The major distinguishing feature of the X-chromosome center is its relative size , 36% of the chromosome length , which is substantially less than the 47–52% on the autosomes . Second , we find that the chromosome tips have extremely low recombination rates; the terminal domain of each chromosome end is a region of effectively zero recombination , a pattern observed previously only for the right tip of the X [65] and more recently for chromosome III [68] . Every chromosome terminus contained a series of nonrecombining markers , and these domains ranged in size from 200 kb ( IR ) to 1300 kb ( XR ) , averaging 600 kb . We previously showed that the allele frequencies in the RIAILs depart from the neutral expectation , implicating selection during the application of the cross design [40] . We extend that analysis here , estimating expected allele frequency skew using our simulations that explicitly incorporate marker spacing and recombination domain structure . Chromosome I ( p<0 . 001 ) and chromosome II ( p = 0 . 001 ) exhibit significant allele frequency departures from the neutral expectation ( Figure 3 ) . The other chromosomes exhibit allele frequencies consistent with neutrality ( III , IV , V , X: p = 0 . 449 , 0 . 213 , 0 . 155 , 0 . 323 for observing the largest allele frequency skew by chance ) . In addition to selection on individual alleles , a more subtle form of selection is likely to operate in a cross of divergent selfing strains: epistatic selection to maintain coadapted combinations of alleles . Such selection should decrease the recombination fraction between coadapted loci without altering allele frequencies [70] . We compared the genetic lengths we observed for the RIAIL chromosomes to the expected genetic lengths determined by the RIAIL simulations , which employed 50 cM meioses and yielded expected lengths of approximately 300 cM for each autosome and 214 cM for the X chromosome . Chromosomes I , II , and III were shorter than expected in the absence of selection ( one-sided p = 0 . 011 , 0 . 002 , 0 . 010 , respectively; Figure 2B ) , while the others were not different from their expected lengths . For chromosomes I and II , the shortened genetic length is attributable at least in part to selection on single loci causing associated allele frequency skews . Chromosome III , however , is about 11% shorter than expected , despite no evidence of selection altering single-locus allele frequencies and no sign of distortion relative to the WormBase map of chromosome III ( Figure S1 ) . The simulations were performed under the assumption that male meiosis is identical to hermaphrodite meiosis , and that oogenic and spermatogenic meioses within hermaphrodites are identical , with exactly one cross-over per chromosome per meiosis . That chromosomes IV and V exhibited the expected lengths suggests that the different settings for meiosis do not alter global crossover rates , although we cannot test sex-differences in local patterns of recombination frequencies . We next sought evidence for epistatic selection generating associations between alleles on different chromosomes [70] , [71] . We calculated p-values for Fisher's Exact Test for the 877 , 079 pairs of non-syntenic SNPs and found that the distribution of p-values is uniform; 1 . 2% of tests were significant at p<0 . 01 , and 0 . 09% were significant at p<0 . 001 . No tests were significant at the Bonferroni-corrected threshold . An analysis of the false discovery rate , based on permutations of genotypes by chromosome , found no threshold at which the FDR fell below 0 . 5 . The maximum observed r2 between nonsyntenic sites was 0 . 087 , demonstrating the absence of strong correlations among chromosomes . Segregating modifiers of recombination rate may influence the number or distribution of recombination breakpoints in the genomes of recombinant inbred lines [72] , [73] . Such modifiers may be detected as QTLs for breakpoint number . We counted the breakpoints on each chromosome and mapped the number as a quantitative trait using structured nonparametric interval mapping [74]–[76] . The total number of breakpoints varies among the RIAILs from 6 to 29 with mean 15 . Total breakpoint count links significantly to chromosome II ( lod = 3 . 80 , genome-wide p = 0 . 026; Figure 4 ) . The Hawaii allele of the QTL is associated with slightly higher breakpoint numbers on every chromosome . Meiosis in C . elegans involves regulatory proteins that are unique to individual chromosomes or pairs of chromosomes , raising the possibility that segregating modifiers of recombination may have effects limited to individual chromosomes [77] . Similarly , modifiers may act in cis to alter recombination probabilities . To address these possibilities , we considered the number of breakpoints on each chromosome separately ( Figure 5 ) . Chromosome I breakpoint number exhibited a very significant linkage to chromosome I ( lod 7 . 21 , genome-wide p<0 . 001 by structured permutation ) . A second QTL , located on chromosome II , reached nominal genome-wide significance ( lod 3 . 62 , p = 0 . 050 ) . Chromosome II breakpoint number exhibited significant linkage to chromosome II ( lod = 4 . 154 , p = 0 . 008 ) , and X chromosome breakpoint number linked to the X chromosome ( lod = 3 . 98 , p = 0 . 022 ) . Breakpoint number on chromosomes III , IV , and V did not link to any QTLs , even at the less stringent p-values required for significance at the chromosome-wide level . The linkages of chromosome I and II breakpoint number to their own chromosomes are readily interpreted as artifacts attributable to unbalanced allele frequencies shaped by selection ( Figure 3 ) . As selection drives one allele to low frequency , the presence at a nearby locus of the allele from the same donor increases the probability that a breakpoint will be present between the two loci . Thus a selective sweep causes an association of linked alleles from the disfavored genome with high recombination breakpoint numbers . In our cross , presence of a Hawaii allele in the center of chromosome 1 all but guarantees the presence of a breakpoint to its left , due to the strong selection against Hawaii alleles at the gene zeel-1 . Genotype data from 1460 N2-CB4856 SNPs ( Table S2 ) distinguished only 41 haplotypes among 125 wild isolates ( Table S3 ) . Of the 1460 loci assayed , 101 exhibited genotyping failures in one or more wild isolates , consistent with deletions or SNPs in those strains interfering with the genotyping assay . These genotyping failures exhibit significant LD with adjacent SNPs , and in strain JU258 , where large deletions have been identified by tiling array experiments [53] , 15 of the 25 such calls fall within deletion predictions . The segregating genotyping failures are dramatically overrepresented on the chromosome tips and arms , particularly IIL and VR ( Table 2 ) , and depleted from the centers ( Fisher's exact test , p = 10−13 ) . Despite the additional information provided by these putative deletion genotypes , they distinguished only two haplotypes otherwise identical according to SNP genotypes . Among wild isolates from recent systematic collections , most haplotypes are confined to a single locality , though each locality may harbor multiple haplotypes ( Table S3 ) , as others have observed [23] , [24] , [45] , [47] , [50] . The only exceptions are haplotype 25 , shared between Le Blanc and Hermanville in France [23] ( ∼310 km apart ) , and haplotype 40 , shared between Mecklenbeck and Roxel in Germany [50] ( ∼5 km apart ) . Among the classical wild isolates from the CGC , a collection assembled without systematic sampling , SNP haplotypes are often shared among distant localities . Haplotype 1 is shared by N2 , from Bristol , England , PX176 from Eugene , Oregon , and TR388 and TR389 , from Madison , Wisconsin . Haplotype 19 is shared by AB2 , from Adelaide , Australia , CB4855 , from Palo Alto , California , and CB4858 , from Pasadena , California . The similarities among classic strain haplotypes raise the possibility that these strains are not independent wild isolates , a point to which we return in the Discussion . The SNPs are derived entirely from a comparison of N2 and CB4856 sequences , creating a strong ascertainment bias . In a panmictic population of constant size , ascertainment from a pair of chromosomes should bias the allele frequency spectrum observed in the rest of the population , yielding a uniform distribution [78] . In our data , the allele frequency is strongly skewed , with a dramatic excess of alleles observed only once ( Figure 5 ) . The skew is not consistent with a simple explanation in terms of population expansion , because the two alleles are not equally represented among the minor alleles . Instead , the allele found in CB4856 is almost always the rare allele ( 83% of sites; Figure 5 ) . For 461 SNPs ( 32% ) , the Hawaii allele is unique to the Hawaiian strain , while no alleles are unique to Bristol , nor to haplotype 1 . At two sites , only haplotypes 1 and 2 have the Bristol allele , and at just 12 of the 1460 sites is the Bristol allele found in fewer than 10 of the 41 haplotypes . The excess of Bristol alleles is explained by the combination of ascertainment bias and population structure . The effects of these phenomema are revealed by the sequence of allelic states along the wild isolate chromosomes . Considering a single wild isolate and the two ascertainment strains , there are three possible genealogies for each nonrecombining segment of the genome ( Figure 6A ) . Because we observe only those SNPs that arose as mutations on the branches connecting N2 and CB4856 , the three genealogies predict distinct patterns of allelic states in the wild isolate genome ( Figure 6B ) . Under panmixis , we should expect the genealogies to be equally common , but because our sample is conditioned on the presence of a SNP between N2 and CB4856 , genealogies 1 and 3 , which have more opportunity for such SNP-generating mutations to occur , should be overrepresented . In our data , however , the genealogy with CB4856 most closely related to the wild isolate ( genealogy 3 ) appears to be absent ( Figure 6C ) . Instead , the wild isolate chromosomes are mosaics of the other two genealogies , consistent with ongoing genetic exchange among such strains to the exclusion of the CB4856 lineage . The excess of N2 alleles characterizes every strain ( Figure 7 ) ; the least N2-like of the strains , haplotype 39 from the Portuguese island of Madeira and haplotype 40 from northern Germany , carry 58% and 57% N2 alleles ( p<10−7 for each under the null hypothesis that alleles are equally likely , as expected in the absence of structure ) . The only evidence for recent genetic exchange involving CB4856 is the X chromosome of haplotypes 29 ( MY1 ) and 39 ( JU258 ) , which share a run of 30 out of 31 CB4856 alleles ( Figure 6C ) . Much of the rest of the MY1 X chromosome is highly N2-like , but the JU258 X chromosome contains a significant excess of CB4856 alleles ( 59%; p = 0 . 002 ) , uniquely among all the wild isolate chromosomes . The wild isolate chromosomes differ in their distributions of SNP genealogies ( Figure 6C ) . The centers of chromosomes I and V , in particular , are almost entirely N2-like ( genealogy 1 ) in the wild isolates , while the majority of wild isolate chromosomes exhibit outgroup-like ( genealogy 2 ) haplotypes across the centers of chromosomes II and X . For almost every chromosome , at least one strain retains a chromosome whose haplotype is largely consistent with genealogy 2 , in which N2 and CB4856 are more closely related to one another than to the wild strain ( Table 3 ) . These haplotypes represent repositories of allelic variation that exceeds that available in N2-CB4856 comparisons . For chromosome V , however , only one wild isolate has more than 40% CB4856 alleles , and most strains are entirely N2 through the center of the chromosome . The wild isolate carrying the least N2-like haplotype varies by chromosome , meaning that there is no single ‘next-best’ strain for SNP discovery genome-wide . The most useful strains for each chromosome are indicated in Table 3 . Pairwise similarity among haplotypes is plotted in Figure S2 . We attempted to characterize the global population structure of C . elegans using the Bayesian approach of structure 2 . 2 , which estimates the proportion of each strain's ancestry derived from each of a fixed number of ancestral populations [79] , [80] . The analysis strongly favored multiple ancestral populations and conferred the highest likelihood on a population history involving three ancestral populations now extensively admixed ( Figure 7 ) . The ancestral populations correspond roughly to a Bristol-like strain , a Hawaii-like strain , and a third population . The proportions of ancestry inferred for each wild isolate correspond roughly to the fractions of each genotype drawing from the three genealogies possible given our SNP ascertainment scheme . Consequently , the CB4856 alleles present in the wild isolates largely represent recent shared ancestry not with CB4856 but with a common ancestor of both N2 and CB4856 ( genealogy 2 ) . To the extent that much genealogical information is missing in genomic regions characterized by genealogy 2 , due to ascertainment bias , the interpretation of the third ancestral population inferred by structure is unclear . We calculated bounds on the minimum number of recombination events , Rmin , required to explain the haplotype data under the assumption that each mutation is unique ( i . e . an infinite sites model ) [81] . The lower bound on Rmin is 40 or greater for each chromosome and is 90 for chromosomes III and X ( Table 4 ) . These numbers are substantially higher than those calculated from previous data sets , reflecting the larger number of markers in our analysis . To assess the global pattern of linkage disequilibrium , we calculated r2 for each pair of sites on each chromosome , excluding sites with minor allele frequencies less than 0 . 1 , and we made a rough estimate of ρ , the population effective recombination parameter , by nonlinear regression of r2 on physical distance separating the sites . Sites exhibit high correlations across megabase scales and even among unlinked sites ( Figure S3 ) , consistent with findings from microsatellites [24] , [50] , AFLPs [23] , SNPs [52] , [82] , and sequence data [45]–[47] . Considering all pairs of linked sites , r2 decays to half its initial value over a distance of 3 . 3 Mb ( Figure S4 ) , an LD half-length orders of magnitude higher than observed in most obligately outcrossing species , including Caenorhabditis remanei [82] , Drosophila melanogaster [83] , and maize [84] , which exhibit half-lengths measured in tens to hundreds of base pairs , and humans , where the number is in the tens of kb [85] . Even in Arabidopsis thaliana and rice , partial selfers like C . elegans , the LD half-length is measured in kb rather than Mb [86] , [87] . To gain a finer-scale understanding of LD , we estimated ρ for 2 Mb windows centered on each SNP and for whole recombination rate domains ( Figure 8A; Table 4 ) . Variation in estimates of ρ along the chromosomes echoes the variation in recombination rates seen in the RIAILs , with higher in arms and lower in centers . The similarity continues to the pattern of rate differences between the left and right arms of each chromosome , with the exception of chromosome I , where selection in the RIAILs resulted in a compressed genetic map on IL and where balancing selection at the same interval among wild strains may result in reduced LD and elevated estimates of ρ [40] . The half-lengths of LD for arm domains range from 500 kb ( IL ) to 9 . 9 Mb ( IVR ) . The center-domain half-length is shortest on the X chromosome ( 3 . 5 Mb ) , while several chromosome centers exhibit no meaningful decay of LD with distance ( IIC and VC ) . Treating each arm and center domain as an observation ( Figure 8B ) , and recombination rate are well correlated ( r = 0 . 692 , p = 0 . 001; r = 0 . 860 , p<10−5 , when IL is excluded ) . The estimated population effective recombination rate is about 40% the meiotic recombination rate c estimated from the recombination fraction in the RIAILs , with the left arm of chromosome I a notable outlier . Linkage disequilibrium extends among unlinked chromosomes . We calculated r2 for all unlinked pairs of sites and found an excess of linkage disequilibrium across the entire range of r2 . With a false discovery rate of 5% , 77 , 447 of 254 , 343 nonsyntenic pairs ( 30% ) exhibited linkage disequilibrium , and 1918 pairs were in LD with zero false discoveries . Nonsyntenic associations extend primarily between chromosomes 2 , 3 , and X ( Figure 9; Figure S2 ) . In many strains these three chromosomes exhibit haplotypes consistent with genealogy 2 ( Figure 6C ) , implying that both population structure and ascertainment bias may contribute to the elevated LD . The potential to map loci at high resolution by association in wild C . elegans populations relies on appropriate levels of historical recombination to break correlation among markers while preserving correlations between markers and functional variants . To assess the utility of C . elegans for association mapping , we explored the correlations between the 907 non-singleton SNPs in out dataset that are not missing any data and two traits , copulatory plugging and epistatic embryonic lethality , that we have phenotyped in the wild isolates and whose underlying causative variants are known [40] , [41] . By Fisher's exact test , 14% of all tested SNPs are significantly associated with copulatory plugging after Bonferroni correction for 907 tests ( Figure 10 ) . The known plg-1 locus is on chromosome III [6] , [41] , where the most significant associations were observed , but significantly associated SNPs were also located on chromosomes I , II , and X . Mixed-model approaches to control for family and population structure have been successful at identifying SNPs associated with traits in a background of high relatedness among strains [88] , [89] . We incorporated pairwise similarity ( identity-by-state , IBS ) and admixture proportions estimated by structure into a mixed-model analysis using EMMA [88] . Only ten SNPs , all on chromosome III , remained associated with copulatory plugging in the mixed-model analysis incorporating the IBS matrix . Eight SNPs are in perfect LD with one another and with the trait; these SNPs are spread across roughly 2 Mb of chromosome spanning the causal locus at 8 . 86 Mb . Results were similar whether or not the structure results were incorporated into the analysis ( Figure 10 ) . The distribution of p-values from mixed-model analysis are nearly uniform ( Figure S5 ) , demonstrating the efficacy of the mixed-model approach for controlling background relatedness among strains . The epistatic embryonic lethality involves two tightly linked genes mapping to the left side of chromosome I , with the two haplotypes maintained at intermediate frequency by balancing selection [40] . Fisher's exact test identified only 9 SNPs associated with the phenotype after Bonferroni correction , spanning 1 Mb centered on the causal insertion/deletion polymorphism at 2 . 35 Mb ( Figure 10 ) . The most highly associated SNP ( p = 3 . 6×10−8 ) , at 2 , 318 , 113 , is 22 kb from the causal deletion . Mixed-model analysis incorporating only the pairwise identity matrix reduced the number of significant associations to just two , with the significance of the SNP at I∶2 , 318 , 113 dramatically increased . An additional SNP , very distantly linked at I∶12 , 967 , 075 is falsely weakly associated with the lethality phenotype when structure output is incorporated as an additional fixed effect in the model . Overall , however , the p-values at sites distant from the causal variant are nearly uniform ( Figure S5 ) .
The genotype data from our 20-generation cross and from a global panel of wild isolates reveal the landscape of recombination and diversity across the C . elegans genome . The RIAIL genotype data corroborate the domain structure of the C . elegans genetic map , with low recombination centers and high recombination arms , and we found the first clear evidence for recombination rate domains on the X chromosome . We used a segmented linear regression approach to estimate positions for the boundaries of the recombination rate domains . These boundaries show that the autosome centers are very similar to one another in relative size despite substantial differences in absolute size . The arms vary substantially in both absolute and relative size , and they vary substantially in recombination rate as well . Part of the variation among arms is explained by chromosome size , with shorter chromosomes forced to fit their obligatory crossover into a smaller physical distance . All of the chromosomes exhibit large subtelomeric regions that effectively exclude nearly all recombination events . The tip domains , previously characterized as regions of high gene density on the basis of small genetic distances between mutations , are in fact physically large domains in which genes are almost perfectly linked . Overall we estimate that more than 7 Mb of the C . elegans genome ( 7% ) falls in the tip domains of extremely low recombination . Despite the nonrecombining regions at the end of each chromosome , the RIAILs have a dramatically expanded genetic map and an expected mapping resolution of 225 kb , making them a useful tool for mapping QTL . Two patterns confirm that local sequence features shape the recombinational landscape , despite the existence of potent mechanisms of chromosome-scale regulation of crossover events [90] . First , the low recombination central domains are not physically centered on the chromosomes , as would be expected if recombination rate is shaped merely by position in relative chromosomal coordinates . Second , the recombination rate variation we observe within domains , though not sufficient by itself to exclude constant-rate domains , is well mirrored by variation observed from the two- and three-point cross data compiled in WormBase . These repeatable patterns of small-scale rate variation establish that recombination is responsive to local variables . Many questions about the C . elegans recombinational landscape remain unanswered . Each chromosome has one sharply defined arm-center boundary and one with a more gradual change in rate . The gradual boundary is closer to the pairing center on all but chromosome III , where neither boundary is as sharp as is typical for other chromosomesand where epistatic selection may distort the evidence of recombination rate variation . The role of temperature and sex in regulating crossover position also remains unclear , as our results , which include male and hermaphrodite meioses at 25°C , are similar to WormBase maps , derived primarily from hermaphrodite meioses at 20°C . Selection on the left arms of chromosomes I and II resulted in shorter than expected genetic maps , causing underestimation of meiotic recombination rates along those arms . Epistatic selection may also have compressed the genetic map of chromosome III . Epistatic selection may be common in C . elegans , because strains occur primarily as inbred , selfing lineages , within which coadapted alleles at unlinked loci have ample opportunity to arise and persist . Experimental data from laboratory crosses [40] , [61] and from ecological genetics of natural populations [24] provide strong support for selection against recombinant chromosomes and interstrain hybrids . Each mating during the RIAIL cross involved the random selection of an equal number of offspring , two , from each mating pair , giving the design the character of a selection-minimizing mutation accumulation experiment [91] . Consequently , selection must be extremely strong to have altered allele frequencies among the RIAILs . Moreover , because both N2 and CB4856 are viable strains with similar developmental rates , the selection must involve an interaction between alleles of the two strains . Strong epistatic selection clearly obtains in the chromosome I case , where paternal-effect-by-zygotic epistasis between tightly linked loci causes embryonic lethality [40] . Selection against the CB4856 alleles on IIL may be due to partially penetrant epistatic lethality or sterility , or possibly to a substantial growth rate defect such that the worms with the slow-growth genotypes remained early larvae at the time their more mature siblings were picked for subsequent crosses; growth rate variation is known to segregate in C . briggsae crosses [47] . The selected region of chromosome II , which spans the interval from roughly 0 . 5 Mb to 2 . 2 Mb , does not exhibit elevated linkage disequilibrium with other regions of the genome , which might be expected in the event of epistatic selection . One scenario is that the selected region , where CB4856 contains large deletions relative to N2 [53] , may interact weakly with many regions of the genome , such that the interacting loci experienced little individual selection during the cross . The selected region is also strongly enriched for rapidly evolving F-box and MATH-domain genes , which exhibit evidence for positive selection in nature [92] , [93] , increasing the potential for coadaptation with other regions of the genome . The shorter than expected map of chromosome III is not associated with allele frequency skew or apparent distortion of the recombination rate distribution compared to the WormBase map ( Figure S1 ) . There are four possible explanations for the observation . First , the short map may be due to chance ( p = 0 . 010 ) . Second , it may be due to epistatic selection involving multiple close pairs of sites , resulting in a short but proportionate map . Third , chromosome III may truly have a smaller genetic length than the other chromosomes . Both the WormBase map and data from other studies documenting the 50 cM length of chromosome III argue against this possibility [94] . Finally , the RIAIL map may truly be distorted but the WormBase map is erroneous in some details . The WormBase map derives from thousands of independent crosses performed over many decades in many labs , and the composite map may not accurately reflect the underlying recombination probabilities in any single cross . We detected apparent QTLs accounting for recombination breakpoint number on chromosomes I and II that are clearly due to selection-driven allele frequency skew . Allele frequency skews are common in experimental crosses and attempts to map recombination modifiers must take them into account . Nevertheless , these skews lead only to false linkages of recombination modifiers to their own chromosomes ( false cis-acting modifiers ) . We identified distant linkages for chromosome I breakpoint number and for total breakpoint number , and others have identified such distant linkages in other species [73] . These QTLs may represent true modifiers , but the strong evidence for highly constrained meiosis in C . elegans , with nearly complete interference [1] , and the expectation that RIAIL designs will be poorly powered to detect modifiers [72] suggest that the approach of using breakpoint number to map recombination rate modifiers may suffer from additional unidentified biases . The 1460-SNP genotypes of 125 wild isolates represent only 41 distinct genome-wide haplotypes , consistent with the well-established prevalence of selfing among C . elegans in nature . Individuals from single localities are often genotypically identical , though we also observe substantial diversity among strains within localities . The recent collections from France and Germany confirm that strains from different localities are typically distinct , with minor exceptions for proximate collections . Those results contrast with the pattern evident among the less systematically collected strains acquired over many years by the Caenorhabditis Genetics Center ( CGC ) , where identical haplotypes are found among strains collected in far corners of the globe . The pattern suggests that these older collections may include strains whose origins are discordant with those implied by their locality data , perhaps as the result of sample mislabeling during their histories in the lab . Recent findings by McGrath and colleagues [43] confirm these concerns . They determined that LSJ1 , a strain maintained at a lab in California for decades , is most likely an early derivative of the same strain from Bristol that later gave rise to the laboratory strain N2 , which carries haplotype 1 . LSJ1 carries haplotype 2 , which differs from N2 at just one SNP among the 1460 genotyped , but it also differs by functional mutations in two genes , npr-1 and glb-5 [43] . The N2 allele at these loci are present exclusively in strains of haplotypes 1–4 , and the N2 npr-1 allele occurs in all such strains with the exception of LSJ1 . The implication is that the N2 mutations arose in the laboratory subsequent to the separation of the Bristol strain into its LSJ1 and N2 derivatives , and that strains carrying the npr-1 and glb-5 mutations are laboratory-derived descendents of N2 . Our genotype data corroborate documentary evidence suggesting that haplotypes 3 and 4 may be derived from laboratory crosses between N2 and a derivative of the Bergerac strain ( haplotype 7 ) , as foreseen by Egilmez et al . [48] on the basis of patterns of Tc1 transposon content . The likely laboratory origin of haplotypes 1–4 has several consequences . One is that all wild strains described from the Midwestern United States ( TR388 , TR389 , and TR403 ) are dubious . Another is that the allelic variants cloned from haplotypes 1–4 , including those in npr-1 , glb-5 , and perhaps scd-2 , likely originated in the laboratory . Moreover , early inferences about C . elegans population biology may have been influenced by inclusion of multiple samples of similar laboratory strains as putative wild isolates from different geographic locations; of the 32 strains characterized for Tc1 patterns by Hodgkin and Doniach [6] , 12 carry haplotypes 1–4 . Finally , the reliability of locality data from other early collections is called into question . A potential mixup involving the provenance of CB4555 , DR1349 , and CB4858 , presumed derivatives of a strain from Pasadena , has been noted previously [6] , and our data show CB4858 to be very distinct from CB4555 and DR1349 , with the latter two carrying dubious haplotype 4 in common with strain DH424 . We found that CB4858 shares haplotype 20 with strains from other localities , including AB2-4 , from Adelaide , Australia , and CB4855 , from Palo Alto , California . The genotypic similarity among CB4858 , CB4855 , and AB2-4 , which has been noted previously [6] , [45] , [50] , superficially suggests that they may share an ancestor in a laboratory . However , distinct chemoreceptor pseudogenization [59] and Tc1 patterns [6] provide evidence for the distinctness of CB4855 from the other strains , and AB4 and CB4858 appear quite distinct from one another in other SNP datasets [46] , [52] . Our 1460 SNPs also fail to distinguish among recently collected strains known from other data to be distinct; for example our haplotype 33 includes strains known to vary at a microsatellite locus [50] . We used the RIAIL genotypes as a standard against which to evaluate wild isolate genotypes , and this control allowed us to identify 101 loci at which wild strains segregate alleles distinct from N2 and CB4856 . Third alleles likely represent deletions overlapping the target SNP or imply the presence of additional SNPs that disrupt hybridization of the genotyping oligos . These variants are strongly enriched in chromosome arms and tips , particularly IIL and VR , previously identified as enriched in deletions based on hybridizations of genomic DNA to microarrays [53] . The elevated levels of putative deletion polymorphisms are not strictly attributable to recombination rate , as the levels are highest in the chromosome tips , which are very recombination poor . Variation among chromosomes also points to sequence-specific properties influencing these polymorphisms . C . elegans geneticists have long recognized that the Hawaiian strain , CB4856 , collected from a pineapple field in 1972 [6] , is divergent relative to other wild isolates [46] , [52] , [57] , [59] , with some loci dramatically diverged uniquely in this strain [46] . Our data confirm that CB4856 has experienced genetic isolation from all other sampled strains . The large excess of alleles unique to Hawaii , the excess of N2 alleles among all other strains , and the prevalence of two of the three possible genealogies for wild isolate chromosomes all point to the lack of recent reproductive contact between the population in which CB4856 resides and the remainder of the global C . elegans population . Every other wild isolate exhibits long stretches of N2-like alleles ( genealogy 1; Figure 6 ) , consistent with a recent common ancestor for N2 and the wild isolates for those regions of the genome . However , most wild isolates also carry large regions of genome consistent with genealogy 2 , implying that these strains retain allelic variation beyond that present in the N2-CB4856 comparison . Consequently , the period of isolation of CB4856 must be short relative to the average coalescence time of C . elegans alleles . Population genetic analyses of resequencing data from selected genomic regions support the same conclusion; the Hawaiian strain is often nested well within the genealogy for particular loci [45]–[47] . The short period of isolation suggests that hyperdivergent sequences unique to the Hawaiian strain may represent targets of positive selection in Hawaii [46] rather than evidence for ancient divergence between lineages . Stronger inferences about C . elegans population history are confounded by a severe and unusual SNP ascertainment problem , intermediate between phylogenetic ascertainment bias [95] and population genetic ascertainment bias [78] . The problem is worsened by the presence of population structure [96] , [97] , a variable whose effect on ascertainment bias depends on the nature of the structure , which is unknown in this case . The striking variation among chromosomes in haplotype patterns ( Figure 6 ) may represent differences among chromosomes in the recency of common ancestry between N2 and CB4856 , influencing the probability of observing genealogy 2 in wild isolates , or it may represent true differences among chromosomes in the prevalence of genealogy 2 , due perhaps to selection . One reassuring observation is a strong qualitative correspondence between the haplotype pattern we observe for CB4858 and the genomewide SNP density between N2 and CB4858 inferred from whole genome resequencing [51] . The correspondence implies that our genealogical model of haplotypes from ascertained SNPs accurately reflects SNP density independent of N2-CB4856 divergence . The excess of genealogy 1 through the center of chromosome V among nearly all wild isolates may therefore represent a selective sweep favoring an N2 allele . As all wild isolates should be similarly affected by ascertainment bias , we can infer that the relative divergence of JU258 , a strain from Madeira , is not attributable to its origin from an island , as is sometimes supposed . Several strains from Northern Germany ( e . g . , MY2 ) exhibit similarly divergent haplotypes . At the same time , JU258 is unique among wild isolates in carrying a chromosome with a significant excess of CB4856 alleles , consistent with very modest reproductive contact between ancestors of those strains subsequent to the apparent isolation of CB4856 from all others [53] , [59] . Estimates of the frequency of outcrossing in wild C . elegans vary substantially [23] , [24] , [45] , [55] , [98] , but all estimates derived from patterns of linkage disequilibrium point to very low rates . The first evidence for recombination among wild chromosomes appeared only in 2000 [52] , and as recently as 2003 it was possible to invoke a single outcrossing event to explain C . elegans genotype data [46] . Our much denser dataset finds support for a large number of recombination events , with a minimum of 90 events required to explain variation on each of chromosome III and X . Despite the evidence for ample recombination , linkage disequilibrium is high within and among C . elegans chromosomes . Our estimate of the population effective recombination parameter is strongly correlated with our estimate of recombination rate from the RIAILs , much more than is observed in Arabidopsis thaliana [86] , although the scale over which rates are estimated may influence these analyses . Strikingly , the magnitude of is only about 40% that of , the estimated meiotic recombination rate . In a random sample of chromosomes , in the absence of ascertainment bias and population structure , is an estimator of 4Nec ( 1-s ) [99] , where Ne is the effective population size and s is the selfing rate . The effects of ascertainment bias and population structure prevent rigorous quantitative inference from our estimate of ρ; simple ascertainment bias is expected to elevate r2 [100] , but confounding structure irremediably complicates the matter . Supposing that our estimate reflects biological phenomena and not merely statistical artifact , there are two general explanations for the extremely low value of . First , we may infer that the effective population size is very small and that the selfing rate is very large . Both s and Ne have to be at the extremes of biological plausibility for this model to fit the observed relationship between and , such that the product of the population size and outcrossing rate ( 1-s ) is roughly 0 . 1 . For example , the effective population size estimated from nucleotide polymorphism level , ( at equilibrium , π = 4 Neμ; empirically , from mutation accumulation experiments[101] and from population resequencing [45] ) , implies a low outcrossing rate of ∼2×10−6 . Although this very rough estimate of outcrossing rate is less than an order of magnitude smaller than other estimates based on linkage disequilibrium in C . elegans [23] , [45] , direct estimates of outcrossing from heterozygote frequencies are much higher , in the range of 10−2 and greater [24] , [55] . These direct estimates , in conjunction with our estimate of ρ , imply an effective population size smaller than 10 . The disconnect between population genetic and direct estimates of outcrossing rates yields a second explanation for the low population effective recombination rate — selection against outcross progeny or recombinant genotypes , i . e . , outbreeding depression [24] , [61] . Heterozygotes produced by outcrossing may have low reproductive success and their offspring , with recombinant genotypes , may experience epistatic selection against deleterious combinations of alleles [24] . Outbreeding depression has been observed repeatedly in the laboratory [40] , [61] , including in the genotypic patterns evinced by the RIAILs on chromosomes I , II , and III . Moreover , a longitudinal study of wild populations of C . elegans provided strong evidence of selection against recombinant genotypes in nature [24] . That selection can influence is evidenced by the elevated estimate on the left arm of chromosome I , where zeel-1/peel-1 haplotypes are maintained by balancing selection [40] . Outbreeding depression may explain some of the strong linkage disequilibrium among unlinked sites ( Figure 9 ) , as epistatic selection against recombinants can preserve correlations among chromosomes . Because such patterns of LD among chromosomes are expected in the presence of population structure , however , strong inferences about the causes of LD are not possible . Despite the exceptional levels of linkage disequilibrium across the C . elegans genome , we have demonstrated the feasibility of mapping common , large-effect variants by association . Ordinary correlations between alleles and phenotypes resulted in large numbers of false positive associations , but use of a mixed-model approach to control for background similarity among strains [88] , [89] was successful . The two traits we mapped , copulatory plugging and embryonic lethality , are best case scenarios for association mapping , with intermediate frequencies and Mendelian inheritance . Even in these cases , associations in regions of high LD necessarily span large intervals , more than 2 Mb in the case of plg-1 . High-resolution association mapping in the C . elegans isolates collected to date is most likely to be fruitful for associations with markers on chromosome arms . The very-high-resolution ( ∼20 kb ) association detected for embryonic lethality reflects the exceptionally low LD around the loci responsible for the trait , attributable to the long-term maintenance of the alleles by balancing selection . The low LD around zeel-1 and peel-1 further confirms that the alleles are ancient and not involved in genome-wide differentiation between the two incompatibility classes [40] . We have used high-density SNP genotyping to extensively characterize patterns of recombination in a large panel of C . elegans recombinant inbred advanced intercross lines . These lines provide a powerful permanent resource for high-resolution genetic mapping of phenotypic variation . We also genotyped a large collection of wild isolates , allowing us to define a set of isolates with distinct haplotypes and to describe in detail the genetic history of the C . elegans population . These results call into question commonly held beliefs about the origins of a number of isolates . Further insights into C . elegans population biology await broader surveys of sequence variation among the isolates .
We generated recombinant inbred advanced intercross lines [102] from a cross between N2 and CB4856 . We performed reciprocal crosses , yielding two classes each of male and hermaphrodite progeny differing in their mitochondrial and X chromosomes . We performed each of the four possible crosses among these strains , yielding four classes of F2 hermaphrodites and a single class of F2 males , ignoring the male mitochondrial genome , which is not transmitted . We performed the four possible crosses among these F2s , with each class of cross contributing 64 male and 64 hermaphrodite worms to the 512-worm F3 population , at which point we initiated random pair mating with equal contributions of each pair to each generation [62] . The random pair mating continued until the tenth generation . Each cross plate contained a single male and a single hermaphrodite , and each generation some crosses failed due to poor male mating , evident from the absence of male offspring among the progeny . Other crosses failed due to segregating sterility , as evidenced by the failure of the hermaphrodite to produce any offspring . In addition , in some cases crosses failed because worms crawled to the edge of the plate and desiccated . To expand the population , we derived two lines from each plate containing tenth generation hermaphrodites . Each of the lines was then propagated by selfing a randomly selected hermaphrodite for each of 10 generations . Worms were cultured using standard methods [103] and were maintained at 25°C during the construction of the RIAILs . We acquired 125 wild isolates from three main sources . Forty-three strains received from the Caenorhabditis Genetics Center come from unsystematic collections from sites in Europe , North America , and Australia since the 1940s . The origins of most of these strains are recounted in Hodgkin and Doniach [6] , and the sources of the others ( JU258 , LSJ1 , PB303 , PB306 , PX174 , PX176 , PX178 , and PX179 ) are given in WormBase [67] . Two strains lack locality data . PB303 and PB306 were isolated by Scott Baird from isopods obtained from biological supply companies; the geographic origins of the isopods are unknown . LSJ1 derives from a laboratory in California , but it may represent an independent culture of the Bristol strain that gave rise to N2 [43] . The CGC received the strain in 1995 . The remaining wild isolates come from two systematic field collections . Haber et al . [50] collected 23 strains in northern Germany in 2002 . We acquired these strains from the CGC . Barriere and Felix [23] , [24] collected C . elegans from localities across France and we acquired from them 59 strains collected from 2001 through 2005 . We collected DNA from each RIAIL and wild isolate using a salting-out protocol [104] applied to populations of each strain . We genotyped the strains using Illumina's GoldenGate assay [105] . The assay interrogated 1536 loci reported in public databases as SNPs between N2 and CB4856 . The databases contained 1099 confirmed SNPs and more than 17 , 000 SNPs predicted from sequence but not confirmed . 795 confirmed SNPs passed Illumina's design criteria . These were supplemented with 741 unconfirmed high-confidence SNPs with good design scores to make up the final set of 1536 . This set was selected with the SNPdome algorithm ( Illumina ) to ensure uniform coverage of the C . elegans genome and to minimize gaps . We used the RIAIL genotypes to validate the SNPs and confirm their map order . From the 1536 assay results , we identified 1205 high-quality SNPs with the following properties: N2 and CB4856 DNA samples were assigned different , homozygous genotypes with Illumina confidence scores >0 . 5; fewer than 5% of the 236 RIAILs had confidence scores <0 . 5; fewer than 2 RIAILs were called as heterozygotes . For these 1205 SNPs , we examined the wild isolates and assigned genotypes to calls with confidence scores >0 . 35 . For the 285 SNPs that yielded some confidence scores between 0 . 35 and 0 . 5 , fluorescence intensities were individually inspected and calls assigned manually when unambiguous . For many of the 1205 RIAIL-confirmed SNPs , one or more wild isolates failed to give any genotyping signal . We identified a threshold of normalized intensities of both fluors ≤0 . 009 at which 768 wild isolate genotypes gave no signal ( 0 . 5018% of all calls ) while the RIAILs gave only 8 genotypes at the same level ( 0 . 0028% ) , a 180-fold enrichment for the wild isolates . As these failed wild isolate genotypes exhibit linkage disequilibrium with well-genotyped SNPs , they likely represent mutations that disrupt the hybridization of the Illumina oligos to the genotyping interval . We assigned a third-allele call to these genotypes . The remaining 331 SNP assays were individually examined to assign genotype calls . For 46 assays , N2 and CB4856 yielded the same genotype , implicating false-positive SNPs predictions . An additional 29 SNPs produced uninterpretable fluorescence intensity scatterplots . We were able to assign genotype calls for 196 SNPs which failed to pass the confidence threshold due primarily to low intensity . The remaining 70 SNPs exhibited more than two clusters of genotypes in plots of fluorescence intensities . We found that the extra clusters were due to hybridization of the SNP-assay oligos to additional loci which themselves exhibited segregation . As a result , each cluster could be assigned a homozygous genotype call on the basis of linkage disequilibrium with adjacent SNPs among the RIAILs . The final dataset included 1460 SNPs . We excluded one RIAIL from subsequent analysis because its genotypes included a large proportion of ambiguous calls . The resulting dataset includes 236 RIAILs and 125 wild isolates scored at 1460 SNPs . The 527 , 061 genotypes include 1450 third allele ( putative deletion ) calls among the wild isolates , 654 Ns for bad data , and 180 heterozygote calls . Eight of the RIAILs exhibited short tracts of residual heterozygosity . The mitochondrial genotype for each RIAIL was determined by PCR-RFLP , using primers 5′-ctcggcaatttatcgcttgt and 5′- cttactcccctttgggcaat and digesting with PmeI . We estimated a genetic map for the RIAIL cross using r/qtl [74] and found that 6 SNPs had expected physical positions on chromosomes other than those to which they mapped . These may represent errors in the genome assembly or in oligo production; the oligo sequences map uniquely in the genome assembly . The expected and mapped physical positions of these SNPs are in Table S4 . Analyses of RIAILs employed the 1454 physically mapped SNPs; the complete dataset is provided in Table S1 . We considered the mismapped SNPs in analyses of WI haplotypes but excluded them from analyses that required physical positions . The complete wild isolate dataset is provided in Table S2 . In all cases where a RIAIL genotype contained an allele from one strain flanked by alleles from the other parental strain ( i . e . , a single-marker segment ) , we re-examined the plots of fluorescence intensities to confirm the genotype call; such a pattern is expected for a genotyping error and can strongly bias estimates of map lengths and breakpoint counts [71] . We estimate bin size as the distance from the end of a chromosome to the midpoint of the first breakpoint-containing interval or as the distance between the midpoints of successive breakpoint-containing intervals . This approach ignores bins created by multiple independent breakpoints within a single interval and uses interval midpoints rather than outside markers to avoid overlapping bins . Expected bin size is the per-base-pair sum of the squares of the bin lengths [106] . We estimated genetic distances in r/qtl using the Haldane map function , treating observed recombination fractions as though they had been observed in a backcross . The marker density is sufficiently high that the exact form of map function employed has little effect on estimated genetic distances . We defined the tip domains of each chromosome to include all markers between the chromosome ends and the first recombination breakpoint observed in the RIAILs . The midpoint of this most distal recombinant interval was chosen as the tip-arm domain boundary . The non-tip markers were included in a segmented linear regression analysis , using the segmented package in R [107] , to identify arm-center domain boundaries . To estimate confidence intervals for the domain boundaries , we used simulations of the RIAIL chromosomes . We simulated 1000 RIAIL populations for each chromosome , using the known pedigree . Each gamete received a meiotic chromosome with 0 or 1 breakpoints ( i . e . , complete interference [4] ) , the position of the breakpoints determined by the relative recombination fractions of the centers and arms estimated from the RIAILs . The tips were specified to be non-recombining and the two arms of each chromosome were assigned equal recombination probabilities per base pair; that is , intra-chromosomal differences in rate between arms were not modeled . Each chromosome was simulated as a sequence of markers with one marker for every kilobase of chromosome . We then sampled markers at spacing defined by the genotyped SNPs , yielding a dataset of RIAIL chromosomes simulated with discrete , constant-rate recombination domains . We estimated domain boundaries for the simulated chromosomes by segmented linear regression . The 95% confidence intervals vary in size depending on the size of the chromosome and the difference in recombination probability between adjacent domains . On average the intervals span 1 . 1 Mb . The simulated RIAIL chromosomes were also used to estimate expected allele frequency skews and expected genetic lengths for each of the chromosomes . The RIAIL allele frequencies at each marker were estimated using the sim . geno function in r/qtl [74] to infer missing data . WormBase [67] genetic maps are derived from data available on June 7 , 2008 , for 4542 genes with experimentally determined map positions and known physical positions . As our analyses of these data are qualitative , we made no effort to screen these data for quality , as evident from several obviously mismapped data points in Figure S1 . We performed non-parametric interval mapping [76] in r/qtl [74] . The RIAILs differ in their relatedness as a result of the derivation of two selfing lines from each 10th generation intercross hermaphrodite . The paired lines exhibit substantially higher similarity ( mean percent bases shared ±standard deviation , 69 . 6±11 . 4% ) than unpaired lines ( 52 . 8±9 . 5% ) , so that background similarity could inflate lod scores at markers unlinked to QTLs . Moreover , the significance of the lod scores would be overestimated by conventional permutation , because the RIAILs are not exchangeable; permuted datasets would break the associations between genetically and phenotypically similar RIAILs [75] , [108] . Note that the mean similarity among unpaired lines is greater than the expected 50% because of the influence of selection on allele frequencies during RIAIL construction . For this reason we have not used simulated genotypes [108] to assess QTL significance . Instead we used a structured analysis and structured permutations . We split the dataset into two subsets with each RIAIL pair split between the two . We performed linkage scans separately for the two subsets and summed the lod scores . We permuted the two subsets separately 1000 times to derive genome-wide significance estimates for each phenotype . Estimation of population structure used a dataset of 40 haplotypes ( haplotype 21 , which differs from haplotype 20 only by a single putative deletion allele , was excluded , as the analysis treats these genotypes as missing data ) and 1454 SNPs . We ran structure 2 . 2 [80] ten times at each of five values of K , the number of ancestral populations . We used the linkage model [79] with a burn-in period of 10 , 000 replicates followed by 50 , 000 replicates to collect estimated parameters and likelihoods . The outputs of the repeated runs at each K were aligned using CLUMPP 1 . 1 . 1 [109] and Figure 8 generated using distruct 1 . 1 [110] . We computed lower bounds on Rmin for each chromosome using HapBound and upper bounds using SHRUB [81] . We used a dataset with 1318 SNPs , after excluding all sites with missing data or putative deletion alleles . We used Haploview 4 . 0 [111] to calculate r2 between all pairs of the 1042 sites with minor allele frequencies greater than 0 . 1 in the 40-haplotype dataset . We used these r2 values to estimate ρ per basepair and its standard error by nonlinear regression using equation 3 of Weir and Hill [112] , implemented with the R function nls . This simple method of moments estimator roughly approximates a likelihood estimator . Estimates of the half-length of LD represent the distance at which the expected value of r2 from the nonlinear regression drops below half its initial value . To estimate ρ in sliding windows , we used the r2 values among SNPs within 1 Mb to either side of each focal SNP . These 2 Mb windows are the smallest practicable windows given our marker density . We also estimated ρ for whole arms and centers , using the domain boundaries estimated from the RIAILs and shown in Table 1 . We estimated the distribution of r2 among nonsyntenic sites in the absence of association from 100 permutations of chromosomes among the 40 wild isolate haplotypes , preserving allele frequencies and chromosomal haplotype frequencies but breaking correlations among chromosomes . The means of the ranked nonsyntenic r2values across permutations provides an estimate of the number of false discoveries at each quantile of the r2 distribution . Permutations and calculations were performed in R , and r2 was calculated using the LDmat function in the popgen library ( http://www . stats . ox . ac . uk/̃marchini/software . html ) . The dataset included 784 sites with no missing data and minor allele frequencies greater than 0 . 1 . We excluded singleton SNPs and those with missing data and used the resulting 40×907 matrix to estimate an identity-by-state kinship matrix using EMMA [88] . We did not remove SNPs in perfect linkage disequilibrium with other SNPs because we sought to discern the genomic extent of intervals associated with traits . We estimated the significance of associations in the mixed-model analysis using likelihood ratio tests with the function emma . ML . LRT , incorporating the kinship matrix and in some cases the ancestral population admixture assignments from structure ( K = 3 ) as fixed effects . | C . elegans is a model system for diverse fields of biology , but its ability to serve as a model for quantitative trait gene mapping depends on its recombination rate in the laboratory and in nature . The latter is a function of how worms mate and migrate in the wild . We examined the patterns of recombination in a population that we put through thousands of meioses in the laboratory and in a collection of strains isolated from nature . The data suggest that meiotic recombination rate is highly regular in worms , with discrete domains whose boundaries we identify . The pattern in natural strains suggests that population structure , population size , outcrossing rate , and selection combine to suppress the overall effects of recombination . Moreover , some “wild” strains appear to be laboratory contaminants . Nevertheless , the history of recombination in wild worms is sufficient to permit correlations between genotype and phenotype to pinpoint the loci responsible for phenotypic variation . | [
"Abstract",
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"evol... | 2009 | Recombinational Landscape and Population Genomics of Caenorhabditis elegans |
Homeostatic temperature regulation is fundamental to mammalian physiology and is controlled by acute and chronic responses of local , endocrine and nervous regulators . Here , we report that loss of the heparan sulfate proteoglycan , syndecan-1 , causes a profoundly depleted intradermal fat layer , which provides crucial thermogenic insulation for mammals . Mice without syndecan-1 enter torpor upon fasting and show multiple indicators of cold stress , including activation of the stress checkpoint p38α in brown adipose tissue , liver and lung . The metabolic phenotype in mutant mice , including reduced liver glycogen , is rescued by housing at thermoneutrality , suggesting that reduced insulation in cool temperatures underlies the observed phenotypes . We find that syndecan-1 , which functions as a facultative lipoprotein uptake receptor , is required for adipocyte differentiation in vitro . Intradermal fat shows highly dynamic differentiation , continuously expanding and involuting in response to hair cycle and ambient temperature . This physiology probably confers a unique role for Sdc1 in this adipocyte sub-type . The PPARγ agonist rosiglitazone rescues Sdc1−/− intradermal adipose tissue , placing PPARγ downstream of Sdc1 in triggering adipocyte differentiation . Our study indicates that disruption of intradermal adipose tissue development results in cold stress and complex metabolic pathology .
Mammals have an extraordinary ability to defend their body temperature , and their homeothermy is supported by high calorie expenditure; indeed for mice , a transition from a warm , “thermoneutral” ( 30–33°C ) temperature downward to the prescribed laboratory housing temperature ( typically 20–24°C ) increases the metabolic load by 50–60% [1] , [2] , [3] , [4] . Metabolic mechanisms that promote efficiency are therefore key , especially for mice chronically housed under conditions that constitute ( mild ) cold stress . There is a well-established cascade of sensory and reactive components of non-shivering adaptive thermogenesis , often starting with cold-activated local and sympathetic neural response mechanisms [5] , [6] , [7] , [8] , although non-neural , cellular level mechanisms have also been described [9] . These sensors induce activation of both white and brown adipose tissues , to enable circulatory warming via oxidation of lipids [10] . Although physiologists have stressed the importance of insulation for many years , there are no studies that describe adaptive changes of skin/fur in mice housed in mild cold stress . Since the responses to cold stress clearly impact many processes , including macrophage activation [5] , the immune response to tumorigenesis [11] , and obesity [12] , factors that mitigate cold stress are important to understand . Serendipitously , our studies of mice with a mutation in syndecan-1 ( Sdc1 ) have revealed a role for this molecule in maintaining normal intradermal fat function and alleviating cold stress . Syndecan-1 ( Sdc1; CD138 ) is an abundant heparan sulfate proteoglycan that is expressed by most epithelial cells , and by stromal , endothelial and hematopoietic lineages during active phases of their development [13] . Its function is often dominated by its constituent heparan sulfate side chains , which are proposed to enable growth factor signaling by promoting ligand/receptor complex formation [14] , [15] . Despite the implication of Sdc1 in the activity of a great many growth factors and cell adhesion molecules , Sdc1−/− mice are viable , fertile and grossly normal . Their only obvious phenotype is their smaller size; they have the same body composition as wild type mice , but are systematically smaller throughout growth and development by approximately 13% [16] . These mice do show highly significant phenotypes such as tumor resistance [16] , [17] , altered stress responses and wound healing , and changes in B cell development and microbial pathogenesis [18] . More recently , Sdc1−/− mice have been shown to have defects in lipoprotein particle metabolism [19] , [20] , leading to altered levels of circulating VLDL . Here , we describe a general physiological change in Sdc1−/− mice that has the potential to influence a wide variety of stress responses , including tumor development . We show that these mice are cold stressed in normal housing conditions , and that this is associated with a deficiency of intradermal fat . Brown adipose tissue in Sdc1−/− mice shows elevated UCP1 and increased p38α activation , and this stress checkpoint is also more systemically activated in intraperitoneal organs . Furthermore , our experiments support a key function for Sdc1 as a VLDL receptor for undifferentiated adipocytes , and as an essential trigger for adipocyte differentiation in vitro . Expansion of intradermal fat in Sdc1−/− mice can be rescued by administration of the PPARγ agonist , rosiglitazone , in vitro and in vivo . We hypothesize that Sdc1 is required by intradermal fat , and not by white adipose tissue , because intradermal fat is constantly remodeled in response to both ambient temperature and the hair cycle . Our model proposes that inhibiting Sdc1 activity in vivo could induce p38α activation to modulate a number of physiologies .
As part of our investigation of the tumor-resistant phenotype of Sdc1−/− mice , we analyzed baseline metabolic parameters , seeking systemic differences [16] . In general , liver-associated glycogen provides a carbohydrate buffer to meet short-term calorie needs , and the levels of circulating triglycerides reflect an intricate sum of fat uptake , mobilization and oxidation [21] . We evaluated the dynamic range of both these calorie reserves after challenge by two metabolic stressors , cold and fasting . BALB/c mice showed 70% reduced liver-associated glycogen after 90 minutes of 4°C cold exposure , or an overnight fast ( Fig . 1A ) . Circulating triglycerides were reduced to approximately 100 mg/dL ( Fig . 1B ) . We found that glycogen was depleted in livers from Sdc1−/− mice ( by nearly 50% , from 32 to 19 mg/g liver , p = 1×10−6; Fig . 1A ) , and the circulating levels of triglycerides were not normal ( also 50% reduced , from 151 to 86 mgs/dL , p = 0 . 003; Fig . 1B ) . These are broadly similar to the calorie depletion patterns observed in wild type BALB/c mice stressed by cold or fasting [22] . Cold stressed Sdc1−/− mice showed plasma levels of triglycerides of only 41 mgs/dL , which is out of the normal range . During experiments designed to assay metabolic stress-related responses , we noticed that Sdc1−/− mice housed at room temperature ( defined for this study as 20–23°C ) , were unusually prone to entering torpor after overnight fasting [23] , [24] . Thus , assay of the metabolic activity of mice housed in metabolic cages at room temperature showed that some Sdc1−/− mice showed a sudden steep decline in O2 uptake after 24 hours of fasting , and entered a semi-comatose state ( Fig . 1C , D ) . None of the wild type mice showed this behavior ( p = 0 . 02 , 2-sided Fisher exact ) . Consistent with other studies , this state of torpor was reversible by shifting the housing temperature to 31°C ( Fig . 1D ) . Torpor is a conservative physiological response associated with decreased cardiac activity , and is typically observed in normal mice only in response to dual stressors , both cold and fasting [3] , [24] , [25] . This result suggested to us that Sdc1−/− mice were abnormally cold-stressed . To test this hypothesis , we examined the main systemic modulator of temperature homeostasis , the brown adipose tissue ( BAT ) , for evidence of activation of a thermogenic response . Brown adipose tissues are specialized to react to cold stress via a sensory mechanism that includes βadrenergic- and/or cardiac natriuretic peptide-dependent activation of mitogen activated protein kinase-14 ( MAPK14 ) /p38α [26] , [27] , [28] . Activated p38α in BAT induces the uncoupling and biosynthesis of mitochondria , generating heat fueled by oxidizing local fat depots; the effect is to warm incoming blood to maintain thermal homeostasis [12] , [29] , [30] . We assayed BAT from Sdc1−/− mice , and observed that the lipid reserves were severely depleted ( Fig . 1E ) . Chronic demands for heat generation are known to lead to calorie depletion of BAT and enhanced VLDL clearance [22] . Chronic cold stress is also known to sensitize mice and affect subsequent responses to acute stressors [31] . We therefore tested molecular markers of cold stress in BAT , and the response of these mice to acute cold ( transfer to 4°C for 90 minutes ) . We assayed activation of p38α assayed as phospho-T180Y182 p38α ) since this signaling hub is required for thermogenesis [26] , [27] , [32] . After cross-checking with isoform-specific antibodies , we showed that phospho-p38α was increased in BAT of Sdc1−/− mice housed at room temperatures , to levels approximately equivalent to that observed in BALB/c mice upon acute cold stress ( Fig . 1F , G ) . Furthermore , Sdc1−/− mice showed super-activation after transfer to 4°C . Uncoupling protein-1 , UCP1 , is a heat-generating mitochondrial protein that is fundamental to the response of white and brown adipose tissues to cold stress [30] , [33] . The expression of UCP1 protein was increased by 10-fold in the BAT of acutely cold-exposed mice ( Fig . 1H ) . UCP1 protein levels were increased by 2 . 0-fold ( p = 0 . 005 ) in BAT of Sdc1−/− mice in RT housing ( Fig . 1H ) . We measured markers of transcriptional activation of BAT; PGC1α mRNA ( Peroxisome proliferator-activated receptor gamma coactivator 1-alpha , PPARGC1A ) is crucial for inducing mitochondrial proliferation and was elevated 2 . 0-fold in chronically housed Sdc1−/− mice and super-induced upon acute cold stress ( Fig . 1I ) [34] , [35] . Elovl3 [36] was increased 3 to 9-fold in Sdc1−/− mice in RT housing , and has been associated with cold stress before . We hypothesized that Sdc1−/− null mice perceive abnormal levels of cold stress in normal housing conditions , and searched for the underlying physiology . Since the reactive component of thermogenesis , brown adipose tissue , appeared to be functionally normal , we turned instead to a tissue that has been proposed to be key to thermal insulation , the intradermal layer of fat in the skin [37] . In Sdc1−/− skin , we found the intradermal fat layer was dramatically reduced , by at least 75% ( Fig . 2A ) . The fat layer comprised of fewer , smaller adipocytes , measured histologically ( Fig . 2E ) . All the other tissue types comprising the skin were normal in Sdc1−/− mice ( epidermis including hair follicles , dermis and muscle; data not shown and Fig . S1 , S2 ) . We considered the possibility that the deficiency of intradermal fat in Sdc1−/− mice reflected a generalized loss of white adipose tissue . We therefore measured fat content of Sdc1−/− mice using dual-energy X-ray absorptiometry ( DXA ) imaging [38] , and found that the size of fat depots were no different for peri-pubescent Sdc1−/− mice ( 7 weeks old ) , with a minor ( though statistically significant ) decrease in body fat for 12 week-old mice ( Fig . 2B ) . Note that Sdc1−/− mice are approximately 13% smaller than BALB/c wild type mice throughout embryogenesis and adulthood [16] . Measurements of individual fat pads ( mammary glands and gonadal/peri-uterine fat pads ) confirmed the DXA result ( data not shown ) , and histological analysis of white adipose tissues showed that the size of adipocytes in white adipose tissue was not different in Sdc1−/− mice; neither were the expression of several molecular markers of white adipose cells ( Fig . S3 ) . Though the regulation of intradermal fat is almost entirely uncharacterized , it is known to expand during folliculogenesis to support the anagen phase of hair growth [39] ( data redrawn in Fig . S4 ) . Indeed a recent report described the functional interaction of a mesenchymal cell type in the intradermal fat with folliculogenesis [40] . Furthermore , intradermal fat is directly responsive to ambient temperature . Thus when BALB/c mice are housed at 31°C for 2 weeks , intradermal fat thins out to only 20% of the thickness typical of normal housing temperatures ( Fig . 2C ) . This “minimum” thickness corresponds to the thickness observed in Sdc1−/− mice housed at room temperatures . Indeed , it is known that mice housed at room temperatures are relatively cold-stressed [1] , [4] , [23] , [41] . Intradermal fat depots may be particularly important for determining the physiology of mice housed between isothermal and room temperatures , since intradermal fat expansion shows a dynamic response for this temperature range ( the additional cold stress imposed by housing at 4°C did not increase the intradermal fat layer; Fig . 2C ) . The intradermal adipocyte depot therefore responds to entirely different cues compared to other adipocyte depots . When we examined anagen stage skin from Sdc1−/− mice , the intradermal fat layer was the same thickness as control mice ( Fig . 2D ) , reflecting an equivalent number and size of adipocytes ( Fig . 2E ) . Anagen stages comprise approximately 2 weeks out of 4 for each cycle period , and the proportion of samples from Sdc1−/− mice that were in non-anagen was approximately the same as control mice ( Fig . 2D ) , suggesting that the follicle cycle time was not grossly affected by this mutation . ( We define non-anagen as absence of follicular penetration below the dermis ) . Furthermore , neither the initiation of the first hair cycle ( scored as the gross appearance of hair at day 6 ) , nor the follicular density during anagen was affected by the absence of Sdc1 ( Fig . 2D , S1 ) [40] . We conclude that the effect of Sdc1 is specific to intradermal fat and not other adipocyte reserves , and that it is important only to the expansion of adipocytes in response to ambient temperatures , and not to the local signaling that regulates hypertrophy during anagen . Interestingly , deficient intradermal fat expansion together with symptoms of chronic cold stress in Sdc1−/− mice were associated with systemic signaling changes in organs inside the body cavity . Thus , when the activation status of key metabolic and signaling hubs was assayed in lung and liver , p38α was consistently hyper-phosphorylated in Sdc1−/− mice ( Fig . 3A ) . Assay of tissue lysates shows that the relative activation of p38α was higher in Sdc1−/− mice . Activation of p38α results in downstream activation of a key anti-cancer checkpoint , p53 [42] , together with a number of regulators of cell cycle entry , including the cyclin-dependent kinase inhibitors , p16 ( CDKN2A/p16Ink4a ) , p21 ( CDKN1A/Waf1 ) and p27 ( CDK1B/p27Kip1 ) [43] . Overall , analysis of mRNA expression in Sdc1−/− livers showed only moderate changes , including significant induction of 140 genes and decreased expression of 134 genes at a false discovery rate ( FDR ) of 10% ( Table S1 ) . Of these mRNAs , Elovl3 mRNA ( Fatty acid elongase 3 , for very long fatty acids ) was increased by approximately 60-fold in Sdc1−/− livers ( Fig . 3B ) , and has been previously associated with cold stress . Indeed , this enzyme was cloned because it was induced in brown adipose tissue after exposure to βadrenergic agonists or cold , and has been observed before in liver [36] , [44] . Expression typically responds in order to compensate for changes in peroxisomal fatty acid oxidation [44] . There was also an 11-fold increase in expression of Gpr12 mRNA , a sphingosine-lipid activated G-protein coupled receptor which induces cAMP and downstream signaling pathways . Amounts of Fgf21 mRNA were strikingly variable , suggesting that this gene may be cyclically regulated or highly reactive to environment . Although expression trended higher in Sdc1−/− mice , the increases in this so-called “starvation hormone” were therefore not statistically significant [25] . Interestingly , there was also a 4-fold repression in a key transactivator of the Hippo pathway , Tead2 . Inhibition of Hippo signaling is required for adipogenesis to proceed , and is mediated by sphingosine-associated lipid ligands of GPCR receptors [45] . Expression was significantly reduced for Defensin B1 ( 12-fold ) and retinoic acid induced mRNAs ( Raet1 , 10-fold; Fig . 3B ) . We conclude that the changes observed were targeted and included mRNAs for proteins previously implicated in cold stress responses . Sdc1−/− mice showed other phenotypes consistent with their chronic cold stress . Thus , when exposed to 4°C cold for 90 minutes , Sdc1−/− mice showed decreased blood glucose ( whereas normal mice did not ) ( Fig . 3C ) , presumably due to their low liver glycogen stores . Despite their lack of thermo-insulation , the body temperature of Sdc1−/− mice is normal at room temperature ( Sdc1−/− mice , 37 . 3±0 . 17°C; BALB/c mice , 37 . 0±0 . 14°C ) . When exposed to an acute cold stressor ( 4°C for several hours ) , Sdc1−/− mice resisted the drop in body temperature that is typical for cold-naïve BALB/c mice ( Fig . 3D ) . It is likely that this reflects a higher thermogenic capacity , which could be confirmed by measuring the thermogenic effect of a single norepinephrine injection . Furthermore , peri-gonadal WAT from Sdc1−/− mice showed seams of “browning” ( observed histologically , Fig . S3 ) which was quantified by Western blotting of UCP1 protein ( Fig . 3E ) [8] . Our hypothesis proposes that the metabolic phenotype of Sdc1−/− mice derives from a specific deficiency of intradermal fat , and the consequent cold stress . In order to test that proposal , we housed Sdc1−/− mice at thermoneutral temperatures . Under typical housing conditions , mice use considerable energy to maintain their body temperature , illustrated here as a spike of 50% increase in O2 consumption for either Sdc1−/− or wild type mice when the cage temperature was dropped from 31°C to 23°C for an hour ( Fig . 4A ) . Thermoneutral conditions are defined as the temperature at which mice show basal metabolic rate; for mice this temperature range is 29–33°C [2] , [12] . Sdc1−/− mice were therefore housed at 31°C for 2 weeks , to test whether Sdc1-associated phenotypes were reversed . As expected , both control and Sdc1−/− mice showed thin layers of intradermal fat after acclimating to warm temperatures ( Fig . 4A ) . In support of our hypothesis , liver glycogen stores and circulating blood triglycerides were restored to almost normal levels ( Fig . 4B ) . Levels of p38α activation were low and equivalent for wild type and Sdc1−/− livers ( Fig . 4C ) . Sdc1−/− mice showed minor/no statistical difference in energy expenditure ( either at 23°C or 31°C , calculated per mouse or per g body weight , or using a multivariate regression analysis; Fig . S5B , C ) [41] , [46] , [47] , [48] . Sdc1−/− mice have a food intake approximately equal to wild type mice ( per g body weight ) , and normal RER ( Fig . S5 ) , although they are relatively hyperphagic post-fasting ( Fig . 4D ) . In sum , by housing Sdc1−/− mice in warmer temperatures , the intradermal fat layer thinned out to approximately match the intradermal fat layer of wild type mice . Under these conditions , all the systemic effects that we have described were rescued . To investigate the molecular basis for this phenotype , we turned to previously published studies . Several groups have implicated the heparan sulfate associated with Sdc1 in lipid uptake from lipoprotein particles . Specifically , Esko and colleagues observed that Sdc1 was required for the uptake of VLDL particles by liver [19] , [49] . Interestingly , those studies showed that circulating levels of triglycerides were higher in C57Bl6 Sdc1−/− mice; in contrast , we show that BALB/c Sdc1−/− mice have lower triglyceride levels . ( Indeed , we confirmed the higher triglyceride levels in C57Bl6 Sdc1−/− mice: specifically , 74 . 7±7 . 9 mgs/dL; Sdc1−/− , 102 . 1±11 . 2 mgs/dL; p = 0 . 04 ) . These strains are highly discrepant with respect to lipogenesis; indeed female BALB/c mice ( like many strains ) do not become obese on high fat feeding ( data not shown , and [41] , [50] , [51] ) . Overall , the literature suggests that for cells not specialized for VLDL uptake , ie . cells other than white fat adipocytes , Sdc1 could be a key player in the capture of VLDL particles and the uptake of associated fats . Furthermore , Orlando and colleagues showed that over-expression of Sdc1 in fibroblasts was sufficient to confer the ability to take up the dye dissolved in the triglyceride in di-I-labeled VLDL particles , via an endocytic mechanism [52] . A prior study from Bernfield and colleagues showed that expression of Sdc1 was up-regulated early during adipogenesis in the 3T3-L1 cell line in vitro , and that Sdc1 stabilized lipoprotein lipase , important to mobilizing fatty acids from complex triglycerides [53] . We therefore hypothesized that knockdown of Sdc1 would inhibit the differentiation and/or the uptake of lipoprotein particles during adipocyte differentiation . We determined the effect of Sdc1 knock-down on the differentiation of 3T3-L1 cells , a culture model of adipocyte differentiation . We confirmed that Sdc1 protein was expressed and induced during adipocyte differentiation ( Fig . 5A ) , as has been reported previously [53] . Knock-down of Sdc1 ( using siRNA ) profoundly reduced the accumulation of intracytoplasmic lipid inclusions that is characteristic of adipocyte differentiation ( revealed by Oil Red O staining; Fig . 5B ) . Mechanistically , we hypothesized that lack of fat accumulation by adipocytes could be the result of either reduced uptake of serum-associated VLDL , or the inhibition of differentiation of 3T3-L1 cells . To distinguish between these options , differentiation-associated markers were assayed in Sdc1 knock-down 3T3-L1 cultures: expression of reporter mRNA species such as PPARγ ( peroxisome proliferator-activated receptor gamma , the hub of adipogenic differentiation-associated transcription ) , FABP4 ( fatty acid binding protein-4 ) , Lpl ( lipoprotein lipase ) , FASN ( fatty acid synthase ) and CD36 ( thrombospondin receptor ) [54] , [55] , [56] , together with several adipocyte-associated proteins ( FasN , Cd36 , activated phospho-IRS1 ) . These were all reduced in parallel to Sdc1 knockdown ( Fig . 5C ) . These experiments were repeated using ear mesenchymal stem cells ( eMSCs ) from BALB/c and Sdc1−/− mice; eMSCs are a primary cell culture model useful for the interrogation of genetically modified mouse strains [57] , [58] , [59] . Immunohistological sections of ear show that this is a rich source of intradermal differentiated adipocytes along with undifferentiated cells ( Fig . S6D ) . These cells show a similar pattern of Sdc1 expression during differentiation as 3T3-L1 cells ( Fig . S6A ) . This model of adipocyte differentiation also required Sdc1 for differentiation , shown by loss of Oil Red O staining ( Fig . 5D; matching nuclear stains are shown in Fig . S6C ) . There was reduced expression of differentiation-associated mRNAs by Sdc1−/− eMSCs ( Fig . S6B ) . This was reproduced in Sdc1−/− eMSCs of three different strains ( BALB/c ( Fig . 5D and S6 ) and for C57BL6 and FVB mice , Fig . 6C ) . Overall , we conclude that loss of function of Sdc1 inhibits the differentiation of pre-adipocytes . Since Sdc1 ( more specifically , the heparan sulfate associated with Sdc1 ) has previously been shown to bind VLDL and mediate the uptake of triglycerides ( tG ) , we evaluated the effect of the competitive inhibitor of heparan sulfate , heparin , on VLDL-tG internalization in this cell model . We found that the uptake of di-I labeled VLDL-tG was dramatically inhibited in pre-adipocytes ( 90% ) in the presence of heparin ( Fig . 5E ) . In contrast , VLDL uptake was almost heparin-independent in adipocytes . Since adipocytes express several specific lipoprotein receptors ( for example VLDLR and LDLR; mRNA expression is shown in Fig . 5C ) , it is likely that heparan sulfate proteoglycans are redundant and functionally insignificant . This outcome suggested that Sdc1 is part of the trigger mechanism for adipocyte differentiation . Since activation of PPARγ by synthetic ligands reduces the need for an upstream adipogenic trigger , we treated 3T3-L1 cells with the PPARγ agonist , rosiglitazone ( Rosi ) . Rosiglitazone induced robust adipocyte differentiation , regardless of Sdc1 ( Fig . 6A ) , suggesting that the activity of Sdc1 is required upstream of PPARγ activation . Reduced Sdc1 activity had no effect on VLDL uptake by adipocytes induced by rosiglitazone administration ( Fig . 6B ) , confirming that Sdc1 is not an important component of exogenous lipoprotein uptake in differentiated adipocytes . Since a PPARγ agonist can rescue the effect of loss of Sdc1 in vitro , we tested whether this rescue would also work in vivo . There are no specific reports in the literature that describe the modulation of intradermal fat in response to any drug , despite a wealth of data that describe changes of metabolism induced specifically by various PPAR agonists [60] . Note that intradermal fat is not the same as subcutaneous fat , though definitions are sometimes not explicit . We treated mice with rosiglitazone for 5 days , a treatment that induced classic PPARγ- dependent changes in white adipose tissues , including the induction of mRNAs for proteins important to uncoupling/browning of white adipose tissue ( UCP1 , PGC1α and Elovl3; Fig . 6C ) . We found that rosiglitazone administration increased the amount of intradermal fat in wild-type mice; indeed , detailed examination of the histology of the skins of mice similarly treated by Varga and colleagues showed similar changes [61] . More importantly for this study , this treatment substantially rescued the intradermal fat layer of Sdc1−/− mice ( Fig . 6D ) . This supports our proposal that the absence of Sdc1 generates a cell-autonomous loss of differentiation in intradermal pre-adipocytes that results in a deficient thermal “blanket” and chronic , unalleviated cold stress .
Many responses to cold have been documented , and these differ according to the severity of the cold stress , and whether the cold stress is acute or chronic . Studies of mice illustrate the redundancy of mechanisms , since different strains emphasize different strategies [8] . They include increased length of fur , altered vascularization , decreased metabolic and activity rates , and lower body temperatures; these are accompanied by behavioral responses such as nest building , neural adaptation and shivering [37] , [62] , [63] , [64] . Interestingly , we found that Sdc1−/− mice show substantial defects in a dynamic layer of adipocytes localized under the epidermis in skin , called intradermal fat . Its thickness is regulated by ambient temperature , notably across the 31–20°C temperature range from thermoneutrality to mild cold stress . Skin provides the only barrier that modulates heat loss from body temperature ( 37°C ) to the environment ( RT/22°C ) . We calculated the theoretical thermal conductivity of mouse skins containing a thin 40 µM layer of adipose tissue ( observed in Sdc1−/− mice at 22°C , and all mice housed at thermoneutrality ) , and the thicker 200 µM layer observed at 22°C in BALB/c mice ( Fig . S7 ) . There is a 1 . 8-fold increase in heat loss through a 40 µM intradermal fat layer compared to skin containing 200 µM ( all other factors being equal ) . This layer of insulating adipose tissue could therefore have a surprisingly key role for physiology; furthermore , the mechanism that regulates this highly dynamic tissue is likely to be an important determinant of metabolism . We have shown that Sdc1−/− mice show symptoms of abnormal cold stress at normal housing temperatures . Thus the thermogenic response of BAT is activated , there is evidence of browning in WAT , and mice are susceptible to fasting-induced torpor . They show chronic cold stress-associated phenotypes such as glycogen depletion and depleted circulating triglycerides [22] . Altered demands on liver function are reflected by the induction of the biosynthetic enzyme , Elovl3 , also a marker of cold stress [36] , [44] and Pgc1α . These mice show abnormal responses to acute cold stressors; this is typical of chronically cold-adapted mice , or animals pre-exposed to cold [31] , [65] . Room temperature housing is already known to be an important determinant of specific physiologies . Thus the obese phenotype of UCP1 mice was not apparent until mice were moved from 20°C to 29°C housing [12] , [33] . Macrophages in brown and white adipose tissues showed significant alternative activation in mice housed at 22°C ( or acutely challenged at 4°C ) compared to those housed at 30°C [5] . Indeed , absent macrophages impaired metabolic adaptations to the cold . Antigen-specific ( tumor-directed ) CD8-positive T cells were activated in mice housed at 31°C compared to 22°C [11] , leading to much-reduced tumor growth in warm temperatures . These two immunosuppressive cell types therefore show opposite trends for activation in cool and warm temperature housing , so the net result of ambient temperature for any given physiology may be difficult to predict a priori . Both cells types secrete , and are governed by , systemic cytokines . There is another example of a skin-associated phenotype that is known to generate a cold stress , by a contrasting mechanism . Thus Scd1 ( stearoyl-CoA desaturase ) -deficient mice show depleted Δ9 monounsaturated 16∶1 and 18∶1 fatty acids in skin sebocytes . This results in loss of skin barrier homeostasis and acute cold sensitivity [66] , . There are similarities and differences between Scd1−/− mice and Sdc1−/− mice; they both show hyperactive BAT , and widespread up-regulation of thermogenic response genes ( UCP proteins and PGC1α in muscle , BAT and WAT ) . However unlike Scd1−/− mice , Sdc1−/− mice do not show a highly elevated metabolism rate , acute cold sensitivity , or exacerbation of metabolic hyperactivity and dehydration at thermoneutral temperatures , reflecting the difference in underlying mechanisms . Note that at this point we cannot exclude the possibility that there is an altered demand for heat generation due to the relatively higher surface area/volume ratio of the smaller ( 13% ) Sdc1 mice . Indeed , this factor applies in general to metabolic studies of mouse strains that vary in size [41] , [46] , [47] , and to genetically modified mice that are smaller than their control counterparts ( for example , Fgf21 transgenic mice ( 40–50% smaller ) [68] , Igf1−/− ( 69% ) [69] and ApoE−/− ( 22% ) [70] ) . However , results from Sdc1 knockdown ( or knock out ) in pre-adipocytes , suggest that Sdc1 is required to trigger adipocyte differentiation , leading us to hypothesize that the systemic effect of absent Sdc1 may reflect a cell-autonomous function in intradermal adipocytes . Different types of adipocytes are programmed to respond to cold stress is different ways . Thus , white adipose tissues are induced to release triglyceride stores , and induce a browning/beige response . Brown adipose tissues are induced to take up lipids , mobilize their own , and induce thermogenesis via uncoupling of mitochondria utilizing β−oxidation of fatty acids [6] , [9] , [71] , [72] , [73] . Our observations suggest that intradermal adipocytes have a distinct response; they accumulate lipid in response to cold stressors , and the intradermal fat expands by 4-fold . In common with other types of adipocytes , these intradermal adipocytes are depleted in response to a PPARα agonist ( WY14643; data not shown ) . We have shown that loss of Sdc1 dramatically inhibits differentiation of adipocytes in vitro . Intradermal fat is highly kinetic compared to other adipose reserves , expanding and collapsing every month , and adjusting in response to ambient temperature . We propose that this underlies the highly specific nature of the lesion produced by absent Sdc1 ( the size of WAT deposits are not affected in Sdc1−/− mice ) . We have shown that Sdc1 is important for VLDL uptake in pre-adipocytes , and suggest that this may be important to its role in differentiation . The PPARγ agonist , rosiglitazone , can rescue the effects of Sdc1 deficiency , and we conclude that Sdc1 is likely to be a component of the sensory trigger for intradermal fat differentiation . No other studies have monitored the response of intradermal fat over this temperature range . We hypothesize that mice with a sufficient insulating response are able to alleviate mild cold stress , albeit altering their metabolic equilibrium , probably with chronic changes of systemic cytokines . In contrast , though the body temperature is maintained in Sdc1−/− mice exposed to cool housing temperatures , the intradermal fat layer of Sdc1−/− mice does not expand sufficiently to alleviate this stressor . This results in “unalleviated” cold stress , notably associated with systemic p38α activation ( Fig . 7 ) . Sdc1−/− intradermal fat shows normal depletion in warm temperature housing , and normal expansion during the hair cycle . ( Adult mouse skin is in anagen stage almost half the time , and the hair cycles are asynchronous , in patches throughout the mouse pelt [74] , [75] ) . The implication is that Sdc1 is involved only in the cold-sensitive trigger for adipocyte differentiation . The dynamic process of intradermal fat accumulation and involution , together with the effect of Sdc1 , is illustrated in Fig . 7 . MAPK14/p38 is an essential mediator of the cold stress response for brown adipose tissues , inducing the expression of the uncoupler protein , UCP1 , in response to β3-adrenergic stimulation of PKA [26] , [76] . This is one of two types of MAPK pathway dedicated to “stress” responses; it is also activated in response to stressors that are osmotic , oxidative , or immune in origin for other cell types , and modulates outcomes for a great variety of signaling pathways [43] , [77] . Specifically , p38α activation leads to activation of anti-cancer checkpoints p53 and CDK inhibitors [78] , and is known to control susceptibility to tumor development and metastasis [43] , [79] . Typically , by inducing thermogenesis in BAT , β3-agonists relieve cold stress , and suppress further responses . However , in Sdc1−/− mice , the effects of cold stress are inadequately buffered , and the activation of p38α becomes widespread , throughout the intraperitoneal organs of Sdc1−/− mice . We predict that this could have the side effect of enhanced protection against the development of pathologies typically held in check by p38α . In conclusion , we suggest that without Sdc1 , the ability of intradermal adipocytes to respond to environmental temperature cues is impaired . This leads to an inadequate thermogenic response and a disproportionate cold stress at temperatures below thermoneutral conditions . Humans and mice have similar strategies for thermoregulation , which is a conclusion emphasized by the relatively recent discovery of human BAT [80] , [81] . There may be opportunities to inhibit the process of intradermal fat differentiation ( specifically inhibiting the Sdc1-mediated function ) to engage the systemic hyper-activation of stress checkpoints in both mice and men .
Heparin sodium salt , fatty acid free bovine serum albumin , dexamethasone , 3-isobutyl-1-methylxanthine ( IBMX ) and insulin were all from Sigma-Aldrich ( St Louis , MO ) . DiI-labeled Very Low Density Lipoprotein ( VLDL ) was obtained from Kalen Biomedical ( Montgomery , MD ) . Paraformaldehyde was from Electron Microscopy Sciences ( Hatfield , PA ) . Rosiglitazone was from Cayman Chemicals ( Ann Arbor , MI ) . Oil red O was obtained from Amresco ( Solon , OH ) . Antibodies used for immunoblot or staining were anti-p38 , P-p38 , p38α , p38γ , P-p53 , p27 , FAS , caveolin-1 ( Cell Signaling Technology , Danvers , MA ) , p16 ( Santa Cruz Biotechnology , Santa Cruz , CA ) , β-actin ( Sigma-Aldrich ) , p21 , p-IRS1 and anti-mouse IgG-Alexa 488 ( Invitrogen , Carlsbad , CA ) , CD36 and UCP1 ( Abcam , Cambridge , MA ) , vinculin ( Chemicon , Billerica , MA ) , donkey-anti-mouse IgG-HRP and normal goat serum ( Jackson Immuno Research Laboratories , West Grove , PA ) , and goat-anti-rabbit IgG-HRP ( Molecular Probe , Carlsbad , CA ) . S1ED anti-mouse syndecan1 antibody was a kind gift from Dr . Alan C . Rapraeger [82] . The generation of Sdc1−/− mice has been described previously ( McDermott et al 2007 ) . Note that all the assays describe BALB/c female mice , except where indicated . Mice were housed at room temperature ( 20–23°C ) unless otherwise specified . For isothermal housing , mice were individually caged , housed at 31°C ( ±1°C ) for 2 weeks in a controlled environment facility , and monitored daily . For cold tolerance testing , mice were individually housed in a cold room ( cage temperature: 4∼5°C ) . A Thermalert TH-5 monitoring thermometer with a RET-3 mouse rectal probe was used to measure body temperature . Mice were maintained on a 12 h light and dark cycle with free access to water and chow diet ( LabDiet# 5020 , St Louis , MO or Teklad#8604 , Harlan Laboratories , Madison WI ) . To administer a PPARγ agonist to mice in vivo , diet was formulated with Rosiglitazone ( 0 . 0015% ) by Harlan Laboratories ( Madison , WI ) . Serum was prepared for analysis and kept at −20°C until needed . Samples for skin sections were paraformaldehyde-fixed ( 4% ) overnight and then paraffin-embedded for evaluation . For individual assay of multiple metabolic parameters ( O2 , CO2 , food and water consumption ) , mice were transferred to a LabMaster modular animal monitoring system from TSE Systems ( Chesterfield , MO ) , acclimated for 1 week prior to measurements , and phenotyped as described [47] , [48] , [83] . BALB/cJ stock and Sdc1−/− BALB/cJ mice 7 to 12 weeks of age were anesthetized ( 3% isoflurane ) and scanned using a Lunar PIXImus densitometer ( GE/Lunar Corp , Madison , WI ) . Calibration of the instrument , animal placement , and scan analysis were conducted as suggested by the manufacturer . One investigator did all DXA determinations . All data presented for body composition exclude the head , by placing an exclusion region of interest ( ROI ) over the head . Blood glucose level was measured from serum using QuantiChrom glucose assay kit ( BioAssay Systems , Hayward , CA ) that utilizes an improved o-toluidine method . In some experiments , blood glucose level was measured directly from the mice after bleeding at the tail tip with OneTouch Ultra 2 Blood Glucose Monitoring System ( LifeScan , Milpitas , CA ) . Liver glycogen amount was measured as follows: liver tissues were homogenized in PBS with a Polytron PT2100 ( Kinematica , Lucerne , Switzerland ) . Liver glycogen amount was measured using a colorimetric enzymatic procedure with minor modifications [84] . Briefly , liver tissues were homogenized with a Polytron PT2100 ( Kinematica , Lucerne , Switzerland ) and amyloglucosidase ( Roche Diagnostics , Indianapolis , IN ) was added for 10 mins at 55°C to break down glycogen to glucose; aliquots were added to a reaction mixture containing glucose oxidase ( MP Biomedicals , ( Solon , OH ) ; mixture includes peroxidase from MP BioMedicals , N , N-dimethylanaline and 4-aminoantipyrine from Acros Organics ( Pittsburgh , PA ) ) for 30 mins at 37°C , and absorbance was measured at 550 nm . Triglycerides in serum were measured using the EnzyChrom triglyceride assay kit from BioAssay Systems ( cat#ETGA200 ) . Tissues were freshly ground frozen , mixed with extraction buffer ( isopropanol∶Triton-X100∶water = 5∶2∶2 ( v/v ) ) and homogenized . After a brief centrifugation at 14000 g for 5 min , 4°C , the supernatant containing extracted triglyceride was collected and processed as described by the manufacturer . This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Experimental protocols were approved by the University of Wisconsin School of Medicine and Public Health Animal Care and Use Committee . The number of mice used to perform this study was minimized , and every effort was made to reduce the chance of pain or suffering . Tissues were dissected and stored in liquid nitrogen until analysis . Frozen tissue was ground into powder and homogenized in RIPA lysis buffer ( 20 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1 mM EGTA , 1% NP-40 , 1% sodium deoxycholate ) supplemented with protease and phosphatase inhibitors ( Thermo Scientific , Rockford , IL ) . Protein concentration was determined using the BCA protein assay . Lysates were analyzed by SDS-PAGE , followed by transfer to PVDF membranes and assay using the antibodies described . Total RNA was isolated from cells and most tissues using the RNeasy Mini Kit ( Qiagen , Valencia , CA ) ; for fatty tissues the RNeasy Lipid Mini Kit ( Qiagen ) was used instead . Array processing , and cDNA synthesis for qPCR assay was done according to the Supplemental Experimental Procedures . Mouse 3T3-L1 preadipocytes were from the American Tissue Culture Collection ( ATCC ) . Cells were maintained in Dulbecco's modified Eagle's medium supplemented with 4 . 5 g/L of glucose ( Life Technologies ) , 10% fetal bovine serum and 100-U/ml penicillin and streptomycin . Cells were differentiated into adipocytes as described by others ( Wilsie et al . , 2005 ) ; briefly , confluent 3T3-L1 preadipocytes were induced to differentiate using MDI medium ( 100 µg/ml 3-isobutyl-1-methylxanthine , 100 ng/ml dexamethasone and 1 µg/ml insulin ) for 4 days , followed by 1 µg/ml insulin for an additional 4 days . For some experiments , the PPARγ agonist , rosiglitazone ( 2 µM ) was added to MDI media for 2 days , and cultures were re-fed every 2 days . To knock-down Sdc1 , 3T3L1 preadipocytes were transfected with Sdc1 siRNA ( Dharmacon ThermoScientific , Rockford , IL ) using Lipofectamine RNAiMAX Transfection Reagent ( Life Technologies ) according to the manufacturer's instructions . Briefly , cells were seeded at 90% confluency in either 4 well chambers or 24 well plates . On the next day , cells were transfected with 15 pmol of anti- Sdc1 or non-targeting siRNA . To provide a gross assay of lipid accumulation , the formation of oil droplets in cells was analyzed using Oil Red O staining . Cells were fixed for 60 mins at 23°C with 3% paraformaldehyde , and stained with filtered 0 . 21% Oil Red O solution for 10 min , followed by four washes with PBS . Cells were photographed , and accumulation was quantified by dissolving the dye in isopropanol and measuring the optical density at 510 nm . Skin samples were taken from the same anatomical site ( either belly or back , as indicated ) , paraformaldehyde-fixed ( 4% ) overnight and oriented during paraffin-embedding . Tissues sections were deparaffinized , re-hydrated and stained with H&E . Cultured cells were fixed with 3% paraformaldehyde for 20 min . After blocking with 5% normal goat serum for 30 mins at room temperature , cells were incubated overnight with a rabbit polyclonal anti mouse-Sdc1 “S1ED” antibody ( gift from Dr . Alan Rapraeger ) , and the anti-mouse IgG-Alexa 488 secondary antibody for 1 h at room temperature . Ear mesenchymal stem cells were prepared according to the methods described by Rim et al ( 2005 ) and Mori and MacDougald [57] , [58] , and differentiation was induced as for 3T3-L1 cells , with the following modifications: eMSCs were incubated in an MDI differentiation medium ( containing 0 . 5 mM IBMX , 1 µM dexamethasone and 1 . 7 µM insulin ) for 5 days , followed by DMEM/F12 culture medium with 10 nM insulin for 3 more days . VLDL uptake by preadipocytes and adipocytes was assayed as previously described ( Wilsie et al . , 2005 ) with some minor modifications . 3T3-L1 preadipocytes were seeded , or matured adipocytes differentiated , on 4 well glass chamber dishes . Cells were incubated with DiI-VLDL ( 4 µg/ml ) diluted into DMEM with 1% fatty acid free bovine serum albumin ( FAF/BSA ) in the presence or absence of either heparin ( 200 µg/ml ) . After 3 h at 37°C , cells were fixed with 3% paraformaldehyde for 20 min , and mounted in ProLong Gold Antifade Reagent with DAPI ( Life Technologies ) . Cells were visualized on a confocal microscope ( BioRad MRC1024 ) , and fluorescence intensity was quantified by Image J software . Data are expressed as mean +/− standard error of the mean and statistical analysis was performed with unpaired one-tailed t tests using Microsoft Excel software . The results with calculated P values less than 0 . 05 are considered statistically significant . | All mammals strive to maintain a fixed body temperature , and do so using a remarkable array of different strategies , which vary depending upon the degree of cold challenge . Physiologists many decades ago observed that a fat layer right underneath the epidermis ( and above the dermal muscle layer ) thickens in response to colder ambient temperatures . This “intradermal fat” provided insulation within days of climate changes . We have found that syndecan-1 , which functions as a facultative lipoprotein uptake receptor , is required for intradermal fat expansion in response to cold exposure . This is a highly specific phenotype not shared by other adipocytes . When intradermal fat is absent , mice do not adapt normally to cold stress , and show altered systemic physiologies , including increased brown adipose tissue thermogenesis and hyper-activation of a stress checkpoint ( p38α ) , designed to protect the body against mutagenic and oxidative stressors . The phenotypes associated with loss of Sdc1 function are reversed when mice are housed in warm temperatures , where defense of body temperature is not required . This study is the first to show that intradermal fat can be genetically regulated , with systemic effects on physiology . | [
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] | 2014 | Syndecan-1 Is Required to Maintain Intradermal Fat and Prevent Cold Stress |
Live attenuated vaccines ( LAVs ) , if sufficiently safe , provide the most potent and durable anti-pathogen responses in vaccinees with single immunizations commonly yielding lifelong immunity . Historically , viral LAVs were derived by blind passage of virulent strains in cultured cells resulting in adaptation to culture and a loss of fitness and disease-causing potential in vivo . Mutations associated with these phenomena have been identified but rarely have specific attenuation mechanisms been ascribed , thereby limiting understanding of the attenuating characteristics of the LAV strain and applicability of the attenuation mechanism to other vaccines . Furthermore , the attenuated phenotype is often associated with single nucleotide changes in the viral genome , which can easily revert to the virulent sequence during replication in animals . Here , we have used a rational approach to attenuation of eastern equine encephalitis virus ( EEEV ) , a mosquito-transmitted alphavirus that is among the most acutely human-virulent viruses endemic to North America and has potential for use as an aerosolized bioweapon . Currently , there is no licensed antiviral therapy or vaccine for this virus . Four virulence loci in the EEEV genome were identified and were mutated individually and in combination to abrogate virulence and to resist reversion . The resultant viruses were tested for virulence in mice to examine the degree of attenuation and efficacy was tested by subcutaneous or aerosol challenge with wild type EEEV . Importantly , all viruses containing three or more mutations were avirulent after intracerebral infection of mice , indicating a very high degree of attenuation . All vaccines protected from subcutaneous EEEV challenge while a single vaccine with three mutations provided reproducible , near-complete protection against aerosol challenge . These results suggest that informed mutation of virulence determinants is a productive strategy for production of LAVs even with highly virulent viruses such as EEEV . Furthermore , these results can be directly applied to mutation of analogous virulence loci to create LAVs from other viruses .
Vaccines against virus pathogens have been licensed in the United States since 1914 [1] and most are inactivated or live-attenuated viruses . Inactivated vaccines are viruses that have been killed using either formaldehyde ( polio virus , influenza virus , and hepatitis A virus ) or β-propiolactone ( influenza virus ) rendering the virus unable to replicate after vaccination [2] . Live-attenuated vaccines ( LAVs ) are live viruses that either have been mutated , most commonly by blind passage ( e . g . , [3] ) , or exhibit host incompatibility to reduce virulence after vaccination [4] . Smallpox , measles , mumps and rubella virus ( MMR ) , varicella virus ( chicken pox ) , rotavirus , and yellow fever virus vaccines are LAVs that are currently FDA approved [5 , 6] . LAVs have an advantage over inactivated vaccines in their ability to mimic natural virus infection , thus inducing a potent immune response that results in high levels of neutralizing antibodies that persist for longer times as well as inducing T cell responses to epitopes scattered throughout the virus genome [7] . One LAV in particular , the yellow fever virus ( YFV ) 17D vaccine , induces a neutralizing antibody response in >95% of vaccinees that can persist for >35 years [8] . The YFV 17D vaccine was generated by blind serial passaging of a virulent YFV strain in mouse and chicken tissues [3] leading to the accumulation of 31 amino acid mutations in comparison with the Asibi parental strain [9] . Similar blind serial passaging has been used to generate other LAVs including the oral poliovirus vaccine [10] , the Venezuelan equine encephalitis virus ( VEEV ) TC83 vaccine [11] , the chikungunya virus ( CHIKV ) 181/25 vaccine [12] and the Japanese encephalitis vaccine SA14-14-2 [13] . While effective in attenuating virulent viruses , serial passaging introduces mutations in the virus genome that have unknown mechanisms of action and can exhibit minimal genetic differences compared to virulent parental strains [5] . For example , the LAV poliovirus vaccine strain contains 10 essential attenuating mutations [14] and reversion of these mutations yielding virulent viruses can occur rapidly in vaccinees [15] putting unimmunized populations at risk . LAVs are often contraindicated in young or immunocompromised populations because of safety concerns [16] . Inactivated vaccines are inherently safer than LAVs , but often , they require formulation with adjuvants and multiple booster immunizations to achieve protective antibody responses . These antibody responses can wane over time , leading to inadequate protection and reemergence of the pathogen [17] . Inactivation techniques ( e . g . formalin ) can also disrupt the natural epitopes on the surface of virus particles changing the antibody repertoire [18] . Furthermore , inactivated vaccines may not effectively stimulate the adaptive immune response to generate memory T cell responses [19 , 20] . A rationally-designed LAV would preserve the natural epitopes of the virus while also effectively stimulating both the humoral and adaptive immune response; yet be sufficiently attenuated for administration to most populations and resistant to reversion . The mosquito-borne alphaviruses are members of the Togaviridae family of medically-important viruses that cause encephalitis ( EEEV , VEEV , and western ( WEEV ) equine encephalitis ) or arthritis and arthralgia ( e . g . , CHIKV , Sindbis virus , and Ross River virus ) [21] . EEEV is endemic in the Eastern US and is among the most virulent acutely infectious viruses known , resulting in a 30–70% mortality rate in symptomatic cases and long-term neurological sequelae in most surviving humans [22 , 23] . Currently , there are no licensed antivirals or an approved vaccine for any of the alphaviruses . A formalin-inactivated EEEV vaccine that is given to at risk workers was developed by the United States Army in the 1960s and remains under investigational new drug status [24 , 25] . However , the vaccine is poorly immunogenic and requires repeated booster immunizations to maintain adequate serum neutralizing antibody levels [24] . An inactivated EEEV/WEEV vaccine is available for veterinary use , but this also requires multiple booster shots in endemic areas [26] . For an EEEV LAV to be licensed , two main outcomes would need to be achieved: 1 ) adequate virus attenuation to prevent potential adverse events with this highly virulent virus [27] , and 2 ) sufficient virus replication for induction of highly protective immunity . To begin to design an EEEV LAV , we chose four target loci for inclusion , mutations at each of which had been shown to affect either EEEV virulence or the virulence of other encephalitic alphaviruses in animal models . These included: 1 ) A locus in the 5’ untranslated region ( UTR ) that was originally identified in the VEEV blind passage TC-83 LAV that alters the secondary structure of the UTR compared to wild-type ( WT ) VEEV strains and increases the binding and translation suppression of IFIT-1 , an interferon-induced antiviral effector protein [28] . 2 ) A five amino acid deletion of a nuclear localization signal in the capsid protein that reduces shutoff of host cell transcription [29–32] . 3 ) A three amino acid charged-alanine change in the E2 glycoprotein that greatly reduces heparan sulfate ( HS ) binding by the virus [33 , 34] . 4 ) Deletion of the four miR-142-3p microRNA binding sites in the EEEV 3’ UTR that leads to efficient EEEV infection of myeloid cells and induction of virus-attenuating systemic interferon-α/β ( IFN-α/β ) [35] . We designed LAV candidates containing mutations in each of the loci , singly or in combination , creating a series of LAV candidates . Mutations were designed such that reversion to WT phenotypes would require more than a single nucleotide change as is often the case with LAVs derived through blind passage [3 , 10–12] . The LAVs were screened for defects in virus growth in vitro , attenuation in a mouse model of EEEV pathogenesis and protection against high dose subcutaneous ( sc ) or aerosol EEEV challenge . LAVs with mutations in three or four virulence loci were fully attenuated in a mouse model and conferred complete protection against sc challenge and partial or complete protection against high dose aerosol challenge . Critically , the vaccine viruses containing three or more mutations were completely avirulent from an intracerebral inoculation route , a contrast with multiple LAVs derived from blind passage ( e . g . , YFV 17D and VEEV TC83 ) and suggesting a large margin of safety . Together , our data demonstrates that rational design of LAVs through mutation of known virulence loci can be effective tool in generating LAVs against alphaviruses and potentially other RNA virus pathogens .
To generate the LAV candidates , we disrupted four virulence loci in the wild-type ( WT ) EEEV strain , FL93-939 . We specifically designed the mutations to resist reversion by mutating either multiple nucleotides , multiple amino acids or creating a deletion; thus , at least two nucleotide changes would be required for reversion ( Table 1 ) . First , to increase sensitivity to IFIT-1 , nucleotides 4 and 6 in the 5’ UTR were mutated from guanine to adenine ( 5’U4&6 ) to disrupt the 5’ terminal stem-loop structure similar to the VEEV TC-83 LAV 5’ UTR [28] . Second , to eliminate shut-off of host transcription , amino acids 65–69 comprising a nuclear localization sequence , were deleted from the capsid protein ( C65-69 ) [32] . Third , three lysine residues at amino acids 71 , 74 , and 77 in the E2 protein were mutated to alanine to disrupt binding of virus particle to cell surface heparan sulfate ( HS ) ( E71-77 ) increasing virus replication in lymphoid tissues and significantly reducing virus spread within the CNS [33 , 34] . Finally , 260 nucleotides were deleted in the 3’ UTR to remove the miR-142-3p binding sites to allow for efficient replication in myeloid cells ( 3’U11337 ) and the induction of systemic IFN-α/β [35] . The mutations were incorporated individually or in combination into the EEEV cDNA clone to generate the 14 LAV candidates ( Table 1 ) . Virus stocks of the single mutants were sequenced and confirmed the presence of the desired mutation within the rescued virus stock . A triple mutant virus possessing the WT EEEV 5’UTR was omitted due to a desire to test triple and quadruple mutation viruses that encode a 5’UTR mutation . This mutation is critical for attenuation of the VEEV TC83 vaccine strain [11 , 28] , an investigational new drug vaccine candidate that is given to at risk-laboratory workers [36] . To determine whether incorporation of the mutations into the EEEV cDNA clone affected virus growth in vitro , Vero cells were infected with equal genomes of each of the LAV vaccine candidates to compensate for differences in the specific infectivity ( genome equivalents [GE] to PFU ratio ) of the HS-binding and non-HS binding mutants ( Table 1 ) [33 , 37 , 38] . Genome numbers were calculated based on the number of genomes equivalent to a MOI = 1 for WT EEEV . The growth kinetics of the LAV candidates were separated into two main groups: those that should bind efficiently to HS and those that contained the E71-77 mutation and presumably lacked the ability to bind to HS ( Fig 1 ) . Similar to previously published results , the HS binding-deficient mutants exhibited slower growth kinetics compared to the HS-binding LAV candidates due to inefficient binding of virus particles to the cell surface [33 , 34] . At 12 hpi , there was 10 , 000-fold increase in virus replication of the HS-binding LAVs compared to the non-HS-binding LAVs ( Fig 1 ) . By 48 hours , this difference was eliminated as yields became similar between the HS binding and the non-HS biding ( E71-77 ) viruses most likely due to eventual infection of all cells by the non-HS binding viruses . Importantly , incorporation of any of the four mutations singly or in combination into the LAVs had no detrimental effect on virus growth in this context . Vero cells do not express miR-142-3p [35] , therefore differential effects of the presence of the miR-142-3p binding sites should not be evident . These results demonstrate that all of the LAVs are viable , have similar growth kinetics in vitro , and can be considered for potential further testing in vivo . To begin to examine the ability of the LAV candidates to function as attenuated and immunogenic vaccine vectors in vivo , we examined virulence of the single mutants as well as all combinations of the mutations following a primary subcutaneous ( sc ) or intracerebral ( ic ) infection ( Table 2 ) . Female outbred CD-1 mice ( 6 weeks old ) were infected with equal genomes of the viruses corresponding to 103 pfu ( 1 . 5 x 105 genomic equivalents ) of WT EEEV subcutaneously ( sc ) , in each rear footpad or intracerebrally ( ic ) , and monitored daily for morbidity and mortality . Following sc infection , the WT EEEV and single mutation E71-77 virus resulted in limited survival ( 8 . 3% and 4 . 2% , respectively ) with a small increased mean time to death ( MTD ) for the E71-77 mutant ( Table 2 ) , similar to previously reported results [33] . The 3’U11337 LAV candidate was significantly attenuated compared to WT ( mutant 91 . 3% survival; P<0 . 0001 , Log-Rank Test ) . Survival of the 3’U11337 virus was higher in these experiments than previously published results [35] potentially due to use of the outbred CD-1 model . Infection with both 5’U4&6 ( survival 45 . 8%; P<0 . 0001 ) and C65-69 ( survival 91 . 7%; P<0 . 0001 ) single mutants also resulted in significant attenuation compared to WT . Combinations of two , three , or all four mutations resulted in further attenuation compared to WT EEEV and the individual mutants ( Table 2 ) . Of the double mutants , only the C65-69 E71-77 ( 79 . 2% survival ) or E71-77 3’U11337 ( 65 . 2% survival ) mutants caused mice to succumb to infection after the primary infection . Mean time to death ( MTD ) also increased for the mice succumbing to infection for 5’U4&6 ( 8 . 2 ± 1 . 3 days ) , C65-69 E71-77 ( 6 . 9 ± 0 . 6 days ) , and E71-77 3’U11337 ( 6 . 4 ± 1 . 5 days ) LAVs compared to WT ( 5 . 0 ± 1 . 1 days ) . Subcutaneous infection with all other LAV candidates yielded 100% survival . Furthermore , mice that survived infection with these viruses did not lose weight , suggesting a high degree of attenuation ( Fig 2 ) . In contrast with a sc infection , the YFV-17D LAV is lethal after an ic infection of immunocompetent mice [39] , while the VEEV TC-83 LAV is lethal after an ic infection in some mouse strains but not others [40] . Potentially reflecting this retained virulence , both the YFV-17D and the VEEV TC83 vaccines can cause adverse events in humans [41 , 42] . Therefore , we determined whether the LAV candidates were similarly attenuated after an ic infection . Outbred CD-1 mice were infected ic with equal genomes of each LAV and monitored for morbidity and mortality . WT , E71-77 , and 3’U11337 LAV candidates were 100% lethal after ic infection ( Table 2 ) . Similar to sc infection , both 5’U4&6 ( 16 . 7% survival ) and C65-69 ( 33 . 3% survival ) were attenuated compared to WT . For the double mutant viruses , C65-69 3’U11337 and E71-77 3’U11337 were both 100% lethal within 6 days of infection . By contrast , all mice infected with double mutant viruses containing the 5’U4&6 mutation exhibited increased survival after ic inoculation . 5’U4&6 3’U11337 was the least attenuated ( 33 . 3% survival ) followed by 5’U4&6 E71-77 ( 83 . 3% survival ) . Interestingly , the combination of 5’U4&6 and C65-69 caused no mortality ( Table 2 ) nor weight loss ( Fig 2 ) . Finally , all combinations of three or four mutations resulted in 100% survival ( Table 2 ) . These results demonstrate that combining at least three mutations in these virulence loci fully attenuates the EEEV LAV candidates after both sc and ic infection . One hypothesis for the ability of the YFV 17D vaccine to induce long-term neutralizing antibody levels suggests that limited vaccine replication early after infection results in strong stimulation of both the innate and adaptive immune responses with few pathological manifestations [43 , 44] . We hypothesized that with our EEEV LAVs , low levels of virus replication would be required to produce an avirulent virus with potent neutralizing antibody responses . However , combining multiple attenuating mutations into a single LAV could potentially reduce virus replication to the point where effective antibody responses are not generated . To compare in vivo replication with the EEEV LAV candidates , we measured replication in both the popliteal lymph node ( PLN ) , an initial site of virus replication , and the serum , 24 hours post infection ( hpi ) . In the PLN , in the absence of robust myeloid cell replication ( viruses with WT sequences in the 3’ UTR ) , on average ~103 pfu/LN were detected at 24 hpi infection ( Fig 3A ) . The level of virus replication was not uniform between all of the mice suggesting individual variation in the level of miR-142-3p expression between mice or the presence of escape mutants that have eliminated the miR-142-3p binding sites ( DW Trobaugh and WB Klimstra manuscript in preparation ) . Between ~102 and ~103 pfu/LN were detected on average in the PLN after infection with either the 5’U4&6 , C65-69 , and E71-77 single mutation viruses . 3’U11337 replicated to the highest level ( ~105 pfu/LN ) , most likely due to the ability of viruses containing this mutation to replicate in myeloid cells within the PLN [35] . Adding a second mutation to each LAV candidate resulted in a reduction for all of the LAV candidates in mean pfu/LN compared to the single mutation LAV candidates ( Fig 3A ) . For example , combining 5’U4&6 and C65-69 reduced virus replication in the PLN compared to both 5’U4&6 and C65-69 single mutants but not significantly . In fact , only 1 mouse had detectable levels of 5’U4&6 C65-69 in the PLN after 24 hours . Also , adding either 5’U4&6 or C65-69 to the E71-77 LAV backbone reduced virus replication at 24 hpi by 10-fold compared to E71-77 alone , which was not significantly different than either 5’U4&6 or C65-69 alone . A similar reduction was seen after the addition of a second mutation to viruses bearing the 3’U11337 mutation . The 5’U4&6 ( P = 0 . 31 ) and C65-69 ( P<0 . 01 ) mutations decreased virus replication by at least 10-fold compared to 11337 . There was a slight but not significant reduction in E71-77 3’U11337 virus replication compared to 3’U11337 alone [33 , 35] . The addition of a third mutation continued to reduce virus replication in the PLN . The 5’U4&6 C65-69 E71-77 virus exhibited the lowest level of virus in the PLN with detectable levels in only 2 of 7 mice . Combining the 5’U4&6 mutation with C65-69 3’U11337 led to a further 10-fold decrease in virus replication compared to C65-69 3’U11337 alone . Similarly , addition of 5’U4&6 to E71-77 3’U11337 reduced virus replication by 10-fold compared to E71-77 3’U11337 alone . Finally , only 3 of 8 mice had detectable levels of the virus with four mutations in the PLN . We also measured virus levels in the serum at 24 hpi to determine whether the mutants produced similar levels of serum viremia compared to WT . At 24 hpi , WT and E71-77 had the highest level of serum viremia ( ~104 pfu/ml ) compared to all of the other mutants ( Fig 3B ) . Serum viremia of the other single mutants was reduced either 100-fold ( C65-69 ( P<0 . 05 ) ) or 10-fold ( 5’U4&6 and 3’U11337 ) compared to WT . Serum viremia of the LAVs containing two mutations were further reduced by 10- to 100-fold from WT and E71-77 . 5’U4&6 3’U11337 had the lowest level of serum viremia , which was detected in only a single mouse . The highest level of serum viremia for the double mutants was in the C65-69 E71-77 group . Similar to virus replication in the PLN , serum viremia was further reduced with the addition of the third and fourth mutations . Only a single mouse infected with 5’U4&6 C65-69 3’U11337 or 5’U4&6 C65-69 E71-77 3’U11337 had serum viremia in contrast to mice infected with 5’U4&6 E71-77 3’U11337 where only a single mouse was below the limit of detection . Together , these results demonstrate that increasing the number of mutations in the vaccine candidates led , generally , to reduction in early virus replication in the PLN and serum viremia compared to WT EEEV . However , considerable variability was encountered likely due to the specific effect of mutations on myeloid cell infection , virus spread , and IFN resistance . We have previously demonstrated that the 3’U11337 single mutation virus induced high levels of serum IFN-α/β compared to WT EEEV within 12 hpi infection , and this serum IFN-α/β was required for the attenuation of 3’U11337 in vivo [35] . Furthermore , combination of the E71-77 and 3’U11337 mutations resulted in even greater induction of serum IFN-α/β at 12 hpi , possibly by increasing the access of myeloid cell replication-competent viruses to the PLN and spleen [33 , 35] . Production of IFN-α/β early after virus infection is an important factor in B and T cell activation and differentiation leading to robust acquired immune responses [45 , 46] . Therefore , we next determined whether the LAV candidates would induce serum IFN-α/β independently of the 3’U11337 and E71-77 mutations or whether these mutations were required for IFN-α/β production . Serum was harvested from mice at 24 hpi and serum IFN-α/β was quantitated by bioassay . As expected , 3’U11337 elicited the highest and most consistent levels of serum IFN-α/β of the single mutant viruses ( Fig 4 ) [35] . In contrast to our previous data [35 , 47] , some WT infected mice had serum IFN-α/β levels at 24 hpi suggesting variability in the outbred CD-1 population or the generation of escape mutants that can replicate in myeloid cells ( DW Trobaugh and WB Klimstra manuscript in preparation ) . When double mutants were considered , the 3’U11337 mutation was required for consistent IFN-α/β production between mice in a group and levels were slightly but not significantly augmented by the presence of E71-77 . IFN-α/β was suppressed compared to E71-77 3’U11337 when E71-77 was combined with any mutation other than 3’U11337 . With three or four mutation viruses , 3’U11337 was required for detection of IFN-α/β in any animals and the 5’U4&6 E71-77 3’U11337 virus elicited the highest and most consistent levels . In general , higher levels of PLN replication ( Fig 3A ) were reflected in higher serum IFN-α/β levels ( Fig 4 ) between viruses suggesting that PLN replication is a major factor in induction of the IFN-α/β response . Overall , PLN replication , which 3’U11337 promotes most directly , is required for consistent serum IFN-α/β production by the LAV candidates and the E71-77 mutation may sustain these levels in the presence of another attenuating mutation . Three weeks after the primary sc vaccination , mice that survived were challenged either sc ( 104 or 105 pfu ) or with a standard dose ( 50–100 LD50 ) aerosol infection of WT EEEV FL93-939 encoding nanoLuciferase ( nLuc ) as a self-cleavable protein , which is similarly virulent to the unmodified parental FL93-939 strain [48] . Mice were monitored daily for morbidity and mortality , and on day 4 ( aerosol ) or day 6 ( sc ) post challenge , mice were imaged using an IVIS Spectrum-CT in vivo imager to quantify virus replication in the brain . After sc challenge , mock-infected controls that succumbed to the challenge ( 6 of 8 mice ) died by day 7 ( Fig 5A ) and had high levels of virus replication in the brain detected by IVIS imaging ( Fig 5B ) . The single C65-69 E71-77 immunized mouse that died after sc challenge on day 4 after infection did not completely recover prior to challenge . All other LAV-vaccinated mice survived the high dose sc challenge with no observable weight loss after infection ( S1 Fig ) demonstrating that the LAVs induce complete protection against a sc challenge . All aerosol-challenged control mice succumbed to infection within 5 days ( Fig 6A ) . On day 4 , mock-immune mice had 100-fold more virus replication as measured by IVIS quantitation of nLuc activity in the brain compared to non-challenged control mice ( Fig 6B and 6C ) . In general , mice that survived the aerosol challenge did not have discernable weight loss ( S2 Fig ) or detectable levels of virus replication by IVIS in the brain on day 4 post challenge ( Fig 6B and 6C ) . Surprisingly , the 5’U4&6 C65-69 vaccinated mice rapidly succumbed to aerosol challenge with only 1 out of 7 mice surviving ( 14 . 3% ) with all of the sick mice having 10-100-fold increases in virus replication in the brain versus uninfected controls . In contrast , six out of eight mice ( 75% survival ) vaccinated with the single mutant C65-69 survived the aerosol challenge; the two sick mice had 100-fold increases in virus replication . Only 1 mouse vaccinated with the 5’U4&6 C65-69 E71-77 or 5’U4&6 E71-77 3’U11337 mutants succumbed to the aerosol challenge . Interestingly , there was a delay in virus replication in the brain of a 5’U4&6 C65-69 E71-77 3’U11337 vaccinated mouse that succumbed to infection . The single mouse had low-undetectable levels of nLuc in the brain on day 4 post infection , but by day 7 , virus replication was 100-fold higher ( Fig 6D ) . Together , the challenge results demonstrate that the EEEV LAV candidates protect uniformly against a sc challenge and the three-four mutation viruses protect partially to completely against the aerosol challenge . Little is known regarding the correlates of protection required for , not only protection from an EEEV infection , but from all alphaviruses [24 , 49] . The inactivated EEEV vaccine given to at-risk workers uses a PRNT80 neutralizing antibody value of 1:40 as demonstrative of adequate protection; however , this has not been experimentally validated [24] . Since some vaccinated mice succumbed to the aerosol challenge , we determined whether or not we could identify a serum antibody neutralization value required for protection from an aerosol infection . Serum was harvested from vaccinated mice at 3 weeks post inoculation , one day prior to aerosol challenge . Sera were tested against a chimeric virus encoding the Sindbis nonstructural genes and the structural genes of WT EEEV FL93-939 [50] , derived from the challenge virus , for neutralizing activity in a standard PRNT assay using commercially available anti-EEEV sera as a control . At a 1:20 dilution of serum , mice immunized with the viruses containing only a single mutation all exhibited close to or achieved 100% neutralization ( Fig 7A ) . Not all mice that were vaccinated with viruses containing more than one mutation had complete neutralization at a 1:20 dilution . In fact , substantial variability was seen in the mice vaccinated with viruses with two , three or four mutations and some sera from these mice exhibited less than 80% neutralization . This suggests that attenuation of these viruses may affect the production of neutralizing antibodies . Also , the presence of neutralizing activity at a 1:20 dilution did not guarantee protection from aerosol challenge . Notably , mice immunized with C65-69 , 5’U4&6 C65-69 , 5’U4&6 C65-69 3’U11337 , and 5’U4&6 C65-69 E71-77 3’U11337 LAVs exhibited neutralizing activity at a 1:20 dilution but still succumbed to the aerosol infection and several animals with 100% neutralization also succumbed . Interestingly , all but one animal that succumbed included the C65-59 mutation that decreases shutoff of cellular transcription . Variability was also seen in PRNT80 values from the vaccinated mice . The highest average PRNT80 values were elicited by 5’U4&6 E71-77 and C65-69 E71-77 double mutation viruses . Average PRNT80 values for the three or four mutation viruses were generally equivalent to or lower than the single and double mutation viruses ( with the notable exception of 5’U4&6 C65-69 ) . The majority of immunized mice that had PRNT80 values below the LOD did not survive the standard dose aerosol challenge ( Fig 7B ) , however , two did survive . All mice with a PRNT80 above 1:256 survived standard dose aerosol challenge regardless of the virus used for vaccination . Below this PRNT80 value , there was not a direct association of neutralization capacity with protection . For example , with C65-69 and 5’U4&6 C65-69 , some mice that had a PRNT80 value above 1:128 also succumbed to disease . Protective responses in mice with low serum PRNT80 values suggest that other immune responses such as production of mucosal IgA or CD4+/CD8+ T cell activation may also play a role in protection from EEEV aerosol challenge . Finally , to assess the dose-responsiveness of aerosol protection afforded by the viruses with the most desirable attenuation properties , we immunized mice as above with the 3 or 4 mutation viruses and subjected them to a high dose aerosol challenge ( >1000 LD50; Fig 8A ) . All vaccine viruses provided over 50% protection from mortality ( P<0 . 05 versus mock ) and weight loss ( S3 Fig ) and the 5’U4&6 E71-77 3’U11337 protected all of the mice . Each LAV vaccinated mouse that succumbed to the high dose aerosol infection had 10-fold lower levels of nLuc signal in the brain compared to mock mice on day 4 ( Fig 8B and 8C ) suggesting there is some low-level protection afforded by these LAVs but not complete protection . In this case , a mouse succumbed to challenge with a neutralization titer of >1:512 ( Fig 8D ) suggesting greater stringency than the 50-100LD50 challenges . Together , our results demonstrate that the LAVs containing three or four mutations are attenuating in vivo via both sc and ic infection , protect against sc infection , and generate sufficient immune responses for protection from stringent aerosol challenge in vaccinated mice . To further differentiate between the 3 and 4 mutant LAV candidates to identify and optimal vaccine candidate , we investigated whether these viruses induced different host immune responses . Since we have already determined that myeloid cell replication is required for serum IFN production ( Fig 4 ) , we wanted to determine whether myeloid cell replication was required for the production of inflammatory cytokines and chemokines during immunization ( Fig 9 ) . LAVs that were competent for myeloid cell replication ( 11337 mutation ) elicited higher levels of IP-10 , MCP-1 , MCP-3 , MIP-1β , IFN-γ , and IL-18 when compared to mock mice and the LAVs without the mutation . When comparing the 3 and 4 mutant LAVs , 5’U4&6 E71-77 3’U11337 immunization resulted in significantly higher levels of IP-10 and MCP-3 compared to the other 3 or 4 mutant LAVs . 5’U4&6 C65-69 E71-77 had the lowest cytokine response of all the 3 and 4 mutant LAVs most likely due to its inability to replicate in myeloid cells . Furthermore , the addition of C65-69 to 5’U4&6 E71-77 3’U11337 resulted in lower cytokine responses compared to 5’U4&6 E71-77 3’U11337 . All other cytokine and chemokine responses were not significantly different from background . Next , we wanted to further evaluate the 3 and 4 mutant LAV candidates to determine whether they induced quantitatively different CD8+ T cell responses after immunization . We immunized C57Bl/6 mice with equal genomes of the 3 or 4 mutant LAVs sc in both rear footpads , and early epitope-specific CD8+ T cell responses in the spleen were measured using an EEEV-specific peptide ( RSFRFSRV ) located in the nsP2 protein . On day 6 post immunization , splenocytes were harvested to quantify IFN-γ+ CD8+ T cell responses . The LAVs that were competent for myeloid replication ( 11337 mutation ) had higher frequencies ( Fig 10A ) and numbers ( Fig 10B ) of EEEV-specific IFN-γ+ CD8+ T cells ( nsP2 ) compared to media control and the only non-myeloid tropic LAV , 5’U4&6 C65-69 E71-77 . There were no significant differences in frequency or number of IFN-γ+ CD8 T cells when comparing between the LAVs that replicate in myeloid cells ( 5’U4&6 C65-69 3’U11337 , 5’U4&6 E71-77 3’U11337 , or 5’U4&6 C65-69 E71-77 3’U11337 ) . However , a trend for greater CD8+ T cell abundance was evident with the 5’U4&6 E71-77 3’U11337 , which induced significantly higher cytokine levels in the serum and was most protective from aerosol challenge . Together , these data demonstrate that incorporating myeloid cell replication into the LAVs by eliminating the miR-142-3p binding sites ( 11337 ) induces a more robust inflammatory response in the serum and T cell response in the spleen .
LAVs are an effective tool in combating medically important pathogens . However , LAVs can induce adverse events in some individuals limiting their use and distribution . Historically , LAVs have been generated by blind passaging in cell culture or animal models until attenuation was achieved . This serial passaging led to the accumulation of mutations in the virus genome that decreased virus virulence . While the mutations could be identified by sequencing , their specific mechanisms of attenuation were rarely known . This has remained true for very widely used LAVs such as the YFV 17D LAV [9] or the poliovirus LAV [14] , which have been given to hundreds of millions of individuals . For example , with the exception of a substitution to positive charge in the DIII loop of the YFV E protein that confers enhanced interactions with heparan sulfate [51] , the molecular attenuation mechanisms conferred by some or all of the 31 specific mutations in YFV 17D LAV have not been well characterized [9] . In addition , attenuating mutations selected by blind passage of LAVs are not designed to resist reversion and often involve single nucleotide changes [3 , 10–12] . This can lead to rapid reversion to non-attenuated phenotypes [15 , 40] . Here , we have studied the effects upon attenuation , lymphoid tissue tropism , elicitation of neutralizing responses and protection from sc or aerosol WT EEEV challenge of four mutant loci in the genome of EEEV whose mechanisms of action are known and whose mutant sequences are specifically designed to resist reversion . These loci/mutations were chosen to increase IFN sensitivity ( 5’U4&6 ) [28]; decrease HS binding and neurovirulence and increase virus particle access to lymphoid tissue ( E71-77 ) [33]; decrease shutoff of host cell transcription , thereby , increasing host cell responses to infection and potentially antigen presentation in infected cells ( C65-59 ) [32]; and eliminate miR-142-3p restriction , thus increasing myeloid cell replication , direct antigen presentation and cytokine responses ( 3’U11337 ) [35] . Combinatorial mutation of these known virulence loci had no effect on virus replication in the Vero mesenchymal cell line ( Fig 1 ) demonstrating that these mutations have no direct inhibiting effect upon virus genome replication in mammalian cells . The mutation that disrupts HS binding , E71-77 , limits growth for all of the viruses containing this mutation , due to inefficient cell binding and lower infectivity in vitro [33 , 34] . Growth kinetics of the viruses should be different in myeloid cells due to the presence or absence of the miR-142-3p binding sites [35] . In addition , Vero cells are defective in IFN-α/β production and , thus , the effects of IFN-mediated antiviral activity are not accounted for in our in vitro testing . In contrast with Vero cells , in mice , incorporation of at least three mutations in virulence loci rendered the viruses completely attenuated ( no mortality or morbidity ) after either a sc or ic infection . Avirulence from an ic inoculation route suggests a degree of safety beyond that provided by passage-derived live attenuated vaccines such as YFV 17D or TC83 both of which can cause morbidity or mortality in mice from this route [39 , 40] . However , additional experiments will be required to determine if murine avirulence is strongly associated with avirulence in primates and whether or not LAV candidates highly attenuated in mice could be given to immunocompromised or juvenile human populations , which are contraindicated even for safe LAVs such as YFV 17D [16] . We have also demonstrated that all but one mouse that survived the vaccination process were completely protected ( no morbidity or mortality ) from sc challenge with EEEV . Even the 5’U4&6 C65-69 double mutant virus , which elicited very low neutralization titers ( most mice <80% at 1:20 dilution of serum ) , was completely protective . Therefore , as with current use of the inactivated EEEV IND vaccine to protect at-risk humans , detectable neutralization titer is a reasonable measure of protection from the natural route of infection in mice . Standard and high dose aerosol challenge yielded substantially less protection , and this was distributed through all mutation groups ( 1–4 mutations ) ; although , of the 6 double mutation viruses , 5 provided complete protection from standard dose aerosol challenge . Among 3 or 4 mutation viruses the 5’U4&6 E71-77 3’U11337 virus elicited the highest level of protection to >100LD50 challenge ( 1 of 11 mice succumbed ) and the virus was completely protective even after even a >1000LD50 aerosol challenge . This minor variability in protective responses after aerosol challenge may reflect immune response differences between individual mice in the outbred CD-1 model used here . A PRNT80 value >1:512 was associated with complete protection against all aerosol doses while the relationship of neutralization titer to protection from aerosol challenge at lower PRNT80 levels was not as clear . However , all mice that exhibited less than 70% plaque neutralization at a 1:20 dilution of serum did succumb to aerosol challenge . A direct association of replication competence in vivo with protection is implied by the fact that viruses with two mutations produced higher neutralization titers and greater protection against challenge than viruses with three or four mutations and their replication in the PLN and serum was generally higher . However , reactogenicity profiles with the two mutation viruses are possibly unacceptable in that mice succumbed to the vaccination dose with the E71-77 3’U11337 and C65-59 E71-77 viruses and none were avirulent after ic inoculation . Similarly , the 5’U4&6 C65-69 E71-77 3’U11337 four mutation virus showed less protection after aerosol challenge than any of the three mutation viruses . Future work will examine whether these LAVs induced different mucosal immune responses that can provide sterilizing immunity upon an aerosol infection . The predicted activity of several of the mutations was recapitulated in vivo . The 3’U11337 mutation that greatly increases myeloid cell infection by EEEV in vitro and in vivo [35] did increase PLN replication and IFN-α/β in serum in most combinations . In the context of vaccines , this is likely to increase immune responses in a number of ways including infection of antigen presenting cells , increasing multiple aspects of immune stimulation . Consistent with this , we observed that incorporation of this mutation into the LAV induced higher pro-inflammatory cytokine levels in sera and higher frequency and numbers of virus-specific CD8+ T cells at 6 days post-infection compared to the non-myeloid tropic LAVs . However , neutralization titer values were not highly reflective of the presence of this mutation as the highest average PRNT80 values were produced by double mutants lacking this mutation , potentially as a consequence of lower IFN-α/β induction by these viruses in vivo . Similarly , we predicted that the 3’U11337 mutation would increase PLN replication and serum IFN-α/β induction in most contexts . The single mutation did increase PLN replication versus the WT virus but this was not associated with higher serum IFN-α/β at 24 hpi . Supporting the prediction , the combination of E71-77 with 3’U11337 exhibited the highest PLN replication and IFN-α/β production of the double mutants and also , this combination was reflected in highest PLN replication and IFN-α/β induction among the triple mutants . Previous models of infection with single mutant viruses assayed IFN titers at 12 hpi . By 24 hpi , escape mutants may be generated during WT EEEV infection that have increased tropism for myeloid cells ( DW Trobaugh and WB Klimstra , manuscript in preparation ) . This myeloid cell replication would then lead to higher serum IFN-α/β production similar to 3’U11337 [35 , 47] . Notably , presence of the C65-69 mutation appeared to result in incomplete aerosol challenge immunity in several contexts . The mutation deletes a nuclear localization signal , greatly reduces shutoff of host cell transcription after infection [32] and likely interferes with any other nuclear activities of the capsid protein [52 , 53] . However , it has no effect upon replication in vitro in the absence of an IFN-α/β response . It could be predicted that , in vivo , this mutation would increase antigen presentation in infected cells and , possibly increase serum IFN responses . In mice , viruses within mutation groups ( e . g . 1 mutation versus 2 , 3 or 4 ) that possessed this mutation showed generally lower replication in the PLN and lower serum IFN-α/β induction . This mutation provided the poorest protection and neutralization titers when present alone or in combination with the 5’U4&6 mutation , and this combination of mutations was also the only double mutant exhibiting complete avirulence after ic inoculation . Furthermore , neutralization titers and protection were greatest among 3 mutation viruses when this mutation was omitted . However , it also should be noted that C65-69 was possessed by several two or three mutation viruses that were highly protective . Therefore , it appears that effects of this mutation on protective responses may be more reflective of a context-dependent effect upon replicative fitness in vivo rather than the specific activity of the mutant locus . The 5’U4&6 mutation did not appear to have a distinct or consistent effect upon virus replication , IFN-α/β induction , neutralization titer or protection in vivo . For example , it was present in the double mutant with the lowest neutralization titers as well as the triple mutant with the highest . Not surprisingly , the primary impact of the mutation: increased sensitivity to genome binding by the IFN induced antiviral effector protein IFIT-1 [28] , does not have a readily apparent association with immunogenicity . However , the mutation clearly provided an attenuating effect in the context of the single mutant or the triple mutants . Therefore , this mutation is possibly not associated with modulation of the immune response beyond its attenuating properties . Ultimately , our data suggest that informed mutation of virulence loci can generate safe and effective LAVs even for viruses with the extreme virulence of EEEV . Our studies do suggest that knowledge of particular attenuation mechanisms can provide some predictive value regarding attenuation and immunogenicity in vivo . However , beyond what may be a specific circumstance with WT EEEV related to its unusual lack of tropism for myeloid cells , informed derivation of a LAV will require empirical assessment of the balance between attenuation and multiple aspects of immunogenicity . Our results also suggest that attenuation and immunogenicity must be considered as separate aspects of informed vaccine design . In our case , with one exception , double mutant viruses elicited the highest and most consistent neutralization responses and provided 100% protection against normal dose aerosol challenge . However , several were unacceptably virulent from a sc inoculation and only one was avirulent from an ic inoculation . Therefore , the margin of safety for these viruses may not be acceptable in humans . Three and four mutation viruses were completely avirulent from both sc and ic inoculation , but none provided complete protection from aerosol challenge and neutralization titers were not as consistent or high on average as with the double mutants . However , only one mouse died in four aerosol challenge experiments after immunization with 5’U4&6 E71-77 3’U11337 and all mice did survive the high dose aerosol challenge . Interestingly , among the three or four mutation viruses , this virus elicited the highest levels of MCP-1 , MCP-3 and IL-18 and showed a trend towards production of higher numbers of CD8+ T cells , possibly underlying its superior protective efficacy . Since the extent to which murine results are applicable to humans is not clear , we propose that a range of viruses exhibiting ic and/or sc avirulence as well as a high degree of aerosol protective efficacy be tested in non-human primate models , including the sc-avirulent , aerosol-protective double mutants with highest neutralization titers ( 5’U4&6 E71-77 and C65-69 E71-77 ) as well as the 5’U4&6 E71-77 3’U11337 triple mutant and the 5’U4&6 C65-69 E71-77 3’U11337 quadruple mutant . Furthermore , complete characterization of the avirulence/immunity relationships in NHPs will be required including examination of multiple aspects of the disease profile such as febrile responses after immunization and immune response generated by the vaccines such as serum antibody class , subclass , presence of antibodies at respiratory mucosal surfaces , compete cytokine analysis , stimulation of CD4+ and CD8+ T cells and their effector activities , and assessment of their relationship to protective efficacy .
All animal procedures were carried out under approval of the Institutional Animal Care and Use Committee of the University of Pittsburgh in protocols 15066059 and 18073259 . Animal care and use were performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Research Council . Approved euthanasia criteria were based on weight loss and morbidity . Baby hamster kidney cells ( BHK-21; ATCC CCL-10 ) and murine C3H/An connective tissue L929 cells ( ATCC CCL-1 ) were maintained in RPMI-1640 supplemented with 10% heat-inactivated donor calf serum ( DCS; Gibco ) and 10% tryptose phosphate broth ( Moltox ) . African green monkey Vero cells were obtained from ATCC ( CCL-81 ) and maintained in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) . All media contained 100 units/ml penicillin , 0 . 5 mg/ml streptomycin , and 2 mM L-glutamine . The cDNA clone for the WT EEEV strain FL93-939 was generously provided by Scott Weaver ( University of Texas Medical Branch , Galveston ) [54] . The LAV candidates containing mutations in the 5’ UTR ( nucleotide mutations: G4A and G6A ) , capsid protein ( deletion of amino acids 65–69 ) , E2 protein ( amino acid mutations: K71A , K74A , and K77A; 71–77 ) [33] and 3’ UTR ( deletion of nucleotides 11337–11596 ) [35] were created using the Quick Change II XL mutagenesis kit ( Stratagene ) . The following primers were used; 5’U4&6-S: CTA ATA CGA CTC ACT ATA GAT AAG ATA CGG TGT AGA GGC AAC CAC CCT ATT TC , 5’U4&6-AS: GAA ATA GGG TGG TTG CCT CTA CAC CGT ATC TTA TCT ATA GTG AGT CGT ATT AG; C65-69-S: CCA ACC CTC CAG CAG GAC CGA AGC CTG CGC CCA AGC CTA; C65-69-AS: TAG GCT TGG GCG CAG GCT TCG GTC CTG CTG GAG GGT TGG; E71-77-S: CCT ACA TGA GTT TCA TGA ACG GCG CAA CGC AGG CAT CAA TAG CGA TCG ACA ACC; E71-77-AS: GCC GTT CAT GAA ACT CAT GTA GGC CAA ATC GAC; 3’U11337-S; GAC ATT AAC ATC TTG TCA ACC GGC AGC GCA TAA TGC TGT CTT TTA TAT C; 3’U11337-AS: GAT ATA AAA GAC AGC ATT ATG CGC TGC CGG TTG ACA AGA TGT TAA TGT C . Fragment swapping strategies were also used for constructing the different combinations of LAVs using the restriction sites ( Mlu I , EcoR I , and Not I ) . All of the LAV candidates were verified by DNA sequencing . Viruses containing all combinations of the four mutant loci were created with the exception of a triple mutant with a wild type 5’ UTR . LAV vaccine RNAs were generated using Not I linearized cDNA to make capped , in vitro transcribed RNA ( mMessage mMachine , Ambion ) . The RNA was electroporated into BHK-21 cells , and the supernatants were harvested 16–20 hours after electroporation . The supernatant was clarified by centrifugation and stored at -80°C in single use aliquots . Virus titers were determined by a standard plaque assay on BHK-21 cells . To quantify the number of genomes in each LAV stock , 20 U RNase ONE ( Promega ) was added to 200 μl virus stock ( 60 min at 37°C ) to eliminate free RNA . After incubation , virus supernatant was added to Tri-reagent and frozen at -80°C . Polyacryl carrier was added to each sample , and RNA was isolated according to protocol provided by manufacturer . cDNA was reverse transcribed from 100ng of RNA as previously described [55] using T7-FL93-nsP2-AS: GCG TAA TAC GAC TCA CTA TAT GAC AAC CAA CGA GTG TGG G . Quantitative determination of the number of genomic equivalents ( GE ) in each LAV stock was performed using SYBR green on a MiniOpticom thermal cycler ( Bio-Rad ) and previously described conditions [55] with the primers FL93-nsP2-S: AGA GTG GCT GAC GTT CGC AC , and T7: GCG TAA TAC GAC TCA CTA TA to quantify positive-strand RNA . The EEEV GE standard curve was based on 10 fold dilutions of in vitro transcribed EEEV replicon RNA [47] . To sequence the 5’UTR mutation , viral RNA stocks prepared as described above were decapped using RNA 5’ Pyrophosphohydrolase ( RppH; New England BioLabs ) according to manufacturers’ guidelines . Viral RNA ( 100 ng ) was incubated with RppH in NEB Buffer 2 for 1 h at 37°C followed by addition of 500mM EDTA and heat inactivation at 65°C for 5 min . Viral RNA was cleaned using RNeasy MinElute Cleanup Kit ( Qiagen ) according to manufacturer’s guidelines and eluted with 14 μl of RNase-free water . 5’ and 3’ ends of the viral RNA were then ligated together using 10mM ATP , 50% PEG8000 , 40 units ( U ) /μl of RNase Inhibitor and 10 U of T4 RNA ligase 1 ( New England Biolabs ) for 16 h at 16°C . Reverse transcription ( RT ) of the ligated RNA was performed using SuperScript IV reverse transcriptase ( Thermo Fisher Scientific ) and a random hexamer primer according to manufacturer’s guidelines . The random hexamer primer was first annealed to the ligated RNA by heating at 65°C for 5 min in the presence of 10mM dNTPs followed by incubation on ice for 5 min . Reverse transcription ( RT ) was preformed using the following conditions: 23°C for 10 min , 50°C for 30 min , and 80°C for 10 min . cDNA amplification was performed using GoTaq polymerase ( Promega ) and the following conditions: 95°C for 2 min , then amplification for 40 cycles ( denaturing: 95°C for 35 sec , annealing: 55°C for 30 sec , extension: 65°C for 1 min ) . A lower amplification temperature was used due to the presence of the poly A tail in the ligated viral RNA [56] . RT for sequencing of the other virulence loci was performed using Superscript IV VILO ( ThermoFisher Scientist ) according to manusfacturer’s guidelines using the anti-sense ( AS ) primers described below for C65-69 and E71-77 and Oligo ( dT ) for 3’U11337 . PCR amplification was performed as described above . The PCR product from all reactions was excised from a 2% agarose cell using Promega Wizard SV Gel and PCR cleanup system . The PCR product was sequenced at the University of Pittsburgh Genomics Research Core . The following primers were used for PCR amplification and sequencing: 5’U4&6: EEEV 3’U-11208-11228-S: CCG CCA CCG CGT GGT CGT GGC , EEEVnsp1-AS: TGA CTT GAC GAA TGG GCT GTC TGC GT; C65-69: S- CCA TAA CCC TCT ACG GCT GAC CT , AS- CTG TAA CCG TGT CCC CTG GT , E71-77: S- AGG AGA ACC AGG AGA GAT TTG GA , AS- GCA CGC TTG TGA GTG TAA C; 3’U11337: EEEV 3’U-11208-11228-S: CCG CCA CCG CGT GGT CGT GGC , EEEV-T7-CSE AS- TAA TAC GAC TCA CTA TAG GGC GTA TGG AAA AAA TTA ATA TGA TTT TGT AAA TTG ATA TAA AAG ACA GC . Virus growth curves were performed as described previously with some modifications [47] . Vero cells were infected in triplicated in 24-well plates with equal genomes of each LAV stock corresponding to a multiplicity of infection ( MOI ) of 1 pfu per cell of WT EEEV . Supernatant was collected at time zero and indicated time points for titration by plaque assay on BHK-21 cells . Outbred 5-6-week-old female CD-1 mice ( Charles River ) were infected subcutaneously ( sc ) in each footpad or intracerebrally ( ic ) with equal genomes of the LAV vaccine stocks corresponding to 103 pfu of WT EEEV ( 1 . 5 x 105 genomic equivalents ) in OptiMEM ( Gibco ) . Mice were monitored twice daily for morbidity and mortality . Serum was collected via the submandibular vein at indicated time points and stored at -80°C until use . For virus challenge studies , mice were aged for 21 days , and bled on day 21 prior to challenge . For tissue harvest , popliteal lymph nodes ( PLN ) were harvested at 24 hours post infection ( hpi ) and placed in 100 μl PBS containing 1% DBS for virus titration . Biologically active serum IFN-α/β collected at 24 hpi was measured using a standard biological assay on L929 cells as described previously [47 , 57] . The IFN-α/β concentration in sera samples was set as the dilution of sample required for 50% protection from cytopathic effect compared to protection conferred by an IFN standard . To generate the IFN standard , murine IFN-α or IFN-β sequences were cloned into a previously described Sindbis virus replicon [58] . Capped , in vitro transcribed IFN-α or IFN-β encoded replicon RNA was electroporated into BHK cells and incubated overnight at 37°C . The next day , the supernatant was initially clarified by centrifugation ( 4000 rpm for 30 min at 4°C ) followed by ultracentrifugation at 24 , 000 rpm for 6 h at 4°C . The supernatant was then acidified to pH = 2 . 0 with 0 . 02 N H2SO4 and incubated overnight at 4°C . The supernatant was neutralized to pH = 7 . 0 with 0 . 2N NaOH and concentrated using Amicon Ultra-4 <10mw centrifugal filter units ( EMD Millipore ) . Concentration of mouse IFN-α and IFN-β was determined using the IFN-α/β bioassay and known IFN standards . For sc challenge , on day 22 after primary infection with the LAV candidates , mice were infected sc in both footpads with either 1 x 105 pfu or 1 x 104 pfu of 20% sucrose purified WT EEEV FL93 expressing nanoLuciferase ( nLuc ) as a self-cleavable protein ( TaV ) [48] . Aerosol challenge experiments were performed as previously described [59 , 60] . Briefly , mice were challenged with high doses of 20% sucrose-purified WT EEEV FL93-nLuc TaV using the AeroMP exposure system ( Biaera Technologies , Hagerstown , MD ) inside a class II biological safety cabinet and either an Aeroneb nebulizer ( Aerogen ) or 3-jet Collison nebulizer ( CH Technologies ) . All mice were monitored twice daily for morbidity and mortality . On day 4 ( aerosol ) or day 6 ( sc ) post challenge , mice were injected with 10 μg of Nano-Glo substrate sc in 500 μl PBS as previously described [59] . Four min after substrate injection , the mice were imaged using the IVIS Spectrum CT Instrument ( PerkinElmer ) using the autoexposure setting . The total flux ( photons per second ) in the head region was calculated for each animal using Living Image Software 4 . 5 . 1 with all images set to the same scale . Images of representative animals are shown from each LAV vaccine candidate . A chimeric SINV ( TR339 ) encoding the EEEV FL93 structural proteins was generated from in vitro transcribed RNA in BHK cells as previously described [50] . Serum collected on D21 from mice immunized with the LAV candidates was heat inactivated at 56°C for 30 min . The serum was serially diluted ( 2-fold dilutions ) and incubated with ~100 pfu of SINV-EEEV for 1 h at 37°C . Anti-EEEV ascites serum ( ATCC ) was used as a positive control . After incubation , Vero cells were infected in 6 well plates for 1 h at 37°C in a plaque assay . After overlay with agarose immunodiffusion grade ( MP Biomedicals ) , plates were incubated for 2 days followed by overlay with neutral red for at least 6 h to count plaques . Percent neutralization was calculated based on the number of plaques in each serum dilution compared to the number of plaques in non-antibody treated control wells . A best fit non-linear curve was used to calculate the 80 percent reduction dilution ( GraphPad Prism ) . CD-1 mice were infected as described above and serum was collected at 24 hpi infection stored at -80°C until use . A mouse 26-plex ProcartaPlex immunoassay ( ThermoFisher Scientific ) and a Bio-Plex Pro II array washer were used according to manufacturer’s guidelines and 25 μl of serum . Samples were run on a Bio-Rad Bio-Plex II suspension array system in BSL-3 containment . Background values were subtracted from calculated cytokine concentrations . C57BL6 mice ( 6 weeks ) were immunized with equal genomes of the triple and quadruple LAVs ( 1 . 5 x 105 genomic equivalents ) in both rear footpads . On day 6 , spleens were harvested , and an intracellular cytokine staining was performed as previously described [61] . Splenocytes were stimulated with 1μM of an EEEV-specific nsP2 peptide ( RSFRFSRV , >95% purity GenScript ) ( D . W . Trobaugh and W . B . Klimstra manuscript in preparation ) for 5 hr in the presence of brefeldin A ( GolgiPlug , BD Biosciences ) . Cells were washed with PBS and stained with GhostDye UV450 ( Tonbo Biosciences ) . Next , cells were incubated with 1/200 dilution of anti-CD16/32 for 15 min at 4°C followed by surface staining with 1/100 dilution anti-CD3 PerCP-Cy5 . 5 ( 145-2C11 ) and anti-CD8 APC-Cy7 ( 53–6 . 7 ) for 20 min . After permeabilization with BD CytoFix/CytoPerm , cells were incubated with 1/100 dilution of anti-IFN-γ FITC for 20 min 4°C . Cells were then fixed with 4% PFA overnight . Data was collected on a BD LSR II and analyzed with FloJo software ( TreeStar ) . All antibodies were purchased from Tonbo Biosciences unless specified . All statistical analysis was performed using GraphPad Prism software . Statistical significance for survival curves was determined by Mantel-Cox log rank test . In general , WT EEEV was compared to the single mutants , the single mutants were compared to the double mutants incorporating the single mutations , the double mutants were compared to the triple mutants incorporating the double mutations , and the triple mutants were compared to the quadrupole mutant . Comparisons indicated in figure legends were determined by one-way analysis of variance with Turkey’s multiple-comparison test of log-transformed data or two-way analysis of variance with multiple comparisons using the Bonferroni method . | Live-attenuated vaccines ( LAVs ) mimic a natural virus infection and elicit high levels of neutralizing antibodies that can persist for long times . Historically , LAVs have been created by blind passaging of the virus leading to attenuating mutations in the viral genome with no known mechanism of action . We have used an informed approach to create a LAV for eastern equine encephalitis virus ( EEEV ) . EEEV is one of the most highly virulent mosquito-borne viruses in the United States , and there is currently no approved vaccine or antiviral therapeutic . Here , we created a series of LAVs by combining mutations of four alphavirus virulence loci that have known functions . We demonstrate that viruses containing at last three mutations are highly attenuated after both a subcutaneous and intracerebral infection of mice and provide protective immunity against both a subcutaneous and aerosol challenge . We have also identified a key mutation , elimination of the miR-142-3p microRNA biding sites in the EEEV 3’ untranslated region , as critical for myeloid cell replication and essential for eliciting optimal cytokine responses , T cell responses , and protection from challenge . In summary , our results provide a rationale for an informed approach to the generation of LAVs against arboviruses . | [
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... | 2019 | Rational design of a live-attenuated eastern equine encephalitis virus vaccine through informed mutation of virulence determinants |
Axonal transport is responsible for the movement of signals and cargo between nerve termini and cell bodies . Pathogens also exploit this pathway to enter and exit the central nervous system . In this study , we characterised the binding , endocytosis and axonal transport of an adenovirus ( CAV-2 ) that preferentially infects neurons . Using biochemical , cell biology , genetic , ultrastructural and live-cell imaging approaches , we show that interaction with the neuronal membrane correlates with coxsackievirus and adenovirus receptor ( CAR ) surface expression , followed by endocytosis involving clathrin . In axons , long-range CAV-2 motility was bidirectional with a bias for retrograde transport in nonacidic Rab7-positive organelles . Unexpectedly , we found that CAR was associated with CAV-2 vesicles that also transported cargo as functionally distinct as tetanus toxin , neurotrophins , and their receptors . These results suggest that a single axonal transport carrier is capable of transporting functionally distinct cargoes that target different membrane compartments in the soma . We propose that CAV-2 transport is dictated by an innate trafficking of CAR , suggesting an unsuspected function for this adhesion protein during neuronal homeostasis .
Adenoviridae is a family of greater than 150 nonenveloped double-stranded DNA viruses that infect all vertebrate classes . Whilst adenoviruses ( Ads ) are commonly associated with respiratory , ocular and gastrointestinal tract infections , many serotypes cause clinical manifestations in other tissues , including the central nervous system ( CNS ) [1]–[4] . Interest in Ad biology has been rekindled by at least two events: Ads have re-emerged as life-threatening pathogens in immunosuppressed hosts and young military recruits [5] , and they are currently the most common viral vectors used in clinical gene transfer trials . Importantly , Ad infections can be lethal in immunocompromised patients due to genetic defects ( SCID ) , during haematopoietic stem cell transplants or by pharmacological agents ( e . g . during solid organ transplant ) [2] . For brain-directed gene transfer , Ad vectors , in particular canine serotype 2 ( CAV-2 ) [6] have unique characteristics . In the CNS of rodents , dogs and primates ( including human tissue ex vivo ) , CAV-2 vectors preferentially transduce neurons and undergo efficient axonal transport ( [7]; our unpublished data ) . We previously demonstrated that following interstriatal injections in rodents , CAV-2 was transported to afferent structures such as the contralateral and ipsilateral cortex , substantia nigra , thalamus and basal nuclei of Meynert [7]–[9] . In addition , following injection into the mouse gastrocnemius , CAV-2 preferentially transduced motor neurons of the sacral dorsolombar rachis [7] . CAV-2 vectors also lead to >1 year in vivo transgene expression in rodent CNS [8] , [9] without accompanying immunosuppression . In addition to their potential in addressing fundamental neurobiological questions [9]–[11] , these molecular tools could also be used for treatment of neurodegenerative disorders [12] . Although there are a handful of exceptions , most Ad attachment and trafficking studies have used epithelial-like cells and serotypes from human subgroup B , C and D ( e . g . Ad2 , 5 , 35 and 37 ) . Many human serotypes , as well as CAV-2 , bind with high affinity to the coxsackievirus and adenovirus receptor ( CAR ) [13]–[16] , a widely expressed cell adhesion protein involved in tight junction formation in epithelial cells and myocardial cells , and highly expressed in the developing brain [17]–[21] . Many CAR-tropic Ads are endocytosed in clathrin- and Rab5-associated pathways in epithelial cells [22]–[24] . Following receptor-mediated internalisation , subgroups C Ads are thought to undergo a stepwise disassembly , starting with detachment of the fibre from the virus at the cell surface , followed by a passage through early endosomal compartments in which acidification serves as a disassembly trigger [25] , [26] . Although the mechanism is poorly understood , intra-endosomal signals likely release vertex proteins , which may lead to protein VI-mediated membrane lysis [27] and escape of the virion into the cytosol [25] . The metastable virions may then be targeted via dynein and microtubule-dependent mechanisms towards the nucleus in some cell types [28]–[30] . In spite of initial reports demonstrating that Ad vectors can be transported retrogradely in neurons in vivo [31] , [32] , little is known concerning their brain cell receptors , the endosomal compartment ( s ) entered during trafficking or the determinants for their long-range transport . Axonal transport is crucial for neuronal differentiation and homeostasis , which depend on the efficient long-distance delivery ( up to 1 meter in humans ) of signals and cargoes [33] . This pathway relies mainly on the microtubule-based motors kinesins and cytoplasmic dynein , and their coordination with F-actin-based motors [33] , [34] . Alterations in components of the axonal transport machinery are associated with a growing number of neurodegenerative conditions , including Alzheimer's , Parkinson's , Huntington's and motor neuron diseases [33] , [35] . In spite of its importance , we are only beginning to understand how the machinery of axonal transport is regulated . The dual nature of Ads as ubiquitous pathogens and potential gene transfer vectors for the CNS , imposes an in-depth analysis of the molecular mechanisms involved in the virus-neuron interaction . Here , we characterised the binding , internalisation and axonal transport of an Ad that preferentially infects neurons . Our data suggest that the neuronal binding of CAV-2 is CAR-dependent and its internalisation involves clathrin-coated pits and the small GTPase Rab5 . In contrast to the established paradigm of Ad trafficking in epithelial cells , long-range CAV-2 transport in axons is mainly vesicular , and depends on the sequential maturation of transported endosomes , which switch from Rab5 to Rab7 . We found that CAV-2 axonal motility is bidirectional , with a bias for the retrograde direction . Carriers of CAV-2 also transported tetanus toxin and neurotrophin receptors and surprisingly still contained CAR . We also demonstrated that similarly to whole virions the fibre knob ( FK ) protein could be found in CAR+ organelles . We therefore propose that the intrinsic neuronal properties of CAR are responsible for the efficient trafficking of CAV-2 in neurons . More globally , our data demonstrates that distinct receptor-mediated endocytic events determine the sorting of diverse cargoes to nonacidic vesicles , which are then recruited in a Rab7-dependent manner to the long-range retrograde transport pathway , in a process that allows selected pathogens to reach the CNS .
CAV-2 vectors preferential infect neuronal cells in vivo and in mixed brain cell cultures , however the binding determinants responsible for this tropism have not been addressed . Although the 150 Ad serotypes can bind numerous co-receptors [36] , [37] , our previous studies suggested that CAR is the main receptor for CAV-2 [16] , [38] . To study the neuronal link between CAR and CAV-2 , we incubated Cy3-labelled CAV-2 virions ( CAV-Cy3 ) with primary spinal cord motor neurons ( MNs ) on ice to allow binding , but prevent internalisation . Cells were then fixed and stained for endogenous CAR . Interestingly , CAR was found in two distinct compartments in MNs . In addition to a plasma membrane localisation seen also in sparse epithelial-like cells copurifying with MNs , CAR was also found in a large intracellular pool ( Figure S1A ) . We found that >70% of CAV-Cy3 colocalised with CAR on neurites in MNs and dorsal root ganglia neurons ( DRG ) ( Figures 1A , B and S1B ) . Moreover , when MNs were pre-incubated with saturating concentrations of recombinant fibre knob ( FK ) , the adenovirus protein responsible for CAR binding , and then treated with CAV-2 , virion uptake was reduced by 76% compared to control ( Figure 1C ) . We then examined the early steps of CAV-2 entry in MNs by transmission electron microscopy ( TEM ) . At 1 minute post-internalisation , electron dense CAV-2 virions were associated with structures resembling clathrin-coated pits , often present at cell-to-cell contacts ( Figures 1D , S1C and data not shown ) . By indirect immunofluorescence , we also found extensive colocalisation between clathrin heavy chain and CAV-2 ( Figure S1D ) . These results are in good agreement with previous reports showing that in epithelial cells , CAR-tropic Ads undergo clathrin-associated endocytosis , and are consistent with our current understanding of CAV-2 internalisation in these cells [16] , [39] . We next assessed CAV-2 internalisation in MNs . To this end , we again incubated MNs with CAV-Cy3 on ice and then replaced the medium with warm medium to induce internalisation . Cultures were incubated at 37°C for 45 minutes , then shifted back to 4°C and incubated with anti-Cy3 antibody to detect surface-bound virions . We found that MNs internalised >75% of CAV-2 under these conditions ( Figure 1E ) . Upon internalisation in epithelial cells , most CAR-tropic Ads are believed to rapidly exit endosomal compartments to reach the cytoplasm [30] from where the capsid may interact directly or indirectly with cytoplasmic dynein [29] , and be transported towards the nucleus . To determine if a similar process was also at the basis of the axonal transport of CAV-2 , virions were incubated with MNs at 4°C then shifted to 37°C , fixed at different times and then visualized by TEM . At 2 to 5 minutes post-internalisation , the majority ( >90% ) of the virions were inside intact endosomal membranes ( Figure 1F ) . Surprisingly , this pattern did not change significantly ( ∼90% ) 30 to 45 minutes post-internalisation , when live imaging of CAV-2 axonal transport was optimal ( 3 independent experiments , 97 virions in total; see below ) . At these later time points , membrane-enveloped virions could be detected close to structures morphologically similar to microtubule tracks ( Figure 1G , black arrow ) . Together these results suggest that CAV-2 binds CAR , is endocytosed in clathrin-coated pits and , unexpectedly , remains within endosomal compartments associated with microtubules in MNs . The above results prompted us to characterise the motility of intracellular CAV-2 using established vesicular transport markers by live cell imaging . Initially , we incubated CAV-Cy3 with primary MNs , and axons were then imaged by confocal microscopy . Using this approach , we detected bidirectional transport of CAV-2 ( Figure 2A and B , Video S1 ) . Whilst the majority ( 87% ) of motile virions were transported towards the soma , some ( 13% ) showed anterograde movement ( Figure 2C , lower quadrant ) . In addition , some single virions changed direction during imaging ( Figure 2A and B , asterisk and red dotted line ) , suggesting that either CAV-2 structures associates with molecular motors of different polarity or that dynein-dependent bidirectional transport [40] influences its kinetic properties . Bidirectional CAV-2 transport , with a preference for retrograde motility , was also found in cultures of embryonic DRG ( data not shown ) , suggesting a similar mechanism in sensory neurons . The kinetics of transport were analysed by determining the speed distribution profile of CAV-2 in MNs ( Figure 2D ) . CAV-2 retrograde transport appeared to be bimodal with peaks at 0 . 60 and 1 . 30 µm/s ( Figure 2D , blue line ) , which is consistent with fast retrograde transport [41] . In contrast , the anterograde transport profile was more discontinuous ( Figure 2D , red line ) . While characterising CAV-2 transport kinetics , we noticed a delay in the onset of long-range axonal transport . Although our results suggested that CAV-2 is rapidly internalised ( <5 min; Figure 1F ) , we detected primarily oscillatory movements at early times post-internalisation ( Figure S2 , top panel ) . Only after 25 minutes were we able to detect long-range movements ( Figure S2 , middle panel ) , with robust vectorial transport beginning after ∼30 minutes ( Figure S2 , middle and lower panels ) . In contrast to the efficient escape from endosomes by CAR-tropic Ads , our TEM data showed that the majority of CAV-2 remained trapped in vesicles when axonal transport is most efficient . To directly address the possibility that CAV-2 axonal transport is mediated by a membrane compartment , we co-incubated MNs with CAV-Cy3 and AlexaFluor647-dextran , which is a fluid phase marker used to identify endocytic organelles . Consistent with our TEM observations , we found the majority ( ∼75% ) of virions were co-transported with dextran ( Figure 2E and F ) . These data suggest that CAV-2 uses a vesicular transport pathway to reach the MN soma . The stable association of CAV-2 with the endosomal lumen is inconsistent with the canonical mechanism regulating productive CAR-tropic Ad infections , and may represent a key determinant for efficient axonal transport of CAV-2 . Because the exit of Ads from endosomes is triggered by the acidification of their lumen , CAV-2 might enter nonacidic pH compartment ( s ) allowing its stable sequestration during axonal transport . To test this hypothesis , we assessed the association of CAV-2 with a fragment of tetanus toxin ( TeNT HC ) , which is internalised via a clathrin-dependent mechanism coupled to axonal retrograde transport and is sorted to carriers characterised by neutral pH [42] , [43] . To this end , we co-incubated MNs with CAV-Cy3 and fluorescently-labelled TeNT HC [41] . In fixed samples , CAV-Cy3 colocalised with TeNT HC in axons and somas ( Figure 3A ) . Furthermore , using live-cell imaging we found that more than 85% of CAV-2 was co-transported with TeNT HC ( Figure 3B and Video S2 ) . Our previous work showed that TeNT HC carriers also contain neurotrophins and their receptors [44] . Accordingly , CAV-2 carriers were also positive for the neurotrophin receptor p75NTR ( data not shown ) . To directly assess the pH of the transport carriers containing CAV-2 , MNs were incubated with CAV-2 covalently labelled with carboxyfluorescein ( CAV-FC ) , a probe previously used to measure the pH of endosomes reached by Ads during endocytosis [45] . CAV-FC-infected MNs were incubated with the ionophores nigericin and monensin , exposed to L15 media at different pHs , and the ratio of the emission intensities upon sequential excitation at 458 and 488 nm was determined . Under these conditions , the calibration curve of the pH-dependent fluorescence of CAV-FC was obtained ( Figure 3C ) . We then assayed the pH of CAV-FC-containing structures in neurites compared to cell bodies ( Figure 3D ) . Consistent with the co-transport of CAV-2 with TeNT HC , we found that the majority of axonal CAV-FC was within a pH-range of 6 to 7 ( Figures 3D ) . Interestingly , we detected numerous acidic ( pH<6 ) CAV-FC structures in the soma , whereas only very few axonal CAV-FC could be observed at or below pH 6 ( Figure 3D ) . To test the presence of CAV-2 in nonacidic structures in axons using an alternative approach , MNs were incubated with CAV-Cy3 , AlexaFluor647-dextran and Lysotracker-488 , a probe that is sequestered in acidic compartments . Consistent with the above results , axonal CAV-2/dextran-positive carriers were Lysotracker-488-negative ( Figure 3E ) . Furthermore , our quantitative analyses of the extent of colocalisation between CAV-2 and lysotracker confirmed the higher association of virions in acidic organelles in cell bodies of MNs versus neurites ( Figure 3F ) . Taken together , these data demonstrate that the majority of CAV-2 is retrogradely transported in axons inside a nonacidic vesicular compartment , which is also used by endogenous ligands , receptors and other pathogens . Progression along the endocytic pathway is tightly regulated in time and space . In many cell types , the classical endosomal pathway involves early endosomes containing Rab5 , which then mature into late endosomes characterised by the presence of Rab7 on their cytosolic face [46] . Because axonal transport of TeNT HC requires the sequential activities of Rab5 and Rab7 [44] , we asked if these small GTPases were also associated with CAV-2 transport . MNs were incubated with CAV-Cy3 for 5 or 45 minutes , fixed and stained for endogenous Rab5 and Rab7 . At 5 minutes post-internalisation , we found numerous Rab5/CAV-2 structures lacking Rab7 , both in axons ( Figure 4A ) and in cell bodies ( data not shown ) , demonstrating that the virions associated with early Rab5+ endosomes immediately after internalisation . However , at 45 minutes post-internalisation we detected virions mainly in Rab7+ structures ( Figure 4B ) . Quantitative analysis of the distribution of Rab5 , Rab7 and CAV-2 showed that at 5 minutes post-internalisation , 40% of CAV-2 was in Rab5+ compartments whereas at 45 minutes post-internalisation , only 11% of the virions colocalised with Rab5 . In contrast , at 45 minutes 44% of virions colocalised with Rab7 , and 16% were Rab5/Rab7 double positive ( Figure 4C ) . These ratios are in good agreement with the colocalisation between transported TeNT HC and Rab7 [44] . To address the functional relationship between CAV-2 transport and Rab7 activity , we microinjected MNs with plasmids expressing GFP-tagged fusion proteins of either wild-type Rab7 ( GFP-Rab7WT ) or its dominant-negative N133I mutant ( GFP-Rab7N133I ) [47] . The axonal transport of CAV-2 was then assayed using live-cell imaging in GFP and GFP-Rab7 expressing neurons . In agreement with the degree of colocalisation observed with the endogenous protein , CAV-Cy3 colocalised with GFP-Rab7WT in somas ( Figure 4D ) and axons ( 32%; 5 independent experiments , 107 virions in total ) ( Figure 4E and F ) . Furthermore , the GTPase activity of Rab7 was essential for axonal transport of CAV-2 since overexpression of GFP-Rab7N133I strongly impaired CAV-2 movement ( Figure 4G and H ) , compared to overexpression of GFP or GFP-Rab7WT ( Figure 4G and H ) . In agreement with previous reports [48] , the inhibitory effect of GFP-Rab7N133I is linked to its expression levels . As a consequence , sub-threshold GFP-Rab7N133I expression did not alter the axonal transport of CAV-2 ( Figure 4H; outlier in the GFP-Rab7N133I sample ) . Conversely , strong overexpression of GFP-Rab7WT caused a partial , yet not significant , inhibition of this process ( Figure 4H ) . These results suggest that Rab5 to Rab7 vesicular maturation is required for CAV-2 progression along the axonal endocytic pathway . Axonal transport is mainly powered by the microtubule-dependent motors cytoplasmic dynein and kinesins [34] . To further understand the determinants of bidirectional CAV-2 transport , we stained MNs previously incubated in the presence of CAV-Cy3 with antibodies specific for subunits of motor complexes . Dynein heavy chain ( Figure 5A ) and p50/dynamitin , a subunit of the dynein-dynactin complex ( data not shown ) , were associated with more than 60% of virions , suggesting that this ubiquitous retrograde motor plays a major role in the axonal transport of CAV-2 . Secondly , we found a lower , albeit significant , colocalisation of virions with the heavy chain of kinesin-1 ( KHC ) ( Figure 5B ) . Although these data do not exclude the possibility that the bidirectional transport of CAV-2 is due uniquely to dynein , they favour the likelihood that both cytoplasmic dynein and kinesin play a role in this process . To directly demonstrate the involvement of these motor proteins in CAV-2 transport , we overexpressed p50/dynamitin , a treatment that disrupts endogenous dynein-dynactin complex [49] . In p50/dynamitin-expressing MNs , CAV-2 transport was strongly inhibited ( Figure 5C and F ) compared to GFP-expressing cells ( Figure 5E ) . Similarly , overexpression of the tetratricopeptide ( TPR ) domain of kinesin light chain 1 [50] also reduced the frequency of motile virions ( Figure 5D and F ) , suggesting that the axonal transport of CAV-2 require coordination between plus and minus-end microtubule motors . Although the binding of Ads to CAR may induce downstream signalling [51] , CAR's role in Ad infection has been considered primarily as a docking site prior to integrin-mediated internalisation . Consistent with this , deletion of CAR cytoplasmic tail had no significant effect on Ad internalisation in epithelial cells [52] . Yet , CAV-2 is one of a handful exceptions in the Adenoviridae family: the external capsid , in particular the penton base , does not contain a recognisable integrin-interacting motif [16] , [53] , [54] . Therefore , we asked whether CAV-2 and CAR were associated during endocytosis and the subsequent axonal transport . As mentioned previously , CAR staining in MNs showed a plasma membrane as well as an intracellular localisation ( Figure S1A ) . After 45 minutes post-internalisation , 80% of axonal CAV-2 was found in CAR+ structures ( Figure S3 ) . Furthermore , upon incubation of MNs with TeNT HC and CAV-2 , followed by an acid wash to remove extracellular-bound ligands whilst preserving internalised probes [44] , anti-CAR immunostaining revealed high colocalisation levels of CAR , CAV-2 and TeNT HC in neurites ( ∼70%; Figure 6A ) . The colocalisation of CAV-2 and TeNT HC in axonal carriers prompted us to use a biochemical approach based on TeNT HC-coupled to superparamagnetic nanobeads to isolate these transport vesicles [44] . Using western blot analysis , we detected an ∼250-fold enrichment of CAR in these organelles ( Figure 6B ) , further supporting the notion that CAR and CAV-2 co-inhabit a pool of axonal transport vesicles . To directly monitor CAR neuronal trafficking , we used fluorescently-labelled CAV-2 fibre knobs ( FK-Cy5 and FK-Cy3 ) to visualise CAR entry and transport in MNs . Initially , we tested the specificity of labelled-FK binding to CAR by transfecting CAR-negative NIH 3T3 cells with a plasmid encoding a GFP-CAR fusion protein . Transfected cells were then incubated with FK-Cy5 and fixed . We found that only GFP+ cells bound FK-Cy5 , strongly supporting a CAR-specific binding of the CAV-2 fibre knob FK-Cy5 ( Figure S4A ) . Consistently , preincubation of MNs with unlabelled FK blocked FK-Cy5 labelling ( Figure S4C ) . When MNs were incubated with FK-Cy5 followed by acid wash , FK and CAR colocalised in discrete puncta ( >95% , Figure 7A ) , suggesting that this viral protein and its cellular receptor are linked during endocytosis . Furthermore , FK-Cy5 was retrogradely transported in the same carriers as TeNT HC and displayed a bidirectional transport similar to CAV-2 ( Figure 7B ) , suggesting that CAR-mediated binding and internalisation is coupled to axonal transport . Accordingly , we also found FK-Cy5 in GFP-Rab7+ axonal carriers ( data not shown ) . To further understand the role of CAR in CAV-2 binding and endocytosis , we took advantage of a CAR-ablated FK variant ( FKm ) , which bears a single-point mutation in the CAR binding site [15] . We incubated MNs on ice with labelled-FK or FKm . In these conditions , FKm was not able to bind MNs ( Figure S4B ) . Together , these results strongly suggest that in neurons , CAR can be endocytosed and trafficked bidirectionally in axons , and that this protein may dictate internalisation and subsequent axonal transport of CAV-2 . The above results suggest that Ads take advantage of an innate trafficking of CAR to access the CNS . This prompted us to investigate its intracellular dynamics in vivo . Sciatic nerve ligation represents a powerful system to study axonal dynamics . To specifically monitor CAR axonal transport , we injected FK-Cy3 in the tibialis anterior and gastrocnemius muscles of C57BL/6 mice after ligation of the sciatic nerve . Eight hours post-injection , we examined the distributions of CAR and FK-Cy3 . Consistent with our hypothesis , CAR accumulated inside axons in both proximal and distal parts of the ligation site ( Figure 8A ) . However , only distal sections showed a clear signal correspondent to retrogradely-transported FK-Cy3 ( Figure 8A , right panel ) . Intra-axonal CAR was also observed by staining for CAR in transverse sections of unligated sciatic nerve ( Figure 8B ) . CAR distribution was not significantly affected by the presence of FK-Cy3 since similar CAR staining patterns were also observed in the absence of FK ( Figure 8A left panel , B , and data not shown ) . These data suggests that CAR undergoes constitutive bidirectional transport in sciatic nerve in situ .
A better understanding of the interactions between adenovirus and neurons was essential and overdue . To our knowledge , this is the first study to address the determinants of Ad neurotropism and axonal transport . Axonal transport has been described for a handful of viruses , including rabies , herpes simplex type I ( HSV-1 ) , measles , West Nile and poliovirus . Although less common than the above pathogens , both human and canine Ad serotypes are associated with brain pathologies [3] , [4] . Notably different mechanisms of axonal transport have been described: direct interaction with molecular motors for HSV-1 and rabies viruses [55] versus endosomal trafficking for poliovirus [56] . Our proposed model goes partially against the paradigm derived from prototype Ad trafficking studies performed in epithelial cells . We propose that the recognition of CAV-2 on the neuronal surface is primarily CAR-dependent . Internalisation involves CAR and clathrin-coated vesicles that acquire the early endosomal marker Rab5 , yet apparently does not induce capsid disassembly and endosomal escape . These latter axonal vesicles mature into Rab7+ compartments that still contain CAR , and have the advantageous characteristic of being nonacidic . After a lag phase , long-range transport of CAV-2 entrapped in vesicular organelles becomes sustained and bidirectional , probably involving the concerted action of dynein and kinesin . Crucially , our data also suggest an innate function of CAR in axons dictating CAV-2 transport . Endocytic progression is required for Ad infection and has been shown to differ mechanistically for different Ad serotypes [28] . The lag phase observed before the onset of CAV-2 axonal transport , which is not seen in epithelial cells infected by CAV-2 or Ad2/5 [30] , [39] , was also similar to that observed for TeNT HC and p75NTR [44] . Although further studies will be needed to pinpoint the underlying causes of this delayed onset , a likely explanation is that it is due to cargo sorting and/or endosome maturation . The association of CAV-2 initially with Rab5+ early endosomes and then with a transport compartment containing Rab7 is also similar to TeNT HC trafficking . Interestingly , Rab7 effectors RILP and ORP1L can mediate the recruitment of cytoplasmic dynein to endosomes in HeLa cells [57] . Whether Rab7 also directly recruits the dynein complex in axons is unknown , but might explain why , by reaching organelles containing Rab7 , CAV-2 undergoes efficient axonal transport . Although other serotypes can reach Rab7+ compartments [45] , there appears to be a functional difference between some of those found in axons and epithelial cells , one difference being that a population of Rab7+ endosomes in axons have lumens that are neutral . Using a marker described to traffic inside pH-neutral carriers ( TeNT HC ) , CAV-2 linked to a pH-sensitive dye [43] and Lysotracker , we showed that in contrast to virions in the cell body that can reach acidic organelles ( pH 5–5 . 5 ) , the majority of axonal CAV-2 carriers had a pH ranging from 6 to 7 . These data , combined with previous report of the pH of axonal organelles [58] demonstrate that the presence of Rab5 and Rab7 offer no indication of the pH of the endosomes or other organelles under investigation . Neurons appear to differ in the regulation of endosomal acidification that occur in their axons versus cell body . By entering nonacidic organelles in axons , CAV-2 could remain stably and efficiently associated with long-range carriers until delivered to the soma , where endosomal acidification could occur , triggering the exit from these compartments . In light of these results , it is tempting to speculate that human Ad serotype 5 ( HAd5 ) , which can be retrogradely transported in vivo [31] , [32] and escapes endosomes when the pH drops below 6 in epithelial-like cells [45] , could take advantage of a similar protective endocytic pathway to reach the neuronal cell body . Interestingly , when HAd5 and CAV-2 vectors were mixed and co-injected in the rodent brain , both are capable of axonal transport to afferent regions . However CAV-2 vectors are 50–100 fold more efficient when transgene expression is used as a readout at distal sites [7] . Does HAd5 use a pathway similar to CAV-2 ? There are notable similarities and differences between HAd5 and CAV-2 that may affect their axonal transport . In the case of CAR as a binding site , our data have consistently suggested that CAV-2 is “CAR-tropic” while other studies have reported that HAd5 uses CAR , as well as other cell surface molecules for binding and internalisation [37] . CAV-2 is also more thermostable than HAd5 ( unpublished data ) . A priori , we would predict that if an HAd5 virion binds CAR it could be taken up and transported in a manner similar to that seen by CAV-2 . Using real time confocal microscopy we detected fast axonal transport of HAd5 in primary neurons ( our unpublished data ) suggesting , but not demonstrating , similarities in transport . We do not know if the increased thermal stability of CAV-2 versus HAd5 plays a role during vesicular maturation at , for example , the axon soma interface . The interaction with integrins via the HAd5 penton base may also make the HAd5 capsid more sensitive to disassembly triggers in the lumen of a Rab7 vesicle in axons . The motility of CAV-2 showed an average retrograde speed above 1 µm/s , consistent with fast axonal transport . Notably , we found a minor population of CAV-2 and FK carriers undergoing bidirectional transport . Similar bidirectional transport was detected using FK to monitor CAR trafficking in axons . This feature is not unique to Ad: HSV-1 shows bidirectional transport with a bias for the retrograde direction during infection and displays a preferential anterograde transport during the phase of egress [55] , [59] . However , bidirectional HSV-1 transport is via direct recruitment of motors to the viral capsid . The association of CAV-2 and CAR with organelles undergoing bidirectional movement is particularly interesting because the regulation of bidirectional transport is still poorly understood . In this regard , CAR- or CAV-2-containing endosomes could represent an ideal tool to address how vesicular cargo coordinates the recruitment of both classes of microtubule-dependent molecular motors , or how a main retrograde motor , such as cytoplasmic dynein , may switch to an anterograde direction [40] . Dynactin may be a potential regulator of kinesin- and dynein-driven transport since it is able to simultaneously bind these two classes of motors . Interestingly , p50/dynamitin , a subunit of the dynactin complex , colocalised with CAV-2 , and p50/dynamitin overexpression inhibited the axonal transport of virions . The observed impairment of CAV-2 transport by inhibition of either cytoplasmic dynein or kinesin-1 suggests that coordination between these two classes of motors is necessary to ensure efficient axonal retrograde transport , as previously observed for TeNT HC carriers and mitochondria ( reviewed in [33] ) . A priori , one could envisage that the internalised cargo , via its interaction with specific integral membrane proteins , dictates the directionality of the transport . In this light , although TeNT HC and CAV-2 share a high number of axonal carriers , together they move exclusively in the retrograde direction . In contrast , anterograde moving organelles contain CAR and CAV-2 , but lack TeNT HC . This observation suggests the existence of discrete sorting steps during internalisation or en route endosomal maturation , which alter the ability of transported endosomes to recruit or activate anterograde and/or retrograde motor complexes . This may be achieved by engaging specific adaptor proteins able to co-ordinate motor complex activity , as in the case of huntingtin , which controls the directionality of vesicular carriers in cortical neurons via an Akt-dependent phosphorylation switch [60] . Although CAR is the main receptor for many Ad serotypes , little is known regarding its intracellular dynamics in neurons . In addition to a plasma membrane targeting , we found that CAR is also present on an internal vesicular pool . By means of competition experiments , we showed that the binding to CAR is an essential step for the entry of CAV-2 . CAV-2 and its recombinant FK are taken up in CAR-containing vesicles , suggesting that the virus and its receptor could be endocytosed together and then co-transported . Notably though , our assays do not address whether fibres detach from the capsid , which is an early step in virion disassembly in epithelial cells [28] . Given the average size of CAV-2+ vesicles ( 100–110 nm ) versus the CAV-2 icosahedra core ( ∼90 nm [61] plus the projecting fibres ( 30 nm ) ) , the most obvious prediction is that the fibres would be detached . However , the CAV-2 fibre shaft , in contrast to other Ads [36] , is particularly flexible due to the presence of two hinges [61] . This added suppleness may allow the fibre to fold over whilst still attached to CAR in the lumen of the endosomes . By using fluorescently-labelled CAV-2 FK , we also demonstrated that CAR undergoes endocytosis and bidirectional transport in cultured MNs and in sciatic nerve axons . These findings introduce a paradigm shift for the CAR-mediated endocytosis of Ads . As mentioned above , the available in vitro evidence is that CAR functions as a primary attachment site and that integrins are responsible for virus internalisation via the interaction with motifs in the Ad penton base . The homotrimeric FK could bind three CAR D1 domains simultaneously [13] , [15] , [62] . In this light , it will be critical to determine if the FK induces clustering of CAR , which in turn triggers internalisation of ligand-receptor clusters , or if other mechanisms are involved . Interestingly , the affinity of the CAV-2 FK to CAR is 5 to 7-fold times greater than that of HAd5 knob-CAR and the highest reported for an Ad [15] . The roles of CAR as an adhesion molecule and key component of tight junctions are well established [18] . Although CAR is highly expressed in the developing brain [17] , its neuronal function ( s ) remains speculative . Based on its direct interaction with actin , a potential role of CAR in neurite outgrowth has been proposed [63] . Recently , this association has been extended to several cytoskeletal components , suggesting a more general role of CAR in actin and microtubule dynamics [63] , [64] . Notably , our ex vivo and in vivo data demonstrate that CAR is found inside axons even in absence of an exogenous “ligand” , and also link CAR directly or indirectly to the axonal transport machinery . Together , our observations suggest that CAV-2 is taking advantage of an axonal trafficking pathway involving CAR and that allows virions to be efficiently transported to the CNS . The nature and regulation of axonal transport pathways are of crucial interest since their impairment has been linked to several neurodegenerative disorders . In this context , some Rab7-associated axonal organelles may be the hallmark of a long-range , vectorial axonal transport . Because CAV-2 , like TeNT , is able to reach this compartment , it may have a preferential and efficient access to the CNS . Indeed , this Rab7+ nonacidic axonal compartment may offer ultimate protection against degradation during long-range transport , allowing pathogens , virulence factors , as well as endogenous molecules , to be delivered intact to the cell body of neurons .
All experiments were carried out under license from the UK Home Office in accordance with the Animals ( Scientific Procedures ) Act 1986 and following approval from the Cancer Research UK Ethical Review Committee . Labelling reagents , AlexaFluor488-Lysotracker , AlexaFluor647-dextran , carboxyfluorescein and AlexaFluor-conjugated secondary antibodies were from Invitrogen . Mouse monoclonal anti-CAR antibody ( MoAb . E ( mh ) ; a gift from Steven Carson , University of Nebraska ) was used at 1∶500 in western blot analyses . Rabbit polyclonal anti-CAR antibodies ( 1∶300 ) ( Ab1605; a gift from Joseph Zabner , University of Iowa ) , monoclonal anti-Rab5 ( 1∶200; Synaptic System ) , polyclonal anti-Rab7 ( 1∶200 ) [44] , polyclonal anti-FK ( 1∶300 ) [65] , anti-DHC ( 1∶100; Santa-Cruz ) anti-p50/dynamitin ( 1∶200; BD Bioscience ) , anti-KHC ( 1∶100; Chemicon ) were used in immunofluorescence ( IF ) studies . Monoclonal anti-Cy3 ( 1∶200; Abcam ) was used on live cells . Anti-MBP was purchased from Boehringer ( Mannheim , Germany ) . p50/dynamitin and TPR construct were kindly provided by Michael Way ( CRUK , London ) . The plasmid expressing GFP and CAR was a gift from Joseph Zabner . Paramagnetic Fe-beads were purchased from G . Kisker GbR . Rat spinal cord MNs were purified from E13 . 5 embryos as described previously [43] and used from day in vitro 5 onwards . CAV-Cy3 was prepared from the E1-deleted vector CAVGFP [66] by direct post-purification labelling with Cy3 [39] . CAV-Cy3 has a physical particle ( pp ) to infectious unit ( IU ) ratio of 25∶1 [66] . The vector was propagated , purified , and titred as previously described [7] , [66] . Multiplicities of infection are in pp/cell . For internalisation assays , MNs were incubated with CAV-Cy3 on ice and either fixed or shifted to 37°C for 45 minutes , back on ice , incubated with anti-Cy3 to label cell-surface virions and then fixed . Indirect immunofluorescence ( IF ) experiments were performed as follow . After fixation , MNs were permeabilised with 0 . 1% Triton X-100 for 5 minutes at room temperature ( RT ) , followed by blocking with 3% bovine serum albumin ( BSA ) for 1 hour at RT . Primary and secondary antibodies were diluted in blocking solution and incubated sequentially for 1 hour at RT . Samples were then mounted with Mowiol ( Harco ) and imaged by confocal microscopy . For live cell experiments , MNs were incubated with CAV-Cy3 and AlexaFluor488-TeNT HC or AlexaFluor647-dextran or AlexaFluor488-Lysotracker at 37°C , washed with DMEM containing 30 mM HEPES-NaOH , pH 7 . 4 and imaged . Live and fixed samples were imaged by confocal microscopy ( Zeiss LSM 510 equipped with a 63× , 1 . 4 NA Plan Apochromat oil-immersion objective ) . Images were processed using Zeiss LSM 510 software . For live cell imaging , 100–150 frames were acquired ( 0 . 2 frames/s ) and analysed as previously described [42] . Kymographs were generated using MetaMorph ( Molecular Devices ) . Vertical single line-scans through the thickness of each process were plotted sequentially for every frame in the time series . Acid wash was performed to release proteins bound to the cell surface by incubating the cells for 5 minutes at room temperature in 100 mM citrate-NaOH , pH 2 . 0 , 140 mM NaCl and washed with PBS . Virus binding was quantified using the spot count option of the Imaris software and normalized to the total amount of membrane measured by voxel counting using ImageJ . CAV-2 was directly labelled with carboxyfluorescein according to a previous report [45] . Briefly , carboxyfluorescein can be used as intracellular pH sensor by measuring the ratio of emission intensities upon sequential excitation at 458 and 488 nm ( I488/I458 ) . CAV-CF-infected MNs were imaged live and after obtaining the calibration curve ( with MNs treated with 10 µg/ml of nigericin and monensin+L15 at various pHs ) , axonal versus somatic particles emission intensities were analysed . Intensities and ratios were measured using imageJ ( version 1 . 37 ) . Magnetic isolation of TeNT HC carriers was performed as previously described [44] . Quantification of CAR enrichment in carriers by western blot was performed using ImageJ . TeNT HC was isolated and labelled as previously described [44] . CAV-2 FK ( residues 358–542 ) construct was cloned into pPROEX HTb ( Life Technologies ) , expressed with a cleavable His6-tag , and purified as previously described [15] . The CAV-2 FKs were dialysed in PBS 0 . 1 M Na2CO3 pH 9 . 3 and labelled using Cy5 mono-reactive dye pack ( Amersham Bioscience ) for 45 minutes at RT . The elution of labelled protein was performed with 2 ml of PBS using NAP5 column ( GE Healthcare ) pre-equilibrated with 10 ml PBS . The final dye/protein ratio ( ∼2 . 4 ) was determined using a NanoDrop ND-100 spectrophotometer . For TEM analysis , MNs were incubated for various time periods with CAV-Cy3 . Cells were washed twice with 0 . 2 M Sorensen's buffer and fixed with 2 . 5% glutaraldehyde ( Agar ) in Sorensen's buffer , containing 70 mM sucrose for 1 h at 4°C . After washing , MNs were post-fixed with 1% osmium tetroxide for 30 minutes , washed , dehydrated in an ascending ethanol series and embedded in araldite over 2 days . Thin sections were stained with methanolic uranyl acetate and lead citrate . Sections were imaged with a JEOL 1010 transmission electron microscope . Under isoflurane anaesthesia ( National Veterinary Services , Stoke on Trent , UK ) , an incision was made along the left flank of adult C57Bl/6 mice to expose their sciatic nerve , which was ligated at the mid-thigh level . Immediately following ligation , the tibialis anterior and gastrocnemius muscles were exposed and FK-Cy5 ( 6 µg in 8 µl ) was slowly injected intramuscularly using a Hamilton microsyringe . The needle was left in place for 1 minute to prevent leakage . The wound was sutured and the animals were allowed to recover . After approximately 8 hours , the mice were terminally anaesthetized with sodium pentobarbitone and perfused transcardially with 4% PFA ( TAAB ) in 0 . 1 M PBS . The ligated sciatic nerve was removed , post-fixed for 4 hours in the same fixative and then cryoprotected in 30% sucrose in PBS . The animals were housed in a controlled temperature and humidity environment and maintained on a 12 hour light/dark cycle with access to food and water ad libitum . | Adenoviruses commonly cause subclinical morbidity in the ocular , respiratory , and gastrointestinal tracts , and less frequently , adenovirus-induced disease can be fatal for newborns and immunocompromised hosts . In addition , adenoviruses can reach the central nervous system ( CNS ) and cause associated encephalitis and tumours . On the flip side , during the last two decades , adenovirus vectors have become powerful tools to treat and address diseases of the CNS . Despite the fact that axonal transport of adenoviruses was reported more than 15 years ago , nothing was known concerning how adenoviruses access the CNS . The characterization of their interactions with brain cells was therefore long overdue . In this study , we describe the axonal trafficking of an adenovirus that preferentially infects neurons and reaches the CNS through long-range axonal transport . We show that this adenovirus exploits an endogenous vesicular pathway used by the adhesion molecule CAR ( coxsackievirus and adenovirus receptor ) . Our study characterizes this endogenous route of access , which is likely to be crucial to neuronal survival , neurodegenerative diseases , gene transfer vectors , and adenovirus-induced morbidity . | [
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"microbiology/cellular"... | 2009 | CAR-Associated Vesicular Transport of an Adenovirus in Motor Neuron Axons |
A novel form of copy number control ( CNC ) helps maintain a low number of Ty1 retrovirus-like transposons in the Saccharomyces genome . Ty1 produces an alternative transcript that encodes p22 , a trans-dominant negative inhibitor of Ty1 retrotransposition whose sequence is identical to the C-terminal half of Gag . The level of p22 increases with copy number and inhibits normal Ty1 virus-like particle ( VLP ) assembly and maturation through interactions with full length Gag . A forward genetic screen for CNC-resistant ( CNCR ) mutations in Ty1 identified missense mutations in GAG that restore retrotransposition in the presence of p22 . Some of these mutations map within a predicted UBN2 domain found throughout the Ty1/copia family of long terminal repeat retrotransposons , and others cluster within a central region of Gag that is referred to as the CNCR domain . We generated multiple alignments of yeast Ty1-like Gag proteins and found that some Gag proteins , including those of the related Ty2 elements , contain non-Ty1 residues at multiple CNCR sites . Interestingly , the Ty2-917 element is resistant to p22 and does not undergo a Ty1-like form of CNC . Substitutions conferring CNCR map within predicted helices in Ty1 Gag that overlap with conserved sequence in Ty1/copia , suggesting that p22 disturbs a central function of the capsid during VLP assembly . When hydrophobic residues within predicted helices in Gag are mutated , Gag level remains unaffected in most cases yet VLP assembly and maturation is abnormal . Gag CNCR mutations do not alter binding to p22 as determined by co-immunoprecipitation analyses , but instead , exclude p22 from Ty1 VLPs . These findings suggest that the CNCR alleles enhance retrotransposition in the presence of p22 by allowing productive Gag-Gag interactions during VLP assembly . Our work also expands the strategies used by retroviruses for developing resistance to Gag-like restriction factors to now include retrotransposons .
The Ty1 and Ty2 retrotransposons of Saccharomyces belong to the Ty1/copia group of long terminal repeat ( LTR ) retrotransposons which replicate in a manner analogous to retroviruses [1] . Ty1 is the most abundant of five retrotransposon families ( Ty1-Ty5 ) in the S288C reference genome of Saccharomyces cerevisiae , followed by the related Ty2 element [2 , 3] . Recently , Ty2 has been shown to outnumber Ty1 in some Saccharomyces genomes [2–4] , but Ty1 remains the more widely studied retrotransposon [1] . Ty1 contains two overlapping ORFs , GAG and POL , and many elements are transpositionally competent and transcriptionally active [5] . An abundant full-length Ty1 mRNA is transcribed which serves as a template for translation and reverse transcription . Two translation products are produced: Gag ( p49 ) and Gag-Pol ( p199 ) , of which the latter comprises only 5–10% of total translation products due to its production requiring a +1 ribosomal frameshifting event . Gag , Gag-Pol and Ty1 mRNA accumulate in the cytoplasm in distinct foci called retrosomes [6–9] . Virus-like particles ( VLPs ) assemble from Gag and Gag-Pol proteins within retrosomes and encapsidate Ty1 mRNA , and tRNAiMet , which is used to prime reverse transcription . VLP maturation occurs via the activity of the POL-encoded enzyme , protease ( PR ) . Pol is cleaved from p199 via a PR-dependent autocatalytic event , followed by PR cleavage of Gag at its C-terminus ( from p49 to p45 ) and Pol at two internal sites to form mature PR , integrase ( IN ) , and reverse transcriptase ( RT ) . Once maturation occurs , reverse transcription of the packaged genomic Ty1 RNA forms a cDNA copy that is integrated into the host genome . Because Ty1 insertions can mutate cellular genes and mediate chromosome instability by homologous recombination with elements dispersed in the genome , it is beneficial to the host to control the process of retrotransposition [10–13] . Natural isolates of S . cerevisiae and its closest relative S . paradoxus maintain lower copy numbers of the Ty1 retrotransposon in their genomes compared to the reference laboratory strain S288C [2–4 , 14] , without the support of eukaryotic defense mechanisms such as RNAi or the presence of innate restriction factors like the APOBEC3 proteins [15–19] . The maintenance of Ty1 copy number is due at least in part to a mechanism called copy number control ( CNC ) , which was first observed in an isolate of S . paradoxus that lacks complete Ty1 elements but contains numerous solo-LTRs [20] . The Ty1-less strain supports higher levels of Ty1 transposition compared to standard lab strains , as monitored using a Ty1 tagged with the his3-AI retrotransposition indicator gene [21] . Additionally , Ty1his3-AI mobility decreases dramatically when the naive genome is repopulated with Ty1 elements [20] . Introduction of a transcriptionally repressed Ty1 element on a multi-copy plasmid also inhibits Ty1his3-AI mobility . Based on these observations , CNC is conferred by a factor produced directly by the Ty1 element . The CNC phenotype , which includes decreased levels of transposition [20] , the reduction of mature Ty1 RT and PR protein levels , and the absence of detectable mature IN [22] , is dependent on the GAG open reading frame [20] . Overexpression of Ty1 fused to a GAL1 promoter on a multi-copy plasmid has been shown to override CNC , suggesting that CNC can be saturated [23–26] . Recently , we found that CNC functions through the protein product encoded by a subgenomic internally-initiated Ty1 sense transcript , called Ty1i ( internal ) RNA [26] . Transcription of Ty1i RNA initiates within GAG , about 800 nucleotides downstream of the full-length , transposition-competent Ty1 mRNA . The first AUGs are in the same reading frame as Ty1 Gag , resulting in synthesis of a 22 kD protein ( p22 ) that shares the same sequence as the C-terminal half of Gag [26 , 27] . This shared sequence includes the PR cleavage site , which is utilized within the inhibitory protein to form p18 [26] . Ectopic co-expression of p22 or p18 with Ty1 dramatically inhibits Ty1 mobility . p22/p18 co-immunoprecipitates with Ty1 Gag and co-localizes with Ty1 Gag in the cytoplasm . Ectopic expression of p22/p18 disrupts normal retrosome formation and VLP assembly , followed by a block in maturation and reverse transcription within the VLPs that are able to form . In addition , p18 interferes with the nucleic acid chaperone ( NAC ) function of Gag , further disrupting Ty1 replication [27 , 28] . It is not clear which insult by p22/p18 is most destructive , but collectively these effects result in the strong inhibition of retrotransposition observed during CNC . Retroelement restriction mechanisms have been aided by studying resistance mutations in retroviruses and/or sequence variation determining viral tropism . An example of particular relevance is the discovery that capsid ( CA ) , a cleavage product of retroviral Gag , is the target of several restriction factors including Friend virus susceptibility factor–1 ( Fv1 ) , tripartite motif 5 alpha ( TRIM5α ) , and myxovirus resistance protein 2 ( Mx2 ) , among others [29–33] . Viruses that escape restriction by these factors typically carry mutations in the CA-encoding region of the gag gene [29 , 34–38] . In the case of Fv1 and TRIM5α , viral escape mutations disrupt binding between the restriction factor and CA by altering CA surface residues [37–40] , while Mx2 escape mutations in CA are not fully understood but likely alter the interactions between CA and host factors [32 , 33 , 41] . While Fv1 , TRIM5α , and Mx2 bind the incoming viral capsid during the early stages of retroviral infection , the sheep restriction factor enJS56A1 is known to interact with Jaagsiekte sheep retrovirus ( JSRV ) Gag at later stages when the integrated provirus is undergoing translation and particle assembly [42] . Resistance to enJS56A1 is conferred by mutations in the signal peptide of the JSRV envelope glycoprotein , which is hypothesized to ultimately alter the ratio of JSRV to enJS56A1 Gag levels to favor JSRV particle production [43] . Remarkably , Fv1 and enJS56A1 are both derived from endogenous retroelement gag genes [42 , 44] , similar to p22 . To further understand the mechanism of CNC , we carried out forward genetic screens for CNC-resistant ( CNCR ) Ty1 elements . Almost all of the CNCR elements contain missense mutations within GAG that map within predicted helices important for VLP assembly and maturation . Computational and functional analyses reveal three domains within the Ty1 Gag protein: TYA , CNCR and UBN2 . All resistance mutations recovered map within the CNCR and UBN2 domains encoded by GAG . Importantly , several mutations are not present in p22 coding sequence , supporting the idea that p22 targets Gag to inhibit retrotransposition . Most CNCR mutations in GAG do not markedly alter Ty1 fitness or the interaction between Gag and p22 , but prevent co-assembly of Gag and p22 into VLPs , which improves VLP maturation and progression through the retrotransposon life cycle .
We hypothesized that the generation of CNCR Ty1 mutants may identify a molecular target of p22 . Since previous work implicated a physical interaction between Gag and p22 [26] , isolating resistance mutations in GAG would suggest that this interaction is important for CNC . To identify Ty1 mutants that are resistant to the effects of p22 and its processed form p18 , we designed a system that allowed for simultaneous expression of wild type p22/p18 and a randomly mutagenized Ty1his3-AI element fused to the regulated GAL1 promoter carried on a low copy centromere-based ( CEN ) plasmid ( pGTy1his3-AI/CEN ) . The purpose of using a low copy plasmid for pGTy1his3-AI expression was to minimize CNC saturation that occurs with overexpression of Ty1 on a multi-copy plasmid . In addition , the Ty1 copy number provided by a low copy centromere-based plasmid does not result in detectable CNC [22] . Isogenic , repopulated Ty1-less S . paradoxus strains containing 1–38 copies of Ty1-H3 were analyzed , representing a wide range of Ty1 copy numbers naturally found in yeast ( S1 Table ) [2 , 3 , 14] . All strains carried a deletion of SPT3 , which encodes a transcription factor required for expression of full length Ty1 mRNA from nucleotide 238 ( Ty1-H3 , Genbank M18706 . 1 ) and the synthesis of Ty1 Gag and Gag-Pol [45] . Ty1i RNA , which initiates internally at nucleotide ~1000 , is still produced in spt3Δ mutants [26 , 45 , 46] . pGTy1his3-AI/CEN provided Ty1 mRNA , Gag and Gag-Pol and the strains were analyzed for CNC ( Fig 1 ) . Ty1 mRNA produced from this plasmid contains the his3-AI indicator gene , allowing transposition levels to be analyzed by growth on media lacking histidine . As expected , increasing Ty1 copy number resulted in decreased Ty1 mobility , with the strongest decrease observed in the presence of 38 genomic copies of Ty1 ( Fig 1A ) . These strains were immunoblotted for p22/p18 levels in the presence and absence of pGTy1his3-AI expression using p18 antiserum , which detects both Gag-p49/p45 and p22/p18 [26] . Under both repressing ( glucose ) and inducing ( galactose ) growth conditions , p22 levels in cell extracts increased similarly with copy number ( Fig 1B ) . Because p22 was not detected in the lowest Ty1 copy strain ( 1 Ty1 ) containing pGTy1his3-AI , these results confirmed that pGTy1his3-AI does not produce detectable p22 . It remains possible that increasing chromosomal copies of Ty1 stimulated p22 production from pGTy1his3-AI , but this seems unlikely considering that p22 levels do not increase in the 38 Ty1 copy strain containing pGTy1his3-AI compared to an empty vector in either growth condition ( Fig 1B ) . Therefore , genomic Ty1-H3 elements , and not the pGTy1his3-AI mutant library , were the source of p22 in the screen . When pGTy1his3-AI expression was induced by galactose , Gag-p49/p45 was detected and the maturation of p22 to p18 was observed , supporting previous findings suggestive of p22 cleavage by Ty1 PR [26] . In addition , mature RT ( p63 ) and IN ( p71 ) were present only in low copy number strains ( Fig 1C ) , confirming another feature of the CNC phenotype [22] . To further establish that cleavage of p22 was Ty1 PR-dependent , wild type or PR-defective pGTy1/2μ multi-copy plasmids were introduced into the 38 Ty1 copy strain ( Fig 1D ) . As expected , neither Gag-p49 expressed from the PR- Ty1 nor p22 expressed from genomic elements were cleaved to form mature products Gag-p45 and p18 , respectively ( Fig 1D ) . To search for pGTy1his3-AI CNCR mutations , we utilized the 38 Ty1 copy strain described above , which produced the highest level of p22 of the strains tested ( Fig 1B ) . pGTy1his3-AI was mutagenized by propagation in a mutator strain of E . coli ( XL–1 Red , Agilent Technologies , Santa Clara , CA ) and 20 , 000 transformants were screened for an increase in Ty1HIS3 mobility following induction on medium containing galactose ( see Materials and Methods ) . The CNC region ( nucleotides 238–1702 [48] ) was sequenced from pGTy1his3-AI plasmids that conferred an increase in Ty1 mobility when compared to wild type plasmid controls . Nine unique mutations were present in GAG ( S2 Table; XL–1 Red ) . A restriction fragment encompassing the CNC region was subcloned from the mutant plasmids into a fresh pGTy1his3-AI vector to eliminate contribution of background mutations and no loss of CNCR was observed . To avoid bias based on our mutagenesis method and to generate additional CNCR mutations , GAG and POL were mutagenized separately by error-prone PCR , followed by gap-repair with pGTy1his3-AI in the 38 Ty1 copy strain . While GAG mutagenesis by PCR revealed 8 new CNCR mutations from 500 colonies , POL mutagenesis revealed only 1 CNCR candidate from 6 , 000 colonies ( S2 Table ) . While most of the mutations ( 8 of 9 ) isolated via XL–1 Red mutagenesis were single base changes , 5 of 8 mutations recovered via PCR mutagenesis had more than one base change . Interestingly , all GAG mutations recovered with either method were missense , suggesting that they function at the level of the Gag protein . The only CNCR pGTy1his3-AI plasmid isolated from POL mutagenesis contained two missense mutations within RT ( D518G/V519A ) . To quantify the level of resistance to p22/p18 , the frequency of Ty1his3-AI mobility was determined for the mutants alongside wild type controls ( Fig 2 , S4 Table ) . In the 38 Ty1 copy strain , most mutant plasmids produced mobility frequencies between 11- and 63- fold higher than wild type ( Fig 2A ) . Four candidates from the CNCR screen ( Gag N183D , K186Q , I201T , and A273V ) exhibited stronger resistance , ranging from 233- to 424-fold higher than wild type ( Fig 2A ) . Because it was possible to obtain Ty1 mutations that globally increased transposition , rather than acting specifically in the presence of p22/p18 , CNCR mobility was also measured in the 1 Ty1 copy strain ( Fig 2B ) . Importantly , all CNCR mutants transposed at similar or decreased levels compared to wild type in the absence of p22/p18 , indicating that we obtained CNC-dependent mutations . Three CNCR mutants , Gag P173L , Gag K250E , and RT D518G/V519A , showed a decrease in Ty1his3-AI mobility in the absence of p22/p18 , indicating that these mutations negatively impacted Ty1 fitness ( Fig 2B ) . Percent recovery of Ty1his3-AI mobility with CNCR plasmids was calculated by dividing Ty1his3-AI mobility in the presence or absence of p22 ( Percent CNC Recovery , Fig 2C ) . As expected , the three CNCR mutations with decreased fitness showed higher percent recovery , with K250E and RT D518G/V519A at >100% , due to the fact that these plasmids result in higher transposition frequencies in the presence of p22 than in its absence . Since overall Ty1his3-AI mobility was extremely low , further studies were not performed with these mutants . The remaining four mutations conferring >10% CNCR include those resulting in Gag amino acid changes N183D , K186Q , I201T , and A273V . These elements exhibited 20–30% recovery , indicating that while the strongest CNCR mutations dramatically increase transposition in the presence of p22 ( Fig 2A ) , they are only partially resistant to the effects of p22 . Note that both classes of recovery should be expected since the mutant screen balanced transposition fitness and CNCR . Consequently , we focused on the N183D , K186Q , I201T , and A273V GAG mutations , since they conferred the strongest levels of resistance recovered with no effect on fitness of Ty1 . In an effort to increase CNCR , cells containing double mutant pGTy1his3-AI-K186Q/I201T or pGTy1his3-AI-A273V/I201T were tested for Ty1his3-AI mobility . Gag K186Q/I201T was defective for transposition and was not studied further . Gag A273V/I201T was able to transpose , but experienced decreased fitness . In the 38 Ty1 copy strain , the levels of Ty1his3-AI mobility with A273V/I201T was lower than either single mutant , but still 85-fold higher than wild type ( Fig 2A ) . In the 1 Ty1 copy strain , A273V/I201T exhibited Ty1his3-AI mobility at 8% of wild type levels ( Fig 2B ) . Interestingly , the double mutant did exhibit increased CNC recovery ( 45% ) , but at the expense of overall Ty1 mobility ( Fig 2C ) . The loss of fitness in the double mutants reinforces the idea that mutations conferring a high level of CNCR may have been missed in the screen since they compromise Ty1 fitness . In addition , Ty1 Gag may be genetically fragile since it cannot tolerate wholesale alterations in sequence , a feature that is also observed with HIV–1 CA [49] . To determine if the CNCR elements are resistant to p22/p18 in a genomic context , wild type or CNCR pGTy1his3-AI/CEN plasmids were expressed in Ty1-less S . paradoxus , and cells with 1–2 wild type or CNCR Ty1his3-AI genomic insertions were identified . Next , an empty vector or a p22-producing plasmid , pTy1-ATGfs ( S3 Table ) , was introduced into these strains . In the absence of p22 ( Table 1 , empty vector ) , genomic CNCR Ty1 elements N183D , K186Q , and I201T transposed at similar levels to the respective wild type control ( <2-fold change ) , confirming that they do not globally increase Ty1his3-AI mobility in a chromosomal context . In contrast , CNCR Ty1 A273V displayed a 4 . 3-fold increase in Ty1 mobility compared to wild type in the presence of empty vector , indicating that A273V may not be CNC-dependent in all contexts . This may be due to the fact that A273V is the only mutation tested that maps within GAG and p22 coding sequence , thus changes in both proteins could be affecting Ty1 mobility . Dramatic differences in CNC were observed when the wild type and CNCR Ty1his3-AI elements are challenged with p22 ( Table 1 , pTy1-ATGfs ) . While p22 expression decreases wild type Ty1his3-AI mobility 56- to 120- fold , CNCR Ty1his3-AI mobility is partially resistant to the effects of p22 , decreasing 2- to 13-fold . A key feature of CNC is a decrease in Ty1 mobility of a single genomic Ty1his3-AI in the presence of elevated Ty1 copies [48] . In contrast , additional chromosomal copies of CNC-defective Ty1 elements , which are elements that do not produce p22/p18 but retain the ability to undergo retrotransposition , increase the level of Ty1his3-AI mobility [26] . To determine how chromosomal CNCR elements influence Ty1his3-AI mobility ( Table 2 ) , S . paradoxus containing a wild type Ty1his3-AI genomic insertion was repopulated with unmarked CNCR elements carrying the N183D , K186Q , I201T , and A273V mutations . It is important to note that Ty1his3-AI RNA is not preferentially packaged in cis [23] and can serve as the genomic RNA in mixed particles containing wild type and CNCR Gag , with the latter likely being produced in excess due to increased genomic copy number . Compared with the starting strain , Ty1his3-AI mobility in strains repopulated with +14 and +21 wild type Ty1 elements decreased 31- and 620-fold , respectively . Repopulation with +14–20 CNCR elements did not alter Ty1his3-AI mobility , supporting the idea that CNCR mutations relieve the inhibitory effects of p22 produced by the additional chromosomal elements . However , the fact that additional CNCR elements do not stimulate Ty1his3-AI mobility probably reflects the partial resistance phenotype imparted by the CNCR mutations . Since little is known about the structure of Ty1 or other LTR-retrotransposon Gag proteins , we submitted Ty1 Gag protein sequence for secondary structure prediction using I-TASSER [50–52] , and several other structural prediction servers ( see Materials and Methods ) . These analyses predicted that a central portion of Ty1 Gag contains nine helical regions ( Fig 3A , gray boxes ) , which overlap previously identified conserved Gag domains A , B and C of Ty1/copia family of retrotransposons [53] . Using profile-based methods , we identified two annotated protein families ( Pfam ) within Ty1 Gag called TYA ( TYA transposon protein , PF01021 ) and UBN2 ( gag-polypeptide of LTR copia-type , PF14223 ) ( Fig 3A ) . The TYA domain is found strictly in yeast and corresponds to an unstructured region in the N-terminal half of Gag between residues 17–114 . The UBN2 domain maps to the C-terminal half of Gag between residues 245–356 , roughly overlapping Ty1/copia conserved Gag domains B and C [53] , and is represented across multiple plant and fungal species . Of the 11 single GAG mutations that impart resistance to p22 , 9 mapped within the helical domains , with 4 mapping within the UBN2 domain . Mutations outside of the UBN2 domain clustered between amino acids 170–220 , which we refer to here as the CNCR domain ( Fig 3A ) . The CNCR domain contains sequences belonging to Ty1/copia Gag conserved domain A [53] , which is characterized by an invariant tryptophan residue corresponding to Ty1 Gag W184 . Interestingly , CNCR alleles lie in close proximity to the W184 codon . Ty1-H3 Gag ( Uniprot P08405 ) sequence was used in a profile hidden Markov model search to identify closely related Gag proteins and an alignment was generated to highlight variations in amino acid sequence in CNCR ( Fig 3B ) and UBN2 ( Fig 3C ) domains ( refer to S1 Fig for a full alignment ) . Redundant and partial Gag sequences were purged and the Gag sequence of the Ty2-917 element ( GenBank KT203716 ) , which was isolated as a spontaneous HIS4 mutation [58] , was added to the hits . In total , we generated a multiple alignment of 15 sequences representing Ty1 and Ty1-like Gag proteins from 9 different yeast strains in the Saccharomycetaceae family [60 , 61] . While substitutions of CNCR residues do not naturally occur in known Ty1 Gag sequences from S . cerevisiae , substitutions in all but one of the CNCR residues ( E287 ) are found in the alignment of non-Ty1 Gag proteins , including those from Ty2 elements , the second most abundant Ty1/copia retrotransposon found in the S . cerevisiae S288C genome [2 , 3] , and Ty1-like elements present in Saccharomyces kudriavzevii and Lachancea kluyveri ( Fig 3B and 3C ) . Most substitutions are different than those recovered in our CNCR screen , with the exception of D180N and T218A ( Fig 3B ) . Considering all 10 CNCR residues altered in our screen , 6 of these are not conserved from Ty1 to Ty2 . To determine if Ty2 undergoes CNC , we analyzed the retrotransposition-competent Ty2-917 element [62] . Unlike pGTy1-H3 , which confers CNC when GAL1-promoted Ty1 mRNA transcription is repressed , a multi-copy pGTy2-917 plasmid does not inhibit Ty1his3-AI mobility [48] . This result suggests that a p22-like protein is either not produced by Ty2-917 or does not affect Ty1 movement . A transcriptionally silent pGTy2-917 also did not affect Ty2-917his3-AI mobility , demonstrating that Ty2-917 is not under a Ty1-like form of CNC ( S5 Table ) . In fact , a transcriptionally active Ty2-917 carried on a multi-copy plasmid stimulated Ty2-917his3-AI mobility 1 . 5-fold . Whether all Ty2 elements respond the same way as Ty2-917 will require further investigation . Considering the relationship between Ty1 CNCR mutations and Ty2 residues in the CNCR domain ( Fig 3B ) , we introduced an empty vector or the p22 expression plasmid into a strain containing Ty2-917his3-AI to determine if Ty2 mobility was sensitive to inhibition by p22 ( S5 Table ) [26] . A decrease in Ty2-917 mobility of less than 2-fold was observed in cells expressing Ty1-p22 , supporting the idea that Ty2-917 is not sensitive to Ty1 CNC . Since CNCR residues map to putative helical domains in Gag , a series of mutations were made in hydrophobic residues within several predicted helices ( Fig 3A ) and their impact on Ty1 transposition and protein levels was analyzed when the mutant elements were expressed from the GAL1 promoter ( Fig 4 ) . We substituted the invariant tryptophan residue found in Ty1/copia Gag proteins to alanine ( W184A , helix 1 ) and tested several published Gag mutations designed to interrupt hydrophobic faces of Gag helices ( IM248/249NR , L252R , both in helix 4; LF339/340RD , I343K , both in helix 9 ) [55] . All helix substitutions abolished Ty1his3-AI mobility when expressed in both 1 and 38 Ty1 copy strains ( -p22 and +p22 , respectively; Fig 4A ) . Mature RT was not detected in whole cell extracts from the 1 or 38 Ty1 copy strains expressing mutant Ty1 , indicating that Gag-Pol maturation did not occur ( Fig 4B ) . When helix 1 ( W184A ) or helix 4 ( IM248/249NR and L252R ) was perturbed , Gag was stable and present in both immature ( p49 ) and mature forms ( p45 ) , while p22 maturation to p18 was similar or slightly decreased . In contrast , Gag-p49 and p22 from helix 9 substitutions ( LF339/340RD and I343K ) did not undergo maturation . We analyzed Gag W184A ( helix 1 ) , L252R ( helix 4 ) and I343 K ( helix 9 ) for VLP assembly by sedimentation of total protein extracts through 7–47% sucrose gradients ( Fig 4C ) . This analysis was performed in the 1 Ty1 copy strain to prevent further disturbance by p22 during VLP formation . Wild type Gag migrated primarily as larger complexes , which are probably comprised of assembled VLPs ( Fig 4C , fractions 5–9 ) . W184A and L252R VLP assembly was perturbed , as Gag was present in every fraction of the sucrose gradient . There was less cleavage of Gag-p49 to p45 compared to wild type and both forms were present in each fraction . More cleavage of Gag-p49 to p45 was visible in the higher percent sucrose fractions , suggestive of Ty1 PR activity in these fractions . Gag I343K , which did not exhibit Gag cleavage ( Fig 4B ) , remained at the top of the sucrose gradient and did not form higher order structures ( Fig 4C ) . CNC is associated with altered abundance and maturation of Ty1 proteins , including loss of mature RT and IN [20 , 22] . Disturbing Gag helices in which CNCR mutations mapped also affected Ty1 protein maturation ( Fig 4 ) . Therefore , Ty1 protein levels produced by CNCR pGTy1his3-AI elements were examined . Cell extracts were immunoblotted for Gag-p49/p45 and p22 using the 38 Ty1 copy strain ( Fig 5A ) . When wild type pGTy1his3-AI was expressed , there was slightly more p18 than p22 . Strikingly , expressing the four CNCR pGTy1his3-AI plasmids resulted in a lower level of p18 , indicating less cleavage of p22 and/or decreased stability of p18 . Mature RT levels were also recovered in the CNCR strains , suggesting that VLP maturation improved ( Fig 5B ) . Ty1 VLPs were isolated from the 38 Ty1 copy strain expressing wild type pGTy1his3-AI or pGTy1his3-AI-I201T to determine levels of Ty1 protein and RNA within assembled particles ( Fig 6 ) . Equal amounts of VLP preparations were immunoblotted for the detection of Gag , RT and IN . Gag protein levels were similar between wild type and I201T VLPs ( Fig 6A ) . Since wild type VLPs represent different stages of maturation , the samples contained Ty1 precursors Gag-PR-IN-RT ( Gag-Pol; p199 ) , PR-IN-RT ( p154 ) , IN-RT ( p134 ) , and PR-IN ( p91 ) . In addition , RT antibodies reacted with two bands of unknown origin around 65 and 90 kD , which we reported previously ( Fig 6B , asterisks ) [26] . As expected from previous work [22] , mature IN ( p71 ) was not detected from wild type VLPs isolated from the 38 Ty1 copy strain ( Fig 6C ) . I201T VLP maturation occurred more efficiently , as indicated by increases in mature IN ( Fig 6C ) and a decrease in the unknown RT-reactive proteins in I201T VLPs ( Fig 6B , asterisks ) . To determine if the increase in Ty1 mature protein products was due to less p22/p18 present in VLPs , dilutions of wild type and I201T VLPs were immunoblotted with p18 antiserum ( Fig 6D ) . p18 , rather than p22 , was the main protein observed in wild type VLPs , likely due to cleavage by Ty1 PR within VLPs [26] . The level of p18 within I201T VLPs was lower than that observed in wild type VLPs , raising the possibility that less p18 within assembled CNCR VLPs results in increased Ty1 protein maturation or stability . Ty1 RNA packaging , as demonstrated by protection from digestion when whole cell extracts are treated with the nuclease benzonase [63] , is markedly decreased in the presence of p22/p18 [27] . To determine if a CNCR mutation functions by increasing the level of RNA packaged into VLPs , RNA extracted from purified WT and I201T VLPs was subjected to Northern blotting ( Fig 6E ) . Total cellular RNA was examined to control for Ty1 mRNA expression . Wild type and I201T RNA extracts from cells or purified VLPs contained similar levels of Ty1his3-AI transcript , suggesting that CNCR does not function through the enhancement of Ty1 RNA packaging , at least in the case of I201T . Interestingly , p22/p18 shares sequence with two regions implicated in Ty1 nucleic acid transactions: the NAC region of Gag ( amino acid residues 299–401 ) and the N-terminus of PR ( known as p4 in Gag ) that participates in reverse transcription [27 , 28 , 64] . The first region was extensively examined using recombinant mature p18 , which lacks p4 sequence [27] . Recombinant p18 possesses NAC activity and can bind Ty1 RNA , but truncated versions that lack NAC activity still inhibit Ty1 retrotransposition , suggesting that NAC activity is dispensable for p22/p18 function [27] . To test whether PR/p4 is implicated in CNC , we measured the mobility of a single genomic Ty1his3-AI element in presence of transcriptionally repressed wild type pGPOLΔ Ty1 plasmids [26] , or derivatives carrying altered p4 regions . Wild type pGPOLΔ plasmids reduced Ty1his3-AI mobility by 150-fold compared to an empty GAL1 plasmid ( S6 Table ) . The pGPOLΔd1 plasmid , which carries a deletion in PR/p4 that blocks successful reverse transcription [64 , 65] , and pYES2-p45 lacking p4 [27] reduced Ty1his3-AI mobility by 140- and 160-fold , respectively . These results are supported by the observations that ectopic expression of mature p18 alone , which does not contain PR/p4 sequence , inhibits pGTy1his3-AI mobility , and expression of p22/p18 or p22 mutant for the PR cleavage site exhibit similar levels of inhibition [26 , 27] . Together , our results show that the PR/p4 region is not required for CNC . To determine if the low level of p18 detected in I201T VLPs was related to altered binding of p22/p18 with wild type versus Gag I201T , pGp22-V5 and wild type pGTy1his3-AI or pGTy1his3-AI-I201T were co-expressed in a Ty1-less strain . Endogenous Gag produced by chromosomal Ty1 elements and GST-p18 have been shown to interact via a GST pull-down assay [26] . Functional p22-V5 , which carries an internal V5 tag that is present in both p22 and p18 , was expressed from a low copy CEN plasmid to maximize CNCR imparted by the Gag mutations . Quantitative mobility assays revealed that wild type Ty1his3-AI mobility decreased 783-fold in the presence of p22-V5 , while Ty1his3-AI-I201T mobility only decreased 5-fold , confirming the CNCR phenotype ( Table 3 ) . Utilizing the V5 tag on p22/p18 , co-immunoprecipitations ( co-IP ) were performed from total cell extracts and analyzed for the level of Gag . We detected co-IP of p22-V5/p18-V5 with wild type Gag ( S2 Fig ) , which confirms previous pull-down analyses with p18 tagged with GST [26] . p22-V5/p18-V5 co-immunoprecipitated 1201T ( S2A Fig ) , K186Q ( S2B Fig ) or wild type Gag with comparable efficiencies . To track Gag and p22 and p18 independently during VLP assembly , the fractionation pattern of Gag was examined by sucrose gradient sedimentation as in Fig 4C using total protein extracts from cells expressing wild type pGTy1his3-AI or pGTy1his3-AI-I201T alone or co-expressed with pGp22-V5 ( Fig 7 ) . In the absence of p22-V5 , wild type and I201T Gag-p49/p45 assembled into VLPs and migrated to fractions 6–9 ( Fig 7A and 7B ) . In the presence of p22-V5 , the fractionation pattern of wild type Gag was more dispersed , as reported previously [26] ( Fig 7C ) . While Gag was present throughout the gradient , it was found at the highest concentration in fractions 4–9 . In contrast , p22-V5 when co-expressed with wild type pGTy1his3-AI collects as both p22-V5 and p18-V5 and was present in the highest concentration at the top of the gradient ( Fig 7D ) . More p18-V5 co-sedimented with wild type Gag than p22-V5 , but p22-V5 and p18-V5 were detected in all fractions . The co-sedimentation of wild type Gag and p18-V5 supports the idea that cleavage by Ty1 PR occurs in complexes migrating to the lower half of the gradient . Surprisingly , p18-V5 was also present at the top of the gradient , which contains most of the soluble proteins in the extract . Considering that the introduction of the internal V5 tag does not alter the requirement of Ty1 PR for cleavage ( S3 Fig ) , p22-V5 may be cleaved by Ty1 PR outside of fully assembled VLPs , perhaps in the Gag complexes present in retrosomes . Alternatively , p22-V5 may be cleaved within VLPs , but not all p22-V5/p18-V5 remains stably associated with the particles . Regardless , our results suggest that a fraction of p22-V5/p18-V5 co-assembles with wild type VLPs . Expression of pGTy1his3-AI-I201T and pGp22-V5 resulted in a Gag fractionation pattern similar to that observed in the absence of p22-V5 ( Fig 7E ) . Interestingly , p22-V5/p18-V5 was detected at the top of the gradient but did not co-sediment with I201T VLPs ( Fig 7F ) . These results suggest that the CNCR conferred by I201T results from the exclusion of p22-V5/p18-V5 from VLPs , perhaps during a step in the assembly process . However , the fact some p18-V5 is produced in these cells suggests that the restriction factor does gain access to PR .
To understand the mechanism of inhibition of Ty1 retrotransposition by the self-encoded restriction factor p22 , we isolated and characterized Ty1 element CNCR mutants . All but one of the recovered resistance mutations mapped within GAG and altered Gag protein sequence . More than half of the mutations mapped outside of p22 coding sequence , including the three strongest CNCR mutations recovered ( N183D , K186Q , and I201T ) . Importantly , most CNCR mutations do not reflect simple gain-of-function since the mutations do not increase Ty1 mobility in the absence of p22 ( Fig 2B ) . These results , along with the observations that the mutant centromere-based pGTy1his3-AI plasmids do not produce detectable p22 levels ( Fig 1B ) or confer CNC [22] due to their low copy number , demonstrate that Gag is the primary molecular target of p22 . Although we focused on GAG mutations as they represent the vast majority of resistance mutations recovered , one CNCR mutant contains two sequence changes ( D518G/V519A ) in the Ribonuclease H ( RH ) domain of RT ( D518G/V519A ) within POL that dramatically affected Ty1 fitness in the absence of p22 ( 0 . 4% recovery of wild type mobility ) . The RH domain of RT is responsible for degrading the RNA template during reverse transcription ( reviewed in [66] ) , and the decrease in Ty1 mobility is probably due to the fact that D518 is a conserved residue predicted to be involved in metal chelation [67] . Mutations resulting in decreased Ty1 RT activity , including active site mutations within the polymerization domain or host mutations that inhibit RT activity by altering cytoplasmic manganese levels can be suppressed by mutations in the RH domain [67 , 68] . This suppression has been attributed to allosteric communication between the RT polymerization domain and the RH domain [67] . Full-length cDNA is not detectable in cells undergoing CNC [22] , because there is a low level of the initial reverse transcription product minus strand strong-stop DNA [69] . The failure of RT is likely a downstream effect of the alteration in VLP maturation and the absence of mature IN [22 , 26] . Although the resistance mutation in RH may bypass the primary defect imposed by p22 , it may promote a conformation of RT/RH that allows a low level of activity . The data presented here greatly extends previous work suggesting that a Gag/p22 interaction is central to the mechanism of CNC [26 , 27] . Strains undergoing CNC experience a decrease in Ty1 retrotransposition as Ty1 copy number increases [20 , 22 , 26] . A decrease in mobility of a genomic Ty1his3-AI element was not observed when additional genomic copies carry CNCR mutations , indicating that mutations in Gag , including those that map outside of p22 coding sequence , can relieve CNC in a genomic context . We have not identified resistance mutations with greater than 30% recovery of Ty1 mobility without additionally affecting the fitness of the element ( Fig 2 ) . Similarly , combining CNCR mutations resulted in a loss of Ty1 fitness , rather than a combinatorial increase in resistance . These results illustrate the delicate balance between resistance to p22 and overall fitness of Ty1 . A similar tradeoff between resistance and fitness exists for mutations in HIV–1 CA that confer resistance to the restriction factor TRIM5α [40] . The inability to achieve complete resistance to TRIM5α is attributed to the genetic fragility of HIV–1 CA , meaning that it is highly sensitive to mutation , and the fact that TRIM5α can bind multiple surfaces on the CA lattice [40 , 49] . Similarly , some CA mutations conferring resistance to the Mx2 restriction factor also have a negative impact on HIV infectivity [41] . Ty1 Gag is a multifunctional protein but unlike retroviral Gag is not cleaved into the functionally distinct proteins such as matrix , CA , and nucleocapsid ( NC ) . Even so , Ty1 Gag is responsible for executing the same functions as retroviral CA and NC . Thus our inability to obtain fully resistant Ty1 elements strongly suggests some of the same rules concerning genetic fragility apply to Ty1 Gag , namely that its function is very sensitive to mutation . Secondly , p22 may bind multiple surfaces of the VLP during different stages of assembly , making it difficult to attain full resistance by mutating Gag at only one or two residues . A third consideration is that the surfaces or protein domains that interact with p22 may be the same or overlap with domains important for Gag function . In support of this idea , we recovered Gag mutations , P173L and K250E , that confer CNCR , yet negatively impacted Ty1 fitness in the absence of p22 ( Fig 2 ) . K250 is located within predicted helix 4 , an amphipathic helix important for VLP maturation ( Fig 3A ) and perturbation at this site may prevent p22-mediated effects , perhaps by altering VLP assembly and maturation dynamics . However , this alteration in VLP function was not efficient in the absence of p22 . Like other infectious agents , the presence of Ty elements in Saccharomyces has resulted in positive selection for certain host genes , suggesting there is an ongoing “genetic conflict” or evolutionary arms race between Ty elements and their host [70] . In our screen , we recovered mutations that mapped within Gag but not p22 coding sequence and found it difficult to recover mutations that fully restored Ty1 retrotransposition in the presence of p22 . Gag and p22 share coding sequence in the natural setting , and this is likely to influence a Ty1-p22 arms race for the adaptation of Ty1 to inhibition via p22 . Our bioinformatic analysis of Ty1 Gag revealed 9 predicted helical stretches and two Pfam domains: TYA in the N-terminal half and UBN2 in the C-terminal half of the protein ( Fig 3A ) . Four CNCR mutations mapped within UBN2; although no CNCR mutations were isolated in TYA . UBN2 is a Gag sequence motif that is found in Ty1/copia retrotransposons across plants and fungi . Because UBN2 can be recognized by profile-based methods across a wide range of organisms , this domain is likely involved in a conserved function of Gag . Because known mutations that affect VLP assembly fall within this domain [55] , it is reasonable to hypothesize that the UBN2 domain is involved in VLP assembly . UBN2 also overlaps with , but does not fully encompass , the NAC region of Gag ( Fig 3A ) [28] . Recent work demonstrates that p18 interferes with Gag NAC function [27] . It would be interesting to investigate whether CNCR mutations modulate NAC activity of Gag , although we showed that I201T VLPs do not exhibit enhanced levels of Ty1 RNA packaging ( Fig 6E ) . Additionally , only V336I and Q350R S395L mapped within the NAC region ( S2 Table , Fig 3A ) , suggesting that CNCR does not primarily alter Gag NAC functions . The remaining CNCR mutations mapped to a central region of Gag , which we named the CNCR domain . Predicted helix 1 within the CNCR domain was frequently mutated in our screen and overlaps with conserved domain A present in all Ty1/copia elements that surrounds an invariant tryptophan ( Gag W184 for Ty1 ) [53] . Ty2 Gag differs from Ty1 Gag at many positions and some CNCR mutations are present within Ty2 sequences , raising the possibility that Ty2 is naturally resistant to Ty1 CNC . Ty1 and Ty2 are closely related retrotransposons based on their near identical LTR sequences , with a single nucleotide deletion defining Ty2 LTR sequences [2 , 71] . Phylogenetic analyses suggest that S . cerevisiae recently acquired Ty2 elements from S . mikatae by horizontal transfer [3 , 72] . We showed that Ty2-917 is neither under CNC by Ty2-917 nor inhibited by GAL1-promoted expression of p22 from Ty1 . CNCR residues are also altered in a Tsk1 element from L . kluyveri [57] and a Ty1-like element present in S . kudriavzevii and hybrids thereof [3 , 56] . Further mutational analyses of specific CNCR residues within these elements will be required to address the role of naturally occurring CNCR residues in Ty1 and Ty2 . In addition , the overlap between Gag and p22 coding sequence may create specificity even within Ty1 elements . It will be interesting to determine if Ty1 elements in natural Saccharomyces isolates confer CNC on Ty1-H3 or whether they have evolved specificity for elements in their native genomes . Though the structural role of CNCR residues within Ty1 Gag’s helical domains remains to be determined , the mutational analyses presented here define the importance of key hydrophobic residues within these predicted regions . Mutation of the conserved W184 residue within helix 1 to alanine resulted in complete loss of transposition and formation of mature Pol proteins , as well as abnormal VLP assembly ( Fig 4 ) . Other Gag helix mutations we tested were previously analyzed in the context of a truncated Gag protein containing amino acids 1–381 , which is deleted for 21 residues at the C-terminus of p45 and lacks a complete NAC domain [28 , 55] . The truncated Gag is still able to form particles and thus was used to address the role of certain residues in particle assembly . In the context of 1–381 particles , L252R was reported to completely disrupt particle assembly , while LF339-340RD has no effect . IM248/249NR and I343K both alter the migration of 1–381 particles through a sucrose gradient and assemble into “giant” particles when visualized by negative staining and TEM . These mutations had not been characterized in the context of a full length Ty1 element nor analyzed for effects on Ty1 mobility . Therefore , our work showed that altering these hydrophobic residues within helices severely hinders Ty1 mobility ( Fig 4 ) . Our results regarding particle assembly with these Gag substitutions differ from previous work , as L252R was capable of forming higher order complexes and I343K was not ( Fig 4C ) . These conflicting results could be due to the differences in multimerization and VLP assembly using full length Gag compared to truncated 1–381 Gag used by others [55] . Importantly , since most CNCR mutations mapped to helices required for normal Ty1 transposition , protein processing and VLP assembly , our results indicate that inhibition by p22 disturbs a central function of Gag . Mutations exhibiting the highest level of CNCR ( N183D , K186Q , I201T , and A273V ) were associated with reduced levels of p18 ( Fig 5 ) , which is cleaved from p22 near its C-terminus by Ty1-PR , and there was less co-assembly between p22 and Gag I201T compared to wild type Gag ( Fig 7 ) . These results suggest that the reduction in p18 levels in the presence of the CNCR VLPs may result from diminished access to PR . More p22/p18 associated with purified wild type VLPs when compared with I201T VLPs ( Fig 6D ) . When total cellular protein from cells co-expressing p22-V5 and wild type or Ty1his3-AI-I201T was analyzed by sucrose gradient sedimentation ( Fig 7 ) , the majority of p22-V5 and p18-V5 remained in fractions containing soluble protein rather than in fractions containing VLPs or higher order assembly intermediates . Because Ty1 PR-mediated processing is thought to occur only within assembled VLPs and cleavage of p22 and p22-V5 was Ty1 PR-specific ( Fig 1D and S3 Fig ) , p22/p18 may be capable of moving in and out of the VLP . Perhaps this occurs by diffusion of p22/p18 through VLP pores , which are permeable to ribonuclease A ( 15 . 7 kD ) but not to benzonase ( 30 kD ) treatment in vitro [73–75] . Consequently , p22/p18 may still be within the acceptable size limit to enter and exit VLP pores . Alternatively , once p22/p18 co-assembles with Ty1 proteins in VLPs and maturation is initiated , p22/p18-containing VLPs may be subject to dissociation and degradation . Although we did observe a modest shift in Ty1 Gag fractionation towards the top of the gradient in the presence of p22/p18 , Gag was not concentrated in the first two fractions with p22/p18 . Lastly , our results raise the possibility that Ty1 PR may function outside of stably assembled VLPs , perhaps in assembly intermediates present in retrosomes , which are cytoplasmic foci containing Ty1 mRNA and proteins [7 , 9 , 76] . In support of this idea , few if any VLPs are detected in cells containing retrosomes resulting from endogenous Ty1 expression , VLP assembly increases dramatically when Ty1 is overexpressed from a strong promoter , and assembly occurs within retrosomes [7 , 25 , 77] . Recent work also shows that steady state Gag expressed from endogenous Ty1 elements does not co-migrate with unprocessed Gag-p49 [76] , suggesting that Gag cleavage can occur in the absence of detectable VLPs . Finally , several earlier studies demonstrate the presence of mature p45 resulting from endogenous Ty1 expression [8 , 24 , 78–80] . Together , our results suggest that p22 cleavage may occur in the same spatiotemporal environment as pre-VLP Gag cleavage . We observed varying degrees of p22 cleavage and/or p18 stability in the presence of altered Ty1 Gag proteins . While p22/p18 levels were comparable to WT in Gag L252R , p18 was not detected in Gag LF339-340RD and Gag I343K . CNCR mutations ( N183D , K186Q , I201T , and A273V ) and the helix-altering Gag W184A and IM248-249NR were associated with decreased levels of p18 , but we cannot distinguish if these changes represent a decrease in p22 cleavage or a reduction in p18 stability . It is interesting to consider that some loss-of-function changes in Gag ( W184A and IM248-249NR ) and the gain-of-function CNCR mutations both result in decreased p18 levels . Perhaps p22 cleavage is a read-out for both Ty1 PR activity , which can be affected by several different situations , such as Gag:Gag-Pol ratio and particle assembly [47 , 81–83] , or access of the p22 substrate to Ty1 PR . Whereas the helix mutations alter VLP assembly , the resistance mutations likely affect access to PR , since p22 is excluded from CNCR VLPs . Thus , reduced p22 cleavage can occur in both loss-of-function and gain-of-function contexts . Although the CNCR mutations in Gag might affect Gag/p22 binding , co-IP experiments performed using standard washing conditions did not support a simple interaction between p22 and Gag involving CNCR residues ( S2 Fig ) . In addition , sucrose gradient fractionation indicated that most p22-V5/p18-V5 was present in the fractions containing soluble proteins and did not co-sediment with VLPs . Perhaps p22 is capable of binding several forms of Gag , whether monomeric , small assembly intermediates , or intact VLPs , and perhaps these interactions inhibit VLP assembly or maturation with different potencies . If the crucial binding substrate of p22 is multimeric and represents a minority of Gag molecules present in the cell , co-IP analysis may not show differences in binding . Interestingly , retroviral CA-binding restriction factors TRIM5α and the Gag-derived Fv1 bind to their CA target after polymerization of the lattice [84 , 85] . We are considering that the interaction between p22 with polymerized/assembled Gag alone may be the defining and initial insult to Ty1 replication . Retroviral studies involving sensitivity and escape from host restriction factors show similarities to the Ty1-p22 system . Mx2 restriction of HIV–1 is thought to involve inhibition of viral uncoating and/or nuclear entry and requires Mx2-CA binding [41 , 86] . However , known Mx2 escape mutations in the CA gene do not significantly alter binding between Mx2 and CA [41] , which demonstrates that viral escape mutations can promote replication in ways distinct from the disruption of restriction factor-target binding . In the case of the resistant provirus enJSRV26 , increasing the levels of enJSRV26 Gag expression relative to the restriction factor enJS56A1 Gag protein is enough to allow JSRV replication in sheep [43] . Increased expression of enJSRV–26 Gag is achieved by mutation of the signal peptide in the envelope glycoprotein , which modulates proviral gene expression . Similarly , increasing the level of Ty1 expression can overcome CNC [48] . We favor the hypothesis that understanding how p22 is excluded from CNCR VLPs is central to understanding CNC . Since the steady state level of Gag was unaffected in CNCR mutants ( Fig 5A ) , perhaps the ratio of Gag:p22 is specifically higher within retrosomes comprised of CNCR Gag . In summary , we have shown that mutations in Gag confer resistance to the p22 restriction factor produced by Ty1 during CNC . These mutations are beneficial only in the presence of p22 and do not globally increase Ty1 mobility . CNCR mutations allow for VLP maturation , which may be the step in Ty1 replication most sensitive to CNC , by excluding p22 from assembling particles . Identification of the Gag multimerization states that bind p22 and host factors that modulate Gag assembly , in combination with studies examining VLP assembly dynamics and structure , especially regarding the newly identified Gag domains , will deepen our understanding of retroelement control .
Strains are listed in S1 Table . Strains repopulated with Ty1 elements were obtained following pGTy1 induction as described previously [20] . Ty1 insertions following repopulation experiments were estimated by Southern blotting as in [48] . Standard yeast genetic and microbiological procedures were used in this work [87] . Refer to S3 Table for plasmid descriptions and sources . Directed mutagenesis was carried out by overlap PCR using the following primer sequences: W184Ab; 5’-ATGTTTTAACAGCATTTGGAAAGTCATTAGGTGAGGTTAAC; W184Ac , 5’-GACTTTCCAAATGCTGTTAAAACATACATCAAATTTTTAC; L252Rb , 5’-ATACTTTTGGATCTAATTTTCATGATATCCGTATAATCAACG; L252Rc , 5’-TCATGAAAATTAGATCCAAAAGTATTGAAAAAATGCAATCTG; IM248/9NRb , 5’-AAAGAATTTTCCTGTTATCCGTATAATCAACGGATAGGAT; IM248/9NRc , 5’-TATACGGATAACAGGAAAATTCTTTCCAAAAGTATTGAAA; LF339/40RDb , 5’-GGATATCTAAGTCCCGTTCAGCGACTGTCATATTTAGATG; LF339/40RDc , 5’-GTCGCTGAACGGGACTTAGATATCCATGCTATTTATGAAG; I343Kb , 5’-AAATAGCATGCTTATCTAAGAACAGTTCAGCGACTGTCAT; I343Kc , 5’-CTGTTCTTAGATAAGCATGCTATTTATGAAGAACAACAGG . For pBJM24 , the plasmid markers were switched from URA3 to TRP1 , as described previously [26] . Galactose-inducible centromere ( CEN ) vectors expressing p22-V5 were created by PCR amplification of Ty1-H3 p22 coding sequence 1038–1613 with the internal V5 tag and flanking GAL1P and CYC1 TT sequences using pBDG1568 as a template [26] and primers: cla1_galp , 5’-CATGTTTCATCGATACGGATTAGAAGCCGCCGAGC; cyc1ttrevSacII , 5’-CATGTTTCCCGCGGGAGTCAGTGAGCGAGGAAGC . The insert was cloned into an empty URA3/CEN vector ( pRS416 ) using ClaI and SacII sites . The V5 tag is located between nucleotides 1442 and 1443[26] . pTy2-917his3-AI ( pBDG631 ) was constructed by digestion of pGTy917 with BglII and pBJC42 ( his3-AI , pBDG619 ) with ClaI , fill-in synthesis of the linearized vector and his3-AI fragments using DNA polymerase I followed by blunt end-ligation . pGPOLΔd1 was constructed by BglII digestion and reclosure of pGTy1his3-AId1 ( kindly provided by Jef Boeke [54 , 64] and Joan Curcio [88] ) , which deletes the majority of POL . pYES2-p45 was constructed by PCR using primers specific for the coding sequence of p45 and the amplification product was cloned into the multi-copy GAL1-promoted expression vector pYES2 [27] . Recombinant plasmids were verified by restriction enzyme analysis or DNA sequencing . Plasmid mutagenesis was performed by transforming 50 ng of pBDG606 ( S3 Table ) into XL–1 Red ( Agilent Technologies ) cells and sub-culturing transformants for 3–4 days at 37°C . Gap repair was performed with pBDG606 using mutagenized GAG template and AatII ( upstream of GAL1P ) and BstEII ( within PR ) sites , while POL mutagenesis was performed using BstEII and XbaI ( within his3-AI ) sites . Primers flanking these restriction sites ( GAG: USAatII , 5’-ATAATACCGCGCCACATAGC; RP1 , 5’-CATTGATAGTCAATAGCACTAGACC; POL: USBsteIIf , 5’-GCACGACCTTCATCTTAGGC; 3pLTRrev , 5’-ATCAATCCTTGCGTTTCAGC ) were used in a standard Taq ( ThermoFisher Scientific , Waltham , MA ) PCR reaction with Ty1-H3 as a template to mutagenize the area of interest at a low frequency . XL–1 Red treated pBDG606 or DNA fragments for gap repair were transformed into YEM515 ( see S1 Table ) and plated onto SC-Ura . Transformants , were replica plated on SC-Ura + 2% galactose , incubated at 22°C for 1–2 days , and then replica plated on SC-Ura-His and incubated at 30°C for 2 days . Candidate plasmids were extracted , propagated in E . coli , transformed into YEM514 and YEM515 and retested for pTy1his3-AI mobility . Candidates with at least a 10-fold increase in retromobility in YEM515 were carried forward . After sequencing the CNC region of XL–1 Red treated plasmids , the GAL1 and GAG segments were subcloned into wild type plasmid using AatII and Eco91I sites to eliminate other mutations present outside of the region of interest . In all cases , subcloned GAG mutations conferred a similar level of CNCR as the primary isolates . For the gap repair screen , the entire region amplified by low fidelity PCR was sequenced . The mobility frequency of Ty1his3-AI was determined as described previously [21 , 48] with the following modifications . For strains transformed with only pGTy1his3-AI , single colonies were grown at 30°C overnight in 1 ml of SC-Ura + 2% raffinose and then diluted 1:25 in quadruplicate 1 ml SC-Ura + 2% galactose cultures . Galactose cultures were grown at 22°C for 2 days , and cells were then washed , diluted and spread onto SC-Ura and SC-Ura-His plates . For strains transformed with both pGTy1his3-AI and p22-containing plasmids , similar methods were used for the assay except liquid and solid media also lacked tryptophan . For qualitative mobility assays with pGTy1his3-AI , cells were patched onto SC-Ura and grown at 30°C for 2 days . Cells were replica plated onto SC-Ura +2% galactose and incubated at 22°C for 2 days , followed by replica plating onto SC-Ura-His and incubation at 30°C until His+ papillae appeared . For transposition assays involving chromosomal Ty1his3-AI , a single colony was dissolved in 10 ml water . One microliter of diluted cells was added to quadruplicate 1 ml SC-Ura or YEPD cultures and grown 2–3 days until saturation . The cells were washed , diluted and spread onto SC-Ura or YEPD and SC-Ura-His or SC-His plates , and incubated at 30°C until colonies formed . For strains carrying pGTy1his3-AI , 1 ml of SC-Ura + 2% raffinose was inoculated with a single colony and grown overnight at 30°C , then diluted 1:10 into SC-Ura + 2% galactose and grown at 22°C for 24 h . For growth in glucose , a dilution of 1:100 was used . To detect p22/p18 , 5 ml of culture was processed by trichloroacetic acid ( TCA ) extraction as described previously [26] . To detect all other Ty1 proteins , protein from 10 ml of culture was extracted as previously described [89] and 30 μg of protein was used for immunoblotting . Samples were separated on 10% ( for RT and IN detection or to separate Gag-p49 and p45 ) or 15% ( Gag-p49/p45 and p22/p18 detection ) SDS-PAGE gels and immunoblotted as described previously [26] . Antibody dilutions were as follows: anti-p18 1:5000 [26] , anti-VLP 1:10 , 000 , anti-RT 1:5 , 000 , anti-IN 1:2500 , anti-Hts1 1:40 , 000 , anti-TY ( BB2 , UAB Epitope Recognition and Immunoreagent Core , Birmingham AL ) 1:50 , 000 , anti-V5 1:20 , 000 ( Life Technologies , Carlsbad , CA ) . Ty1-H3 sequence ( GenBank M18706 . 1 ) was submitted to the following online servers for secondary structure prediction: ITASSER [50–52] PredictProtein [90] , Sable [91] , PSIPRED [92] , and SAMTO8[93] . When comparing the secondary structure predictions , the results were consistent , with the same helices predicted by all five servers . The boundaries of the helices varied slightly , but not by more than three residues . The I-TASSER results were chosen for display in Fig 3 . Protein domains in Ty1 sequence were predicted using profile hidden Markov models [94] by scanning Ty1 Gag sequence against the Pfam database . Ty1 related sequences in UniProt were identified using HMMER [94] and aligned using CLUSTALW [95] . Full alignment can be found in the supplemental data ( S1 Fig ) . Protein alignments were visualized using Jalview ( http://www . jalview . org/ ) [96] . ClustalX coloration was used with a conservation color increment of 35 . The raw alignment file is provided as S1 File . VLPs were isolated as described previously [26] , except the cells were induced in SC-Ura + 2% galactose for 24 h at 20°C . Two micrograms of final VLPs were immunoblotted to detect Gag , RT , and IN . A 1:2 dilution series was loaded to detect p18 . Equivalent total cellular RNA and VLP RNA , as estimated by OD600 or total Gag protein respectively , was extracted using the MasterPure yeast RNA purification kit ( Epicentre Biotechnologies , Madison , WI ) and analyzed via Northern blotting as previously described [26] . Antibodies were crosslinked to resin using a Pierce Crosslink IP Kit ( ThermoFisher Scientific ) and following the manufacturer’s instructions . For immunoprecipitations , a 25 ml culture was induced in SC-Ura-Trp + 2% galactose at 20°C for 24 h or until OD600 = 1 . 0 . IP Lysis buffer was supplemented with 1 μg/ml aprotonin , pepstatin and leupeptin and 1 mM PMSF . Cells were broken in IP Lysis buffer plus protease inhibitors by vortexing with glass beads . Equal amounts of protein were applied to Protein A/G agarose crosslinked with 2 μg of V5 Antibody ( Life Technologies ) and allowed to bind for 2 h at 4°C . Beads were washed with IP Lysis buffer and eluted with 20 μl of elution buffer . 1/100 of the input and 1/2 of the pull-down material were loaded per lane . Beads not crosslinked to V5 antibody served as a negative control . A 100 ml culture was induced in SC-Ura or SC-Ura-Trp + 2% galactose at 20°C for 24 h or until the culture reached an OD600 of 1 . Cells were broken in 15 mM KCl , 10 mM HEPES-KOH , pH 7 , 5 mM EDTA containing RNase inhibitor ( 100 U per ml ) , and protease inhibitors ( 16 μg/ml aprotinin , leupeptin , pepstatin A and 2 mM PMSF ) in the presence of glass beads . Cell debris was removed by centrifuging the broken cells at 10 , 000 x g for 10 min at 4°C . Five milligrams total protein in 300–500 μl of buffer was applied to a 7–47% continuous sucrose gradient and centrifuged using an SW41 rotor at 25 , 000 rpm ( ~100 , 000 x g ) for 3 h at 4°C . After centrifugation , 9 x 1 . 2 ml fractions were collected and 30 μg of the input and 15 μl of each fraction was immunoblotted to detect Ty1 proteins . | The presence of transposable elements in the eukaryotic genome threatens genomic stability and normal gene function , thus various defense mechanisms exist to silence element expression and target integration to benign locations in the genome . Even though the budding yeast Saccharomyces lacks many of the defense systems present in other eukaryotes , including RNAi , DNA methylation , and APOBEC3 proteins , they maintain low numbers of mobile elements in their genome . In the case of the Saccharomyces retrotransposon Ty1 , a system called copy number control ( CNC ) helps determine the number of elements in the genome . Recently , we demonstrated that the mechanism of CNC relies on a trans-acting protein inhibitor of Ty1 expressed from the element itself . This protein inhibitor , called p22 , impacts the replication of Ty1 as its copy number increases . To identify a molecular target of p22 , mutagenized Ty1 was subjected to a forward genetic screen for CNC-resistance . Mutations in specific domains of Gag , including the UBN2 Gag motif and a novel region we have named the CNCR domain , confer CNCR by preventing the incorporation of p22 into assembling virus-like particles ( VLPs ) , which restores maturation and completion of the Ty1 life cycle . The mechanism of Ty1 inhibition by p22 is conceptually similar to Gag-like restriction factors in mammals since they inhibit normal particle function . In particular , resistance to p22 and the enJS56A1 restriction factor of sheep involves exclusion of the restriction factor during particle assembly , although Ty1 CNCR achieves this in a way that is distinct from the Jaagsiekte retrovirus escape mutants . Our work introduces an intriguing variation on resistance mechanisms to retroviral restriction factors . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | The Ty1 Retrotransposon Restriction Factor p22 Targets Gag |
Functional residues in proteins tend to be highly conserved over evolutionary time . However , to what extent functional sites impose evolutionary constraints on nearby or even more distant residues is not known . Here , we report pervasive conservation gradients toward catalytic residues in a dataset of 524 distinct enzymes: evolutionary conservation decreases approximately linearly with increasing distance to the nearest catalytic residue in the protein structure . This trend encompasses , on average , 80% of the residues in any enzyme , and it is independent of known structural constraints on protein evolution such as residue packing or solvent accessibility . Further , the trend exists in both monomeric and multimeric enzymes and irrespective of enzyme size and/or location of the active site in the enzyme structure . By contrast , sites in protein–protein interfaces , unlike catalytic residues , are only weakly conserved and induce only minor rate gradients . In aggregate , these observations show that functional sites , and in particular catalytic residues , induce long-range evolutionary constraints in enzymes .
Enzymes facilitate the chemical reactions necessary for life . To function properly , enzymes must reconcile two competing demands: they must fold stably into the correct three-dimensional conformation , and they must display the correct catalytic residues in their active sites . As enzymes evolve , mutations that are functionally beneficial are often deleterious for stability , and vice versa [1–3] . Thus , the patterns of evolutionary divergence observed in enzyme evolution are shaped by the interplay of these two potentially conflicting constraints . Mutations affecting fold stability can occur anywhere in the protein structure , though in general stability effects tend to be more pronounced in the interior , more densely packed regions of a structure than on the protein surface [4 , 5] . By contrast , where mutations affect function in a protein structure is less clear . Site-directed mutagenesis experiments demonstrate that mutations at catalytic residues , unsurprisingly , disable enzyme function [6 , 7] . Accordingly , residues directly involved in protein function tend to be more conserved over evolutionary time than other residues [8–10] . Less intuitively , however , mutations 20 Å or more from a catalytic residue can attenuate catalytic activity in enzymes such as glycosidase [11] , TEM-lactamase [12] , or copper nitrate reductase [13] . Similarly , a study of a small set of α/β-barrel enzymes has found that evolutionary conservation decays continuously with the distance to the nearest catalytic residue [14] . These results suggest that residues far from an active site may be functionally important , but that this importance may decline with distance in physical , three-dimensional space . Here , we analyze a dataset of 524 distinct enzyme structures spanning the six major functional classes of enzymes . We systematically assess how site-specific evolutionary variation in these enzymes relates to the geometric location of residues relative to the nearest catalytic residue . We find that , across all six major classes of enzymes , the constraining effects of catalytic residues extend to most of an enzyme’s structure , irrespective of protein size . These effects exist regardless of whether an active site is located on the surface or in the core of a protein , and they remain even when controlling for other structural features predicting evolutionary variation . Finally , we find that we can use site-specific conservation gradients to accurately recover active sites in more than 50% of enzymes . In summary , these findings demonstrate that functional sites induce long-range evolutionary constraints in enzyme structures .
To systematically explore the relationship between site-specific evolutionary rates and distance to the nearest catalytic residue , we have analyzed 524 diverse enzyme structures . We have chosen these structures as a subset of enzymes analyzed previously for their relationship between protein structure and evolutionary variation [15] . The structures represent all six major classes of enzymes , and no two structures in the dataset share more than 25% of their respective amino-acid sequences . The dataset includes both single subunit proteins ( monomers ) and multi-subunit proteins ( multimers ) , and annotations describing the biological assembly and the location of the catalytic residues are available for each structure ( see Methods for details ) . For each enzyme , we have constructed alignments of up to 300 homologous sequences , selected from the UniRef90 database [16 , 17] . We estimate evolutionary variation at each site in each alignment by calculating a site-specific relative evolutionary rate , using the software Rate4Site [18] . The relative rates are normalized such that a value of one corresponds to the average rate in a given protein , and larger or smaller values represent proportionally larger or smaller rates . For brevity , we will also refer to the relative rates simply as “rates . ” In mathematical expressions , rates will be denoted by the letter K . We first ask whether there is an overall trend toward increased evolutionary conservation near active sites . To address this question , we pool all sites from all structures into one combined dataset and then calculate the mean evolutionary rate as a function of Euclidean distance to the nearest catalytic residue in the respective structure . As expected , we find that evolutionary rates are , on average , the lowest at or directly near catalytically active sites . Moreover , we find that rates increase approximately linearly with increasing distance to the nearest catalytic residue , up to a distance of approximately 27 . 5 Å ( Fig 1A ) . Beyond this distance , rates level off . Importantly , 80% of all residues in our dataset fall within a distance of 27 . 5 Å to the nearest catalytic residue ( Fig 1B ) . Thus , the vast majority of all residues in each protein appear to experience some amount of purifying selection mediated by catalytic residues . We can think of sites in a protein as organized into shells according to their distance to the closest catalytic residue . Each shell is 5 Å in width , the approximate minimum distance between two amino acid side-chains . The boundaries between these discrete shells are indicated in Fig 1B with dashed lines , and we can see clear dips in the distribution at 2 . 5 Å and 7 . 5 Å , the boundaries between the 0th and 1st and the 1st and 2nd shells ( the boundaries between shells become less precise for higher shell numbers ) . We can subdivide the sites of our dataset into these discrete shells and then plot the rate distribution within each shell ( Fig 1C ) . We find that the mean rate for each shell increases up to shell 6 ( 32 . 5 Å ) and then stabilizes . Similarly , the width of the distribution also increases up to shell 6 . Thus , all shells include some proportion of conserved sites , but increasingly distant shells include an increasing fraction of moderately or highly variable sites . The broad rate distributions that we observe within individual shells , in particular within shells distant from catalytic residues , highlight that there are other factors besides distance that also influence the extent and type of selection acting on individual sites . In fact , one important evolutionary constraint is the requirement for proteins to fold stably into their active conformation [19] . This constraint causes sites in the interior of the protein , shielded from the solvent and involved in many inter-residue contacts , to be more evolutionarily conserved than sites on the protein surface ( for a recent review , see [30] ) [4 , 15 , 20–29] . Two structural measures are commonly used to quantify this structural constraint: relative solvent accessibility ( RSA ) [31] and weighted contact number ( WCN ) [32] . RSA measures the exposure of a given residue to a hypothetical small solvent molecule , typically water . RSA is useful for determining if a residue is on the surface or the interior of a protein structure . WCN measures the local packing density of a given residue . WCN is high in the core of the protein , where residues are tightly packed . We have calculated both WCN and RSA for each site in each protein in our dataset . We have based this calculation on the published biological assembly of each protein , so that intra-chain contacts are properly accounted for in the case of enzymes that natively function in a multimeric state . As has been reported previously , on average WCN displays higher correlations with site-specific rate than RSA does , in particular when WCN is calculated with respect to the side-chain coordinates of each residue ( see also S1 Fig ) [33] . However , in our dataset , correlations of rate with WCN are only moderately higher than correlations with RSA , and there are proteins for which RSA outperforms WCN ( S1 Fig ) . Therefore , throughout this work , we consider both WCN and RSA as measures of structural constraints acting on site-specific protein evolution . Importantly , neither WCN nor RSA make any assumptions about catalytic residues in proteins . Both quantities are purely geometric measures of protein structure . Conversely , the distance d to the closest catalytic residue does not explicitly contain information about packing density or solvent accessibility . Yet , in our dataset , the three quantities WCN , RSA , and d are all correlated with each other ( S2 Fig ) . Therefore , we next ask to what extent the distance d captures an evolutionary constraint that is distinct from the constraints captured by WCN and RSA . To address this question , we regress site-specific evolutionary rates K against WCN , RSA , and d , in all possible combinations , and separately for each enzyme in our dataset . We then record the R2 for each model and each enzyme ( Fig 2A ) . We find that the best purely structural model , using both WCN and RSA as predictor variables , explains on average 39% of the variation in rate ( Fig 2A ) . Adding distance as a third predictor to this model increases the average R2 to 44% . Thus , distance explains on average at least 5% of rate variation that cannot be attributed to purely structural factors , and possibly more than that; by itself , distance explains on average 25% of the variation in rate . Some of that variation may be accidentally captured by WCN or RSA , because active sites are frequently located closer to the interior than to the surface of the protein structure . To further assess the independent contribution of distance to the pattern of site-specific rates , we compare model predictions and empirical rates as functions of distance to the active site . We compare rates predicted by the linear models K ∼ WCN + RSA and K ∼ WCN + RSA + d , which are fit for each protein individually . For visualization only , we average within shells , as explained above . We find that a linear model containing only WCN and RSA tends to overestimate site-specific evolutionary rates within the first three to four shells ( green line in Fig 2B and 2C ) . Adding distance to this model removes nearly all of the overestimation ( orange line in Fig 2B and 2C ) . These findings demonstrate that structural metrics alone are unable to accurately predict conservation patterns near active sites . Enzymes often sequester substrates into a buried catalytic core . This sequestration allows them to facilitate chemistry that would otherwise be impossible in the broader cellular environment . For this reason , many enzymes tend to have active sites in the protein interior , where local packing density is high and solvent accessibility is low [19] . For those enzymes , we expect the distance metric d to correlate with WCN and/or RSA . By contrast , if the active site is located on the protein surface , then distance should correlate very little or not at all with either WCN or RSA . To further disentangle active-site effects from WCN and RSA , we can identify individual structures from our dataset in which distance is sufficiently uncorrelated ( defined as r < 0 . 25 ) from both WCN and RSA . Among these structures , we find four for which distance correlates strongly with evolutionary rate ( defined as r ≥ 0 . 55 ) ( see Fig 3 ) . They correspond to the enzymes dihydrofolate reductase ( DHFR , protein databank identifier [PDB ID]: 1DHF ) [34] , superoxide reductase ( SOR , PDB ID: 1DO6 ) [35] , anti-sigma factor SpollAB ( PDB ID: 1L0O ) [36] , and the Serratia endonuclease ( PDB ID: 1SMN ) [37] . All of these enzymes perform different biological functions , and they are active in multimeric conformations . In these proteins , rate correlates more strongly with distance than it does with WCN or RSA ( Fig 3A ) , and the mean rate increases linearly with distance throughout the entire structure ( Fig 3B ) . In all four cases shown , the active sites are located near the protein surface ( mean active-site RSA ranges from 0 . 19 to 0 . 25 ) and away from the protein center ( Fig 3C ) . To analyze the effect of active-site location on rate variation more systematically , we next subdivide our entire dataset into three categories based on active site location , measured by the mean RSA of all catalytic residues in the structure . We define these categories as active site in the protein interior ( mean catalytic-residue RSA < 0 . 05 ) , active site with intermediate solvent exposure ( mean catalytic-residue RSA between 0 . 05 and 0 . 25 ) , and active site on the protein surface ( mean catalytic-residue RSA ≥ 0 . 25 ) . Our dataset contains 98 , 367 , and 59 proteins in these three categories , respectively . As before , we find that the purely structural metrics RSA and WCN tend to overestimate site-specific evolutionary rates near the active site in all three groups ( Fig 4 ) . Moreover , the structure-based models perform worse as the active site moves from the core of the enzyme to the surface . In all cases , incorporating distance into the model corrects most rate overestimation near the active site . Interestingly , all models perform better for active sites in the core than for active sites on the surface ( Fig 4 ) . We interpret this observation as follows: When the active site is located in the core of an enzyme , functional and structural constraints are aligned . The sites most conserved due to function are also the sites most conserved due to structure , and this overall trend is captured well in the linear models . By contrast , when the active site is located on the surface , functional and structural constraints are at odds with each other . The sites most conserved due to function are now the sites least conserved due to structure , and vice versa . In this case , since there are now two opposing trends within one structure , it is more difficult for any linear model to accurately capture rate variation throughout the structure . We have previously seen that approximately 80% of all residues in our dataset fall within the 27 . 5 Å cutoff , inside of which evolutionary variation is reduced in proportion to distance to the nearest active site ( Fig 1B ) . However , the 80% figure may be somewhat misleading , because in that analysis we have pooled all residues from all proteins . Our dataset comprises proteins of very different sizes , from 95 to 1 , 287 amino acids long , and for small proteins every residue falls within 27 . 5 Å of an active site , while for large proteins only one-half to two-thirds of the residues lie within the 27 . 5 Å distance cutoff . To ascertain whether the relationship between functional sites and evolutionary rates depends on enzyme size , we can re-analyze our data by protein size . We define three evenly sized groups: small proteins ( 95–268 sites ) , medium-size proteins ( 270–385 sites ) , and large proteins ( 386–1 , 287 sites ) . Each group contains 175 , 175 , and 174 structures , respectively . We observe that as enzyme size increases , the rate–distance slope decreases ( Fig 5A ) . Distance effects are weaker in larger proteins but also extend further out . The effect remains visible when we analyze the distance–rate relationship for individual proteins and in the context of WCN and RSA ( Figs 5B and S5 ) : purely structural models , which use only WCN and RSA to predict rate , overestimate rate up to shell 3 in small proteins , up to shell 4 in medium-sized proteins , and up to shell 5 in larger proteins . In summary , we see that the leveling off of rate at shell 5 , around 27 . 5 Å , in the pooled dataset , does not represent a universal cutoff but rather an average obtained from combining many different structures into one analysis . For any individual protein , there will generally be a distance effect , but it may extend only to shell 3 or 4 in small proteins while extending to shell 6 ( and possibly beyond ) for very large proteins . As we have seen from the preceding analyses , active sites in enzymes impose a selection gradient that can be detected throughout the majority of the protein structure . This observation leads us to ask whether we can use this gradient to identify active sites when their location is not known . To answer this question , we blindly search for distance–rate gradients in our dataset . We systematically use one residue at a time as a reference point in the structure and fit a linear model of rate versus the distance to that reference point . We record the resulting R2 for each model , and we consider the reference point with the highest R2 as the putative active site in the structure . We find that in 18% of the structures in our dataset , the putative active site coincides with a known catalytic residue ( Fig 6 ) . In an additional 37% of structures , the putative active site falls within 7 . 5 Å of a catalytic residue but is not a catalytic residue itself . A distance of 7 . 5 Å corresponds to one shell , i . e . , it captures residues in direct contact with a catalytic residue . Note that the gap visible between 0 and 2 . 5 Å in Fig 6 corresponds to the closest distance that two side chains can physically contact each other . A putative active site either is a catalytic residue , in which case it has a distance of 0 Å to the nearest catalytic residue ( i . e . , itself ) , or alternatively it has to be at least a distance of 2 . 5 Å away from the catalytic residue . In summary , for more than half ( 55% ) of the 524 enzymes in our dataset , we can use the existing selection gradient to identify either a catalytic residue or an immediate neighbor . As a control , we have also considered a model that places the active site at the core of the protein , at the residue with the overall highest WCN ( since , as stated above , the active site is located in the protein interior for many enzymes ) . We find that this control approach recovers catalytic residues or their immediate neighbors in 31% of enzymes ( S6 Fig ) . Thus , while the control approach can recover active sites in a substantial fraction of enzymes , the selection-gradient-based method performs significantly better ( odds ratio = 2 . 8 , p < 1 . 7 x 10−15 , Fisher’s Exact Test , S7 Fig , S1 Table ) . Many enzymes function as components of multimeric protein complexes . In fact , more than half of the enzymes in our dataset contain multiple subunits in their biological assemblies . The arrangement of and interaction between these subunits could substantially modify how protein structure and protein function shape protein evolution , especially if the active site occurs at the interface of two subunits . In our dataset , we find that residues in protein–protein interfaces are , on average , only slightly more conserved than any other residues , whereas catalytic residues are much more conserved ( Fig 7A ) : residues in interfaces evolve , on average , at a rate of 0 . 91 relative to the average residue , while catalytic residues evolve , on average , at a relative rate of 0 . 10 . To verify that the little conservation we see in interface sites is not an artifact of our enzyme dataset , we have also analyzed rates in a set of 17 non-enzymatic protein–protein complexes , consisting of 30 individual proteins total ( see Methods ) . Again , we find that residues in protein–protein interfaces show only moderate conservation relative to all other residues ( relative rate of 0 . 82 , Fig 7A ) . Moreover , consistent with their weak conservation , protein–protein interfaces induce only very minor gradients of conservation , if any , in both enzyme and non-enzyme proteins ( compare Fig 7B–7D with Fig 1 ) . Thus , protein–protein interactions impose much weaker evolutionary constraints than catalytic sites . The structural metrics RSA and WCN are also sensitive to subunit arrangement , and subunit arrangement can be incorrectly annotated in the biological assembly . To assess whether subunit arrangement and/or annotation errors affect the distance–rate relationship , we re-analyze our enzyme data using three additional variations of analysis choices: ( i ) RSA and WCN are calculated using the biological assembly , and any residues at the interface between subunits are excluded; ( ii ) RSA and WCN are calculated using a single subunit , and no residues are excluded; and ( iii ) RSA and WCN are calculated using a single subunit , and all interface residues are excluded ( S8–S34 Figs ) . In all three cases , our results remain qualitatively unchanged from our prior results: rate increases with increasing distance to a catalytic residue , up to about 27 . 5 Å; distance has an effect on rate variation that is independent from the purely structural metrics WCN and RSA . Thus , in summary , there is a positive distance–rate relationship that is independent of WCN or RSA , and it exists regardless of how we treat multi-subunit enzymes and interface residues .
We have shown that many enzymes exhibit a clear , nearly linear relationship between site-specific evolutionary rates and distance to the nearest catalytic residue . We have found this trend consistently throughout a large dataset of 524 diverse enzymes , and we have found that the relationship extends to most of the residues in any given enzyme structure . Using combined linear models containing RSA , WCN , and distance , we have found that distance explains at least 5% of the variance in rate after controlling for WCN and RSA , and potentially up to approximately 36% ( Fig 3 ) in proteins in which the active site is located near the protein surface . Moreover , models containing only the structural predictors WCN and RSA consistently overestimate evolutionary variation near active sites , through shell 5 ( 27 . 5 Å ) in large proteins . Finally , we have shown that in over half of the enzymes in our dataset , we can recover catalytic residues or their immediate neighbors from the evolutionary gradients they imprint throughout the protein structure . For some enzymes in our dataset , we have found little evidence for functional or structural constraints on site-specific evolutionary rates . There are some proteins for which less than 10% of the variation in evolutionary rate can be accounted for with distance to a catalytic residue , RSA , or WCN . These low correlations suggest that either the rates themselves are uninformative , or that the available PDB structures are not reflective of protein structure in vivo . In the first case , the sequence alignments used to determine evolutionary rate could contain a mix of proteins with very different arrangements in vivo . We have no way of determining the biological assembly of every sequence in the alignment , so differences in corresponding subunit arrangement could bias the site-specific evolutionary rates . Additionally , the RCSB protein database may have conflicting biological assemblies . For example , the biological assembly for human dihydrofolate reductase ( PDB ID: 1DHF ) is classified as a homodimer , while the biological assembly for a separate structure ( PDB ID: 1DRF ) of the same protein is classified as a monomer . We have attempted to control for possible structural variability in the sequence alignments and biological assemblies by re-analyzing all structures as monomers and/or removing residues at the interface of subunits before computing correlations , and the overall trends observed remain the same ( S8–S34 Figs ) . Regardless , all of the factors mentioned here could result in rates that correlate poorly with any structural predictors . The field of molecular evolution has long sought to understand the relationship between protein structure , function , and sequence evolution [30] . Here , we have assessed this relationship by comparing distance to the nearest catalytic residue with site-specific evolutionary rates . Past work has employed covariation analyses to reveal clusters of co-evolving residues in protein structures , deemed “protein sectors” [38] . In specific cases , such as in serine proteases , these protein sectors also correspond to different functional biochemical regions of the structure . A recent reanalysis of the seminal protein sector work demonstrates that , in proteins with just one sector , sequence conservation recovers clusters of functional residues just as well as covariation analyses [39] . Our work demonstrates that not only are clusters of functional residues highly conserved , but that such residues induce gradients of conservation within a structure . This finding of long-range interactions between residues is consistent with the sector model of large regions of co-evolving residues . We have found that active sites are among the most highly conserved sites in proteins , whereas residues involved in protein–protein interactions are only weakly conserved relative to the average site in a protein . Moreover , the gradients of conservation induced by protein–protein interfaces are much less marked than those induced by catalytic sites . This finding is consistent with prior work on protein–protein interactions . While several prior works have found increased conservation in interface regions [40–42] , effect sizes have generally been found to be small . For example , [42] found that the reduction in evolutionary rate in a protein–protein interface was mostly ( though not entirely ) explained by the reduction in solvent accessibility induced by complex formation . Also , complementation assays and computer simulations suggest that protein–protein interfaces can experience extensive divergence without loss of function [43] , again supporting the notion that protein–protein interfaces are frequently not under strong purifying selection . For these reasons , we believe that the rate gradients we have found toward active sites are not strongly confounded by protein–protein binding interfaces . Long-range interactions between residues in a protein have historically been studied in the context of allostery . Initially proposed in 1961 , allostery describes the process by which a small molecule ( ligand ) binds to one area of an enzyme ( allosteric site ) and induces a conformational change at a distant active site [44] . Studies of allosteric interactions shed light on two key aspects of our findings . First , biophysical models have been developed that explain long-range interactions . The Monod-Wyman-Changuex model , the most widely studied of allostery models , proposes that ligand binding stabilizes a biologically active or inactive quarternary structure [44] . Recent studies , however , demonstrate that some monomeric proteins also contain allosteric sites [44] . In G-protein coupled receptors , for example , a simplified model of conserved , physically connected amino-acid residues explains the long-range interactions between allosteric sites and active sites [45] . Our dataset contains a mix of monomeric and multimeric proteins , and we observe long-range interactions in both types of proteins . Thus , our findings suggest that allosteric-like couplings between active sites and distant residues may be more common than previously thought . The physical distance between allosteric ligand-binding sites and active sites ranges from 20 Å in hemoglobin to 60 Å in glycogen phosphorylase [46] . Therefore , the selection gradients we have observed here extend to distances well within the range of experimentally observed allosteric interactions . Second , while the observed selection gradients have allowed us to recover residues in close proximity to the active site in a little over half of the proteins in our dataset , in many proteins ( 45% ) the selection gradient points toward a residue >7 . 5 Å from the active site . It is possible that these non-catalytic residues , which are highly predictive of the overall patterns of evolutionary rates in the structure , may be allosteric sites . Allosteric sites tend to be highly conserved , although typically not as conserved as active sites [47] . In summary , studies of allostery provide biophysical explanations for long-range interactions between residues and may explain why we failed to recover catalytic residues from selection gradients in some proteins in our dataset . That selection gradients can recover active sites has potentially broad applications , even beyond enzymatic proteins . For example , some of us have previously used optimized distance to identify important functional sites in influenza A hemagglutinin ( HA ) [48] . HA , a viral surface protein , interacts directly with sialic acid found on the surface of human cells . Viral infection requires binding of HA to sialic acid , and antibodies bind near the sialic-acid binding region to inhibit viral infection . Residues in that region are thus under strong positive selection for immune escape , and consequently the selection gradient in HA revealed a rapidly evolving functional site . This finding suggests that selection gradients could effectively recover diverse types of functional sites , not only those that are well conserved . More broadly , evolutionary history is a useful predictor of active sites [8 , 9] and binding partners [10] . Assuming that a given structure has been crystalized , the rate gradients we have found here could improve computational predictions of active sites and binding sites .
We selected 524 of 554 previously characterized enzymes [15] to conduct our analysis . We removed 30 structures because they contained chains with no available catalytic residue information , or because the UniRef90 database did not contain enough homologous sequences to construct a diverse alignment . These enzymes consist of 204 monomers and 320 multimers , and no two enzymes in the dataset have more than 25% sequence identity . For each enzyme , we obtained catalytic residue information from the Catalytic Site Atlas [49] . We acquired PDB structures of the biological assemblies for these proteins from the RCSB protein database [50] . A biological assembly represents the functional form of a given enzyme in vivo based on the best experimental data available . When available , we used biological assemblies that are author-provided or both author-provided and software-supported ( labeled “A” and “A+S , ” respectively , in the RCSB protein database ) . If author-provided biological assemblies were not available , we used biological assemblies predicted by PISA ( protein interfaces , surfaces , and assemblies , http://www . ebi . ac . uk/pdbe/pisa/ ) ( labeled “S” ) . PISA biological assemblies are entirely predicted by software . In cases in which there were multiple author-provided biological assemblies , we chose the first of those assemblies listed in the RCSB protein database . In addition to the enzyme dataset , we also compiled a non-enzyme dataset as a control . We selected 17 of 179 protein–protein complexes from the Protein–Protein Interaction Affinity Database 2 . 0 [51] . We selected only non-enzymatic proteins based on interaction classification , absence of enzyme comission ( EC ) number , and UniProt annotations . We also excluded complexes containing antibodies , since antibodies evolve on a different time-scale and by different mechanisms than other cellular proteins . We acquired structures of the protein–protein complexes from the RCSB protein database . To calculate site-specific evolutionary rates , we first extracted the amino-acid sequences from the PDB structures . Using PSI-BLAST [52] , we then queried the UniRef90 database [16 , 17] to retrieve homologous sequences for each enzyme . Among these homologous sequences for each enzyme , we removed sequences with less than 10% pairwise divergence to any other sequence , to reduce phylogenetic bias . Next , we randomly downsampled the homologous sequences to a maximum of 300 sequences per enzyme . Then , we performed a multiple sequence alignment ( MSA ) of the sequences with MAFFT 7 . 215 ( Multiple Alignment using Fast Fourier Transform ) [53] and generated phylogenetic trees with RAxML 7 . 2 . 8 ( Randomized Axelerated Maximum Likelihood ) [54] using the LG substitution matrix ( named after Le and Gacuel ) [55] and the PROTCAT model of rate heterogeneity [56] . We calculated site-specific evolutionary rates with the program Rate4Site 2 . 01 [18] , using the MSAs and phylogenetic trees from the previous step as input . We used the empirical Bayes approach for rate estimation and the JTT ( Jones , Taylor , and Thorton ) model of amino acid replacement [57] . Lastly , we normalized the rates such that the rates for each protein have a mean of 1 . Because these rates are measured relative to the average divergence rate of the entire protein , they are dimensionless . Throughout this work , we refer to these site-specific relative rates as K , or simply “rates . ” For each protein structure , we calculated several predictor variables at each site . First , we calculated the weighted contact number WCN for each residue i as follows: WCNi=∑j≠i1rij2 . ( 1 ) Here , rij is the distance between the geometric center of the side-chain atoms in residue i and the geometric center of side-chain atoms in residue j . To calculate these distances for residue pairs involving glycine , which has no side-chain , we used the location of the Cα in those residues instead . Unless noted otherwise , WCN was calculated using the complete biological assembly of the protein . Next , we calculated the relative solvent accessibility ( RSA ) at each site . To this end , we first calculated the accessible surface area ( ASA ) using the software mkdssp [58 , 59] . We then normalized ASAs by the maximum solvent accessibility for each residue in a Gly-X-Gly tripeptide [31] . Peptide linkages across chains , typically disulfide bridges , were assigned an RSA of zero . Unless noted otherwise , RSA was calculated using the complete biological assembly of the protein . Finally , we calculated the distance d to the nearest catalytic residue for each residue in each structure . Most enzymes have multiple catalytic residues , so we define d as distance to the nearest catalytic residue . As was the case for WCN , distances were measured from the geometric center of the side-chain of one residue to the the geometric center of the side-chain of another residue . And in the case of glycines , the position of Cα was again used in place of the side-chain geometric center . Any residue with d = 0 is therefore a catalytic residue , and conversely , all catalytic residues lie at d = 0 . We defined interface residues as residues for which RSA differed by a minimum of 10% when calculated for the full biological assembly or for a single chain . All interface residues were included in the analyses presented in the main body of the text , but we excluded interface residues in the analyses presented in S17–S34 Figs . For each enzyme in the dataset , we fit the following linear models ( represented in standard R notation ) : K ~ d , K ~ RSA , K ~ WCN , K ~ RSA + d , K ~ WCN + d , and K ~ RSA + WCN + d , where K is site-specific evolutionary rate , RSA is relative solvent accessibility , WCN is weighted contact number , and d is distance to the nearest catalytic residue . All statistical analyses were carried out using the R software package [60] . Linear models are fit to each enzyme individually . After fitting the models , data are then binned for visualization purposes . Plots are generated with ggplot2 [61] . All code and data necessary to reproduce our analyses are available in a Github repository at: https://github . com/benjaminjack/enzyme_distance . Processed enzyme data are also provided as S1 Data . Processed data from the non-enzyme dataset are available as S2 Data . Parameter estimates for each linear model fitted to each enzyme are available in S3 Data . | The basic biochemical functions of life are carried out by large molecules called enzymes . Enzymes consist of long chains of amino acids folded into a three-dimensional structure . Within that structure , a specific cluster of amino acids , known as the active site , performs the biochemical function . Substituting one amino acid for another in the active site typically results in a defective , non-functional enzyme , and therefore mutations at or near enzyme active sites are often lethal . Moreover , even mutations far from the active site have been found to disrupt function . Nonetheless , as organisms evolve , enzymes accumulate random mutations . Where in enzymes’ structures do these mutations accumulate without causing harm ? Here , we observe evidence for extensive interactions between active sites and distant regions of the enzyme structure , in a comprehensive set of over 500 enzymes . We show that active sites tightly control the substitutions that an enzyme can tolerate . This control extends far beyond regions of the enzyme immediately adjacent to the active site , covering over 80% of a typical enzyme structure . Our findings have broad implications for molecular evolution , for enzyme engineering , and for the computational prediction of active-site locations in novel enzymes . | [
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... | 2016 | Functional Sites Induce Long-Range Evolutionary Constraints in Enzymes |
Many Gram-negative pathogens use a type IV secretion system ( T4SS ) to deliver effector proteins into eukaryotic host cells . The fidelity of protein translocation depends on the efficient recognition of effector proteins by the T4SS . Legionella pneumophila delivers a large number of effector proteins into eukaryotic cells using the Dot/Icm T4SS . How the Dot/Icm system is able to recognize and control the delivery of effectors is poorly understood . Recent studies suggest that the IcmS and IcmW proteins interact to form a stable complex that facilitates translocation of effector proteins by the Dot/Icm system by an unknown mechanism . Here we demonstrate that the IcmSW complex is necessary for the productive translocation of multiple Dot/Icm effector proteins . Effector proteins that were able to bind IcmSW in vitro required icmS and icmW for efficient translocation into eukaryotic cells during L . pneumophila infection . We identified regions in the effector protein SidG involved in icmSW-dependent translocation . Although the full-length SidG protein was translocated by an icmSW-dependent mechanism , deletion of amino terminal regions in the SidG protein resulted in icmSW-independent translocation , indicating that the IcmSW complex is not contributing directly to recognition of effector proteins by the Dot/Icm system . Biochemical and genetic studies showed that the IcmSW complex interacts with a central region of the SidG protein . The IcmSW interaction resulted in a conformational change in the SidG protein as determined by differences in protease sensitivity in vitro . These data suggest that IcmSW binding to effectors could enhance effector protein delivery by mediating a conformational change that facilitates T4SS recognition of a translocation domain located in the carboxyl region of the effector protein .
Many Gram-negative bacteria utilize a type IV secretion system ( T4SS ) to deliver proteins and DNA into a recipient cell [1] . The Legionella pneumophila Dot/Icm T4SS is an essential virulence determinant that translocates effector proteins into eukaryotic cells [2–4] . Effector proteins translocated by the Dot/Icm system function to delay endocytic maturation of the vacuole in which the bacterium resides , as well as promote remodeling of the L . pneumophila–containing vacuole into an organelle that resembles the endoplasmic reticulum ( ER ) [5–10] . L . pneumophila replication occurs in the ER-derived vacuole created by these Dot/Icm-dependent processes [2 , 3 , 11] . Currently , over 50 effector proteins have been shown to be translocated into eukaryotic cells by the Dot/Icm T4SS , and recent predictions suggest that there could be as many as 150 effectors [12–26] . Several studies have demonstrated that C-terminal regions of effector proteins are required for translocation into recipient cells by the T4SS [13 , 27–34] . Analysis of Dot/Icm effectors has not revealed significant primary sequence homology in the C-terminal regions of effector proteins . However , C-terminal translocation domains are thought to have shared biophysical properties critical for translocation by the T4SS . Currently , the only crystal structure of a type IV effector available is for the RalF protein of L . pneumophila [35] . RalF has a C-terminal domain of 20 amino acids ( aa ) that is necessary and sufficient for Dot/Icm-mediated protein translocation into host cells [14 , 32] . The RalF crystal structure revealed that this C-terminal domain is largely disordered and separated from the protein's globular domains by a long alpha helix [35] . Thus , for RalF there is evidence that a disordered C-terminal translocation domain is available for interaction with T4SS components and that the availability of this C-terminal domain is sufficient for substrate recognition by the Dot/Icm system . In contrast to RalF , recent studies have identified effectors that require the IcmS and IcmW proteins for efficient translocation [21 , 25 , 36] . IcmS and IcmW are small ( 12 and 18 kDa , respectively ) acidic ( pI ∼4 ) cytoplasmic proteins that have physical properties consistent with chaperone molecules found in many type III secretion systems [37] . L . pneumophila ΔicmS or ΔicmW strains retain the ability to replicate in macrophage-like cell lines , induce contact-dependent formation of pores in macrophage membranes , and secrete the DotA protein into culture supernatant [11 , 36 , 38] . All of these activities are abolished in most other dot and icm mutants , suggesting that IcmS and IcmW are not essential components of the Dot/Icm secretion machinery . In previous studies , the IcmS and IcmW proteins were found to interact and form a complex in the L . pneumophila cytoplasm [21 , 39 , 40] . Additionally , the IcmS protein has been shown to stabilize IcmW in the bacterial cell , indicating that the IcmS–IcmW interaction is biologically significant . There is both genetic and biochemical evidence that IcmS and IcmW associate with a subset of Dot/Icm effectors [21 , 25] , and that translocation of effectors that associate with the IcmSW complex is reduced in ΔicmS and ΔicmW strains [21] . Interestingly , RalF is currently the only effector translocated into host cells independent of the IcmS and IcmW proteins , and it has been shown that IcmSW does not bind to RalF in vitro [21] . These data indicate that IcmS and IcmW play an important role in the translocation of some , but not all of the effectors of the Dot/Icm T4SS , suggesting that the IcmSW complex might be critical for the type IV recognition of a particular class of effector proteins . Here , we set out to further explore this possibility by investigating how the IcmSW complex contributes to the translocation of type IV substrates .
Previous studies indicated that Dot/Icm effector proteins can be separated into different classes based on their requirement for icmS and icmW , suggesting that the IcmSW complex may target a particular set of substrates [21] . The sid ( substrate of icm-dot transporter ) genes encode a diverse group of proteins transported by the Dot/Icm system , some of which were shown to be translocated by a mechanism involving the IcmSW complex [13 , 21 , 25] . These effectors do not demonstrate primary sequence homology . Several of the Sid proteins have regions containing predicted coiled-coil regions and hydrophobic segments that are predicted to represent membrane-spanning regions ( Figure S1 ) . To define the repertoire of Sid proteins utilizing IcmSW for translocation by the Dot/Icm system , reporter proteins were constructed that consist of the catalytic domain of the calmodulin-dependent adenylate cyclase ( Cya ) from Bordetella pertussis fused to the amino terminus of each Sid protein . The production of cyclic adenosine-monophosphate ( cAMP ) resulting from the translocation of a Cya-Sid hybrid into mammalian cells was used to measure productive translocation of each effector [32 , 41] . These data show that all of the Cya-Sid fusion proteins were translocated by L . pneumophila into host cells by a Dot/Icm-dependent mechanism ( Figure S1 ) . When the Cya-Sid proteins were expressed in either ΔicmS or ΔicmW strains , a significant decrease in translocation was observed for all hybrids with the exception of Cya-SidF , which was translocated at nearly wild-type levels by either the ΔicmS or ΔicmW strain ( Figure 1A ) . The icmS or icmW independence observed for SidF translocation was similar to that shown previously for RalF [21] . The defect in translocation of Sid proteins by dot and icm mutants was not due to a loss of stability , as steady-state protein levels of the hybrids were similar in wild-type , ΔdotA , ΔicmS , or ΔicmW strains ( Figure 1B ) . Because the IcmS and IcmW proteins might retain partial activity when they are not in a complex with each other , translocation of the Sid proteins was analyzed in an L . pneumophila strain lacking both icmS and icmW . The translocation of most Cya fusion proteins was reduced in the ΔicmS , ΔicmW double mutant compared to either single mutant . An additive translocation defect was observed for Cya-SidA when data for wild-type L . pneumophila was compared to the single mutants ΔicmS or ΔicmW , and the ΔicmS , ΔicmW double mutant ( Figure 2A ) . Translocation of SidA , SidB , SidC , SidD , SidE , SidG , and SidH decreased roughly 10-fold in the ΔicmS , ΔicmW double mutant compared with the single ΔicmS mutant ( Figure 2B ) . Although SidF translocation was largely independent of icmS or icmW in the single mutants , translocation of SidF was reduced by nearly 100-fold in the ΔicmS , ΔicmW double mutant ( Figure 2B ) . Additionally , IcmSW was isolated in a complex with SidF when the three proteins were co-expressed in Escherichia coli ( unpublished data ) . Translocation of RalF was not severely attenuated in the ΔicmS , ΔicmW double mutant , providing further evidence that RalF is an IcmSW-independent effector ( Figure 2B ) . Equal translocation of SidF by wild-type L . pneumophila in cytochalasin D–treated cells indicates that that delivery of this effector by the Dot/Icm system does not require bacterial internalization ( Figure 2B ) . There was a similar defect in Sid protein translocation by the ΔicmS , ΔicmW double mutant observed when bacterial uptake was blocked using cytochalasin D ( Figure 2B ) . This result indicated that the defect in effector translocation was not due to differences in the vacuoles that the ΔicmS , ΔicmW double mutant occupied compared with wild-type L . pneumophila . Fusion protein levels were similar in wild-type L . pneumophila and the ΔicmS , ΔicmW double mutant , indicating that the translocation defect observed for the double mutant is not due to a difference in steady-state protein levels ( Figure 2C ) . No defect in internalization of the ΔicmS , ΔicmW double mutant was observed when compared to wild-type L . pneumophila , indicating that translocation differences were not due to differences in uptake of the two strains ( Figure 2D ) . The translocation efficiency of each Cya-Sid fusion protein by the single ΔicmS or ΔicmW mutants or the ΔicmS , ΔicmW double mutant was calculated as the percent of cAMP relative to the wild-type control strain producing the same fusion protein . The average translocation defect for each strain was calculated using these efficiency values ( Figure 2E ) . These data show a significant decrease in Sid translocation in both the ΔicmW and ΔicmS single mutants . Although the defect observed for the icmS mutant was larger than the defect for the ΔicmW mutant , this difference was not statistically significant . A significant reduction in translocation efficiency was observed in the ΔicmS , ΔicmW double mutant when compared with the single mutants . These data demonstrate that a functional IcmSW complex is critical for the efficient translocation of multiple effector proteins and suggests that IcmS and IcmW may retain partial activity when they are not in a complex with the other protein . The SidG protein demonstrated a strict requirement for icmS and/or icmW for efficient translocation . SidG contains a predicted coiled-coil region , as well as two distinct hydrophobic segments that may represent membrane-spanning regions ( Figure 3A ) . Deletion analysis was used to map the translocation signal in SidG . Deletion of 35 aa from the SidG C-terminus abrogated translocation of the protein into host cells , demonstrating that this C-terminal region is necessary for Dot/Icm-dependent translocation ( Figure 3B ) . Consistent with this region encoding a translocation signal , a Cya hybrid containing only the C-terminal 20 aa of SidG ( 946–965 ) was sufficient to promote Dot/Icm-mediated protein translocation ( Figure 3C ) . Overall , these data define a C-terminal region in SidG that is necessary and sufficient for Dot/Icm-mediated translocation . To investigate whether the IcmSW complex is important for recognition of the C-terminal translocation domain of an icmSW-dependent effector , we measured translocation of a Cya hybrid containing a C-terminal 30-aa region of SidG containing the translocation signal . These data show that efficient translocation of the Cya-SidG ( 936–965 ) protein did not require the IcmS and IcmW proteins ( Figure 4A ) . Thus , the IcmSW complex does not contribute directly to the recognition of the C-terminal translocation signal in SidG . These experiments suggest sequences that are N-terminal to the SidG translocation signal confer the icmSW-dependent translocation phenotype observed for the full-length fusion protein . To identify sequences necessary for the icmSW-dependent phenotype , N-terminal sequences were deleted and translocation efficiencies of the deletion derivatives were compared between wild-type L . pneumophila and the ΔicmS , ΔicmW double mutant . Efficient translocation of Cya-SidG ( 500–965 ) required the IcmSW complex , whereas an icmSW-independent translocation phenotype was observed for the Cya-SidG ( 600–965 ) protein ( Figures 4B and S2 ) . These data define a central region in the SidG protein as being important for the icmSW-dependent translocation phenotype , and indicate that this internal region negatively impacts SidG translocation in the absence of the IcmSW complex . IcmSW binding to SidG was analyzed by co-purification of recombinant proteins produced in E . coli [21] . N-terminal hexa-histidine-tagged IcmW ( H6IcmW ) and IcmS were produced from a bi-cistronic message in conjunction with M45-epitope-tagged SidG produced from a second vector in the same E . coli cell [21] . H6IcmW and associated proteins were captured from lysates on affinity columns . These data show that SidG co-purified with the H6IcmW/IcmS complex isolated from E . coli cells producing the three proteins ( Figure 5A ) . RalF did not co-elute with the H6IcmW/IcmS complex when produced in the same E . coli cell , as shown previously ( Figure 5A ) [21] . Additionally , SidG produced in the absence of the IcmSW complex did not bind to the affinity column ( Figure 5B ) . These data demonstrate that SidG contains binding sites that mediate specific interactions with the IcmSW complex . Further analysis of SidG interactions with the IcmSW complex revealed that H6IcmW/IcmS complex immobilized on a column was unable to bind SidG protein contained in E . coli lysates ( Figure 5C ) , suggesting that interactions between SidG and IcmSW occur in the cell before SidG folding has been completed . When M45-SidG was co-expressed in E . coli with either H6IcmS or H6IcmW , binding of SidG to these individual components of the IcmSW complex was detected ( Figure 5D ) . The control protein RalF did not co-purify with either H6IcmS or H6IcmW ( Figure 5D ) . These data suggest that IcmS and IcmW each have effector binding activity . LvgA is a factor that has been shown to contribute to the stability of IcmS and IcmW in L . pneumophila cells by interacting directly with IcmS [39 , 40] . SidG did not co-purify with H6LvgA when both proteins were produced in E . coli ( Figure 5D ) . Additionally , translocation of Cya-SidG by an lvgA mutant was not as severely attenuated as that observed for Cya-SidG in the ΔicmS mutant ( Figure S3 ) . These data suggest that LvgA is not interacting with effectors directly to regulate translocation . Deletion derivatives were analyzed to identify potential binding regions for the IcmSW complex on SidG . N-terminal deletion analysis revealed interactions between H6IcmW/IcmS and SidG ( 500–965 ) ( Figure 5E ) . The SidG ( 600–965 ) protein did not interact with H6IcmW/IcmS , indicating that a binding region for IcmSW is located in the SidG ( 500–600 ) region . Interestingly , when C-terminal deletions were analyzed , an interaction between IcmSW and SidG ( 1–500 ) was observed ( Figure 5E ) . This suggests the existence of a second IcmSW binding region located in the SidG ( 1–500 ) region . Further deletion analysis revealed that SidG ( 1–300 ) failed to bind IcmSW . These data suggest that a second binding region for IcmSW resides between residues 300 and 500 . Collectively , these data suggest that the SidG ( 300–600 ) region contains at least two IcmSW binding sites ( Figure 5F ) , the locations of which are consistent with translocation data indicating that regions outside of the C-terminal translocation signal sequence are the targets for IcmSW function . The binding of IcmSW to effectors suggests that this complex plays a role in maintaining full-length SidG in a translocation-competent state . One possible mechanism of IcmSW action would be to prevent deleterious interactions from occurring that prevent SidG from being properly engaged by the Dot/Icm system , which was investigated . In Figure 2C , the absence of the IcmSW complex was shown not have a dramatic effect on steady-state protein levels for the different Cya-Sid hybrids , suggesting that IcmSW is not important for preventing the degradation of effectors . Additionally , we found that IcmSW was not important for maintaining intracellular levels of M45-tagged SidG ( Figure 6A ) , and the half-life of SidG was not affected in the L . pneumophila ΔicmS , ΔicmW double mutant ( Figure 6B ) . These data indicate that the decrease in SidG translocation observed for mutants deficient in the IcmSW complex was not due to reduced cellular levels of SidG . Fractionation of bacterial cells revealed that there was no difference in the sub-cellular localization or solubility of RalF or SidG in wild-type L . pneumophila compared to the ΔicmS , ΔicmW double mutant ( Figure 6C and 6D ) . Some of the SidG protein from bacterial lysates was located in a pellet fraction and could be extracted from this fraction using TritonX-100 . Deletion of the C-terminal 180 aa of SidG ( SidG ( 1–785 ) ) did not affect localization of the protein . When hydrophobic segments in SidG predicted to be membrane-spanning domains were deleted , the resulting SidG ( 1–690 ) protein was found exclusively in the soluble fraction , suggesting that the hydrophobic segments are capable of mediating membrane association ( Figure 6D ) . The SidG ( 691–965 ) protein , which harbors the putative membrane-spanning regions , fractionated with TritonX-100-soluble membrane proteins when expressed in L . pneumophila , providing additional evidence that these hydrophobic regions mediate membrane interactions ( Figure 6D ) . No differences for these SidG deletion derivatives were observed when sub-cellular localization of the proteins was compared in wild-type L . pneumopihla cells to ΔicmS , ΔicmW double mutant cells . Although these data suggest that SidG has the potential to associate with membranes , the IcmSW complex did not affect this localization . To determine whether the absence of IcmSW results in SidG aggregation , gel filtration chromatography was used to compare the mobility of SidG produced in wild-type L . pneumophila to SidG produced in a ΔicmS , ΔicmW double mutant ( see Protocol S1 ) . SidG was not detected in the void volume when lysates from wild-type or ΔicmS , ΔicmW double mutant L . pneumophila were fractionated using a Superdex-200 column , suggesting that SidG protein aggregates were not present in the lysates ( Figure S4 ) . Interestingly , the mobility of SidG isolated from the ΔicmS , ΔicmW double mutant was slightly different from the mobility of SidG isolated from wild-type L . pneumophila . This could reflect a conformational difference in the SidG protein or this shift could be the direct result of IcmSW being associated with SidG . In addition to gel filtration , glycerol gradient centrifugation was used to examine whether SidG aggregates were present in the ΔicmS , ΔicmW double mutant ( see Protocol S1 ) . Consistent with the gel filtration results , glycerol gradients revealed no evidence of SidG aggregation in the ΔicmS , ΔicmW double mutant ( Figure S4 ) . Thus , we conclude that the reduction of SidG translocation in the ΔicmS , ΔicmW double mutant is not the result of rapid degradation of SidG , alterations in the solubility of SidG , or differences in the sub-cellular localization of SidG in the ΔicmS , ΔicmW double mutant . It is possible , however , that IcmSW binding alters the conformation of SidG , and that this could be important for engagement of SidG by the Dot/Icm system . If the primary role of the IcmSW complex is to facilitate a C-terminal signal sequence being displayed by an effector protein , the percent decrease in translocation of an effector observed using a ΔicmS , ΔicmW double mutant would correlate with a drop in the ratio of effector in a conformational state where the C-terminal domain is accessible . Accordingly , increasing the total amount of SidG in the cell , which would increase the total number of SidG molecules in a conformational state where the C-terminal domain is accessible , should suppress the translocation defect in the ΔicmS , ΔicmW double mutant . By contrast , if the primary role of the IcmSW complex is to prevent effector proteins from aggregating , overproduction of the effector should augment the translocation defect . To determine whether overproduction of SidG had any effect on translocation by the Dot/Icm system , Cya-SidG production was increased using a heterologous IPTG-inducible promoter upstream of the gene encoding the reporter . After Cya-SidG production was upregulated by IPTG induction , translocation levels were measured and compared with previous translocation data . As shown in Figure 7 , IPTG induction of the gene encoding Cya-SidG in the ΔicmS , ΔicmW double mutant resulted in a 10-fold increase in translocation compared to the control in which IPTG was not added . IPTG induction of the gene encoding Cya-SidG in the wild-type strain resulted in only a modest increase in translocation , indicating that the amount of SidG in a translocation-competent state is near saturation in the wild-type strain . The hypothesis that IcmSW binding to effectors results in a conformational change that enhances recognition of the C-terminal translocation domain predicts that alterations in the C-terminal region of an effector could also reduce the requirement for IcmSW binding to an effector by enhancing presentation of the translocation domain . To address this possibility , an 8-aa FLAG epitope containing multiple charged residues was positioned at the C-terminus of RalF and SidG fusion proteins having M45-Cya at the N-terminus . The FLAG-tagged RalF and SidG proteins were expressed in either wild-type L . pneumophila or the ΔicmS , ΔicmW double mutant to assay translocation . In wild-type L . pneumophila the Cya-SidGFLAG protein was translocated to levels similar to those of the Cya-SidG protein , indicating that the C-terminal FLAG epitope does not affect SidG translocation ( Figure 8A ) . In the ΔicmS , ΔicmW double mutant , translocation of the Cya-SidGFLAG protein was 100-fold higher than translocation of the control Cya-SidG protein , indicating that inclusion of an amino terminal FLAG tag greatly reduces the requirement for icmSW . The observation that translocation of the Cya-SidGFLAG protein was still reduced in the ΔicmS , ΔicmW double mutant compared to translocation by wild-type L . pneumophila indicates that the FLAG tag does not fully suppress the requirement for icmSW , which suggests that the tag does not disrupt the conformation of the SidG protein to the same extent as IcmSW binding . The Cya-RalFFLAG hybrid was not translocated to detectable levels , consistent with previous results demonstrating that the RalF C-terminal translocation domain is non-functional when masked by an epitope tag ( Figure 7B ) [32] . Both RalF and SidG hybrids were detected at similar levels from wild-type L . pneumophila and the ΔicmS , ΔicmW double mutant using antiserum specific for either the N-terminal M45 epitope or the C-terminal FLAG epitope ( Figure 8B ) , suggesting that protein stability was not altered significantly by addition of the C-terminal FLAG tag . Expression of SidGFLAG with the Cya domain removed resulted in equivalent amounts of steady-state protein levels detected in wild-type L . pneumophila and the ΔicmS , ΔicmW double mutant when total protein was precipitated from whole cells ( Figure 8C ) . When bacteria were lysed in the absence of protease inhibitors , however , the SidGFLAG protein produced by the ΔicmS , ΔicmW double mutant showed signs of degradation by an endogenous protease ( Figure 8D ) . This result suggested that IcmSW binding to SidGFLAG promotes a conformational change in the substrate that affects protease sensitivity following bacterial disruption . A polyclonal antibody specific for the C-terminal 90 aa of the SidG polypeptide was used to examine whether the binding of IcmSW to SidG results in a conformational change that can be detected by protease accessibility . This antibody detected endogenous SidG produced by wild-type L . pneumophila and showed that endogenous SidG levels were similar in the ΔicmS , ΔicmW double mutant ( Figure 9A ) , providing further evidence that the IcmSW complex is not required for SidG stability . To determine whether IcmSW binding alters protease accessibility , RalF and SidG were produced in E . coli in either the absence or presence of the H6IcmW/IcmS complex . Cleavage products from whole cell lysates partially digested with trypsin were detected by immunoblot analysis using antibodies specific for RalF or SidG ( Figure 9B ) . Cleavage products observed for RalF were indistinguishable in the presence and absence of the H6IcmW/IcmS complex . These data indicate that IcmSW does not affect RalF conformation and are consistent with data showing that IcmSW does not bind to RalF . Differences in the appearance of a predominant SidG cleavage product were observed when reactions containing IcmSW were compared to reactions without IcmSW , which is consistent with the hypothesis that IcmSW binding alters the conformation of SidG ( Figure 9B ) . Similar assays were performed on SidG lysates where the trypsin was diluted 1 , 000-fold . A difference in the appearance of a major cleavage product was again observed when reactions containing IcmSW were compared to reactions without IcmSW ( Figure 9C ) . Importantly , cleavage products in a size range between 50 kDa and 60 kDa were detected in reactions without the IcmSW complex , and were less prominent in reactions containing the IcmSW complex . Because the SidG antibody recognizes the C-terminal 90-aa region of the protein , the locations of the cleavage sites must be located between residues 400 and 500 of SidG . These data are consistent with IcmSW interactions with SidG protecting protease-sensitive sites in the region required for IcmSW binding . To more carefully examine the emergence of these cleavage products , a time course experiment was performed with a constant protease concentration ( Figure 9D ) . Again , SidG cleavage products in the 50- to 60-kDa range preferentially emerge in lysates that do not contain IcmSW . Collectively , these results indicate that IcmSW alters the conformation of SidG , and suggest that binding of IcmSW to the central core of SidG protects protease sensitive sites .
Over 50 different L . pneumophila Dot/Icm effectors have been identified , and the total number of effectors is expected to exceed 150 [12–26] . Secondary structure predictions indicate that L . pneumophila effectors are diverse ( Figures S1 and 6D ) . There are effectors with predicted membrane-spanning helices , regions of coiled-coil domains , and homologies found primarily among eukaryotic proteins . How such a large number of effectors with such diverse structural properties can be efficiently recognized by a single secretion system is a question of fundamental importance . Analysis of the RalF protein has shown that substituting individual amino acids at positions near the C-terminus can dramatically reduce Dot/Icm-mediated translocation [32] , which is consistent with studies of other type IV secretion systems where individual amino acid residues critical for type IV translocation have been identified near the C-terminus [29] . The C-terminal 20-aa residues that comprise the translocation signal for the RalF protein were disordered in the X-ray crystal structure [35] . This has led to speculation that a disordered C-terminal domain could be important for effector protein engagement with the type IV secretion machinery . In addition to a C-terminal translocation signal , the IcmSW complex had been implicated as being important for recognition of effector proteins by the Dot/Icm system [21] . Here , we provide data that link the function of the IcmSW complex with the hypothesis that a disordered C-terminal secretion signal is important for effector protein translocation . Our data show that IcmSW can bind to effectors , that the binding region is located outside of the C-terminal translocation domain , and that Dot/Icm-mediated recognition of the minimal C-terminal translocation domain does not require the IcmSW complex ( Figures 3 , 4 , and 5 ) . These data are inconsistent with the IcmSW complex functioning as an adapter that facilitates recognition of a C-terminal translocation domain by binding to both the effector and the Dot/Icm translocation apparatus . If the primary function of IcmSW was to serve as an adapter , then removing the binding site for IcmSW from an effector should result in a translocation defect in wild-type L . pneumophila that is comparable with the translocation defect observed for the full-length effector produced in the ΔicmS , ΔicmW double mutant . Our data show the opposite . We found that removing regions from the effector SidG that contained binding sites for the IcmSW complex eliminated a requirement for the IcmS and IcmW proteins for efficient translocation ( Figure 4 ) . Taken together , these data identify internal regions of an effector protein that have a negative effect on recognition of the C-terminal translocation signal and show that IcmSW binding to these regions neutralizes this negative effect . There are two likely explanations for why IcmSW binding to an effector can neutralize intrinsic signals that interfere with translocation . The first is that IcmSW binding prevents inappropriate interactions between the effector and other proteins . Alternatively , IcmSW binding could prevent interactions from occurring within the effector that masks recognition by the Dot/Icm system . We were unable to show that effectors are more prone to aggregation or degradation in the absence of the IcmSW complex , which would be effects that might result from inappropriate homo- or heterotypic protein–protein interactions ( Figures 6 and S4 ) . The observation that overproduction of the effector protein SidG in an ΔicmS , ΔicmW double mutant suppressed the translocation defect also suggests that the IcmSW complex is not preventing inappropriate homo- or heterotypic protein–protein interactions , as overexpression would most likely promote these interactions and augment the translocation defect ( Figure 7 ) . Importantly , we were able to show that introduction of a small epitope tag to the SidG C-terminus greatly affected the icmSW dependency for translocation , and enhanced susceptibility of SidGFLAG to degradation following lysis of ΔicmS , ΔicmW double mutant L . pneumophila , suggesting that IcmSW binding alters the conformation of SidG ( Figure 8 ) . This evidence was further supported by limited proteolysis experiments that demonstrated conformational changes in SidG induced by IcmSW binding . Thus , our data fit better with a model that predicts IcmSW binding to effectors facilitates the display of the C-terminal translocation domain ( Figure S5 ) . The model we propose also suggests that the IcmSW complex binds to effectors before the proteins have folded completely . Whether IcmSW binding to effectors is co-translational or occurs after synthesis has been completed can not be determined from our data; however , the observation that purified IcmSW does not bind in vitro to effectors expressed separately suggests that IcmSW binds poorly to effectors in their native conformation . Importantly , previous data has shown that IcmS binds very well to members of the SidE family of effectors after transfer from a SDS-PAGE gel to a nitrocellulose membrane , suggesting that IcmS can bind to an immobilized effector in a denatured state [25 , 36] . These data also imply that IcmS can bind to an effector independent of IcmW . Consistent with these previous data , we show here that IcmS and IcmW can bind to the effector protein SidG independent of each other and that binding can be detected only when the proteins are synthesized in the same bacterial cell ( Figure 5 ) . Although effectors may have independent binding sites for IcmS and IcmW , the observation that IcmW is highly unstable when it is not associated with IcmS implies that these proteins function as a complex [21] . It is significant that IcmS and IcmW are relatively small proteins predicted to have an acidic pI , which are properties shared by chaperone proteins that function in the translocation of effector proteins by a type III secretion mechanism [37] . The model we propose for the function of the IcmSW complex has parallels to what is known about the function of chaperones used for effectors translocated through the type III secretion apparatus . Chaperones used by the type III secretion system maintain effectors in a partially unfolded state , which facilitates recognition of an N-terminal secretion signal in type III effectors [42–46] . Recognition of the effector by the type III secretion system stimulates disassembly of the chaperone–effector complex by an ATPase that is essential for secretion system function [44 , 47] . Our data suggest that IcmSW binding to effectors facilitates display of a C-terminal translocation signal . There are at least two proteins in the Dot/Icm system with predicted nucleotide-binding sites that might engage effectors and initiate translocation through the Dot/Icm system [39 , 48] . In particular , the DotB protein is a critical component of the Dot/Icm system that is thought to hydrolyze ATP to energize transport of effectors through the apparatus [48] . It will be interesting to test in the future whether DotB can recognize C-terminal translocation signals directly and has an activity that disassembles the IcmSW–effector protein complex . The crystal structure of RalF and molecular analysis of this protein have provided clues as to why not all Dot/Icm effectors show the same level of dependency for IcmSW for translocation [35] . RalF does not bind to IcmSW , the IcmSW complex does not contribute to RalF translocation , and the RalF protein has a disordered C-terminal translocation signal . According to the RalF data , effectors having a C-terminal translocation domain that is disordered in the absence of IcmSW binding should be engaged by the Dot/Icm system efficiently by mutants lacking the IcmSW complex . This would explain why appending a minimal C-terminal translocation sequence to Cya results in Dot/Icm-dependent translocation by a process that is independent of IcmSW . According to our model of IcmSW function , removal of sequences that are N-terminal to the translocation signal of an icmSW-dependent effector prevents complete folding of the C-terminal domain containing this signal , mimicking what occurs when IcmSW binds to the full-length effector . Although the model we have put forward suggests that IcmSW binding to effectors facilitates presentation of a C-terminal translocation domain , it is possible that IcmSW binding serves other functions that are more subtle and difficult to reveal using the assays available to measure effector protein translocation and our limited knowledge of the type IV secretion pathway . For instance , if type IV effectors are translocated in a partially unfolded state , IcmSW binding could potentially be important for translocation of effectors that are difficult for the type IV machinery to unfold once they have achieved their most thermodynamically stable conformation . Additionally , with over 150 predicted effectors , it remains unclear how translocation of effectors is regulated and whether spatial and temporal regulation of effector translocation is important for L . pneumophila modulation of host cell functions [26 , 39 , 49 , 50] . Furthermore , IcmSW could compete with inhibitory proteins that sequester substrates under unfavorable conditions for translocation . Whether IcmSW binding to effectors plays any role in regulating the timing of effector translocation remains an interesting possibility . As more information on the molecular mechanisms important for type IV secretion become available , it should be possible to test how IcmSW might affect recognition of effectors by other type IV secretion components . In conclusion , these studies on IcmSW function have provided new insight into how effector protein C-terminal translocation domains are presented to the type IV machinery and indicate that future studies on this interesting chaperone complex should reveal new information on biochemical activities important for effector protein translocation through the type IV apparatus .
Bacterial strains , plasmids , and primers used to generate constructs in this study are described in Table S1 . The sidG deletion strain was generated as described , using the primers 69 , 70 , 71 , 103 [21] . L . pneumophila strains were cultivated on charcoal yeast-extract agar ( CYEA ) or ACES yeast-extract broth ( AYE ) ( pH 6 . 9 ) supplemented with chloramphenicol ( 6 . 25 μg/ml ) , or kanamycin ( 20 μg/ml ) where appropriate . E . coli strains were cultivated in Luria-Bertani ( LB ) broth supplemented with chloramphenicol ( 25 μg/ml ) , or ampicillin ( 100 μg/ml ) where appropriate . Translocation assays were performed as described [21] . Briefly , monolayers containing 1 × 105 CHO FcγRII cells were infected with 3 × 106 opsonized L . pneumophila ( MOI = 30 ) expressing Cya hybrid proteins . After 1 h of incubation at 37 °C , 5% CO2 , monolayers were washed with PBS and lysed . Total cAMP was extracted and quantified using cAMP Biotrak Enzymeimmunoassay System ( GE Healthcare ) . Where appropriate , cytochalasin D ( 10 μM ) ( Sigma-Aldrich ) was added to the tissue culture medium 30 min prior to and throughout the infection . Stationary phase L . pneumophila were harvested , corrected for load ( OD600 ) , and precipitated in 10% trichloroacetic acid . Precipitated protein was washed in acetone , resuspended in sample buffer ( 62 . 5 mM Tris-HCl [pH 6 . 8] , 20% SDS , 20% glycerol , 8% β-mercaptoethanol , 3 M urea ) , boiled , and loaded on 10% or 15% SDS-PAGE . Gels were transferred to PVDF , and blocked and probed with antibody specific for either the M45 epitope , DotA , RalF , chloramphenicol acetyltransferase ( Cat ) ( Sigma-Aldrich ) , or FLAG ( Sigma-Aldrich ) . Detection was carried out as described [21] . The SidG polyclonal antibody was generated against purified recombinant H6SidG876–965 by Cocalico Biologicals . Monolayers of 1 × 105 CHO FcγRII cells were cultured in medium containing L . pneumophila–specific opsonizing antibody for 1 h prior to infection . Where appropriate , cytochalasin D was added to ( 10 μM ) 30 min prior to infection . L . pneumophila strains were added to the monolayer ( MOI = 30 ) , centrifuged 5 min at 1 , 000 rpm , heated to 37 °C in a water bath for 5 min , and subjected to incubation at 37 °C , 5% CO2 for 1 h . Gentamycin ( Invitrogen ) was added ( 10 μM ) 15 min after infection where appropriate . Media was aspirated from the monolayers and they were washed 3x with PBS . Tissue culture cells were osmotically lysed with ice-cold H20 for 10 min . Lysates were resuspended and serially diluted , and aliquots were plated on CYEA and incubated at 37 °C to determine colony-forming units ( CFU ) . E . coli BL21-DE3 ( Novagen ) was transformed with an expression vector ( pEC66 ) that harbors a di-cistronic message encoding H6icmW and icmS , as well as a second expression vector that encodes an N-terminal M45 epitope–tagged hybrid to the L . pneumophila gene of interest ( RalF or SidG ) . Cultivation and co-purification of complexes were performed as described [21] . To examine the capture of SidG by H6IcmW/IcmS , 0 . 5-l cultures of E . coli BL21-DE3 expressing H6IcmW and IcmS were cultivated either together or separately from an expression vector producing M45-SidG . Cultures were harvested , resuspended in 22 ml of lysis buffer ( 50 mM Tris-HCl [pH 7 . 5] , 150 mM NaCl , 1 mM DTT , 1 mM PMSF ) , and subjected to lysis by French pressure cell ( single pass at 18 , 000 × psi ) . Cultures were clarified by centrifugation at 15 , 000g for 15 min , and 20 ml of the supernatant was loaded onto a Ni-NTA column ( 1 ml bed volume ) ( Qiagen ) equilibrated with lysis buffer . When produced separately , clarified lysates from E . coli BL21-DE3 expressing M45-SidG were loaded on Ni-NTA columns that were pre-charged with lysates expressing H6IcmW/IcmS and washed with 20 ml lysis buffer . Full-length and truncation derivatives of M45-SidG that were co-expressed with H6IcmW/IcmS were lysed , clarified , and loaded onto Ni-NTA columns under identical conditions . Columns were washed with lysis buffer containing 10 mM imidazole , 5 mM MgATP ( 20 ml ) , 25 mM imidazole ( 10 ml ) , 50 mM imidazole ( 10 ml ) , and eluted with 500 mM imidazole ( 4 × 1 ml ) in lysis buffer ( fraction 2 shown ) . Each fraction is represented at equivalent volumes with the exception of the eluate fractions , which are enriched 5x . Detection of the IcmSW component was verified by mixing the eluate 1:1 with sample buffer , resolving on 15% SDS-PAGE and Coomassie blue staining . Detection of M45-tagged protein was visualized through immunoblotting the fractions with monoclonal antisera specific to the M45 epitope . Co-purification of SidG or RalF with either H6IcmS ( pEC27 ) , H6IcmW ( pEC13 ) , or H6LvgA ( pEC329 ) was performed similarly . L . pneumophila strains expressing N-terminal M45 epitope–tagged effectors were cultured in AYE broth at 37 °C to stationary phase , corrected for bacterial load by absorbance at 600 nm ( 10 ml of OD600 = 4 ) , and harvested by centrifugation . Samples were aspirated and pellets were resuspended in 10 ml of pre-warmed AYE broth ( 37 °C ) supplemented with kanamycin ( 50 μg/ml ) . Cultures were incubated in a 37 °C water bath , where aliquots of the suspension were collected at incremental time points , precipitated in 10% TCA , washed in acetone , resuspended in sample buffer , and analyzed by SDS-PAGE and immunoblotting . L . pneumophila strains expressing M45 epitope–tagged effectors ( Ralf or SidG ) were cultured in AYE broth at 37 °C to stationary phase , corrected for bacterial load by absorbance at 600 nm ( 10 ml of OD600 = 4 ) , and harvested by centrifugation . Bacterial pellets were resuspended in 0 . 5 ml ice-cold 200 mM Tris-HCl ( pH 8 . 0 ) and 0 . 5 ml ice-cold 50 mM Tris-HCl ( pH 8 . 0 ) , 1 M sucrose ) . Then , 10 μl of 0 . 5 M EDTA ( pH 8 . 0 ) and 10 μl of 10 mg/ml lysozyme ( Invitrogen ) were added to the suspension , which was diluted to a final volume of 2 ml with ice-cold H2O . After 30 min on ice , MgSO4 was added to a final concentration of 20 mM . Spheroplasts were harvested by centrifugation at 5 , 000g for 10 min , and resuspended in 5 ml 50 mM Tris-HCl ( pH 8 . 0 ) . Samples were subjected to lysis by sonication ( 3 × 30-sec bursts at 25%–35% intensity ) and were centrifuged at 5 , 000g for 10 min to produce a clarified lysate . The clarified lysate was removed and subjected to ultracentrifugation at 100 , 000g for 1 h ( 4 °C ) . The supernatant fraction was separated from the pellet , and the pellet fraction was resuspended in 1 ml Tris-HCl ( pH 8 . 0 ) . 10% TritonX-100 ( in H2O ) was next added to the resuspended pellet fraction to achieve a final concentration of 1% ( vol/vol ) and incubated on ice for 30 min . Suspensions were subjected to ultracentrifugation at 100 , 000g for 1 h ( 4 °C ) . The supernatant fraction was separated from the pellet fraction , which was resuspended in 1 . 1 ml Tris-HCl ( pH 8 . 0 ) . Each of the four fractions were mixed 1:1 with sample buffer , and subjected to 10% SDS-PAGE . Fractions were equivalently loaded on gels based on the volume of buffer that the fraction received . Samples were transferred to PDVF membrane and probed with monoclonal antisera specific to the N-terminal M45 epitope tag . E . coli BL21-DE3 ( Novagen ) was transformed with an expression vector alone ( pET15b ) or pEC66 that harbors a di-cistronic message encoding H6icmW and icmS , as well as a second expression vector that encodes an N-terminal M45 epitope–tagged hybrid to the L . pneumophila gene of interest ( RalF or SidG ) . Stationary phase cultures were diluted 1:50 in 0 . 5 l and incubated on a shaker ( 275 rpm ) for 2 h at 37 °C . IPTG was added to a final concentration of 1 mM and cultures were cultivated an additional 3 h at 37 °C . Cultures were harvested , resuspended in 10 ml of lysis buffer ( 50 mM Tris-HCl [pH 7 . 5] , 150 mM NaCl , 1 mM DTT ) , and subjected to lysis by French pressure cell ( single pass at 18 , 000 × psi ) . Cultures were clarified by centrifugation at 15 , 000g for 15 min and supernatants were adjusted for total protein concentration by absorbance at 280 nm and dilution with lysis buffer . Next , 100-μl aliquots were added to 40 μl lysis buffer containing 0 , 0 . 5 , 5 , 10 , 20 , or 40 μg trypsin ( Sigma-Aldrich ) on ice . Experments performed at the μg scale were incubated on ice for 1 h , whereas experiments performed at the ng scale were incubated at 37 °C for 1 h . After 1 h , PMSF was added to each sample to a final concentration of 1 mM . Time course experiments were performed similarly , where each aliquot was subjected to 20 ng trypsin and reactions were stopped with 1 mM PMSF . Each 140-μl reaction was next mixed with 100 μl sample buffer , boiled , and subjected to SDS-PAGE and immunoblotting with polyclonal antisera generated against either RalF or SidG786–965 . | Intracellular pathogens often manipulate the activities of the eukaryotic host cell in which they reside by using a specialized transport apparatus known as a type IV secretion system to deliver proteins that directly manipulate host cell processes . How proteins to be delivered into eukaryotic cells are recognized by a type IV section system is not well understood . For Legionella pneumophila , the bacterium that causes a severe pneumonia known as Legionnaires disease , a type IV system called Dot/Icm is used to deliver an estimated 150 different proteins into host cells during infection . In this study , we demonstrate that a complex consisting of the proteins IcmS and IcmW bind many of the substrate proteins transported into eukaryotic host cells by the Dot/Icm system . Binding of the IcmSW complex to Dot/Icm substrate proteins enhanced the efficiency by which the substrate proteins were transported into cells by a process that involved altering the conformation of the substrate protein . Thus , this work defines a step that is important for the type IV secretion process and provides new molecular details on substrate protein recognition by type IV secretion systems . | [
"Abstract",
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] | [
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] | 2007 | The Legionella pneumophila IcmSW Complex Interacts with Multiple Dot/Icm Effectors to Facilitate Type IV Translocation |
For epidemiological work with soil transmitted helminths the recommended diagnostic approaches are to examine fecal samples for microscopic evidence of the parasite . In addition to several logistical and processing issues , traditional diagnostic approaches have been shown to lack the sensitivity required to reliably identify patients harboring low-level infections such as those associated with effective mass drug intervention programs . In this context , there is a need to rethink the approaches used for helminth diagnostics . Serological methods are now in use , however these tests are indirect and depend on individual immune responses , exposure patterns and the nature of the antigen . However , it has been demonstrated that cell-free DNA from pathogens and cancers can be readily detected in patient’s urine which can be collected in the field , filtered in situ and processed later for analysis . In the work presented here , we employ three diagnostic procedures—stool examination , serology ( NIE-ELISA ) and PCR-based amplification of parasite transrenal DNA from urine–to determine their relative utility in the diagnosis of S . stercoralis infections from 359 field samples from an endemic area of Argentina . Bayesian Latent Class analysis was used to assess the relative performance of the three diagnostic procedures . The results underscore the low sensitivity of stool examination and support the idea that the use of serology combined with parasite transrenal DNA detection may be a useful strategy for sensitive and specific detection of low-level strongyloidiasis .
The soil-transmitted parasitic nematode Strongyloides stercoralis is increasingly recognized as a significant human pathogen that deserves consideration for inclusion in the public health interventions that are underway to control other medically important soil transmitted helminths ( STH ) [1] such as Ascaris lumbricoides , Trichuris trichiura and the hookworms Ancylostoma duodenale and Necator americanus [2 , 3] . The current STH control strategy does not include S . stercoralis as a target for chemotherapy . One of factors that has negatively influenced the inclusion of S . stercoralis as a target in the STH control efforts is the limited ability to diagnose an infection based on the standard , WHO-recommended , microscopic identification of larval parasites from stool samples [4] . While highly specific when carried out by experienced technical personnel , the sensitivity of this approach is compromised by the unpredictable , intermittent release of small numbers of larvae by adult parasites residing in the intestine [5] . Because this parasite is difficult to diagnose , the prevalence of S . stercoralis infection in many regions is largely unknown . There is a clear need for an improved approach for the diagnosis of S . stercoralis infection to define prevalence and the impact of intervention measures in the field . In recognition of this need for better diagnostics , serological methods have been devised [6] . While significant advances have been made in terms of sensitivity , detection of specific antibodies is still subject to individual response as well as the antigens used in the tests to measure anti-S . stercoralis antibodies [7] . For increased specificity , nucleic acid-based diagnosis of S . stercoralis from stool samples using qPCR has been introduced . Although specific and amenable to multiplexing for the parallel detection of other pathogens [8 , 9] , this process has limitations , again , due to the intermittent presence of small numbers of S . stercoralis larvae passed in the feces of most patients . Additionally , collection of stool specimens in the field is labor intensive , costly and cumbersome . The use of cell-free DNA in blood and other bodily fluids as biomarkers has gained wide acceptance in clinical laboratories . Cell-free DNA is being applied as a diagnostic marker for cancer , prenatal diagnosis and in infectious diseases , including parasitic diseases such as malaria , trypanosomiasis , leishmaniasis , schistosomiasis , strongylodiasis , and filariasis [10–12] . While most methods use blood , cell-free DNA is also readily detected in urine [12–14] , saliva [15] , stool [16] , and sputum [17] . Cell-free DNA that is initially released into the blood can pass through the glomerular barrier and appear as transrenal DNAs in the urine [13] as small fragments of ~150–300 bp [18] . The advantages of transrenal DNA-based diagnosis of infectious disease include: ( a ) urine collection is non-invasive , ( b ) urine is easy and cheap to collect and process , and , in theory , ( c ) transrenal DNA does not depend on the stage of the parasite or the tissue site of infection . In the current study , we employed a Bayesian Latent Class modeling approach to examine the diagnostic utility of three methodologically distinct diagnostic procedures—traditional comprehensive stool based parasitology , serology that employed a specific recombinant S . stercoralis larval antigen for the detection of anti-parasite antibodies , and a PCR-based analysis of urine for the detection of transrenal parasite DNA [19] . The Bayesian approach was used to address issues of misclassification of data because of different diagnostic targets and , importantly , the lack of a gold standard , to calibrate the diagnostic procedures . The selective modeling approach within a Bayesian framework also allowed us to establish a set of principled , evidence-based expectations about the diagnostic accuracy of the three methods and the overall prevalence , before incorporating the evidence from the observed data with the goal of improving the accuracy in estimates of regional prevalence of S . stercoralis .
Our study was a cross-sectional assessment of diagnostic tests in rural and urban communities in Northwestern Argentina , in the Departments of Oran , San Martin and Rivadavia in Salta province . Eligible communities were those assigned to a sanitary intervention program carried out by the teams from Universidad Nacional de Salta , the Regional Sanitarian Development Association NGO , ADESAR , and the Provincial Ministries of Public Health and First Infancy . The objectives of this collaborative network were to provide medical care and epidemiological surveillance of intestinal parasitic infections in remote villages of the Chaco and Yunga geographic regions . A total of 359 participants provided a stool , a urine , and a serum sample . The study was carried out and reported in accordance with the Standards for Reporting Diagnostic Accuracy ( STARD-BLCM ) guidelines [20] . Ethical approval for the study protocol and the informed consent form were obtained from Comité de Ética , Colegio Médico de Salta , Salta , Argentina dated 19 March 2015 , and Johns Hopkins University ( IRB number 6199 ) dated 30 April 2015 . All participants provided written informed consent prior to sample collection . Parents or guardians provided informed consent on behalf of minor participants . All members of these communities were invited to participate and received anthelmintic treatment free of charge based on the results of stool analysis . Prior to the data collection , we performed a Monte Carlo simulation study that generated datasets of n = 400 observations 2000 times using a latent class analysis model in ( Mplus 7 [21] [22] ) . The sample size was based on these simulations . In the model , based on an earlier study [15] , we assumed that the true Strongyloides prevalence was 30% . Stool examination sensitivity was estimated to be 30%-40% . DNA and serological test sensitivity and DNA detection sensitivity was estimated to be 95% and 85% , respectively , based on previous work [23] . In the model we considered simulations involving four different tests . We assumed that the true prevalence of Strongyloides infection was 30% with stool examination sensitivity 70% , DNA and serological test 95% and antigen capture sensitivity 85% [24] . In this same model we also assumed stool examination specificity 100% , DNA specificity 98% , and serology specificity 75% and antigen capture specificity at 80% [24] . All parameters and standard error biases did not exceed 10% for any parameters in the model . In the absence of a gold standard for diagnosis of this infection and to take into account the inherent data misclassification , we fitted a latent class model using a Bayesian approach to assess the performance of the three diagnostic procedures used in this study . The basic idea behind the Bayesian approach is that all unknown quantities/model parameters such as the true sensitivity and specificity of each diagnostic test as well as the prevalence of the infection are believed to have a distribution that captures uncertainty about these parameter values . This uncertainty is captured by a distribution that is defined before observing the data and is called the prior distribution or prior . Prior data are derived from previous information from publications or experience in the field [7 , 15] . Next , the observed evidence ( i . e . the actual data ) is expressed in terms of the likelihood function of the actual data . The actual data likelihood is then used to weigh the prior and this product yields the posterior distribution . Thus , the posterior distribution is a parameter comprised of the prior distribution and the likelihood function . Such a process allows simultaneous inferences to be made on all model parameters [29] . In our study , for each model parameter , the particular beta prior density was selected by matching the center of the range of the mean of the beta distribution according to Joseph et al . [29] . For stool sensitivity and specificity , we assumed a range of 20–40% ( mean = 30%; beta parameters a = 1 . 9 , b = 4 . 444 ) and a range of 95–100% ( mean = 97 . 5%; beta parameters: a = 420 . 3 , b = 10 . 7 ) , respectively [7 , 30] . For NIE-ELISA serology , we assumed a priori sensitivity of 81–88% , ( mean = 84 . 5%; beta parameters a = 45 . 7 , b = 15 . 1 ) and a specificity of 71–81% , ( mean = 76%; beta parameters a = 68 . 2 , b = 21 . 5 ) [26 , 31] . For the diagnostic sensitivity and specificity for PCR DNA as well as prevalence by age groups we assumed non-informative priors which correspond to beta parameters a and b = 1 . To account for age in the model for the prevalence of Strongyloides infection , <15 years old were considered children and ≥15 years represented adolescents and adults . As a sensitivity analysis , we also changed input values by 10% in each afore-mentioned prior , to evaluate the impact of priors on model outputs . As the examined tests in the present study are based on different biological measurements , we have assumed that they are not correlated to any substantial extent and thus that they are conditionally independent on the latent infection status ( i . e . the latent class in the fitted model ) . The software we used to fit such a model was WinBUGS [32] . Multiple chains were run and results examined to ensure convergence . The percent total agreement between PCR and NIE-ELISA serology results was calculated and Cohn’s kappa statistic was used to assess the overall agreement in results [33] . Analyses were done using the ‘irr’ package in R . ( https://cran . r-project . org/web/packages/irr/irr . pdf )
Table 1 outlines the distribution of the 359 participants in the study by age group ( <15 years and ≥15 years ) , sex , and environmental context ( rural vs urban ) . In the patients examined , 222/359 ( 62% ) were positive for one or more of the diagnostic tests ( Table 2 ) . Serology and the transrenal DNA detection assays defined prevalence of 38% and 31% , respectively . In contrast , stool examination identified only ~8% of the participants as harboring an infection with S . stercoralis . There were no significant differences in infection status by any of the demographic categories used in this study . The concordance in the assay outcomes between stool examination , serology , and the transrenal DNA test was evaluated ( Fig 1 ) . While ~53% of the participants were seropositive or transrenal DNA positive , only ~15% ( 52/359 ) were double positive for antibodies and transrenal DNA . Of the 359 samples examined total percent agreement between DNA and serology was only 61% . The kappa statistic was 0 . 131 with p = 0 . 0122 indicating the poor agreement between the two methods . Of the 30 patients who had detectable levels of parasites in their stool samples , 20 ( 66% ) and 22 ( 73% ) were positive by serological or transrenal DNA analysis , respectively . Over half of the stool-positive patients were also positive for serology and transrenal DNA ( ~4% of all patients ) . Therefore , nearly 70% of the seropositive participants tested negative for detectable amounts of parasite DNA in their urine and ~60% of the patients who were DNA positive had no detectable antibodies that bound to epitopes on the 31 kDa S . stercoralis L3 antigen . Table 3 contains the results from the Bayesian LCA model estimates ( i . e . posterior medians and 95% Credible Intervals ( CrI ) , which are the Bayesian analogs of confidence intervals ) for sensitivity and specificity for each of the three diagnostic tests and the prevalence of S . stercoralis infection for the two age groups . The S . stercoralis infection prevalence in persons <15 years was estimated as 13 . 5% ( 95% CrI 5 . 9–24 . 8 ) and for age ≥15 years this was estimated to be 19 . 8% ( 95% CrI 10 . 7–34 . 2 ) . These estimates are based on Bayesian latent class modeling of collective values between the three diagnostic tests having taken into account associated measurement error from each test , not on the results of any one single test , and thus they are more accurate than the empirically calculated prevalence in Table 2 . The estimate of sensitivity for serology slightly exceeded the estimate of the diagnostic sensitivity for urine-based PCR , but their 95% corresponding credible intervals overlapped , suggesting that the diagnostic performances of these two tests were similar . Specificity of urine based-PCR was estimated to be slightly higher than that estimated for serology , but , again , the 95% credible intervals overlapped with the corresponding estimate for serology . There was no substantial change in these results when the priors were altered by 10% . The Bayesian modeling results confirm the low sensitivity ( 43 . 6: 95% CrI: 25 . 7 to 70 . 4 ) and high specificity ( 97 . 9; 95% CrI: 96 . 5 to 98 . 9 ) of stool examination for S . stercoralis infection .
Soil-transmitted and other helminth infections are of increasing global importance and are the focus of several wide spread mass drug administration efforts to reduce the level of morbidity inflicted on endemic populations by these parasites [2 , 3 , 34] . As these programs progress and the prevalence and intensity of infection declines because of these interventions , it is imperative to employ diagnostic strategies with increasing sensitivity and specificity to monitor and identify lingering infections . Decisions to prematurely suspend regional intervention efforts that are made based on the results of diagnostic tests that provide inaccurate assessments of prevalence and intensity are likely to undermine both short-term and long-term programmatic goals . Indeed , models indicate that helminth control programs that terminate prior to a solid control of transmission will result in reemergence and spread of the parasite into susceptible populations with detrimental public health consequences [35 , 36] . Given the limited sensitivity of many of the standard methods used to monitor the prevalence of helminth infections , it is time to revise the diagnostic strategies for these parasites . The goal of the work presented here was to determine if the detection of parasite-derived transrenal DNA has the potential to enhance the sensitivity of diagnosing S . stercoralis infection over an established and widely used serological assay or the standard parasitological stool analysis . Although it is clear from this work that detection of transrenal DNA and serology have an advantage over conventional stool analysis for the identification of infection , the relative merits of transrenal DNA and serological analysis are more difficult to conclude . While each test identified approximately the same number of participants as infected with S . stercoralis , only about 22% ( 53/243 ) were positive for both assays . It is tempting to conclude that direct detection of a S . stercoralis-derived molecule ( transrenal DNA ) is superior to the indirect measure of detecting antibodies that recognize a restricted set of epitopes associated with a single , stage-restricted parasite protein . However , in the absence of a ‘gold standard’ test , or set of reagents against which the accuracy of these two tests can be measured , such a determination cannot be made . The impact that a lack of gold standard tests has had on the development of molecular-based parasite diagnostics has been expertly reviewed elsewhere [29 , 37–39] . The absence of a gold standard has prompted us [23] and others [29 , 37] to employ Bayesian latent class modeling to generate estimates of specificity and sensitivity for parasite diagnostic tests . For the S . stercoralis diagnostic tests used in this study , latent class analysis confirms the low sensitivity of stool examination and concludes that the diagnostic performance of the NIE ELISA and transrenal DNA tests were similar in terms of diagnostic sensitivity and specificity ( Table 3 ) . The limited concordance of the results from the serological and transrenal DNA tests can be of significant importance when MDA control efforts are evaluated . The low concordance may be due , in part , to the single molecule focus of these two assays . The NIE ELISA uses a recombinant form of a 31 kDa molecule expressed by infective S . stercoralis larvae [27] and was chosen for its favorable sensitivity and specificity profile [26] as well as its performance in clinical settings [7 , 31] . The demonstrated utility of the NIE ELISA notwithstanding , both the sensitivity and specificity of this assay would likely benefit from the strategic inclusion of additional parasite molecules expressed by somatic cells of adults or released components of the parasite’s excretory/secretory products . Likewise , the transrenal DNA assay targets a single repeat sequence , the absence of which does not infer a negative diagnosis [23] . While it is possible that the clinical and/or parasitological status of certain patients preclude the passing parasite-derived transrenal DNAs , it is also likely that Strongyloides DNA was present in the urine but derived from other regions of the parasite’s genome . Identifying these additional transrenal sequences would provide an opportunity to devise a multiplex assay that amplifies several transrenal DNAs to enhance the diagnostic sensitivity and specificity of this approach . The estimates for the half-life of cell-free DNA in the blood of humans range between 4 minutes and 12 hours ( reviewed in [40] ) . Assuming that the proximate source of transrenal DNAs is the cell-free DNA in the blood , this short half-life indicates that detection of Strongyloides-derived DNA in the urine is measuring an ongoing infection . This rapid decay in the blood also suggests that testing for the presence of transrenal DNAs could be a sensitive tool to measure the efficacy of chemotherapeutic elimination of the parasite . In support of the utility of using transrenal DNAs as a marker of successful chemotherapy , Ibironke et al . [41] demonstrated that Schistosoma haematobium transrenal DNA was no longer detectable 14 days after treatment with praziquantel . Diagnostic approaches that can accurately assess changes in disease burden and the impact of chemotherapeutic/public health for programs that are at different levels of control ( breaking transmission , elimination , or post-elimination ) are critical for strategic decision making . Following multiple rounds of treatment , MDA programs require highly sensitive assays to identify hot spots of residual transmission . In most cases , there is an unmet need to replace microscopy , which is not sufficiently sensitive to detect these low-level residual infections . At this time , no single nucleic acid , antigen detection or antibody approach appears to be able to provide an appropriately high-resolution picture of infection status . Given this , it may be time to consider coordinating the results of two or more molecular based assays for the diagnosis of STH’s , including strongyloidiasis . The results presented here suggest that the combined use of assays that detect transrenal DNA and antibodies may be a useful approach . | As international bodies focus efforts on control of the world’s neglected tropical diseases , the critical importance of accurate and sensitive diagnosis becomes a key factor . The problem arises when the infection load in a community is reduced to a level where the standard diagnostic methodologies are insufficiently sensitive to detect the residual infection in the community . There is a need to develop improved diagnostic strategies for many parasitic diseases . One of the more difficult to diagnose helminth parasites is the nematode Strongyloides stercoralis . We have introduced a new approach that detects parasite-specific cell free DNA in urine as a sensitive measure of parasite presence . In the work presented here , we compare the performance of parasitological , serological and urine/DNA-based diagnosis of S . stercoralis infection . Using a Bayesian Latent Class Analysis approach , we provide evidence for the enhanced utility of using both urine and blood for the diagnosis of this parasite . | [
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"m... | 2018 | Transrenal DNA-based diagnosis of Strongyloides stercoralis (Grassi, 1879) infection: Bayesian latent class modeling of test accuracy |
Cholesterol availability is rate-limiting for myelination , and prior studies have established the importance of cholesterol synthesis by oligodendrocytes for normal CNS myelination . However , the contribution of cholesterol uptake through the endocytic pathway has not been fully explored . To address this question , we used mice with a conditional null allele of the Npc1 gene , which encodes a transmembrane protein critical for mobilizing cholesterol from the endolysosomal system . Loss of function mutations in the human NPC1 gene cause Niemann-Pick type C disease , a childhood-onset neurodegenerative disorder in which intracellular lipid accumulation , abnormally swollen axons , and neuron loss underlie the occurrence of early death . Both NPC patients and Npc1 null mice exhibit myelin defects indicative of dysmyelination , although the mechanisms underlying this defect are incompletely understood . Here we use temporal and cell-type-specific gene deletion in order to define effects on CNS myelination . Our results unexpectedly show that deletion of Npc1 in neurons alone leads to an arrest of oligodendrocyte maturation and to subsequent failure of myelin formation . This defect is associated with decreased activation of Fyn kinase , an integrator of axon-glial signals that normally promotes myelination . Furthermore , we show that deletion of Npc1 in oligodendrocytes results in delayed myelination at early postnatal days . Aged , oligodendocyte-specific null mutants also exhibit late stage loss of myelin proteins , followed by secondary Purkinje neuron degeneration . These data demonstrate that lipid uptake and intracellular transport by neurons and oligodendrocytes through an Npc1-dependent pathway is required for both the formation and maintenance of CNS myelin .
Ensheathment of axons by myelin is an evolutionary feature of the vertebrate nervous system that is accomplished by the extended and specialized plasma membranes of oligodendrocytes in the CNS and Schwann cells in the PNS . Myelin contains at least 70% lipids by dry weight [1] , and this high ratio of lipid to protein ensures the insulating properties of myelin to maximize the efficiency of nerve conduction . Among all the lipid species found in the myelin sheath , unesterified cholesterol is a major component [1] . In the mouse CNS , cholesterol in compact myelin represents ∼78% of the total lipid pool [2] , and the availability of cholesterol is the rate-limiting step for myelination [3] . Since the CNS is shielded by the blood brain barrier , cholesterol required for myelination comes entirely from local synthesis [2] . Both neurons and glia obtain the cholesterol they need either through endogenous synthesis or by uptake of lipoprotein particles produced and released within the CNS . That endogenously synthesized cholesterol is critical for CNS myelination in mice is demonstrated by deletion in oligodendrocytes of squalene synthase , the first dedicated enzyme of sterol synthesis [3] . These mutant mice exhibit delayed myelination , suggesting that exogenously supplied cholesterol also contributes to CNS myelin formation . However , whether cholesterol from exogenous sources is required for myelin synthesis , or just a compensatory source when endogenous synthesis is lacking in myelinating glia , has not been explored . An essential component of the pathway through which cholesterol in lipoprotein particles is mobilized from the endolysosomal system is the Npc1 protein [4] , [5] . This multipass transmembrane protein resides in late endosomes and lysosomes [6]–[9] , and functions cooperatively with the Npc2 protein to facilitate cholesterol efflux [10] , [11] . Loss of functional Npc1 disrupts intracellular lipid trafficking , and leads to the sequestration of unesterified cholesterol and glycosphingolipids in late endosomes and lysosomes [12] . Mutations in the human NPC1 gene cause Niemann-Pick type C disease ( NPC ) , a fatal childhood-onset neurodegenerative disorder [13] . Mice with a null mutation in the Npc1 gene ( Npc1−/− ) recapitulate the human disease , and exhibit progressive CNS neuropathology in which intracellular lipid accumulation , abnormally swollen axons , neuron loss and gliosis underlie the occurrence of ataxia and early death [5] , [14] . Notably , both NPC patients and Npc1−/− mice exhibit myelin defects indicative of dysmyelination , particularly in the forebrain [15]–[19] . However , the complex pathology resulting from Npc1 deficiency in both neurons and oligodendrocytes has limited the utility of these global null mutants to provide a detailed understanding of the contribution of exogenous cholesterol to CNS myelination . Here we use mice with a conditional null allele of the Npc1 gene to achieve temporal and cell type specific deletion in order to define effects on CNS myelin . We show that deletion of Npc1 restricted to neurons unexpectedly recapitulates the dysmyelination phenotype of global null mutants . This effect is mediated by a block in maturation of oligodendrocyte lineage cells that is associated with decreased activation of Fyn kinase , an integrator of axon-glial signals that normally promote myelination . Furthermore , we show that deletion of Npc1 in oligodendrocytes triggers a similar , though less severe impairment of CNS myelination , as well as myelin protein loss and secondary neurodegeneration . Our analyses suggest that exogenous cholesterol entering cells and trafficking through an Npc1-dependent pathway is necessary for both the formation and maintenance of CNS myelin .
To confirm the requirement of Npc1 for proper myelination in mice during early postnatal stages , we utilized mice with a floxed Npc1 allele ( Npc1flox ) [20] . Cre-mediated deletion yields a null allele that is functionally indistinguishable from the spontaneous null mutation found in Npc1nih mice ( Npc1−/− ) [5] , [20] . To generate mice with Npc1 deletion in the germline , Npc1flox/flox mice were bred with transgenic mice expressing Cre recombinase under the control of the EIIa promoter [21] . Mice mosaic for the conditionally deleted allele were bred with mice carrying the Npc1− allele to generate compound heterozygotes of the conditionally deleted and null Npc1 alleles ( Npc1Δ/− ) . We also generated mice with Npc1 deletion in adults by using a tamoxifen-regulated Cre recombinase under the control of the cytomegalovirus ( CMV ) promoter ( Cre-ERTM+ ) [22] . Cre-mediated deletion of Npc1 in adults was induced by tamoxifen injections at 6 weeks , an age at which myelination is complete . Mice with adult deletion ( Npc1flox/− , Cre-ERTM+ ) have been shown to recapitulate most features of NPC neuropathology , and reach end-stage by ∼22 weeks [23] . To determine the effect of the timing of Npc1 deletion upon myelination , we compared 7-week-old mice with germline deletion ( Npc1Δ/− ) , 22-week-old mice with adult deletion ( Npc1flox/− , Cre-ERTM+ ) and age matched controls . Myelin basic protein ( MBP , a standard marker for mature myelin [1] ) and FluoroMyelin ( a lipophilic stain for compact myelin ) staining of sagittal midline brain sections revealed a dramatic reduction of myelin proteins and lipids in Npc1Δ/− mice , particularly in the forebrain ( Figure 1A , 1B ) . This striking pattern of regionally selective myelin defects is similar to that previously reported in Npc1−/− mice [14] , [15] , [17] . In contrast , Npc1flox/− , Cre-ERTM+ mice exhibited a staining pattern morphologically similar to that in controls ( Figure 1A , 1B ) . The difference in MBP staining patterns between Npc1Δ/− mice and Npc1flox/− , Cre-ERTM+ mice suggests that Npc1 is required in early postnatal stages for proper myelin formation . Further analysis of myelin-specific proteins demonstrated a decrease in MBP and CNP protein levels in Npc1flox/− , Cre-ERTM+ mice compared to littermate controls , particularly in the cortex ( Figure 1C , 1D ) . We conclude that myelin was properly formed in Npc1flox/− , Cre-ERTM+ mice during postnatal development , but that these mice exhibit loss of myelin proteins at later stages , particularly in the cerebral cortex , after Npc1 deletion at 6 weeks . Axonal loss could contribute to the late stage pathology in Npc1flox/− , Cre-ERTM+ mice , as evidenced by decreased neurofilament levels in these aged mutants ( Figure 1C ) . Taken together , our analysis suggests that lack of myelin in NPC mice is caused by dysmyelination at early postnatal days , followed by loss of myelin proteins at end stage . We next sought to dissect the contribution of different CNS cell types to NPC dysmyelination . We started by deleting Npc1 specifically in neurons , using transgenic mice expressing Cre recombinase under the control of the Synapsin1 promoter ( Syn1-Cre ) [24] . We confirmed gene deletion by staining brain sections with filipin , a fluorescent dye that specifically marks accumulation of unesterified cholesterol [25] . NeuN and filipin co-staining verified that Npc1flox/− , Syn1-Cre+ mice , but not Npc1flox/+ , Syn1-Cre+ controls [23] , developed filipin-positive neurons throughout the brain , including brainstem and cortex ( Figure S1A ) . A subset of neurons remained filipin negative , possibly reflecting mosaic gene deletion . To further verify neuron-specific gene deletion , Syn1-Cre+ mice were crossed to a Rosa reporter line that has been widely used to demonstrate gene deletion in both neurons and oligodendrocytes [26] . LacZ staining revealed widespread positive cells in many brain regions including the cortex , with minimal staining in the corpus callosum , where neuronal cell bodies are lacking ( Figure S1B ) . Co-staining with NeuN or Olig2 showed that these LacZ positive cells were neurons , and not oligodendrocyte lineage cells ( Figure S1C ) , further supporting the notion that we achieved neuron-specific deletion by using Syn1-Cre+ mice . The effect of Npc1 deficiency in neurons upon myelination was first evaluated by MBP immunofluorescence at 3 different ages . At postnatal day 16 ( P16 ) , myelination was actively occurring in the forebrain of Npc1flox/+ , Syn1-Cre+ controls , with abundant MBP-positive myelinating oligodendrocytes populating the cortex ( Figure 2B ) . In contrast , Npc1flox/− , Syn1-Cre+ mutants exhibited a severe paucity of myelin in the same region , with most of the MBP positive cells exhibiting the morphology of pre-myelinating oligodendrocytes ( Figure 2B ) . At 7 weeks , myelination was completed in Npc1flox/+ , Syn1-Cre+ controls , but was greatly attenuated in the cortex of Npc1flox/− , Syn1-Cre+ mutants . No recovery of myelination was observed in mutants aged to 16 weeks ( Figure 2B ) , which is end stage for these mice [23] . Similarly , FluoroMyelin staining revealed a paucity of compact myelin in the corpus callosum of Npc1flox/− , Syn1-Cre+ mutants at 16 weeks ( Figure 2B , bottom panel ) . Although MBP staining was markedly decreased in the cortex of Npc1flox/− , Syn1-Cre+ mutants , other brain regions exhibited a normal staining pattern , reminiscent of the selective defects in myelination observed after global germline deletion ( Figure 1A ) . Regional-specific dysmyelination was further supported by western blots showing decreased levels of myelin-specific proteins including CNP , MBP and MAG in cortex , but not brainstem of Npc1flox/− , Syn1-Cre+ mutants ( Figure 2C ) . Electron microscopy confirmed that the density of myelinated nerve fibers in the corpus callosum was greatly reduced in Npc1flox/− , Syn1-Cre+ mutants at 3 weeks ( Figure 2E ) . Notably , neurofilament protein levels in the cortex were similar between Npc1flox/+ , Syn1-Cre+ controls and Npc1flox/− , Syn1-Cre+ mutants at P16 ( Figure 2C ) , and neurofilament immunofluorescence staining showed no significant axonal pathology ( Figure 2D ) . These data indicate that dysmyelination in the forebrain of Npc1flox/− , Syn1-Cre+ mutants was not secondary to axonal loss . To characterize the mechanism underlying dysmyelination in Npc1flox/− , Syn1-Cre+ mutants , we assessed oligodendrocyte lineage cells at different stages of differentiation . At P16 , Npc1flox/− , Syn1-Cre+ mutants showed a significantly reduced number of CC1-positive mature oligodendrocytes in the forebrain ( Figure 3A , 3C ) but a normal density of NG2-positive oligodendrocyte precursor cells ( OPCs ) ( Figure 3A , 3B ) . As previously reported for global null Npc1 mutants [17] , this deficit of mature oligodendrocytes was not associated with evidence of increased apoptosis ( data not shown ) . The paucity of mature oligodendrocytes was associated with a reduced number of cells in the corpus callosum expressing Sip1 , a signaling protein implicated oligodendrocyte differentiation ( Figure 3C ) [27] . These data indicated that Npc1 deficiency in neurons triggered a block of oligodendrocyte maturation , and prompted us to determine whether signals known to regulate oligodendrocyte maturation and myelination were perturbed in Npc1flox/− , Syn1-Cre+ mutants . We first examined proteins that mediate signaling between axons and oligodendrocyte lineage cells including PSA-NCAM [28] , Lingo1 [29] and Jagged1 [30] , and found no differences between Npc1flox/− , Syn1-Cre+ mutants and controls at P16 ( Figure S2A ) . Similarly , we found no evidence of astrocyte activation in the corpus callosum of Npc1flox/− , Syn1-Cre+ mutants at P16 ( Figure S2B , S2C ) , consistent with prior studies showing that astrogliosis is limited to the thalamus of Npc1−/− mice at two weeks [31] . In contrast , activity of the non-receptor tyrosine kinase Fyn [32] was reduced in the cortex of Npc1flox/− , Syn1-Cre+ mutants , as evidenced by decreased levels of the active form ( phosphorylated at tyrosine 420 ) and concurrently increased levels of the inactive form ( phosphorylated at tyrosine 531 ) ( Figure 3E ) . As oligodendroglial Fyn is an integrator of axonal signals that promote myelination [33] , the decreased activity of Fyn in Npc1flox/− , Syn1-Cre+ mutants suggests that Npc1 deficiency in axons leads to a disruption of axon-glial signaling that is crucial for oligodendrocyte differentiation and myelination . Next , we tested if Npc1 deficiency in oligodendrocyte lineage cells contributes to the pathogenesis of dysmyelination in NPC mice . To accomplish this , we used transgenic mice expressing Cre recombinase under the control of the CNP promoter ( CNP Cre/+ ) [34] . In these mice , Cre is abundantly and specifically expressed in postmitotic oligodendrocytes . Co-staining for Cre and Olig2 , a marker of both OPCs and postmitotic oligodendrocytes , verified that Cre was specifically expressed in a subset of Olig2+ oligodendrocyte lineage cells in various brain regions including brainstem and cortex ( Figure S3B ) . Filipin staining revealed minimal accumulation of unesterified cholesterol in Npc1flox/− , CNPCre/+ mutants ( Figure S3A ) , a finding both consistent with a previous report showing no detectable cholesterol accumulation in oligodendrocytes of Npc1−/− mice [35] and indicative of the cell lineage specificity of this Cre line . Deletion of Npc1 in oligodendrocytes resulted in a dysmyelination phenotype that was initially similar to that caused by Npc1 deletion in neurons . At P16 , Npc1flox/− , CNPCre/+ mutants expressed markedly reduced levels of myelin-specific proteins including MBP , CNP and MAG in the cortex ( Figure 4A , 4B ) . Similarly , compact myelin levels by FluoroMyelin staining were decreased in Npc1flox/− , CNPCre/+ mutants ( Figure 4A ) . This dysmyelination phenotype partially recovered by 7 weeks ( Figure 4A ) , a finding that indicates oligodendrocyte deletion delayed myelination and contrasts with the block produced by neuronal deletion . Myelination in the brainstem of Npc1flox/− , CNPCre/+ mutants was minimally affected ( Figure 4B ) despite robust Cre expression in this region ( Figure S3B , S3C ) . Electron microscopy confirmed diminished density of myelinated nerve fibers in the corpus callosum of Npc1flox/− , CNPCre/+ mutants at 3 weeks ( Figure 4D ) . Similar to neuron-specific mutants , dysmyelination in Npc1flox/− , CNPCre/+ mutants occurred without significant axonal pathology ( Figure 4B , 4C ) . The requirement of Npc1 in oligodendrocytes for proper myelination was further confirmed by using an independent line in which Cre was highly expressed in OPCs ( Olig2Cre/+ mice , Figure S4 ) [36] . Similar to Npc1flox/− , Syn1-Cre+ mutants , Npc1flox/− , CNPCre/+ mutants at P16 showed reduced density of mature oligodendrocytes ( Figure 5A , 5C ) , with normal numbers of OPCs in the forebrain ( Figure 5A , 5B ) , indicating arrest of oligodendrocyte maturation . As the Npc1flox/− , CNPCre/+ mutants aged , they developed progressive motor deficits ( Figure 6C ) , although weight was not affected ( Figure 6A , 6B ) . This led us to examine myelin protein levels in 23-week-old Npc1flox/− , CNPCre/+ mutants . We found decreased levels of myelin proteins not only in cortex , but also in brainstem and cerebellum ( Figure 7A ) , where myelination in early postnatal days was nearly normal ( Figure 4B ) . This suggested that myelin loss was taking place in several brain regions of the aged Npc1flox/− , CNPCre/+ mutants . We found this was associated with only mild changes in the pattern of MBP staining ( Figure 7B ) . Interestingly , the total number of Olig2+ oligodendrocyte lineage cells in the cerebellar white matter was unchanged in aged mutants ( Figure 7C , 7D ) , suggesting that loss of Npc1 did not affect the survival of oligodendrocytes in adult mice . This loss of myelin proteins was associated with secondary neuron loss in the cerebellum . We detected Purkinje cell loss in anterior lobules of 23-week-old but not 7-week-old Npc1flox/− , CNPCre/+ mutants , as demonstrated by calbindin staining of sagittal midline sections ( Figure 7E , 7G ) and by loss of calbindin staining on western blot ( Figure 7F ) . Importantly , no filipin-positive Purkinje neurons were identified in these mice ( not shown ) , supporting the conclusion that Purkinje cell loss was a consequence of non-cell autonomous toxicity . We conclude that Npc1 acts in oligodendrocytes both to promote normal myelination and to ensure the maintenance of myelin in the adult CNS .
Here we used Npc1 conditional null mice to establish the critical role of Npc1 in both neurons and oligodendrocytes for proper CNS myelination . Our findings demonstrate that deletion of Npc1 in neurons alone is sufficient to recapitulate the dysmyelination phenotype that occurs following global germline deletion . These mice display a severe phenotype , particularly in the forebrain , characterized by a lack of mature oligodendrocytes but a normal density of OPCs , indicating that Npc1 deficiency in neurons triggers an arrest of oligodendrocyte maturation . Our data also demonstrate that deletion of Npc1 in oligodendrocytes leads to similar but milder forebrain dysmyelination that largely recovers by 7 weeks , consistent with a delay rather than a block in myelination . Furthermore , we demonstrate that these oligodendrocyte-specific mutants develop ataxia as they age , and that this is associated with decreased myelin proteins and Purkinje cell loss in anterior cerebellar lobules , establishing the occurrence of secondary neurodegeneration . Our results highlight the importance of Npc1 in both neurons and oligodendrocytes for the formation and maintenance of CNS myelin . Significant effort has been devoted to defining the contribution of specific cell types to NPC neuropathology . Studies in chimeric mice , a conditional knock-out model , and several neuron-specific transgenic rescue experiments all demonstrate that neuronal loss can be a consequence of cell autonomous neurotoxicity [20] , [23] , [37]–[39] . Furthermore , these analyses indicate that brain inflammation is a consequence rather than a driver of neuron loss [20] , [23] , [38] , [40] . The role of astroglial cells in NPC neuropathology has been more controversial . While in vitro data suggest that Npc1 deficient astrocytes fail to fully support cultured neurons [41] , both conditional knockout and transgenic rescue experiments failed to establish a significant role for astrocytes in pathogenesis [23] , [38] . A transgenic line that highly over-expresses Npc1 from the GFAP promoter does show some rescue [42] , but the extent of cell type restricted expression in these mice remains incompletely defined . The effects of Npc1 deficiency restricted to oligodendrocytes had not been previously explored . As for effects on CNS myelin , prior transgene rescue experiments using the NSE promoter to drive Npc1 expression demonstrated partial rescue of myelination [39] . These findings are consistent with our observation that neuronal expression of Npc1 plays an important role in oligodendrocyte maturation and myelination . Finally , we note that aged , oligodendrocyte-specific null mutants show evidence of neuron loss . While prior studies firmly establish that neuronal deficiency of Npc1 is sufficient to mediate neurotoxicity [20] , [23] , the data reported here indicate that non-cell autonomous pathways arising from oligodendrocytes also contribute to neuropathology . Oligodendrocyte differentiation and myelination are highly dynamic processes controlled by both intrinsic factors and extrinsic mechanisms [43] . Recent studies of axon-glial communication have identified a series of axonal signals important for regulating myelination . Oligodendroglial Fyn , a Src family kinase , has been suggested to play a central role in integrating diverse axonal signals to initiate myelination [33] . Downstream signaling from activated Fyn kinase promotes oligodendrocyte survival , alters cytoskeleton polarity and increases the expression of myelin genes . Our analysis of neuron-specific Npc1 mutants reveals decreased Fyn activity and a regionally-restricted dysmyelination phenotype similar to that of Fyn knockout mice [44] . We suggest that Npc1 deficiency in neurons disrupts an axon-glial signal vital for promoting myelination . The axonal ligand responsible for oligodendroglial Fyn activation remains elusive . The requirement of Npc1 for Fyn activation raises the possibility that a lipid species , such as cholesterol or a sphingolipid , may contribute to this signal . Additionally , recent neuron-glial co-culture studies demonstrate the role of action potentials in stimulating myelination through Fyn-dependent mechanisms [45] . It is therefore also possible that defective Fyn activation results from decreased electrical activity of axons in Npc1flox/− , Syn1-Cre+ mutants . Recently , a similar role in myelination has been demonstrated for neuron-restriction expression of the PI ( 3 , 5 ) P ( 2 ) phosphatase Fig4 [46] , suggesting that defects in axon-glial signaling may underlie dsymyelination in several disorders . Animal studies of cholesterol metabolism in myelinating glia have highlighted the importance of cell-autonomous production of cholesterol for myelin formation . Mice lacking oligodendroglial squalene synthase , an enzyme required for cholesterol synthesis , exhibit perturbed CNS myelination in early postnatal days [3] . Similarly , deletion of SCAP ( SREBP-cleavage-activating protein ) in Schwann cells , a protein that complexes with SREBP to regulate the expression of genes promoting cholesterol synthesis and lipoprotein uptake , leads to PNS hypomyelination [47] . It is notable that both mouse models partially recover at later stages , suggesting that myelinating glia have the capacity to overcome the lack of endogenous cholesterol production , probably through increased uptake . Here we present in vivo evidence indicating an important contribution of exogenous cholesterol to myelin synthesis . Our findings show that deletion of Npc1 in oligodendrocytes , which eliminates their utilization of cholesterol from the endocytosis of LDL or similar lipoprotein particles , leads to perturbed myelin formation in the CNS . Npc1 deficiency also impairs intracellular trafficking of sphingolipids [48] and endogenously synthesized cholesterol [49] . Nonetheless , the blockade of exogenous cholesterol utilization and the essential role that cholesterol plays in myelination leads us to favor the conclusion that the effects observed here are due to a disruption in the availability of exogenous cholesterol . As shown for other cell types [12] , we speculate that the synthesis of endogenous cholesterol may be up-regulated in Npc1 deficient oligodendrocytes yet insufficient to overcome the lack of exogenous cholesterol , especially during the peak phase of myelination . This suggests that extracellularly-derived cholesterol is indispensible for normal CNS myelination . Although Npc1flox/− , CNPCre/+ mutants form myelin in the brainstem and cerebellum during postnatal development , these regions exhibit loss of myelin proteins in adults . Biochemical studies have shown that in the adult CNS , myelin production and cholesterol turnover decrease to very low levels [2] . It is therefore unlikely that the loss of myelin proteins in these adult mutants results from impaired access to exogenous cholesterol as a consequence of Npc1 deficiency . Rather , we speculate that late-stage pathology stems from the unstable nature of the myelin sheath produced by mutant oligodendrocytes . Studies of cellular models of NPC have shown that cholesterol content is decreased in the plasma membrane of mutant cells [50] , [51] . This change may impact myelin by disrupting membrane fluidity , altering lipid rafts or modulating the function of membrane proteins , and thereby increase the vulnerability of aged mutants . Further analysis of the biochemical composition of the myelin sheath generated by Npc1-deficient oligodendrocytes will help define the mechanism mediating late-onset loss of myelin proteins . Axonal degeneration and neuron loss in these mutants highlights the important role of oligodendrocytes in supporting neuron function and survival . Similar observations have been made in mice over-expressing alpha-synuclein in oligodendrocytes [52] . While this effect may be mediated in part through loss of myelin , other studies have shown that oligodendroglia support axons through metabolic pathways independent of myelination [53] . It is currently unclear which of these mechanisms accounts for Purkinje neuron loss in Npc1flox/− , CNPCre/+ mutants . In summary , the data reported here extend our understanding of the role of cholesterol metabolism in myelination , and demonstrate that exogenous cholesterol is needed by both neurons and oligodendrocytes for the formation and maintenance of CNS myelin . A characteristic feature of Npc1 deficient mice , both global nulls and cell-specific knockouts , is the regionally severe dysmyelination that occurs during early postnatal stages . Fate-mapping studies have established that OPCs originate from heterogeneous regions of the subventricular zone , under the influence of different signaling pathways [54] . We speculate that these regional differences in oligodendrocyte lineage cells lead to distinct responses to axonal signals or to the need for exogenously-derived cholesterol for proper myelination , contributing to severe dysmyelination particularly in the forebrain of Npc1 mutants . While the precise mechanism underlying this regional selectivity remains to be defined , our data establish a critical role for Npc1 in both myelin formation and maintenance . Our findings have important implications for understanding the pathogenesis of NPC disease and may also inform our knowledge of other dysmyelinating/demyelinating disorders .
Animal use and procedures were approved by the University of Michigan Committee on the Use and Care of Animals . Npc1flox/flox and Npc1Δ/− mice were generated as previously described [20] . Other mice used include tamoxifen-inducible CMV-Cre ( Cre-ERTM+ ) ( #004682 , Jackson Laboratories ) , Sny1-Cre ( #003966 , Jackson Laboratories ) , CNPCre/+ mice [34] , Olig2Cre/+ mice [36] and Rosa reporter mice ( #003474 , Jackson Laboratories ) . All mouse strains were maintained on the C57BL6/J background , except Olig2Cre/+ mice which were maintained on the 129/C3H mixed background . Tamoxifen ( Sigma ) was dissolved in corn oil ( Sigma ) at 20 mg/ml and stored at −20C in the dark . The stock solution was warmed to 37C before injection . 6-week-old mice were injected intraperitoneally with 3 mg tamoxifen per 40 g body weight for 5 consecutive days . Motor function was measured using the balance beam test as described previously [20] . Brain lysates were homogenized in RIPA buffer ( Thermo Scientific ) containing Complete protease inhibitor cocktail ( Roche ) and phosphatase inhibitors ( Thermo scientific ) using a motor homogenizer ( TH115 , OMNI International ) . Samples were resolved by 4–20% Tris-glycine gradient gel and transferred to nitrocellulose membranes ( BioRad ) on a semidry transfer apparatus . Immunoreactivity was detected by Immobilon chemilluminescent substrate ( Thermo Scientific ) . Antibodies used were rat anti-MBP ( 1∶2000 , Abcam ) , mouse anti-CNP ( 1∶2000 , Millipore ) , mouse anti-MAG ( 1∶5000 , Millipore ) , mouse anti-Neurofilament 200 ( 1∶5000 , Millipore ) , rabbit anti-NG2 ( 1∶1000 , Millipore ) , rabbit anti-GAPDH ( 1∶5000 , Santa Cruz ) , mouse anti-Cre ( 1∶1000 , Millipore ) , rabbit anti-GFAP ( 1∶5000 , Dako ) , mouse anti-PSA-NCAM ( 1∶1000 , Millipore ) , goat anti-Jagged1 ( 1∶1000 , Santa Cruz ) and rabbit anti-Lingo1 ( 1∶1000 , Abcam ) . 200 µg brain lysates were immunoprecipitated with 10 µg anti-Fyn antibody ( FYN3 , Santa Cruz ) overnight at 4C , followed by incubation with 20 µl Protein A beads ( Santa Cruz ) for 1 h at 4C . The immunoprecipitates were then washed 4 times with protein lysis buffer before being boiled with 2× sample buffer at 100C for 5 min . For the subsequent western blot analysis , anti-Fyn ( FYN3 , Santa Cruz ) , Src pY418 and pY529 antibodies ( Life technologies ) were used to detect total Fyn and phosphorylation of Fyn at Y420 and Y531 , respectively . Mice were perfused with 0 . 9% normal saline followed by 4% paraformaldehyde . Brains were removed and post-fixed in 4% paraformaldehyde overnight . Brains were bisected , with the right hemisphere processed for paraffin embedding and the left hemisphere processed for frozen sections . Prior to freezing , brain tissue was cryoprotected in 30% sucrose for 48 hr at 4C . Brains were frozen in isopentane chilled by dry ice and embedded in OCT ( Tissue-Tek ) . Frozen sections were prepared at 14 µm in a cryostat and used for LacZ staining and subsequent eosin counter staining or immunohistochemical staining for Olig2 ( 1∶500 , Millipore ) and NeuN ( 1∶500 , Millipore ) . For filipin staining , frozen sections were first used for immunofluorescence staining for NeuN or Olig2 , followed by incubation for 90 min in PBS with 10% fetal bovine serum plus 25 µg/ml filipin ( Sigma ) . For FluoroMyelin staining , frozen sections were rehyrated in PBS for 20 min , incubated with FluoroMyelin solution ( 1∶300 , Life Technologies ) at room temperature for 2 hours , and then cleared with four 30-minute washes with PBS . Paraffin-embedded sections were prepared at 5 µm and used for staining with H&E staining or MBP ( 1∶100 , Abcam ) , SMI-31P ( 1∶200 , Covance ) , NG2 ( 1∶100 , Millipore ) , CC1 ( 1∶200 , Calbiochem ) , Calbindin ( 1∶1000 , Sigma ) , Sip1 ( 1∶100 , Santa Cruz ) and GFAP ( 1∶1000 , Dako ) immunofluorescence . For visualization of staining , secondary antibodies conjugated to Alexa Fluor 594 or Alexa Fluor 488 ( Molecular Probes ) were used and images were captured on a Zeiss Axioplan 2 imaging system . For NG2 and CC1 co-staining and Olig2 staining , images were captured on an Olympus FluoView 500 Confocal Microscope system . Quantification of CC1+ or Olig2+ cells was performed using NIH ImageJ software . Quantification of Purkinje cell loss was performed on H&E stained sections . Counts were normalized to the length of the Purkinje layer , as measured by NIH ImageJ software , and reported as Purkinje cell density . Mice were perfused with 0 . 9% normal saline followed by 3% paraformaldehyde and 2 . 5% glutaraldehyde in 0 . 1 M Sorensen's buffer . The corpus callosum was removed and post-fixed in perfusion solution overnight , followed by fixation in 1% osmium tetroxide solution for 1 h at room temperature . After dehydration , tissues were embedded in epoxy resin . For transmission electron microscopy , ultrathin sections were cut , and images were captured on a Philips CM-100 imaging system at 10 , 500× magnification . Statistical significance was assessed by unpaired Student's t test . Statistics were performed using the software package Prism 5 ( GraphPad Software ) . P values less than 0 . 05 were considered significant . | The myelin sheath in the central nervous system is a specialized extension of the oligodendrocyte plasma membrane that serves as an electrical insulator to ensure proper nerve conduction . To accomplish this , myelin is enriched in lipids , particularly unesterified cholesterol , which is an essential and limiting component for myelin formation . Here we determine the contribution of exogenously derived cholesterol to myelination by using a conditional null mutant of the mouse Npc1 gene . Npc1 encodes a transmembrane protein critical for mobilizing exogenously derived cholesterol from late endosomes and lysosomes , and is mutated in patients with Niemann-Pick type C disease , a degenerative disorder caused by impaired intracellular lipid trafficking . We show that mice lacking Npc1 in either neurons or oligodendrocytes exhibit a defect in myelin formation in selected brain regions , with an arrest in oligodendrocyte maturation . In addition , mice with Npc1 deficiency in oligodendrocytes , when aged , show progressive motor dysfunction with myelin breakdown and secondary Purkinje neuron loss . Taken together , our findings demonstrate the role of Npc1 in mediating reciprocal signaling between neurons and glia , and highlight the importance of exogenous cholesterol for CNS myelin formation and maintenance . | [
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] | 2013 | Npc1 Acting in Neurons and Glia Is Essential for the Formation and Maintenance of CNS Myelin |
Cys-loop receptors constitute a superfamily of pentameric ligand-gated ion channels ( pLGICs ) , including receptors for acetylcholine , serotonin , glycine and γ-aminobutyric acid . Several bacterial homologues have been identified that are excellent models for understanding allosteric binding of alcohols and anesthetics in human Cys-loop receptors . Recently , we showed that a single point mutation on a prokaryotic homologue ( GLIC ) could transform it from a channel weakly potentiated by ethanol into a highly ethanol-sensitive channel . Here , we have employed molecular simulations to study ethanol binding to GLIC , and to elucidate the role of the ethanol-enhancing mutation in GLIC modulation . By performing 1-µs simulations with and without ethanol on wild-type and mutated GLIC , we observed spontaneous binding in both intra-subunit and inter-subunit transmembrane cavities . In contrast to the glycine receptor GlyR , in which we previously observed ethanol binding primarily in an inter-subunit cavity , ethanol primarily occupied an intra-subunit cavity in wild-type GLIC . However , the highly ethanol-sensitive GLIC mutation significantly enhanced ethanol binding in the inter-subunit cavity . These results demonstrate dramatic effects of the F ( 14′ ) A mutation on the distribution of ligands , and are consistent with a two-site model of pLGIC inhibition and potentiation .
Synaptic transmission is one of the most important functions of our nervous system , and modulation of post-synaptic receptors is of tremendous importance to understanding the effects of toxins , neuropharmaceuticals , drugs of abuse , and anesthetics , as well as the physiological basis for consciousness . Ethanol is likely the oldest drug known to man , and has been identified as a modulator of synaptic transmission . Ethanol affects the central nervous system by interacting with several proteins , in particular post-synaptic ion channel receptors . Among these , several key targets of alcohol modulation fall in the family of Cys-loop receptors ( see reviews [1] , [2] ) . Cys-loop receptors constitute a family of pentameric ligand-gated ion channels ( pLGICs ) . These receptors are activated by a variety of ligands , from which they draw their names: they include the cation-conducting nicotinic acetylcholine receptors ( nAChRs ) and serotonin-3 receptors , and the anion-conducting glycine receptors ( GlyRs ) and γ-aminobutyric acid-A receptors ( GABAARs ) . In addition to activation by their respective ligands , pLGICs exhibit allosteric modulation by numerous endogenous and exogenous molecules , including alcohols and anesthetics . The dual action of these molecules on pLGICs is particularly interesting . Alcohols and anesthetics potentiate many anionic channels ( GlyRs and most GABAARs [3]–[5] ) , whereas only short-chain alcohols potentiate nAChRs [6]; conversely , longer-chain alcohols and most anesthetics inhibit nAChRs [6] , and both types of modulators inhibit the ρ subtype of GABAARs [7] . Despite their apparent functional diversity , pLGICs share an overall architectural organization , with five subunits and three distinct domains [8] . The extracellular domain ( ECD ) contains the agonist site , at which binding leads to opening of a central pore in the transmembrane domain ( TMD ) . Each TMD contains four transmembrane helices ( M1–M4 ) , with the M2 helices lining the pore; residues in M2 are often described using prime notation , beginning ∼1′ at the N-terminal intracellular end and progressing to ∼20′ at the C-terminal extracellular end of the TMD . A third , intracellular loop domain ( ILD ) is present in some family members , where it modifies functional properties such as desensitization [8] . By definition , allosteric modulators alter the energy landscape for channel activation by binding at a location distinct from the primary ligand-binding site ( see review [9] ) . Modulators including alcohols and anesthetics have been shown to regulate activation by binding at least partially in the TMD [10] , [11] . In addition , at high concentrations some modulators and endogenous steroids can activate GABAAR by themselves [9] , [12] , [13] . Until recently , no high-resolution structures of the pLGIC TMD were available , and the lower-resolution structures [14] or models [15] of human receptors have not allowed definitive characterization of the allosteric binding site ( s ) . Because pLGICs are pharmaceutical targets for large classes of molecules including cannabinoids , steroids , barbiturates , and general anesthetics [9] , [16] , identification of the binding sites and mechanisms of action of these molecules is critical to designing better drugs . Our understanding of pLGIC structure has advanced tremendously in the last five years with the publication of the first crystallographic structures of three different receptors in this family . The first two structures , ELIC and GLIC , were of pLGICs from the prokaryotes Erwinia chrysanthemi [17] and Gloeobacter violaceus [18] , [19] , and have already provided valuable templates for homology models of human receptors such as GlyRs . We previously used a GlyR model based on GLIC to show spontaneous ethanol binding to a site between subunits [20] , consistent with past studies based on lower-resolution pLGIC structures [21] . The third pLGIC to be crystallized , the GluCl channel from the eukaryote Caenorhabditis elegans , was co-crystallized with a partial allosteric agonist bound between subunits [22] , again consistent with functional enhancement mediated by binding in this region . Conversely , the GLIC receptor was recently co-crystallized with the anesthetics propofol and desflurane [23] bound to an intra-subunit pocket in the upper part of the TMD . Resolving the contributions of inter- and intra-subunit binding is critical to understanding the structural basis for pLGIC allostery . One explanation for the observation of both inter- and intra-subunit binding could be the contribution of multiple allosteric sites to different modulatory effects . Like other cationic pLGICs , GLIC is inhibited by most anesthetics [24] and long-chain alcohols , while it exhibits weak potentiation by methanol and ethanol [25] . We previously showed that the mutation F ( 14′ ) A transforms GLIC into a highly ethanol-sensitive channel that is potentiated by alcohols as large as hexanol [25] , thus more closely approximating the properties of GlyRs and GABAARs [4] , [5] . We further demonstrated by molecular dynamics that the enhanced potentiation of the F ( 14′ ) A variant correlated with expansion of the inter-subunit cavity [25] . Thus , inter-subunit ethanol binding may correspond to enhanced function of pLGICs including GlyRs [20] and the GLIC F ( 14′ ) A mutant [25] , while the crystallographically determined intra-subunit binding of anesthetics on GLIC [23] could represent an independent inhibitory site of action . To address this hypothesis and further elucidate the effects of the F ( 14′ ) A mutation in GLIC , we have systematically explored binding of ethanol to GLIC WT and F ( 14′ ) A receptors in molecular dynamics simulations . Four molecular systems were created to study both the WT and mutant , with and without ethanol present in the bulk solvent , and both binding and equilibrium exchange of ethanol in identified TMD cavities was quantified . We also quantified the F ( 14′ ) A mutant with a single ethanol molecule bound in each of the five inter-subunit cavities . In our simulations , ethanol bound in both sites but primarily occupied the intra-subunit cavity of WT GLIC , in contrast to our previous GlyR simulations conducted under identical conditions [20] , but in agreement with the anesthetic co-crystal structures of GLIC [23] . The single point F ( 14′ ) A mutation was sufficient to enhance the average number of ethanol molecules observed in the inter-subunit more than twofold . Given our previous experimental results showing low sensitivity of WT and high sensitivity of F ( 14′ ) A to ethanol [25] , these data support a two-site model for modulation of pLGICs , involving both an inhibiting intra-subunit site and an potentiating inter-subunit site of action .
We performed 1-µs simulations of WT and F ( 14′ ) A GLIC in fully solvated lipid-embedded systems . The protonation state ( pH 4 . 6 ) corresponding to the crystallization conditions of the template GLIC structure was identical to the one proposed by Bocquet et al . [18] and also used by other groups [26] , [27] . Both the WT and F ( 14′ ) A simulations exhibited relatively small deviations from the GLIC crystal structure , with the overall protein Cα root mean square deviations ( RMSD ) under 3 Å in both cases . Indeed , over the last 100 ns , the Cα RMSD relative to the crystal structure was 2 . 43±0 . 12 Å for the WT and 2 . 18±0 . 08 Å for F ( 14′ ) A ( figure 1A , middle panel ) , below the average X-ray resolution of the protein ( 2 . 90 Å ) . Although the overall structures of the WT and F ( 14′ ) A channels were similar throughout the simulations , comparing the ECD and TMD of each protein revealed intriguing differences . In place of the extended ILD found in metazoan pLGICs , GLIC contains only a short linker that cannot be considered an independent domain [28] . The F ( 14′ ) A mutation , which is located in the TMD , was associated with TMD packing rearrangements that led to a larger local change of the structure: the average TMD Cα RMSD over the last 100 ns was 1 . 51±0 . 06 Å for the WT and 2 . 20±0 . 07 Å for the F ( 14′A ) mutated system ( figure 1A , lower panel ) . This increased TMD deviation was compensated in the overall RMSD by decreased structural fluctuations of the ECD: the ECD Cα RMSD over the last 100 ns was 2 . 51±0 . 18 Å for WT but 1 . 96±0 . 10 Å for F ( 14′ ) A ( figure 1A , upper panel ) . Calculating the average RMSD per residue ( figure 1B ) exposed selective deviation of the M2 helix ( residues 222 to 245 ) in the mutated system , approximately 2 . 2 Å for F ( 14′ ) A versus 1 . 2 Å for the WT . A visual inspection of the trajectory revealed a kink in the M2 helix . This kink appeared quickly , after only a few nanoseconds of simulation . Whereas in the WT simulation the average kink angle ( 12 . 46±2 . 36° ) remained close to the crystal structure value ( 8 . 01° ) , the average angle in the F ( 14′ ) A simulation stabilized around double the WT value ( 22 . 18±2 . 04° ) . We observed further indirect effects of the F ( 14′ ) A mutation on the M2 structure via constriction of the channel pore . Monitoring the pore radius across the ∼30-Å TMD throughout the 1-µs WT simulation ( figure 2A ) revealed a pore constriction of radius 2 . 25±0 . 31 Å around residue I ( 9′ ) , which was previously shown to comprise the GLIC hydrophobic permeation barrier [29] . Past studies showed a pore constriction of these dimensions to be wide enough to let some Cl− ion pass through the GlyR pore [20] . Similarly , PMF studies of GLIC have shown that at the same level radii of ∼2 . 4 Å [30] or ∼2 . 5 Å [31] were compatible with a conducting channel . However , the F ( 14′ ) A mutation tightened the pore constriction at the I ( 9′ ) position ( figure 2B ) to an average radius of 1 . 60±0 . 22 Å . Thus , WT GLIC appeared to be completely open , whereas we presumed the F ( 14′ ) A mutant to be mainly closed . This finding is consistent with our previous observation that the F ( 14′ ) A mutation shifts gating over 0 . 5 pH units to the right , corresponding to an approximately four-fold decrease in proton sensitivity [25] . Although the nonconducting F ( 14′ ) A model was structurally distinct from other recent closed [17] or locally-closed [32] pLGIC models , the relevance and relative contributions of these and other possible nonconducting conformations to GLIC gating remain to be determined . Moreover , our F ( 14′ ) A model might only reflect the increased flexibility of M2 upper part , rather than a new GLIC conformation . In both WT and F ( 14′ ) A simulations , we identified two major TMD cavities for each of the five protein subunits . The biggest cavities were intra-subunit , and were located towards the extra-cellular side of the TMD , facing the membrane ( figure 3A–B , violet ) . These cavities were hydrophobic , as confirmed by their negligible hydration and their occupancy by lipid fatty acid chain atoms ( table 1 ) : average lipid occupancy over the second half of the simulation measured 5 . 8±0 . 9 and 6 . 3±0 . 9 atoms per cavity for the WT and F ( 14′ ) A trajectories , respectively . Accordingly , the intra-subunit cavities were mainly lined by hydrophobic residues , with only 20% polar accessible surface area in WT and 22% in F ( 14′ ) A simulations . These cavities did not exhibit systematic changes in volume during the simulations , and did not appear to be influenced by the F ( 14′ ) A mutation , with average volumes of 368±68 Å3 for WT and 392±64 Å3 for F ( 14′ ) A ( figure 3C ) . In our previous work [25] , we adopted the terminology of Nury et al . [23] identifying two interconnected cavities at each GLIC subunit interface: an “inter-subunit cavity” facing the membrane , and a “linking tunnel” facing the pore . However , these cavities were not consistently defined in our 1-µs simulations; in F ( 14′ ) A , they were generally indistinguishable . Therefore , in this work we defined a single inter-subunit cavity associated with each subunit interface , partially exposed to both the membrane and the pore . The inter-subunit cavities were located in roughly the same plane as the intra-subunit cavities relative to the lipid bilayer , but were more hydrophilic , with 41% polar accessible surface in both WT and F ( 14′ ) A simulations , and occupancy by several water molecules in both simulations as well as the previously published crystal structures [18] , [19] . No lipid occupancy was observed in the inter-subunit cavities . We noted that several of the charged or polar residues lining the inter-subunit cavities ( N200 , H235 , N239 , E243 , K248 , Y263 ) are conserved in human pLGICs , supporting the functional relevance of these cavities to gating , modulation , or assembly . In contrast to the intra-subunit cavities , the inter-subunit cavities were dramatically altered by the F ( 14′ ) A mutation . In WT GLIC , each inter-subunit cavity was lined by residues in upmost turn of M2 , including the MTS-accessible residues L ( 17′ ) and V ( 18′ ) [25] , and the M2–M3 loop , and did not penetrate to the level of F ( 14′ ) ( figure 3A ) . The average WT inter-subunit cavity occupied 96±33 Å3 during the second half of the simulation , less than a third of the volume of the average intra-subunit cavity , and was occupied by 1 . 0±0 . 4 water molecules ( table 2 ) . Conversely , the absence of the phenyl group in the F ( 14′ ) A mutant allowed the inter-subunit cavities to extend deeper towards the intracellular side , in some cases contacting the substituted alanines at 14′ ( figure 3B ) . Accordingly , the average inter-subunit cavity volume was enlarged from the beginning of the simulation; by the second half , it increased to 283±45 Å3 ( figure 3D ) , a threefold increase over WT ( table 2 ) . Furthermore , the increased volume allowed occupation by 4 . 4±0 . 8 water molecules , fourfold more than WT ( table 2 ) . To identify sites and consequences of ethanol binding on GLIC , and the effect of the F ( 14′ ) A mutation on ethanol interactions , we ran additional molecular dynamics simulations of both WT and F ( 14′ ) A in the presence of ethanol . We placed each of the previously defined systems in ∼600 mM ethanol by replacing 1% of the bulk water molecules with ethanol . We previously showed that a similar concentration , approximately 3 times the concentration associated with immobilization of organisms [1] , potentiated GLIC WT weakly and F ( 14′ ) A potently [25] . After equilibration , we simulated both systems for 1 µs . Ethanol had a limited effect on GLIC structure , increasing structural deviations in the TMD of both WT and F ( 14′ ) A . For WT , this increase was reflected in an average Cα RMSD with ethanol of 2 . 02±0 . 11 Å over the last 100 ns—a 34% increase over the ethanol-free simulation ( figure 1A , lower panel ) . Similarly , the average Cα RMSD for the F ( 14′ ) A TMD with ethanol was 2 . 68±0 . 08 Å over the last 100 ns , a 22% increase ( figure 1A , lower panel ) . However , structural deviations averaged over the whole protein ( figure 1A , middle panel ) or the ECD ( figure 1A , upper panel ) were similar with and without ethanol for both WT and F ( 14′ ) A . The average RMSD per residue ( figure 1B ) , M2 kink angle ( respectively , 11 . 02±1 . 81° and 24 . 25±2 . 25° for WT and F ( 14′ ) A GLIC versus 12 . 46±2 . 36° and 22 . 18±2 . 04° without ethanol ) and intra-subunit cavity volumes ( figure 3C ) also followed similar patterns with and without ethanol for each system . Whereas ethanol had little effect on the WT pore radius ( figure 2C ) , it partially compensated for the constricted pore in F ( 14′ ) A ( figure 2D ) . The F ( 14′ ) A pore radius at the level of the I ( 9′ ) barrier stabilized around 2 Å in the presence of ethanol ( figure 2D ) , ∼25% larger than in the ethanol-free simulation ( figure 2B ) , and only ∼11% smaller than in the WT simulations ( figures 2A , C ) . Conversely , ethanol selectively increased the average inter-subunit cavity volume in the WT simulation ( figure 3D ) from 96±33 Å3 to 160±35 Å3 ( table 2 ) . The equivalent cavities in F ( 14′ ) A occupied 283±45 Å3 and 274±45 Å3 ( table 2 ) , consistently larger than in WT , but unaltered by ethanol ( figure 3D ) . Our ethanol simulations allowed us to directly observe ethanol occupation of both the intra-subunit and inter-subunit cavities . During the WT simulation , ethanol primarily occupied the intra-subunit cavities ( figure 4A , upper panel ) . An average of 0 . 9±0 . 3 and 0 . 3±0 . 2 ethanol molecules were present in each intra and inter-subunit cavity , respectively , over the second half of the simulation . In contrast , the F ( 14′ ) A mutation increased ethanol occupancy in the inter-subunit cavities almost threefold , approximating the occupancy of the intra-subunit cavities , which was similar to WT ( figure 4A ) . Average occupancies in the F ( 14′ ) A simulation were 0 . 7±0 . 2 ( table 1 ) and 0 . 8±0 . 2 ( table 2 ) for the intra- and inter-subunit cavities , respectively . Ethanol occupation of the F ( 14′ ) A inter-subunit cavities also exhibited substantial variability: for example , one of the inter-subunit cavities was occupied by an average of ∼2 ethanol molecules throughout the second half of the simulation , while another failed to bind ethanol ( table 2 ) . In the intra-subunit cavities , ethanol bound between the M1 and M3 helices of each subunit in a pose similar to that of propofol in the recent co-crystal structure [23] ( figure 4A ) . Ethanol binding corresponded to decreased lipid occupancy in the same cavities ( figure 4B ) : average intra-subunit lipid fatty acid chain atoms decreased from 5 . 8±0 . 9 ( WT ) and 6 . 3±0 . 9 ( F ( 14′ ) A ) without ethanol to 4 . 3±1 . 0 ( WT ) and 3 . 3±1 . 0 ( F ( 14′ ) A ) with ethanol ( table 1 ) . As shown in figure 5A , we observed a negative correlation between the average number of ethanol molecules and lipid atoms occupying each intra-subunit cavity at a given time in the WT ( R2 = 0 . 96 ) and F ( 14′ ) A ( R2 = 0 . 85 ) simulations . Conversely , there was no correlation between the average number of ethanol molecules at a given time and the average volume of the intra-subunit cavities ( figure 5A ) . Thus , ethanol binding in the intra-subunit cavities displaced lipid binding without altering cavity volume . Whereas WT ethanol binding was difficult to observe in the inter-subunit cavities , being occupied less than one-third of the time , ethanol clearly bound in the F ( 14′ ) A inter-subunit cavities near the M2 helices and the channel pore ( figure 4A , lower panel ) . Water occupied some of the same cavities ( Figure 4C ) , and we observed a negative correlation between the average number of ethanol and water molecules occupying each inter-subunit cavity at a given time in both the WT ( R2 = 0 . 69 ) and F ( 14′ ) A ( R2 = 0 . 94 ) simulations ( figure 5B ) . There was also a positive correlation between inter-subunit ethanol occupancy and cavity volume in both the WT ( R2 = 0 . 89 ) and F ( 14′ ) A ( R2 = 0 . 70 ) simulations ( figure 5B ) . Thus , ethanol binding in the inter-subunit cavities may have dual effects of displacing water and increasing cavity volume . Enhanced inter-subunit binding in the F ( 14′ ) A simulation also manifested in a slower exchange time between bound and bulk ethanol . As shown in figure 6 , ethanol exchange in each cavity type was fit by a double-exponential model . The fast component ( roughly 20 ns ) of the exchange likely corresponds to molecules repeatedly moving in/out of cavities before or after binding . For the WT , ethanol present in the inter-subunit site has an exchange time constant ( τ ) of ∼150±20 ns , while the ethanol located in the inter-subunit cavity of the F ( 14′ ) A mutant has a considerably slower exchange , τ∼380±70 ns . While these point to significant relative differences , the values are not trivial to compare to experiments since they are sensitive to the cavity definition , simulation relaxation , and not least that they don't account for molecules re-entering the cavity before reaching bulk water . To further investigate the effects of inter-subunit ethanol binding on F ( 14′ ) A structure , we performed an additional molecular dynamics simulation on the mutant with constrained ethanol molecules . Beginning with the ethanol-free system , we inserted one ethanol molecule in each of the five inter-subunit sites of F ( 14′ ) A , then simulated the system for 500 ns with the ethanol molecules constrained in the cavities . We then continued the simulation for another 500 ns , removing one ethanol molecule every 100 ns . As shown in figure 7 , the F ( 14′ ) A pore radius at the level of I ( 9′ ) stabilized under these conditions around 2 Å , similar to the 600 mM-ethanol simulation and ∼25% larger than in the ethanol-free simulation . Early time points in the simulation trajectories showed even larger deviations: during the first 300 ns , the minimal pore radius at I ( 9′ ) in the constrained F ( 14′ ) A system oscillated between 2 . 0 and 2 . 5 Å , similar to the presumed-open WT system , before stabilizing around 2 Å between 300 and 500 ns . This enlarged pore radius relative to the ethanol-free system was stable upon sequential removal of the constrained ethanol molecules ( figure 7 ) . Conversely , in the presence of 600 mM ethanol , the I ( 9′ ) barrier had become extremely narrow at the beginning of the simulation , with the pore radius falling to as little as 1 Å . Visual inspection of the trajectory showed one of the subunits transiently moving towards the pore and partially occluding it . Following this initial constriction , the radius at the level of I ( 9′ ) progressively increased , stabilizing at 150 ns around 2 Å ( figure 7 ) . Thus , both constrained and spontaneous ethanol binding resulted in initial fluctuations of the pore radius at the I ( 9′ ) constriction point , but subsequently stabilized to similarly expanded dimensions , an effect which was not reversible over a 500-ns time scale .
During all simulations , the backbone structures of GLIC WT and F ( 14′ ) A were relatively stable , with total Cα RMSD under 3 Å . Deviations associated with the F ( 14′ ) A substitution and/or with ethanol solvation were localized to discrete regions of the protein , particularly the M2 helix . The structural integrity of our GLIC models relative to the crystallographic template supported the validity of our simulation conditions . In particular , we chose a physiologically extreme concentration of ethanol for our binding simulations to compensate for the low potency of ethanol for WT GLIC in vitro and to increase our sampling of low-occupancy binding sites within our 1-µs simulations . By replacing 1% of water molecules with ethanol , we approximated a 1 mol-% or ∼600 mM ethanol concentration , approximately 3 times the immobilizing concentration and over 30 times the legal blood alcohol concentration limit to drive a car in the United States [1] . Nonetheless , neither the WT nor F ( 14′ ) A models were systematically disrupted by this high concentration of ethanol; instead , they stabilized on a time scale comparable with the ethanol-free simulations . Our simulations also confirmed the binding of various agents predicted from recent crystal structures . As observed in the earliest GLIC structures [18] , [19] , the membrane-facing intra-subunit cavities in our WT simulations were occupied by lipid , while the more hydrophilic inter-subunit cavities contained water . Furthermore , the ethanol-solvated WT simulation revealed intra-subunit ethanol binding that overlapped with the crystallographic propofol-binding site [23] , consistent with the similar effects of propofol and ethanol on some pLGICs [4] . Despite the overall consistency of our simulations , the F ( 14′ ) A mutation did have structural consequences beyond the absence of the phenylalanine side chain . For example , the mutation increased RMSD through most of the M2 helix , systematically increased the M2 helix kink angle , and constricted the pore radius at the level of the I ( 9′ ) hydrophobic gate . These structural consequences highlight the indirect effects of point mutations that may dramatically alter functional properties , and underscore the value of molecular dynamics simulations in interpreting mutagenesis data . Furthermore , high variability of the M2 region relative to the rest of the protein is consistent with the non-periodic accessibility of mutated M2 residues reported by Parikh and coworkers [33] , and could reflect increased mobility of this region under mutated or otherwise noncrystallographic conditions . The M2 helix may comprise a mobile structural element in which point mutations or the binding of allosteric modulators could influence the equilibrium constant of pore gating transitions . As in our previous work [25] , we imposed an acidic protonation state ( pH 4 . 6 ) corresponding to the crystallization conditions of the template GLIC structure [18] and the presumed open state of the WT receptor [28] . It was recently suggested that GLIC desensitizes on the second time scale [34] , and that the GLIC crystal structure may instead represent a desensitized state [33] , [34]; however , the pore radius in our WT simulations was sufficient to conduct ions [20] and other studies have shown that similar pore radii were compatible with a conductive state of GLIC [30] , [31] . Notably a recent study by Gonzalez-Gutierrez et al . [35] details their infructuous attempts to crystalize ELIC in an open conformation , they conclude that the crystal packing might be more important for energetic conformational equilibrium of LGIC than the presence of agonist or antagonist and mutations favoring the open or close state . The reciprocal should be the same for GLIC , which only introduction of cross-links or non-functional mutations were able to stabilized a locally-closed conformation of GLIC [32] . Given the low deviation of our simulated WT TMD from that of the crystal structure ( Cα RMSD ∼1 . 5 Å ) , our data are consistent with the crystal structure representing an open state . Our group [25] and others [33] also showed that the mutating 14′ position reduced agonist sensitivity , an effect that correlated in this work with constriction of the pore radius and a nonconducting state of the channel . This pore constriction was partially relieved by ethanol binding in the inter-subunit cavity , possibly contributing to the enhanced ethanol potentiation of this mutant . Although our data provide novel insights into ethanol binding to the presumed open state of a pLGIC , alcohols and other modulators may also have relevant interactions with closed , desensitized , or other intermediate states; a complete understanding of allosteric modulation will require modeling of multiple states and the transitions between them . We note that the microsecond timescales of the simulations in this study are still too short to simulate opening , closing , or desensitizing transitions of the channel [28]; instead , our current findings simulate interactions of ethanol with a particular , evidently stable , state of GLIC . The recent determination of GLIC crystal structures in locally closed conformations [32] may lead to valuable new templates for modeling alternative states of this channel; however , the nature of the predominant resting state or states of the channel remain to be determined in detail . Finally , some caution should be exercised when interpreting simulation and experimental results at different pH . These ( as other ) simulations were performed with constant protonation states that attempt to approximate the pH 4 . 6 of the GLIC crystal , while experiments have investigated the pH-response of the channel and used EC10 values to perform the actual modulation studies [25] . This is not easily captured in modeling since it is closer to neutral pH where a true constant-pH simulation algorithm would be needed , which is still not in widespread use , in particular not for massively parallel simulations . The enhanced ethanol potentiation of the F ( 14′ ) A mutant corresponded in our simulations to an approximately threefold increase in inter-subunit cavity volume from 96±33 Å3 to 283±45 Å3 . Given that a single ethanol molecule occupies 97 Å3 , this structural change increased the number of ethanol molecules that could be accommodated by the inter-subunit cavity from ∼1 to ∼3 . Accordingly , in our ethanol simulations , F ( 14′ ) A increased ethanol binding in the inter-subunit cavity as measured by both occupancy and bulk exchange rate . Ethanol occupancy increased inter-subunit cavity volume in WT and F ( 14′ ) A receptors , possibly associated with the displacement of water by the larger ethanol molecules . This enhancement of inter-subunit cavity volume in the presence of ethanol may provide a structural basis for ethanol potentiation . The inter-subunit cavities are poised to influence channel gating , given their close proximity to the ECD-TMD coupling region [36]; indeed , recent microsecond simulations of GLIC indicated that the shape and volume of these cavities is coupled to channel gating [29] . We previously suggested that ethanol binding in the GlyR might induce swelling of the inter-subunit cavities that prevents the channel from closing or desensitizing [20] . A critical role of inter-subunit cavity volume in ethanol effects may also explain pressure antagonism of ethanol on GlyR function [37] . A correlation between inter-subunit cavity volume and pLGIC potentiation was further supported by the recent crystallization of the eukaryotic pLGIC GluCl [22] , in which the partial allosteric agonist ivermectin occupied the inter-subunit interfaces and was associated with enlarged gaps between subunits [22] . Aside from this deviation at the interface , the structure of GluCl aligns closely with that of GLIC , further validating the relevance of this system as a model for eukaryotic pLGIC structure and modulation . Inter-subunit ethanol binding in F ( 14′ ) A was also associated with partial relief from the pore constriction induced by the mutation , which may be directly or indirectly related to changes in inter-subunit cavity volume . Selective occupation of the inter-subunit cavities appeared to be sufficient for this effect , consistent with a negligible contribution of intra-subunit or other binding sites . Based on this observation , ethanol potentiation measured by electrophysiology could reflect compensation for inhibited gating of F ( 14′ ) A relative to WT . Indeed , the ∼10% activation level used to test modulation of F ( 14′ ) A corresponded to the ∼50% activation level of the WT [25]; thus , if ethanol binding had the sole consequence of restoring F ( 14′ ) A to the WT conformation , it would enhance mutant function fivefold . However , our previous electrophysiological studies revealed approximately thirtyfold potentiation of F ( 14′ ) A currents by 600 mM ethanol [25] , indicating that compensation for reduced gating is not the sole mechanism responsible for ethanol potentiation of these channels . The enhancement of WT GLIC by 600 mM ethanol [25] did not correspond to a change in pore constriction , further implicating an alternative or additional mechanism of potentiation . In addition to enhancing ethanol potentiation , we previously reported that F ( 14′ ) A converted longer-chain alcohols as large as pentanol from inhibitors into potentiators [25] . Modulation by hexanol was biphasic , inhibiting at low concentrations and potentiating at high concentrations; heptanol was weakly inhibiting , with a shallow concentration dependence consistent with simultaneous inhibitory and potentiating interactions [25] . Hexanol ( 207 Å3 ) and heptanol ( 236 Å3 ) are too large to bind in the WT inter-subunit cavities ( 96±33 Å3 ) , but would be accommodated by the enlarged cavities in F ( 14′ ) A ( 283±45 Å3 ) . Thus , inter-subunit binding may represent a general mechanism for n-alcohol potentiation of GLIC F ( 14′ ) A . We previously reported that inhibition of GLIC by n-octanol was unaltered by F ( 14′ ) A [25] , supporting a site of inhibitory action independent of enhanced potentiation in this mutant . Recent co-crystal structures of GLIC bound to the anesthetics desflurane and propofol , both of which inhibited the receptor , supported a mechanism for inhibition via the intra-subunit cavities [23] . Consistent with this model , average intra-subunit cavity volume was unaltered by F ( 14′ ) A . Furthermore , we observed equivalent intra-subunit ethanol occupancy in WT and F ( 14′ ) A simulations , substantiating this cavity as a binding site for n-alcohols and supporting its structural independence from the inter-subunit cavity . The only intra-subunit difference we observed between WT and F ( 14′ ) A was the correlation between ethanol and lipid occupancy: ethanol displaced up to 2 lipid atoms per molecule in the F ( 14′ ) A intra-subunit cavity , but displaced only ∼1 lipid atom per molecule in WT . Lipid molecules were resolved in the intra-subunit cavity of the GLIC crystal structure [18] , and exhibited higher occupancy in the WT and F ( 14′ ) A simulation intra-subunit cavities than in bulk membrane; however , the role of lipids in GLIC function remains unclear . In one possible mechanism , lipids could stabilize the open state by occupying the intra-subunit cavities; displacement of lipids by alcohols or other modulators might disrupt this stabilization and inhibit the receptor . A similar mechanism may underlie the critical role of lipids in stabilizing specific states of the nAChR [9] . Ethanol occupied both inter- and intra-subunit cavities in WT and F ( 14′ ) A simulations , suggesting that net modulation might reflect a combination of potentiating and inhibitory binding associated with the inter- and intra-subunit cavities , respectively . By this two-site model , the affinity and efficacy of a given n-alcohol in each cavity determine its net functional effect [38] . For example , we previously reported moderate inhibition of F ( 14′ ) A by low concentrations and potentiation by high concentrations of hexanol [25] . This relatively hydrophobic alcohol might have greater affinity for the more hydrophobic intra-subunit cavity , and might experience greater accessibility to this cavity via partitioning through the lipid bilayer; thus , at low concentrations , inhibitory effects prevail . At higher concentrations , hexanol might bind with lower affinity to the inter-subunit site , but possibly still have greater efficacy when bound in this site , resulting in a net potentiation of the receptor . Heptanol , which is more hydrophobic than hexanol and almost too large for the inter-subunit cavity , might prefer the intra-subunit cavity even at high concentrations , resulting in net inhibition . Recently , a MD study by Lebard et al . [27] described a negligible affinity of ethanol to the pore and proposed pore blocking as an inhibition site for general anesthetics . However , similar multi-site models of allosteric modulation have been proposed by recent simulation studies of GLIC binding to the volatile anesthetic isoflurane [26] , [39] . Most anionic pLGICs , including GlyRs and most GABAARs , exhibit potentiation by alcohols and anesthetics [1] . Thus , these receptors exhibit a similar profile of modulation to GLIC F ( 14′ ) A; and indeed , the position equivalent to F ( 14′ ) in anionic pLGICs is generally substituted with a smaller residue [25] . Structure/function studies have identified several residues critical to alcohol and anesthetic potentiation of GlyRs and GABAARs that map near the GLIC inter-subunit cavities [25] . One early estimate suggested a volume of 189–217 Å3 for the GABAAR potentiating site [40] , between the inter-subunit cavity volumes observed for GLIC WT and F ( 14′ ) A . More recently , molecular dynamics simulations of GlyR models based on either GLIC [20] or the low-resolution nAChR template [21] supported ethanol stabilization of the open state via binding in the inter-subunit cavity . Notably , although the dominant modulation exerted by alcohols and anesthetics on anionic pLGICs is positive ( potentiating ) , mutant labeling studies in both GlyRs [41] and GABAaRs [11] also substantiate a negative ( inhibitory ) modulatory effect exerted via an independent site or sites . Although we cannot rule out contributions of alternative ethanol binding sites , for example in the ECD [42] or ILD [43] , to modulation of GlyRs or GABAARs , the strong correlations of ethanol potentiation with cavity volume , occupancy , and exchange rate in this study highlight an important role for the inter-subunit TMD region . The potent ethanol sensitivity of GLIC F ( 14′ ) A in the absence of an ILD suggests this domain is not critical to pLGIC modulation . Our two-site model of allosteric modulation may be particularly relevant to cationic pLGICs such as nAChRs , which exhibit both potentiation and inhibition by allosteric modulators . Photoaffinity labeling studies localized binding of the potentiator etomidate to an inter-subunit TMD cavity [44] , whereas labeling [45] and simulation studies [26] associated inhibitors such as halothane and isoflurane with an intra-subunit cavity . The low-potency inhibitor benzophenone photolabeled both inter- and intra-subunit cavities as well as the channel pore [46]; if these distinct binding sites confer opposing functional effects , their resulting noncompetitive antagonism might underlie the apparent low potency of this agent . Similar to GLIC , nAChRs are potentiated by short-chain alcohols but inhibited by long-chain alcohols [6] , and structure/function studies have identified TMD residues that contribute independently to potentiation and inhibition [10] . The conservation of F ( 14′ ) in several nAChR subtypes [25] further supports the relevance of WT GLIC as a model for structure , function , and modulation of pLGICs including nAChRs .
The initial GLIC structure was taken from the PDB entry 3EAM [18] . The pdb2gmx program from the GROMACS package [47] was used to add hydrogens according to the residue protonation as defined by Bocquet et al [18] . The mutated F ( 14′ ) A GLIC model was built using the backbone-dependent rotamer library SCWRL [48] to mutate phenylalanine 238 ( 14′ in M2 prime notation ) to alanine , and to rebuild side-chains of the mutated residues and the four closest neighbors in the sequence . The ROSETTA refinement program [49] was used to relax the structure , with protonation identical to wild-type . Each model was inserted into a pure dioleoylphosphatidylcholine ( DOPC ) bilayer and overlapping lipid molecules were deleted , keeping 306 DOPC lipids . The two systems were solvated with roughly 34 , 000 TIP3P water molecules in a hexagonal box . To neutralize the net charge and achieve a physiological ion concentration of ∼100 mM , 61 and 86 water molecules were replaced by Na+ and Cl− ions , respectively . Simulations were performed using GROMACS 4 . 5 . 3 [47] with the Amber 03 force field [50] for protein and ions , TIP3P [51] parameters for water , and the Berger force field for DOPC [52] . All bonds were constrained using the LINCS algorithm allowing a time step of 2 . 5 fs . Particle mesh Ewald electrostatics was used with a 10 Å cutoff for non-bonded interactions and neighborlists updated every 10 steps . Three baths ( protein , water and ion , membrane ) were coupled to a temperature of 310 K using the Bussi velocity rescaling thermostat with a time constant of τT = 0 . 1 ps . The x/y dimensions were scaled isotropically with a Berendsen weak barostat and the z dimension independently to reference pressures of 1 bar , τP = 1 ps and compressibility of 4 . 5 · 10−5 bar−1 . The system was minimized for 10 , 000 steps with steepest descent . It was equilibrated with position restraints of 1000 kJ/mol/nm2 on the protein , then for 10 ns with backbone restraints , and finally for 20 ns with only Cα restraints . Productions run were performed without any restraints for 1 µs . Ethanol was added by replacing 1% of the water molecules that were more than 8 Å away from the protein . None were placed inside the protein pore . The system was again subjected to 10 , 000 steps of minimization . Each system was then used for a 1-µs production run . We also built a F ( 14′ ) GLIC system with one molecule of ethanol docked in each of the five inter-subunit cavities . We used the spontaneously occupied position of ethanol in the inter-subunit cavity in the F ( 14′ ) A simulation to place the five ethanol molecules . Among all the ethanol molecules in the F ( 14′ ) A simulation , we extracted the coordinates of the ethanol molecule staying the longest in the inter-subunit cavity . Over this portion of the trajectory ( ∼700 ns ) , we averaged the ethanol positions and extracted the frame where the ethanol was the closest to the average position . Those coordinates were then imposed on the four other cavities . To keep ethanol molecules in the cavity , distance restraints of 100 kJ/mol/nm2 to the initial position were added during the simulation . The system was then subjected to a 500-ns production run . After this period , one ethanol molecule was replaced by a water molecule every 100 ns . These replacements resulted in a system with 5 ethanol molecules between 0 and 500 ns , 4 ethanol molecules between 500 and 600 ns , 3 ethanol molecules between 600 and 700 ns , 2 ethanol molecules between 700 and 800 ns , one ethanol molecule between 800 and 900 ns , and no ethanol between 900 ns and 1 . 0 µs . In total , five separate microseconds simulations were performed , and ethanol occupancy analyzed independently for the five different subunits of each protein to increase sampling . The M2 kink angle was computed within VMD [53] using a custom script , calculating the angle between the two principal axes of inertia of the top and bottom part of M2 . The bottom part of M2 was defined by Cα of residues 7′–14′ ( 221–238 ) and the top part by Cα of residues 14′–21′ ( 238–245 ) . Average cavity volumes over the course of the simulations were computed in three steps using mdpocket , a module of the Fpocket package [54] . First , mdpocket was used to compute all cavities over the course of the simulation every 5 ns . Second , grids were extracted for intra-subunit and inter-subunit cavities present in at least 20% of the trajectory frames . Onto those 10 cavity grids ( 5 intra-subunit and 5 inter-subunit ) , the largest cavity subspace of each type was selected and superimposed on the other 4 cavities of the same type . Third , average volumes were calculated for each cavity within the previously defined grids . All parameters were according to Fpocket defaults , except the volume calculation , for which we used 10 , 000 Monte Carlo iterations instead of 2 , 500 . Pore radii of the trajectories were computed using the HOLE software [55] , extracted each nanosecond and averaged . Average densities were computed using the Volmap plug-in of VMD [53] with a resolution of 1 Å , and averaged over the second half ( 500 ns ) of each trajectory . | Communication from one nerve cell to the next is an essential process for brain and muscle function . Nerve impulses result in release of transmitter molecules from one cell that bind to receptors on the next cell . Transmitter binding opens a pore in each receptor and ions flow across the membrane , leading to either enhancement or inhibition of new nerve impulses . These receptors are modulated by numerous drugs , including alcohols and anesthetics; identifying the precise location of modulator binding is critical for drug development . We have used computer simulation methods to model alcohol diffusion and binding to a receptor . By modifying a single residue in the receptor , we were able to move the location of the binding site and dramatically alter alcohol modulation , which supports a model with two separate binding sites for enhancement and inhibition in this family of receptors . | [
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] | 2012 | Molecular Mechanism for the Dual Alcohol Modulation of Cys-loop Receptors |
Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking . Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations , selection , drift and spatial constraints , to simulate multi-region sequencing data derived from spatial sampling of a neoplasm . We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from both bulk and single-cell sequencing data . Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours . We also propose a statistical inference framework that incorporates spatial effects within a growing tumour and so represents a further step forwards in the inference of evolutionary dynamics from genomic data . Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors remains challenging . However , mechanistic model-based approaches have the potential to capture the sources of noise and better interpret the data .
Cancer is an evolutionary process fuelled by genomic instability and intra-tumour heterogeneity ( ITH ) [1] . ITH leads to therapy resistance , arguably the biggest problem in cancer treatment today [2] . Recently , seminal studies have attempted to quantify ITH by either looking at subclonal mutations in deep sequencing data from single bulk samples [3 , 4] , or by taking multiple samples of the same tumour , the so-called multi-region sequencing approach ( reviewed in [5] ) . Phylogenetic approaches are then used to reconstruct the ancestral history of cancer cell lineages [6] . However , one important difference between phylogenetic analyses in cancer and classical phylogenetic analyses of species is that each cancer sample is not a single individual , but a mixture of different cancer cell subpopulations and non-cancer cells [7] . The problem is usually tackled by performing subclonal deconvolution of the samples to separate the different subpopulations [3 , 8] . However , these approaches do not account for the spatio-temporal dynamics that generated the data . To study the evolutionary dynamics of individual tumours , mathematical and computational models of evolutionary processes are widely employed [9–12] . Many of these models are rooted in theoretical population genetics , a field that quantifies the evolution of alleles in populations and that is central to the modern evolutionary synthesis [13] . More recently , spatial models have also been used [14–23] . However , seldom have mathematical and computational models of cancer evolution been directly connected to next-generation sequencing data from human tumours . Recent work from us and others has shown that combining theoretical modelling and cancer genomic data allows for measurement of fundamental properties of the tumour evolutionary process in vivo , such as mutation rates and strength and onset of subclonal selection events [22 , 24 , 25] . Here , we study how spatial constrains of a growing tumour impact our ability to infer cancer evolutionary dynamics . We combine explicit spatial evolutionary modelling with synthetic generation of multi-region bulk and single-cell data , thus providing a generative framework in which we know the evolutionary trajectories of all cells in a tumour and can examine the genomic patterns that emerge from the sampling experiment . We show that spatial constrains , stochastic spatial growth and sampling biases can have unexpected effects that confound both the interpretation and inference of the perceived evolutionary dynamics from cancer sequencing data . We also present a statistical inference framework that begins to account for some of these confounding factors and recover aspects of the cancer evolutionary dynamics from various types of multi-region sequencing data as well as single-cell data .
Here we develop and analyse a stochastic spatial cellular automaton model of tumour growth that incorporates cell division , cell death , random mutations and clonal selection ( Material and Methods ) . Each tumour simulation starts with a single ‘transformed’ cell in the centre of either a 2D or a 3D lattice , and we model the resulting expansion of this first cancer cell . All events , such as cell proliferation , death , mutation and selection are modelled according to a Gillespie algorithm [26] . In our model we account for different spatial constraints that are parameterised within our simulation . In order for a cell to divide , a new empty space for its progeny is required within the 8 neighbouring cells if we consider a 2D grid with Von Neumann neighbourhood . If no empty space is present , a cell can generate a new space by pushing neighbouring cells outwards ( choosing a random direction of the push ) . In this scenario , the growth is ‘homogeneous’ and all cells in the neoplasm can divide ( Fig 1A and 1B ) . Because all cells in the tumour can divide , this scenario leads to an overall exponential expansion ( S1A and S1B Fig ) . At some point during the simulation ( Fig 1A–1D ) , within the original tumour population ( blue cells ) , we introduce a new mutant ( a new subclone–red cells ) which may or may not have a selective advantage . In the case of a neutral subclone ( no selective advantage ) , the mutant cells proliferate as all the other cells ( Fig 1A ) . We note that in this case , colouring a new subclone in red at a certain point during neutral growth is arbitrary , and equivalent to the marking of a lineage by a random neutral ( passenger ) mutation . In the case where the subclone has a fitness advantage , the mutant will , on average , grow more rapidly compared to the parental background clone , thus increasing in relative proportion over time ( Fig 1B and S1B Fig ) . We also model ‘boundary driven’ growth , where only cells that are sufficiently close to the border of the tumour can proliferate . Other cells may remain ‘imprisoned’ in the centre of the tumour unable to proliferate because of the lack of empty space around them . Boundary-driven growth has been observed experimentally [27–29] as well as in model systems [30] . The magnitude of this effect is controlled in our simulation with the parameter a , which considers cell location and defines the probability that a cell will push neighbouring cells to create empty spots depending on how far is the cell from the boundary ( see Materials and Methods ) . Boundary driven growth leads to a polynomial expansion ( S1C Fig ) . Importantly , in both the case of neutral mutants ( Fig 1C ) and selected mutants ( Fig 1D ) , the spatial distribution of mutant cells in this scenario is strongly affected by the spatial constraints . At each division , a cell has a certain probability to acquire additional somatic mutations , modelled with a Poisson distribution , with mean u , in line with many other previous models [11 , 24 , 25 , 31 , 32] . Notably , u is the average number of new somatic mutations per division for the whole genome of a single cell . We assume that both daughter cells can acquire mutations , that mutations are unique ( infinite site model ) and we neglect back mutations ( infinite allele model ) . Finally , the large majority of mutations are assumed to be passengers ( neutral ) , with a few driver alterations allowing for subclonal fitness advantages ( e . g . subclonal populations in Fig 1B and 1D ) . This is consistent with large-scale genomic sequencing studies indicating that in any given tumour , the number of driver events is generally small , while the number of passengers is often orders of magnitude larger [31 , 33] . Importantly , our spatial model of tumour growth allows for the simulation of tissue sampling and genomic data generation . For instance , we can simulate the collection of punch biopsies , where spatially localised chunks of tumour are collected ( Fig 1E ) . We can also simulate needle biopsies , where a long and thin piece of tissue is sampled ( Fig 1E ) . We can then simulate the genomic data generation process starting from the cells in the sample and the identification of somatic mutations . For example , we can simulate the sequencing at a given coverage using Binomial sampling of the alleles , the limits of low frequency mutation detection ( e . g . minimum number of reads with a variant , minimum coverage ) , as well as non-uniformity of coverage leading to over-dispersion of the variant allele frequency ( VAF ) of detected mutations . This allows generating realistic data from simulated tumours , e . g . in the case of the simulation of a diploid tumour with one selected subclone in Fig 1E , all needles and punch biopsies contained clonal mutations , shown as a cluster of variants around VAF = 0 . 5 ( Fig 1F ) , and in the case of punch biopsy 1 and needle biopsy 4 , also a subclonal cluster representing the growing subclone . We previously showed , using a non-spatial stochastic branching process model of tumour growth , that assuming a well-mixed population and exponential growth , the expected VAF distribution of subclonal mutations in cancer under neutral growth follows a power-law with a 1/f2 scaling behaviour , where f is the variant allele frequency of subclonal mutations [24] . This has been previously demonstrated to be the scaling solution of the fully stochastic Luria-Delbruck model [34–36] . The 1/f2-like neutral subclonal tail can be observed in all samples of Fig 1F . In the presence of subclonal selection , we expect to observe an additional subclonal ‘cluster’ of mutations all at the same frequency [3] , that are the passenger mutations hitchhiking in the expanding clone ( as we previously described [25] ) . This is exemplified in needle 4 and punch 1 in Fig 1F . We note that a 1/f2-like tail remains in the VAF frequency spectrum of all samples , as a consequence of within-clone neutral dynamics that remain on-going throughout the tumour’s growth [25] . Furthermore , our framework allows simulating single-cell data . For example , from the simulated tumour in Fig 1B we sample individual cells at random and simulate single-cell whole-genome sequencing ( Fig 1G ) . For each representative simulation of spatial constraints in Fig 1 , we modelled the sampling of 6 punch biopsies ( small square regions ) , 2 needle biopsies ( long and thin regions ) , as well as hypothetically sampling the whole tumour . From each sample , we simulated the generation of 100x depth whole-genome data ( see Materials and Methods for details about the sequencing noise model ) . Fig 2A shows the variant allele frequency ( VAF ) distributions of samples from the neutral homogeneous growth case in Fig 1A , with clonal mutations ( truncal ) in grey , subclonal mutations exclusive to the parental background clone in light blue and subclonal mutations within the mutant in pink . All samples show the characteristic 1/f2 distribution corresponding to neutral evolutionary dynamics [24] , as one would expect theoretically [34] . The Area Under the Curve ( AUC ) test for neutrality we previously proposed [25] ( p<0 . 05 means neutrality is rejected ) is reported on top of each VAF plot and shows that even in the presence of a spatial structure , homogeneous ( exponential ) neutral growth follows a 1/f2 distribution ( Fig 2A-i to 2A-iv ) . As we have shown previously , it is possible to recover the mutation rate per cell doubling from the ~1/f2 neutral tail , which in this case without cell death was 10 mutations per division ( ~10−9 mutations/bp/division ) . This was correctly recovered in all samples from Fig 2A ( recovered mutation rate reported in each plot as u ) . In the case of homogeneous growth with subclonal selection ( Fig 2B ) , neutrality could be rejected based in all those samples containing a mix of the background clone and the new subclone ( Fig 2B-i and 2B-iv , see subclonal cluster in purple ) . Specifically , needle 4 and punch 1 showed the expected signature of selection , with a subclonal cluster a consequence of the over-representation of passenger mutations in the expanded clone [3 , 25] . The 1/f2-like tail resulting from the within-clone accumulation of passenger mutations remains in the frequency spectrum [25] . Specifically , in the plots in Fig 2B we report the mutations that were present in the first subclone cell in purple . Those are mutations that increased in frequency by hitchhiking on the selected mutant . Importantly , we note that these mutations are not exclusive to the subclone but are also found in other lineages ( e . g . in the ‘cousins’ of the selected subclone ) . The same dynamics are observed if it is the death rate to decrease , rather than the birth rate to increase ( S2A and S2B Fig ) . Importantly , the cell death d not only increases the rate of genetic drift , as expected , but also the level of clonal intermixing due to the additional stochasticity introduced by high cell replacement ( S2C–S2F Fig , examples of neutral cases ) . Selection could not be detected in other spatially-distinct samples from the same tumour when they did not contain differentially selected populations , and either captured only the background clone ( blue ) or only the selected mutant ( red ) ( Fig 2B-ii and 2B-ii ) . This is correct as in those samples ITH is neutral . This initial spatial analysis produced similar results to our previous well-mixed non-spatial models [24 , 25] . We next investigated the effect of boundary driven growth . Here , only cells close to the borders grow , leaving other cells ‘imprisoned’ inside the tumour mass ( see Materials and Methods for details ) , a pattern called gene surfing that causes radial patterns of cells growing only at the front of the expanding wave ( Fig 2C ) . This has been previously documented both theoretically and experimentally in bacteria [37] , in mathematical models of tumour growth [16 , 17 , 38] , as well as in cancer model systems , where the neutral expansion of the cancer cell population under boundary driven growth led to lineages growing just because they were ‘lucky’ to be in the right place at the right time [29] . This has implications for the impact of the immune system during the evolution of a tumour , which exert a negative selection pressure on the cancer cell population through neoantigen recognition and removal [39] , especially because neoantigen recognition is clone size dependent [40] . Importantly , boundary driven growth leads to non-exponential population dynamics [27 , 28] that also impact the distribution of mutations between the centre and the periphery of a solid neoplasm , as shown in a case of liver cancer sampled at high resolution [41] . The accumulation of subclonal mutations in a neutrally expanding tumour under boundary driven growth is expected to follow a 1/f2 scaling form within most of the detectable frequency range ( f>5% ) , although at low frequency deviations are expected [37] . This is largely driven by the increasing difference in mutational burden between the centre and the border of the tumour , which could lead to rejection of the standard neutral expectation under exponential growth , as seen when the whole tumour is sampled with respect to when only a localised bulk/needle biopsy is collected ( Fig 2C ) . Because the population is no longer homogeneously distributed however , this can lead to significant spatial bias , causing over- or under-representation of mutations in the VAF distributions solely due to spatial effects and not because of selection . This causes deviations from the neutral expectation of the mutant allele distributions that risk being wrongly interpreted as the consequence of on-going subclonal selection , as in Fig 2C . In this scenario , we know that subclonal clusters ( e . g . punch 6 in Fig 2C-iii ) are not differentially selected subclones , but the over-representation of alleles is solely induced by the spatial structure . Furthermore , even when we observe distributions that appear to follow the neutral expectation ( AUC p>0 . 05 ) , boundary driven growth results in much higher mutational loads than would be expected in the well mixed case . Here our inferred mutation rates are up to 10 times higher than the ground truth . This can be observed more explicitly in S3 Fig , where we sample each representative tumour from the centre towards the periphery by taking samples along concentric circles ( S3A Fig ) and compare the mutational loads of the samples ( S3B Fig ) . This was indeed observed in a case of neutrally growing liver cancer [41] and a similar phenomenon is also observed in species evolution [42] . If we combine boundary driven growth and subclonal selection the situation is further complicated: selective effects are now modulated by spatial constraints . In some cases , the selected mutant emerges and remains directly at the front of tumour growth . In this scenario the outgrowth caused by its selective advantage is amplified further just because it occurred at the growing front ( Fig 2D ) . In other cases , the selected mutant may , by chance , remain ‘imprisoned’ within the tumour ( assuming the mechanism of selective advantage is unable to overcome this spatial entrapment ) and stops proliferating despite its selective advantage ( e . g . S4 Fig ) . In both these cases , further sampling biases occur . In the case of punch 5 for example ( Fig 2D-iii ) , where the new subclone is fixed ( clone fraction = 100% ) , there is an overrepresentation of a cluster of mutations that is only due to spatial drift and not selection . These dynamics are recapitulated in larger cohorts of simulated tumours with the same parameters ( S5 Fig ) . The distributions of p-values for the AUC measurements for all simulations for different modes of growth are illustrated in S6A Fig . This figure shows that neutrality is accepted in the majority of homogeneous cases without selection , and it is rejected in the majority of homogeneous cases with selection . In the case of boundary driven growth things are more complicated . In S6B Fig we show the AUC tests for neutrality applied to whole-tumour samples versus punch/needle biopsies . In the case of neutral boundary driven growth , neutrality is accepted in the majority of cases when we use localised punch/needle biopsies , but rejected when the whole-tumour sample is examined . This is due to the deviation from strict neutrality caused by boundary driven growth , that can be detected only when a large region of the tumour is sampled ( and hence differences between centre and periphery of the tumour are captured ) . In the case of selective boundary driven growth , we observe similar dynamics but with the ability of rejecting neutrality if differential selection of the growing subclone is captured within the punch/needle sample . We note that under selective boundary driven growth , the subclone often remains imprisoned , leading to neutral-like dynamics . Similar dynamics to Fig 2B are observed when positive selection is modelled as the probability of growing in the absence of space ( increased pushing probability parameter a ) rather than the increased birth rate . This leads to dynamics dominated by the homogeneous growth of the subclone rather than boundary growth of the background clone ( S7 Fig ) . Moreover , removal of the majority of cells ( 99% ) by treatment leads to enhancement of outgrowth of selected clones due to competitive release ( S8 and S9 Figs ) . We then looked at the pairwise VAF distributions between samples . The amount of subclonal mutations scattered through the frequency spectrum ( Fig 3 ) and the number of subclonal clusters due to sampling bias and spatial drift was significant ( e . g . Fig 3D ) . As per ground truth , only the dark purple mutations should show a subclonal clustering pattern ( e . g . Fig 3B , punch 1 ) . We found that scattered variants were mostly due to the effect of neutral lineages spreading in space , and then subsampled in different ways in each tumour region . In the case of boundary driven growth , sampling bias produces evident clusters that do not correspond to differently selected subclones in the tumour . This makes the reconstruction of the true clonal phylogeny and its evolutionary interpretation problematic . Most of the confounding factors we have described so far result from the limitations of bulk sequencing , where the genomes of many cells are convolved within samples . Single-cell sequencing does not suffer from this particular limitation and promises high-resolution cancer evolutionary analysis devoid of the drawbacks of bulk sequencing [43] . To examine the effect of single cell sequencing , we simulated whole-genome sequencing of 10 single cells taken at random from the tumour and reconstructed their phylogenetic relationship ( Fig 4A-i ) . For the neutral cases ( Fig 4A and 4C ) , the patterns are consistent with a typical 'balanced' neutral tree , wherein all lineages contribute roughly equally to the final cell populations . In a balanced tree , the average distance between the trunk and each leaf of the tree is similar in each lineage . In cases with selection ( Fig 4B-i and 4D-i ) , the selected subclonal lineages are over-represented on the tree ( as reflected in VAF distributions ) , as the red lineage is introduced at time t = 8 and would have been much smaller if it was not selected for . Here the average distance between trunk and any leaf is different in the background vs the new clone . The pattern is even clearer if we sample 400 single cells and performed WGS ( Fig 4B-ii and 4D-ii ) . We note that if we use randomly sampled single cell sequencing and plot the site frequency spectrum ( frequency distribution of mutations within the population of sampled cells ) we recapitulate the VAF distribution , including subclonal clusters and 1/f2 tails ( S10 Fig ) . This is because the site-frequency spectra derived from single-cell sequencing data corresponds to a VAF distribution . However , as whole-genome mutational profiling of single cells is still difficult due to allele dropout [44] , often single-cell genotyping has to be performed instead [45] . In this approach , a bulk sample is sequenced and all mutations in that bulk sample are then tested in single cells for presence/absence of the mutation . Integrating bulk sequencing with single-cell information is extremely powerful [46] , but requires careful interpretation of the results . In Fig 4A-iii we show that this approach , although informative , can lead to very distorted phylogenetic trees where branch lengths are heavily biased by the initial choice of mutations to be assayed , and consequently the signature of selection vs neutrality is not readily identifiable from these data alone . Moreover , significant sampling bias is still apparent for single-cell sequencing when individual cells are not sampled uniformly at random from the whole tumour , but instead isolated in ‘clumps’ from different bulk samples . In Fig 5 we have simulated the collection of 4 single cells from each of the 6 punch biopsies in Fig 2 ( these are the same simulations used to generate Fig 4 ) . The trees are quite different from those sampled in Fig 4 and moreover , it is interesting to see how the underlying patterns of growth are reflected in the mixing of cells from different bulks . For instance , homogeneous growth leads to very high intermixing of cells in different bulks , whereas boundary driven growth tends to spatially segregate bulks . We have quantified the level of intermixing for different modes of growth in all our simulation cohort , highlighting this pattern ( S11 Fig ) . We have observed these patterns in real data from carcinomas vs adenomas , where carcinomas were characterised by clonal intermixing , but adenomas were not [47] . Similar patterns of intermixing have also been found more recently using single-cell seeded organoid sequencing [48] . The spatial effects of drift and sampling bias one can observe are remarkable and represent a major challenge for the correct subclonal reconstruction of tumours growing in three-dimensional space . Due to the inherent complexity , analytical solutions to this problem that take space into the account remain challenging , although some attempts to tackle this difficult question are being undertaken [49] . Understanding the complex impact of spatially growing cell populations on the actual genomic data requires an approach based on computational simulations . Here we devise a statistical inference framework , similar in spirit to what we previously proposed for well mixed populations [25] , that aims at recovering the evolutionary parameters of each individual tumour from the type of data we have discussed so far ( see Materials and Methods for details ) . We constructed a test-set of 34 synthetic tumours simulated with different parameters ( see S1 Table ) and assessed the error in recovering the parameters used to generate these tumours after statistical inference with an Approximate Bayesian Computation–Sampling Monte Carlo ( ABC-SMC ) approach [25 , 50–52] . The details of the inference algorithm are detailed in Material and Methods . We used approximately one million simulation instances to perform parameter inference using priors in S2 Table . We were particularly interested in comparing the accuracy provided by the different spatial sampling methods in recovery evolutionary dynamics . We studied three different sets of tumours . In the first set , we investigated parameter recovery in tumours with homogeneous ( exponential ) growth , with and without selection but with no cell death . In the second set , we added stochastic cell death as an additional factor . In the third set , we studied cases of boundary driven growth where we also examined our ability to recover the extent of the boundary driven parameter a . In all three sets , we studied the differences in the ability to recover parameter if we used a single bulk sample of the whole tumour multi-region punch biopsies , multi-region needle biopsies or single-cell sequencing . Following the inference of the parameters , we calculate the percentage error for each parameter as a difference between the true parameter value and inferred parameter value ( mode of a parameter posterior distribution ) scaled by the true parameter value . Then we plot the distributions of the percentage errors for each parameter per growth model and sampling strategy in Fig 6 . Not surprisingly , the scenario with exponential homogeneous growth without cell death was the one where the evolutionary parameters were the easiest to recover because spatial constrains were limited and the number of unknown parameters lowest ( Fig 6A–6C , “Set 1” ) . In particular , the percentage-error in recovering the mutation rate u was particularly low , especially using single-cell sequencing ( Fig 6A , “Set 1” ) . The mean percent error of the parameters t ( Gillespie time when a new mutant is introduced ) and s ( selective coefficient of the new mutant ) , in the case of homogeneous growth were also within 20% and overall agrees with our previous observations in well-mixed populations [25] . The presence of stochastic cell death , even within a homogeneously growing tumour , introduced significant spatial and sampling biases ( spatial drift ) that led to a higher error in the recovery of the parameters ( Fig 6A–6C , “Set 2” ) . Furthermore , some of the evolutionary parameters became unidentifiable ( mutation and death rate ) . In this scenario , the best sampling strategies to recovery the death parameter d were single-cell sequencing or whole-tumour sequencing , reflecting the need to collect large population of cells for the correct estimation of this parameter ( Fig 6D ) . Boundary driven growth also introduced significant biases that led to higher percent-error values in the recovered parameters ( Fig 6A–6C , “Set 3” ) . Here , single-cell sequencing was best in recovering the boundary driven growth parameter a ( Fig 6E ) . See S12 Fig for summary statistics from the simulations in Fig 6 . The full posterior distributions of each parameter in each context is reported in S13 Fig . Parameter dependency in the inference of t and s combinations is reported in S14 Fig . We performed the same inference approach but with 3-dimensionally growing tumours using a test set of a single simulated ‘target’ tumour and inferred the parameters using approximately 10 million simulated cancers and found similar results ( S15 Fig ) . We do recognise that for complex scenarios that are heavily affected by spatial constrains , such as boundary driven growth , inferred parameter values still suffer from high uncertainty in our ABC framework . This suggests the need for further model development and generation of higher resolution data for high confidence estimation of evolutionary parameters in cancer .
It is now widely accepted that tumour growth is governed by evolutionary principles . Thus , recovering the evolutionary histories of tumours is essential to the understanding patient-specific tumour growth and treatment response . However , these analyses are inevitably based on limited information due to sampling biases , noise of known and unknown nature , lack of time resolved data amongst many others . Despite these limitations , many approaches based on single sampling , multi-region bulk profiling , or single cell sequencing have been developed . Information from such data is often derived using purely statistical bioinformatics methods such as clustering analyses , without consideration of the confounding underlying influence of the cellular mechanics of tumour growth . Here we explicitly investigated spatial effects on the evolutionary interpretation of typical multi-region sequencing data of tumours . We found that the effects of sampling bias and spatial distributions of spatially intermixed cell populations critically depend on the mode of tumour growth as well as the details of the underlying sampling and data generation procedure . Most surprisingly , we could observe clusters of over-represented alleles in the VAF distribution of some tumour samples that were indistinguishable from positively selected subclonal populations , despite emerging solely due to the spatial distribution of cells . Such clusters vary depending on how one samples a tumour , and would therefore cause a major challenge for the evolutionary interpretation of cancer genomic data based on subclonal reconstruction . We furthermore presented a Bayesian inference framework to recover evolutionary parameters from our spatial distributions . Evolutionary parameters such as strength of selection or mutation rates may be important surrogate measurements of evolvability , and hence linked to progression and treatment resistance , as it has been demonstrated for the rates of chromosomal instability [53 , 54] . Again , we observe that our ability to precisely recover certain evolutionary parameters depend on the scenarios of tumour growth and spatial sampling strategies . However , we do believe that although complex , the situation is far from hopeless . More involved statistical frameworks based on first principles of tumour growth can help resolving some of the evolutionary parameters on an individualised patient basis . Importantly , careful spatial sampling and single-cell sequencing can mitigate some of the confounding issues . We do acknowledge that our model has some important limitations , such as the infinite allele assumption ( which could be violated by copy number loss [44] ) . We also recognise that we tested our inference framework only using our own generative model , and that despite the generative model matching the assumptions intrinsic to the inference the posterior parameter estimates still suffered from high uncertainty in some cases , reflecting the complexity of the problem . Also , for computational feasibility we mostly focus on 2D spatial analyses and of a relatively limited number of cells with respect to the billions of cells present in a human tumour . We also acknowledge that we do not offer a closed mathematical formulation for the distribution of alleles under spatial effects , which would be very useful but remains a very difficult problem that can only be tackled partially ( e . g . [37] ) . Additionally , more realistic models of tumour growth dynamics that account for force fields between cells [55] have been developed that could improve on the study of spatial patterns of growth [23 , 56] . For computational feasibility , especially in regards to the necessity of performing statistical inference on the data and generate thousands of simulations , we restricted our analysis to the stochastic cellular automaton model we propose here . Nevertheless , our approach highlights the importance of spatial modelling of real data and the impact of confounding factor in our estimate and understanding of tumour evolution . Importantly , future versions of the model could help guiding optimal sample collection that would minimise the spatial biases in the data . Due to the current technical limitations of these types of approaches , we are still far from direct application in the clinic . Additional effort should also be directed towards the use of measurements from other clinical data , such as imaging , where estimations of necrosis for example , can help parameterise computational models . However , we argue it remains extremely important to understand the confounding factors and spatial biases we expect to find in samples from which often we need to base clinical decisions on . Mathematical modelling of cancer evolution is a growing field with a fast expanding repertoire of models and approaches [11 , 57] , however attention to clinical and biological relevance of modelling approaches is necessary to ensure these efforts are not dead ends .
We developed a computational stochastic model of spatial tumour growth that allows simulating different strategies of multi-region tissue sampling followed by synthetic generation of high-throughput sequencing data . We consider tumour cells as asexually reproducing individuals that die and divide with certain pre-defined probabilities . If b is the birth rate for each cell and d the death rate , then the growth of the population over time t is: N ( t ) =e ( b−d ) t [1] where N ( t ) is a population size at time t , and b-d is the net growth rate . At first , we assume that birth and death rates are constant over time , whereas the overall growth rate can vary over time due to the randomness of each birth or death event , as well as due to spatial constrains that can limit or promote cell division over time . We model spatial constraints with the boundary proliferation parameter a , which models the distance from the border of the tumour within which cells are allowed to proliferate even in the absence of space ( by pushing neighbouring cells outwards ) . When a~1 all cells can proliferate ( homogeneous growth ) , and their growth is equivalent to an exponential expansion . When a~0 , cells can only proliferate if they have an empty space in their neighbourhood , resulting in only a small layer of cells at the tumour border being able to divide . In this case the growth curve can significantly deviate from Eq [1] . In addition to cell division , we also model mutation and selection , where the latter can change birth and/or death rates . We model somatic mutations acquired by each cell after division as a Poisson random variable – Pois ( u ) , where u is the mean mutation rate . Thus , after each cell division , a random set of new unique mutations occur in each cell of the two cells resulting from the division . The majority of these mutations are passenger mutations and hence do not affect a cell’s phenotype . However , they enable us to trace cell lineages uniquely in the final tumour . In addition , we also allow for driver mutation ‘events’ that can lead to positive selection of a subpopulation of cancer cells: a driver event conveys a fitness advantage to that particular cell and its offspring , thus allowing the lineage to increase in frequency . Since we ask what is the distribution of mutations across space , rather than the expected waiting time of driver events as previously analysed [58] , we introduce a driver mutation at a fixed time in our simulations , also to make simulations comparable and computationally efficient . To simulate tumour growth in space with these four stochastic events–birth , death , mutation and selection–we have used a modification of the Gillespie algorithm [26] . Specifically , the simulation framework works as follows: Until a cell reaches a predefined grid boundary , repeat the following steps Details of the data generation and error modelling . At the end of the simulation , we can collect bulk or single-cells and simulate sequencing data generation . Bulk Samples are spatially separated tumour chunks ‘cut out’ from the tumour . We model two different shapes: A bulk sample is a set of adjacent cells from the final tumour population . Each cell has its unique ID , a position on a grid and its list of somatic mutations . From the sampled cells ( in a bulk ) joined list of mutations we can construct the Variant Allele Frequency ( VAF ) distribution as in a real sequencing experiment . To construct a VAF distribution from a simulated bulk tumour sample , we mimic realistic next generation sequencing steps , specifically sequencing coverage and limits of detectability of low frequency mutations . We proceed as follows: This procedure guarantees that the generated read counts reflect the proportions of mutations in the simulated tumour . To model limits of detection of a mutation , after resampling a mutation , we discard it if the corresponding number of reads containing the variant allele is less than 5 ( using the fixed coverage 100 , which accounts for a ~0 . 05 minimum VAF ) . We also performed single cell sequencing taking either random single cells across the whole tumour population , or from spatially structured biopsies ( mimicking bulk tissue collection followed by single-cell isolation ) . We used the obtained single cells to construct maximum parsimony phylogenetic trees . In addition to single cell sequencing , we also model genotyping cells with a given list of mutations , corresponding to targeted sequencing of mutations found using e . g . exome or whole-genome sequencing . To implement this , we take one of the bulk samples as reference genotype and check for the presence of each individual mutation in a random set of 200 cells . Similarly , we use the obtained genotyped single cells to infer phylogenetic trees and check how much the genotyped trees differ from the single cell trees . Due to the complexity captured by our spatial model of tumour growth , we do not have explicit formulas for the stationary probabilities of the stochastic process , and hence cannot derive a likelihood function . Thus , we have to use likelihood-free methods to perform statistical inference on the parameters and compute the posterior distribution of the parameters θ . Here we use Approximate Bayesian Computation ( ABC ) [51 , 59] to infer the parameters of our model . ABC is based on the idea of scanning a large grid of plausible values for θ , and simulating the model many times with such parameters . Outputs of the model are stored and compared using a predefined set of summary statistics that are initially evaluated on real data . We can then rank sets of parameters that lead to the generation of synthetic data that are close to the observed data . We can estimate a posterior distribution p ( θ|D ) for the model parameters θ , using the available data D and the prior for θ . This method is computationally intensive , and requires running several hundred ( ideally thousands or millions ) simulations . In our case we have generated ~74 million simulations that we use to perform the inference step . There are different approaches to implement ABC , the simplest is rejection-sampling . More advanced implementations such as ABC with Markov Chain Monte Carlo ( MCMC ) can result in significant increases in efficiency . In our paper we implemented a simple rejection-sampling algorithm first , and then added Monte Carlo simulation techniques to speed up convergence . The simple ABC rejection-sampling algorithm consists of the following steps: In this study we use uniform priors for all parameters: u~Uniform ( 0 , 100 ) , s , d , a~Uniform ( 0 , 1 ) , tdriver~Uniform ( 0 , 15 ) . One of the most important factors that affect the ABC outcome is the number of simulations that one can afford to run , and the summary statistics were chosen to evaluate the distance between a target and a simulated dataset . Summary statistics can be any quantitative measurement that captures the information from the multidimensional data without losing too much information . As for our distance metric , we use Euclidean and Wasserstein distances between summary statistics for different parameters as discussed below . Wasserstein metric estimates the distance between probability distributions by treating each distribution as a unit amount of dirt piled up on a given metric space and calculates the minimum cost required to convert one pile into another . If x and y are two vectors we want to evaluate the distance of , first we calculate their empirical distribution functions F ( t ) =∑i=1mwi ( x ) l{xi≤t} and G ( t ) =∑i=1nwi ( y ) l{yi≤t} ( for weights wix and wiy we took 1/m and 1/n respectively ) , the Wasserstein distance is defined by evaluating the following: Wp ( F , G ) == ( ∫01|F−1 ( u ) −G−1 ( u ) |p ) 1/p where we took p = 1 for our analysis . We used the R package transport ( https://CRAN . R-project . org/package=transport ) to implement the distance calculation . We used different summary statistics for each sampling scheme . For punch , needle biopsy and the whole tumour sampling–we used the VAF distribution to compute our summary statistics . For the whole tumour VAFs , our ABC procedure was similar to the one in ref [25] . For the bulk samples , since our model implements multi-region sampling , we first evaluate the multivariate VAF distribution ( which is a joint probability distribution of all sampled bulk VAFs ) and then calculated the Euclidean distance between the obtained empirical probability distribution vectors: DEuclidean ( Fsim_data ( VAFbulk1 , … , VAFbulkN ) , Ftarget_data ( VAFbulk1 , … , VAFbulkN ) , ) With single cell samples , we constructed phylogenetic trees per tumour and used different tree-based summary statistics to evaluate the distance . Since the inferred phylogenetic tree branch length is proportional to the number of unique mutations belonging to a node , we decided to compare the vectors of all branch lengths ( between a simulated and target tumour trees ) by computing the Wasserstein distance . For the subclone introduction time tdriver , death rate d and the boundary driven growth parameter a , we chose to compare the vectors of branching times for each node of the phylogenetic trees . Due to computational costs , we are limited to run the ABC framework with a small tumour size ( ~100k cells ) or simulate smaller datasets per inference , both of which can significantly affect the outcome . To therefore speed up our ABC framework we implemented a Sequential Monte Carlo ( SMC ) algorithm to increase the acceptance rate of the simple ABC rejection algorithm . Our ABC SMC algorithm uses sequential importance sampling by running several rounds of resampling around the accepted parameters ( correlating the rounds ) , and gradually decreasing the acceptance threshold while converging to the posterior distribution . This approach significantly increases the acceptance rate of the simulated datasets [60] . Our implementation of the ABC SMC algorithm is as follows: Our ABC-SMC framework tries to recover all the parameters ( referred to as a particle in the algorithm above ) at the same time . We notice that once one of the parameters converges , the acceptance rate decreases significantly . We then decided to fix the converged parameter at the inferred value ( mode of its posterior ) and rerun the inference varying the rest of the parameters until other parameters converge , and repeat the procedure . We found that this significantly improved the convergence speed . For the 2D inference in Fig 6 we started with N = 100 simulated particles , performed r = 10 rounds with quantile Q = 0 . 5 , leading to ~200k simulations for each parameter and ~1M simulations in total . For the 3D inference in S15 Fig we started with N = 1000 simulated particles , performed r = 10 rounds with quantile Q = 0 . 5 , leading to ~2M simulations for each parameter and ~10M simulations in total . The package implements three sampling strategies for the inference: Depending on the strategy , a user would need to provide real or synthetic target data in the form of tumour bulk sample VAFs ( list of R data . frames where each row should correspond to a unique mutation with the following columns: clone ( Clone type label set to 0 ) , alt ( Number of reads with the variant ) , depth ( Sequencing depth ) , id ( Unique mutation ID ) ) , an array of whole tumour sample VAFs or single cell sampling phylogenetic trees . Alternatively , a user can provide a set of parameters ( please refer to the package documentation for the details of each input parameter format ) to simulate a synthetic target tumour to then recover these input parameters . The functions output sequence of files containing sets of inferred parameters corresponding to each SMC round ( that can then be used to construct the posterior distributions for each parameter ) . For Fig 4 and parameter inference framework with single cell sequenced trees we used maximum parsimony phylogenetic algorithm implemented in paup [61] . For the genotyped phylogenetic trees in Fig 4 , we manually constructed input genotype files for paup by recording presence/absence of a given mutation from the sampled 200 cells with respect to the reference mutations list ( in our case mutations list taken from a bulk sample ) . To test for the presence of selection and the mutation rate inference , we fit 1/f2 distribution to the empirical cumulative distributions of sampled VAFs using the R package developed in ref [25] . | Sequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology . However , sequencing data are complex and often difficult to interpret . In particular , the way in which the tissue is sampled and the data are collected impact the interpretation of the results significantly . We argue that understanding cancer genomic data requires mechanistic mathematical and computational models that tell us what we expect the data to look like , with the aim of understanding the impact of confounding factors and biases in the data generation step . In this study , we develop a spatial computational model of tumour growth that also simulates the data generation process , and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results . We then provide a statistical framework that can be used to start overcoming these biases and more robustly measure aspects of the biology of tumours from the data . | [
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"mutation... | 2019 | Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data |
To identify genetic changes underlying dog domestication and reconstruct their early evolutionary history , we generated high-quality genome sequences from three gray wolves , one from each of the three putative centers of dog domestication , two basal dog lineages ( Basenji and Dingo ) and a golden jackal as an outgroup . Analysis of these sequences supports a demographic model in which dogs and wolves diverged through a dynamic process involving population bottlenecks in both lineages and post-divergence gene flow . In dogs , the domestication bottleneck involved at least a 16-fold reduction in population size , a much more severe bottleneck than estimated previously . A sharp bottleneck in wolves occurred soon after their divergence from dogs , implying that the pool of diversity from which dogs arose was substantially larger than represented by modern wolf populations . We narrow the plausible range for the date of initial dog domestication to an interval spanning 11–16 thousand years ago , predating the rise of agriculture . In light of this finding , we expand upon previous work regarding the increase in copy number of the amylase gene ( AMY2B ) in dogs , which is believed to have aided digestion of starch in agricultural refuse . We find standing variation for amylase copy number variation in wolves and little or no copy number increase in the Dingo and Husky lineages . In conjunction with the estimated timing of dog origins , these results provide additional support to archaeological finds , suggesting the earliest dogs arose alongside hunter-gathers rather than agriculturists . Regarding the geographic origin of dogs , we find that , surprisingly , none of the extant wolf lineages from putative domestication centers is more closely related to dogs , and , instead , the sampled wolves form a sister monophyletic clade . This result , in combination with dog-wolf admixture during the process of domestication , suggests that a re-evaluation of past hypotheses regarding dog origins is necessary .
Gray wolves have been dominant predators across Eurasia and North America , often exerting top-down impacts on the ecological communities they inhabit [1] , [2] . As humans expanded out of Africa into Eurasia , they came into contact with gray wolves and , through a complex and poorly understood process , dogs emerged as the first human companion species and the only large carnivore to ever be domesticated . Archaeological evidence provides partial clues about dog origins . For example , dog-like canids first appear in the fossil record as early as 33 , 000 years ago in Siberia [3] . However , it is not clear if these proto-dog fossils are ancestral to living dogs or instead represent failed domestication attempts or simply morphologically distinct wolves [3] . Similarly , the geographic origin of dogs is uncertain , with distinct lines of evidence supporting Southeast Asia , the Middle East , and Europe as potential domestication centers , and ruling out Africa , Australia , and North America [4]–[10] . Nonetheless , several recent studies have begun to illuminate the genetic basis of traits that changed during dog domestication and breed formation , advancing the general understanding of how genetic mechanisms shape phenotypic trait diversity [11]–[14] . For example , a recent study found an increase in copy number of the amylase gene ( AMY2B ) during dog domestication suggesting adaptation to starch-rich diets [15] . Given the unique behavioral adaptations of dogs , including docility and the ability to form social bonds with humans [16] , comparative genomics analyses of dogs and wolves holds great promise for identifying genetic loci involved in complex behavioral traits [14] . However , the demographic context of selection must first be understood to determine how it may have affected patterns of genetic divergence between dogs and wolves . To advance the understanding of dog origins and genetic changes early in dog domestication , we sequenced the genomes of six canid individuals , including three wolves ( Canis lupus ) , an Australian Dingo , a Basenji and a golden jackal ( Canis aureus ) . The three wolves were chosen to represent the broad regions of Eurasia where domestication is hypothesized to have taken place ( Europe , the Middle East , and East/Southeast Asia ) [6] , and specifically , were sampled from Croatia , Israel , and China ( Figure 1 ) . The Dingo and Basenji represent divergent lineages relative to the reference Boxer genome [10] and maximize the opportunity to capture distinct alleles present in the earliest dogs . These lineages are also geographically distinct , with modern Basenjis tracing their history to hunting dogs of western Africa , while Dingoes are free-living semi-feral dogs of Australia that arrived there at least 3 , 500 years ago ( Figure 1 ) [17] . As a result of their geographic isolation , the natural range of wolves has never extended as far south as the geographic sources for these two dog lineages [6] , thus they are less likely to have overlapped and admixed with wolves in the recent past . Sequencing the golden jackal in principle allows us to infer the ancestral state of variants arising in dogs and wolves ( Text S1 , S2 ) , though in practice this was complicated by the observation of wolf-jackal admixture ( see below ) . For some analyses , we also leverage data from a companion study of 12 additional dog breeds ( Text S1 ) . We chose to sequence a small number of individual genomes to high coverage , rather than larger numbers of ( pooled ) individuals at low coverage , to take advantage of recently developed demography inference methods based on small numbers of high quality genomes [18]–[20] . These methods allowed us to disentangle the effects of incomplete lineage sorting ( ILS ) –the discordance from the population phylogeny at individual loci resulting from deep coalescence–and post-divergence gene flow , which pose a particular challenge in analysis of such recently diverged species as dogs and wolves [21] . Combining the results of multiple complementary methods provided us with an integrated , robust view of the shared history of dogs and wolves , including population divergence times , ancestral population sizes , and rates of gene flow . Using polymorphism data from 10 million single-nucleotide variant sites , we investigated: 1 ) the size of the ancestral wolf population at the time of wolf/dog divergence; 2 ) the geographic origins and timing of dog domestication; 3 ) post-divergence admixture between dogs and wolves; and 4 ) lineage-specific characteristics of the recently discovered dog-specific AMY2B expansion [15] .
For each of the six samples , we generated high-quality genome sequences . Cumulative coverage was 72× for the wolves ( 24× average per individual ) , 38× coverage for the two dogs ( 19× average per individual ) , and 24× for the golden jackal , for a total of 335 Gb of uniquely aligned sequence from 11 . 2 billion reads ( Table S1 ) . Surveys of wolf genetic diversity to date have been limited to shotgun sequencing with incomplete genomic coverage [22] , small numbers of sequence loci [23] , limited pooled sequencing ( 6× average from a pool of 12 wolves , 30× average from a pool of 60 dogs ) [15] or lower coverage sequencing ( 9–11× coverage of 4 wolves , 9–14× of 7 dogs ) [24] . Our analyses draw on 10 , 265 , 254 high quality variants detected by our genotyping pipeline ( Text S3 , S4 , S5 ) , of which 6 , 970 , 672 were at genomic positions with no missing data for any lineage ( Tables S2 , S3 ) . We estimate genotype error rates to be very low based on comparison to genotype calls from genotyping arrays ( e . g . heterozygote discordance rates of 0 . 01–0 . 04% , Tables S4 , S5 , Text S5 ) . Further , PCA on the intersection of sequencing and genotyping array variants show the novel samples cluster appropriately , suggesting batch effects due to technology have been minimized ( Figure 2 , Text S5 ) . Genome-wide patterns of heterozygosity provide useful information on long-term effective population sizes . The mean heterozygosity rates ( per nucleotide position ) observed in the genome sequences of the Basenji and Dingo were 9×10−4 and 6×10−4 , respectively ( Figure 3A , Table S6 ) , consistent with a rate of 6×10−4 previously observed in modern dog breeds [22] , and considerably smaller than the rates observed in the three wolf genomes ( 1 . 2×10−3–1 . 6×10−3 ) . This twofold reduction in heterozygosity observed in dogs relative to wolves can be superficially interpreted to reflect a relatively weak two-fold reduction in effective population size of dogs relative to their ancestors , assuming that genetic variation in modern wolves is representative of the ancestral population . To better understand the changes in ancestral population sizes that influenced dogs and wolves , we employed the pairwise sequential Markovian coalescent ( PSMC ) method [20] . This method infers ancestral effective population sizes ( Ne ) over time , based on a probabilistic model of coalescence with recombination and changes in heterozygosity rates along a single diploid genome . We applied PSMC to each of the five genomes ( Figure 3B , Text S8 ) and converted the mutation-scaled estimates of time ( to years ) and population size ( to numbers of individuals ) by assuming an average mutation rate per generation of μ = 1×10−8 and an average generation time of three years [22] , [25] ( see Discussion ) . The inferred tracks of ancestral Ne in dogs show a population decline of at least 16-fold over the past 50 thousand years , from greater than 32 , 000 individuals ( ancestral Ne for Basenji lineage: 32 , 100–35 , 500; for Dingo lineage: 32 , 500–37 , 400 95% bootstrap CI ) to less than 2 , 000 individuals ( Basenji lineage: 1640–1980 at 4 , 000 years ago; Dingo lineage: 704–1042 at 3 , 000 years ago ) . Interestingly , wolves also show a considerable , yet milder , 3-fold reduction in effective population size to present estimates between 10 , 000 and 17 , 000 for the three wolf samples . For clarity , we note that with PSMC the population size trajectories are effective sizes for the lineages that eventually lead to the canid samples as they are known today ( e . g . as Basenji or as Dingo ) and that looking backwards in time eventually trace back to the common ancestral lineage of dogs and wolves . Our observations do not appear to be biased by very recent inbreeding in dogs and wolves , as we found that runs of homozygosity do not affect our inferences of ancestral Ne ( Text S8 ) . These results indicate the ancestral wolf population from which dogs were domesticated was considerably larger than estimated from current levels of diversity in wolves and suggest that simple comparisons of nucleotide diversity in present-day dogs and wolves lead to substantial underestimates of the severity of the bottleneck in dogs . Individual genome sequences include valuable information about phylogenetic relationships between our samples . However , interpretation of these phylogenetic signals is challenging due to the possibility of post-divergence gene flow between dogs and wolves , as well as ILS , which is an expected consequence of the large ancestral population sizes inferred by PSMC . Indeed , we observed predominant ancestral polymorphism in our data: for variant sites with no missing data , and where variants were observed in dogs or wolves , 32 . 0% of variant sites were shared across dogs and wolves , 47 . 3% were private to wolves , 20 . 2% were private to dogs , and only 0 . 5% were fixed between dogs and wolves ( Table S3 ) . Pairwise sequence divergence captures mean coalescent times that are robust to both ILS and moderate levels of gene flow ( see below ) . Thus , to provide accurate estimates of phylogeny given these demographic processes , we constructed a neighbor-joining ( NJ ) tree from a conservative estimator of genome-wide pairwise sequence divergence for all pairs in our seven genomes , including the Boxer reference and using the golden jackal as an outgroup ( Figure 4A , Text S8 , Table S7 ) . Bootstrap support for all nodes was 100% , with dogs and wolves recovered as monophyletic sister clades . Surprisingly , the Boxer reference is only slightly more divergent from the three wolf genomes than it is from the two dog genomes . To evaluate the robustness of our phylogenetic inference , we also constructed a NJ tree using an estimator of sequence divergence for which all possible mismatches between alleles from a pair of individuals are counted ( Table S8 ) . The consensus tree based on this metric places the Chinese wolf at a position sister to a clade of our other wolf and dog samples ( Figure S1 ) , but the bootstrap support for this relationship is low ( 54% ) , suggesting poorer resolution with this estimator . Importantly , both approaches and additional phylogenetic analyses strongly support the hypothesis of dogs forming a distinct clade ( Text S8 , Tables S9 , S10 ) . One important factor that could complicate inference of divergence between dogs and wolves is post-divergence gene flow . To examine admixture in our sampled genomes , we employed the nonparametric ‘ABBA-BABA’ test for gene flow between two divergent populations , such as humans and Neandertals [26] , from individual genome sequences . This method tallies site patterns for four taxa , compares them to those expected under an assumed phylogeny and then uses this comparison to identify significant pattern asymmetries that cannot be explained by ILS or sequencing errors . We applied this test to all dog-wolf sample pairs , using the golden jackal as an outgroup and one of the other four samples as an additional ingroup ( Text S8 ) . We found significant evidence of admixture for three population pairs: Israeli wolf and Basenji , Chinese wolf and Dingo , and Israeli wolf and Boxer ( Figure 4B , see also Table S11 ) . Care should be taken in interpreting these results , as the detected admixture signals may reflect gene flow between lineages ancestral to our contemporary samples . The signal for Chinese wolf and Dingo likely represents ancient admixture in Eastern Eurasia , and the signal observed for Israeli wolf , Basenji , and Boxer likely represents ancestral admixture that occurred in western Eurasia . The resulting phylogeny with admixture edges ( Figure 4A ) is used as the starting point for a more comprehensive examination of joint demographic model for dogs and wolves . We next inferred a complete demographic model for dogs and wolves , including population divergence times , ancestral population sizes , and rates of post-divergence gene flow by jointly analyzing all seven genomes using the Generalized Phylogenetic Coalescent Sampler ( G-PhoCS ) [19] , a recently developed Bayesian demographic inference method . The method is based on a full coalescent-based probabilistic model that considers both ILS ( by modeling ancestral population size ) and post-divergence gene flow ( by allowing lineages to migrate between populations through designated migration bands ) . G-PhoCS conditions its inference on a given population phylogeny , and uses information on local genealogies at a large number of short , unlinked , neutrally evolving loci to generate samples of demographic parameters from an approximate posterior distribution . We applied G-PhoCS to a multiple sequence alignment of the six genomes and Boxer reference in 16 , 434 carefully filtered putative neutral autosomal loci using the NJ tree to indicate the topology of the population phylogeny ( Text S9 , see discussion on alternative topologies below ) . Initially , we considered various migration bands with significant signatures of gene flow ( Text S9 ) . We found evidence of bi-directional gene flow between Israeli wolf and Basenji , as well as Chinese wolf and Dingo , consistent with our findings from the non-parametric ABBA-BABA test . Interestingly , the joint analysis of all genomes indicated that admixture inferred by the ABBA-BABA test for the Israeli wolf and the Boxer is likely a result of gene flow from a population ancestral to Basenji into a population ancestral to Israeli wolves . We base this conclusion on the observation that there is no significant signature of admixture between Boxer and Israeli wolf in the ABBA-BABA test or the G-PhoCS inference when Basenji is also included in the analysis . Using G-PhoCS we were also able to examine signatures of admixture in the jackal outgroup , which cannot be detected using the ABBA-BABA test , and found significant gene flow between the golden jackal and the Israeli wolf , as well as the population ancestral to all dog and wolf samples . Our divergence time estimates imply that dogs and wolves diverged 14 . 9 thousand years ago ( kya ) with 13 . 9–15 . 9 kya Bayesian 95% credible interval ( CI ) , assuming an average mutation rate per generation of μ = 1×10−8 and three years per generation ( Figure 5A ) . Divergence times between wolf populations were tightly clustered at 13 . 4 kya ( 11 . 7–15 . 1 kya ) , and divergence between dogs was estimated to have occurred slightly more recently , at 12 . 8 kya ( 11 . 8–13 . 7 kya; divergence of Dingo ) and 12 . 1 kya ( 10 . 9–13 . 1 kya; divergence between Boxer and Basenji ) . Interestingly , we inferred a divergence time of 398 kya ( 382–415 kya ) between the golden jackal and the population ancestral to dogs and wolves , which is considerably more recent than previously reported [27] . To validate this finding , we ensured that our estimates appropriately account for ancestral gene flow into the golden jackal population ( Text S9 ) and validated the position of our sample within the golden jackal lineage by comparing polymorphism data from that genome to a larger panel of wolves and jackals ( Text S5 , S11 ) . G-PhoCS produced estimates of ancestral effective population sizes compatible with the ones inferred by PSMC , with a large effective population size of 45 , 000 individuals ( 44 , 200–44 , 800 ) for the population ancestral to dogs and wolves , followed by a 22-fold reduction to 2 , 000 individuals ( 700–3 , 200 ) in the population ancestral to all dogs , and a more moderate 3 . 6-fold reduction to 12 , 600 individuals ( 1 , 000–25 , 000 ) in the population ancestral to all wolves . As with our inferences based on PSMC , we estimate a far more severe domestication bottleneck than previously reported [22] , [23] . The main discrepancy between PSMC and G-PhoCS concerns the timing of these changes . G-PhoCS associates this reduction in Ne with the divergence between dogs and wolves at around 15 kya , whereas PSMC infers a gradual population decline starting as early as 50 kya ( Figure 3B ) . As PSMC is based upon the density of heterozygous sites within the genome sequence of an individual , it does not directly infer divergence times . However , one can informally estimate them as the points when Ne trajectories that are overlapping diverge moving forward in time towards the present . The discrepancy between G-PhoCS and PSMC reflects the distinct models used by these methods: G-PhoCS assumes a constant population size for every branch of the phylogeny , which prevents it from characterizing gradual changes in population size , whereas PSMC tends to produce smoothed traces of ancestral Ne , which may limit its ability to capture rapid population bottlenecks . To test which of the inferred models has a better fit to the data , we simulated data under both models , and then used each method to analyze the data simulated under the model inferred by the other method ( Text S8 , S9 ) . These two reciprocal tests indicated that the early and gradual population decline inferred by PSMC is compatible with a more recent dramatic reduction ( Text S8 , Figure S2 ) , and that divergence time estimates of G-PhoCS were not compromised by its inability to model gradual changes in Ne ( Figure S3 ) . Both results support the demographic model inferred by G-PhoCS , which has a relatively recent divergence between dogs and wolves followed by a dramatic reduction in population size . We additionally validated the robustness of our demographic parameter estimates under the set of loci chosen for the analysis as well as assumptions made on intra-locus recombination ( Text S9 ) . The demographic model we inferred using G-PhoCS reflects the population phylogeny estimated in the NJ analysis . To validate the robustness of our inference to this assumption , we considered a series of alternative topologies that correspond to plausible scenarios of the shared histories dogs and wolves . When we assume a model in which each dog population originated from the wolf population corresponding to its geographic origin ( a model of regional domestication , e . g . Figure 5B ) , G-PhoCS infers extremely large rates of post-divergence gene flow between dogs and between wolves . For instance , the total rate of gene flow from Basenji to Boxer is inferred to be mtot = 1 . 24 ( 0 . 93–1 . 59 , 95% Bayesian CI ) , implying a probability near 100% for any Boxer lineage to have migrated from a population ancestral to Basenji . Total rates above 30% were inferred for additional migration bands , such as Basenji-to-Dingo ( 0 . 47 ) , Croatian-to-Israeli wolf ( 0 . 33 ) , and Croatian-to-Chinese wolf ( 0 . 33 ) ( Figure S4 ) . In terms of the number of migrants per generation ( 4Nem ) , these estimates translate into 0 . 26 ( CI: 0 . 15–0 . 38 ) , 4 . 48 ( CI: 2 . 52–6 . 36 ) , and 0 . 89 ( CI: 0 . 56–1 . 23 ) , reflecting large amounts of gene flow , which is unlikely given historical separation of these geographically distinct populations . In contrast , the migration rates estimated in our original inference were considerably lower , with nearly all total rates falling below 10% ( Figure 5 , Text S9 , Table S12 ) , indicating a better fit of that topology to the data . Another set of alternative topologies we examined is one in which the dog clade originates from one of the four branches in the wolf sub-phylogeny ( e . g . Figure 5C ) . Assuming such topologies , G-PhoCS infers that dogs diverged from wolves less than 200 years after wolves diverged from each other ( Figure S5 ) , whereas in the original inference conditioned on the NJ tree , the divergence between dogs and wolves was estimated to have occurred 1 , 400 years before the divergence between wolf populations . All other parameter estimates were not significantly affected by the choice of origin population for the dog clade . Thus regardless of our assumptions on the identity of the wolf population from which dogs originated , we infer that dogs diverged from the sampled wolf populations at about the same time these wolf populations diverged from each other . Additionally , the greater difference between estimated divergence times in our original analysis provides some support for our initial assumption that dogs and wolves form sister clades . Because G-PhoCS does not yet support statistical tests for model selection , we assessed relative support for the alternative models by performing simulations under each model , and comparing the simulated and real data with respect to a series of site configuration statistics informative about the topologies of local genealogies . For every quartet in our sample set that contains the jackal outgroup , we computed the relative frequencies of bi-allelic sites in which each of the two alleles ( denoted A and B ) is present in exactly two of the four individuals . Similar statistics are used in the ABBA-BABA test for admixture , but in our case we were also interested in the frequency of the BBAA configuration , which is the one compatible with the topology of the assumed phylogeny ( see Text S8 for more information ) . We compared frequencies of the three configurations in 20 quartets observed in our data with those observed in data simulated under the three demographic models shown in Figure 5 , denoted as “dog/wolf reciprocal monophyly” ( Figure 5A ) , “regional domestication” ( Figure 5B ) , and “ISW-source” ( Figure 5C ) . This comparison allowed us to draw conclusions regarding the fit of each of these models to the data with respect to the distribution of local genealogies it implies ( Table S10 ) . The three models appeared to be fairly compatible with the data overall , with the reciprocal monophyly model showing the lowest discrepancy ( absolute error = 0 . 43 ) , followed closely by the ISW-source model ( absolute error = 0 . 47 ) and then trailed by the regional domestication model ( absolute error = 0 . 82 ) . The regional domestication model showed the largest discrepancy in quartets including Dingo and at least one other dog , indicating considerably weaker support for the dog clade and its internal structure than present in the data . This implies that the patterns of sequence similarity between dogs are more compatible with a distinct dog clade than they are with similarity solely generated by gene flow between the different dog lineages . The ISW-source model showed high discrepancy in quartets containing the Croatian and Israeli wolves , indicating that the model has problems capturing the phylogenetic relationships between those wolves and the dogs . The reciprocal monophyly model provided the best fit to the data , but it did show some discrepancy in quartets containing both the Dingo and the Chinese wolf . This is perhaps related to the large credible intervals for the rates of gene flow between these populations in the G-PhoCS inference ( CHW→DNG , 0–6%; DNG→CHW , 2–6% ) . In conclusion , these tests show that topological signatures in the data provide strong support for a monophyletic dog clade and somewhat weaker support for a monophyletic wolf clade . Our inference of a pre-agriculture origin of dogs provides an important context for re-assessing the recent hypothesis that copy number expansion at the amylase locus ( AMY2B ) in dogs was an important part of the domestication process [15] . In that study , copy number segregated between species , with only two copies of the gene in each of the 35 wolves genotyped and an average 7 . 4-fold increase across 136 dogs . This finding was interpreted to suggest that AMY2B expansion enabled early dogs to exploit a starch-rich diet as they fed on refuse from agriculture . Surprisingly , and using the corrected depth of coverage to estimate discrete gene copy number , we find the Dingo has just two copies of AMY2B ( Figure 6A , Text S6 ) , suggesting that the AMY2B copy number expansion was not fixed across all dogs early in the domestication process . In a survey of sequence data from 12 additional domestic dog breeds , we find that the Siberian Husky , a breed historically associated with nomadic hunter gatherers of the Arctic , has only three to four copies of AMY2B , whereas the Saluki , which was historically bred in the Fertile Crescent where agriculture originated , has 29 copies ( Figure S6 ) . In order to validate the results , we used real-time quantitative PCR ( qPCR ) to explore the variation in AMY2B copies across additional breed dogs ( n = 52 ) , additional dingoes ( n = 6 ) and a worldwide distribution of wolves ( n = 40 ) ( Text S6 ) . The qPCR results show modern dog breeds on average have a high copy number of AMY2B and that wolves and Dingoes do not ( Figure 6B , Table S13 ) . However , the qPCR results also shows that the AMY2B expansion is polymorphic in wolves ( 16 of 40 wolves with >2 copies Figure 6B ) and thus is not restricted to dogs .
In this study , we generated high-quality individual canid genomes , and used them to uncover the history of dogs and gray wolves . Interpretation of the phylogenetic signals in these genomes was particularly challenging due to high levels of incomplete lineage sorting and post-divergence gene flow . We were able to disentangle the effects of these factors by using an array of recently developed statistical methods that together provided a detailed and robust inference of past demography for these canids . We used methods that rely on different aspects of this dataset: 1 ) whole-genome patterns of heterozygosity in single individuals ( PSMC ) , 2 ) a subset of sites that are informative for post-divergence admixture ( ABBA/BABA analyses ) and 3 ) a set of neutral loci analyzed jointly across all individuals ( G-PhoCS ) . We found evidence of wolf-dog admixture in two divergent dog lineages ( Basenji and Dingo ) . The fact that these lineages have been geographically isolated from wolves in the recent past suggests that this gene flow was ancestral and thus likely impacted multiple ( if not most ) dog lineages [28] , [29] . Admixture has likely complicated previous inferences of dog origins . For instance , the presence of long shared haplotypes in Middle East wolves with several dog breeds [10] may reflect historic admixture rather than recent divergence . Similarly , elevated genetic diversity in East Asian dogs and affinities between East Asian village dogs and wolves [7] , [9] , [24] may be confounded by past admixture with wolves . In areas where village dogs [30] roam freely and wolves have historically been in close proximity , admixture may also be present and exert a non-trivial impact on patterns of genetic variation [21] . Our inferences of ancestral population size from both PSMC and G-PhoCS revealed an unexpected , roughly threefold population bottleneck in wolves . With PSMC , we detect the start of this bottleneck as early as 20 kya , while with G-PhoCS the bottleneck occurs at the timing of dog-wolf divergence , approximately 15 kya . Because our simulations indicated that the timing of abrupt changes in Ne are overestimated by PSMC ( Text S8 , S9 , Figure S2 ) , we place higher confidence in the more recent date inferred with G-PhoCS . Regardless of the method chosen , the bottleneck in wolves appears to have occurred well in advance of direct extermination campaigns by humans and within the timeframe of environmental and biotic changes associated with the ending of the Pleistocene era . Although the specific cause of this bottleneck is unknown , it has important implications for dog domestication . Because of this bottleneck , we expect that at the onset of domestication , there was substantially more genetic diversity for selection to act on than what is observed in modern wolves . Direct comparisons of dog and wolf diversity ( such as comparisons of heterozygosity ) will not show as large a difference and thus previous studies that did not consider a wolf population decline [22] , [23] have underestimated the bottleneck associated with domestication . These previous studies estimated a two to fourfold reduction in dog Ne , a far milder population contraction than the at least 16-fold reduction we infer here . We provide several lines of evidence supporting a single origin for dogs , and disfavoring alternative models in which dog lineages arise separately from geographically distinct wolf populations ( Figures 4–5 , Table S10 ) . Considering a full multi-population demographic model with gene flow , we infer that dogs diverged from wolves at around 15 kya ( CI: 14–16 kya ) . Examination of previous estimates shows a wide range of suggested divergence times [24] , [25] . However , most of the discrepancy between different studies can be traced to differences in the assumed mutation rate . We assume an average mutation rate per generation of 1×10−8 and an average generation time of three years . However , we observed that CpG di-nucleotides , which we filtered out from the data , contribute roughly 30% of mutations in these canid genomes , similar to what was observed in human genomes [19] . Thus our assumptions regarding mutation rate imply a genome-wide rate ( i . e . including filtered sites ) of 1 . 4×10−8 . Other studies of dog domestication assume slightly lower genome-wide rates . For instance , a recent study based on shotgun sequencing data [25] assumes a mutation rate of 1×10−8 and estimates the divergence time to be 14 kya ( CI:11–18 kya ) or 30 kya ( CI:15–90 kya ) , depending on the assumed amount of gene flow . Another recent study [20] assumes an even lower mutation rate of 0 . 66×10−8 and estimates the divergence time at roughly 32 kya . Calibrating the different estimates using the same mutation rate shows a remarkable consistency with our conclusions . Unfortunately , very little is known about dog mutation rates , and estimates of mammalian mutation rates range from 0 . 22×10−8 per year ( i . e . , 0 . 66×10−8 per generation ) [31] to 1 . 8×10−8 per generation [32] . Considering this wide range expands the credible interval for the divergence time of dogs and wolves from 14–16 kya to 11–34 kya . Importantly , our study was able to eliminate much of the uncertainty in the mutation-scaled divergence time ( CI: 0 . 46×10−4–0 . 53×10−4 ) , leaving the mutation rate as the dominant source of uncertainty in dating the origin of dogs . The divergence time between dogs and wolves provides an estimated upper bound for the time of domestication . We can also estimate a lower bound as the divergence time between the Dingo and the population ancestral to Basenji and Boxer , which we infer at 13 kya ( CI: 11–12 kya , 9–25 kya assuming a range of mutation rates ) . Thus , our demographic analysis strongly suggests that domestication occurred between about 11 and 16 kya ( 9–34 kya with mutation rate uncertainty ) , which would place it prior to the adoption of extensive agriculture by humans . This finding is consistent with the fossil record , but it raises questions regarding the hypothesis that the advent of agriculture created a novel niche that was the driving force in dog domestication [15] . Our examination of AMY2B confirmed previously reported high copy numbers across almost all dog breeds [15] . However , we also found variation in copy numbers across wolf populations , and low copy numbers in dog lineages that are not associated with agrarian societies ( Dingo and Husky ) . This suggests a more complicated history of the high copy number variants of AMY2B , which likely existed already as standing variation in early domestic dogs , but expanded more recently with the development of large agriculturally based civilizations in the Middle East , Europe and Eastern Asia . Overall , the genomes sequenced in this study reveal a dynamic and complex genetic history interrelating dogs and wolves . One question that remains unanswered is that of the geographic origin of dogs and the wolf lineage most closely related to them . Our analysis suggests that none of the sampled wolf populations is more closely related to dogs than any of the others , and that dogs diverged from wolves at about the same time that the sampled wolf populations diverged from each other ( Figures 5A , 5C ) . One possible implication of this finding is that a more closely related wolf population exists today , but was not represented by our samples . We consider this unlikely , as we sampled the three major putative domestication regions , and previous SNP array studies demonstrate that wolf populations are only weakly differentiated , indicating that the wolves we sampled should serve as good proxies for wolves in each broad geographic region [10] . Another alternative is that the wolf population ( or populations ) from which dogs originated has gone extinct and the current wolf diversity from each region represents novel younger wolf lineages , as suggested by their recent divergence from each other ( Figure 5A ) . Our inference that wolves have gone through bottlenecks across Eurasia ( Figures 3B , 5A ) suggests a dynamic period for wolf populations over the last 20 , 000 years and that extinction of particular lineages is not inconceivable . Indeed , several external lines of evidence provide support for substantial turnover in wolf lineages . For example , ancient DNA , isotope , and morphologic evidence identify a divergent North American Late Pleistocene wolf [33] and in Eurasia , similarly distinct wolves exist in the early archaeological record in Northern Europe and Russia , 15–36kya [3]–[5] . Presumed changes in available prey ( e . g . megafaunal extinctions ) as habitats shrunk with the expansion of humans and agriculture also suggest the plausibility of wolf population declines and lineage turnover . A remaining alternative to our inferred population phylogeny is that the basal lineage was absorbed into the three lineages sampled . Such a hypothesis is questionable , though , as it requires there to be enough effective gene flow among the three wolf lineages such that no single lineage today serves best as a proxy for the basal lineage in our analysis . If true , the hypothesis that dogs were originally domesticated from a now-extinct wolf population suggests that ancient DNA studies will play a central role in advancing our understanding of the rapid transition from a large , aggressive carnivore to the omnivorous domestic companion that is a fixture of modern civilization .
We selected six samples for genome sequencing and generated single end and long mate pair SOLiD reads . We generated additional paired end ( PE ) sequence data on the Illumina HiSeq platform ( Text S1 ) . For most downstream analyses , we also utilized sequence information from the Boxer reference genome ( CanFam 3 . 0 ) . We aligned sequence reads to CanFam 3 . 0 , with post-processing of aligned reads including the removal of duplicates , local realignment , and base quality recalibration ( Text S3 ) . We then genotyped each sample individually , using the Genome Analysis Toolkit ( GATK ) pipeline [34] . For SNV genotyping and analysis , we excluded repeats of recent origin , CpG sites , regions falling in copy number variants , and triallelic sites , and at the sample level we filtered out genotypes proximate to called indels , with excess depth of coverage , with low genotype quality scores , or where the SNV fell within five base pairs of another SNV ( Text S4 ) . We compared genotype calls based upon sequencing to those from the same samples using the Illumina CanineHD BeadChip , which consists of >170 , 00 markers evenly spaced throughout the dog genome ( Text S5 ) . We also analyzed variants overlapping those generated in a previous SNP array study of a large panel of dogs and wolves [10] , and performed PCA on the combined data set to verify that NGS genotypes clustered with array genotypes for the same lineages ( Text S5 ) . We delineated segmental duplications in our six genomes by identifying regions with a significant excess depth of coverage ( Text S6 ) . For this purpose , we aligned Illumina and SOLiD reads with MrFAST [35] and drFAST [36] respectively . Absolute copy numbers were calculated using mrCaNaVar version 0 . 31 ( http://mrcanavar . sourceforge . net/ ) . In the particular case of the previously reported AMY2B expansion in the dog lineage [15] we also examined patterns of copy number across 52 breed dogs , six Dingoes , and 40 wolves using qPCR ( Text S6 ) . In order to conduct demographic analyses on putatively neutral genomic regions without any apparent functional annotation , we first identified genic region using annotations from the union of refGene , Ensembl and SeqGene annotation databases , with the condition that all annotated transcripts had proper start and stop codons , and contained no internal stop codons ( Text S7 ) . In addition , we defined conserved non-coding elements ( CNEs ) on the basis of phastCons scores [37] ( Text S7 ) . We used the PSMC methods developed by Li and Durbin [20] to infer the trajectory of population sizes across time for the six canid genome sequences ( Text S8 ) . To investigate the extent of gene flow between wolves and dogs subsequent to their divergence , we employed a method recently developed by Durand et al . [18] . This method tests for directional gene flow by testing for asymmetries in allele sharing between a source lineage ( P3 ) , and either of two receiving lineages ( P1 , P2 ) with reference to an outgroup ( O ) . To focus on gene flow most germane to evolutionary processes influencing wolf-dog divergence , we restricted testing to those cases where one of the dog samples was P3 , the other two ( P1 and P2 ) were wolves , and viceversa ( P3 = wolf , P1 and P2 = dogs ) . For more details , see Text S8 . Our main demographic analysis is based on the Generalized Phylogenetic Coalescent Sampler ( G-PhoCS ) developed by Gronau et al . [19] and which we applied to 16 , 434 1 kb loci chosen via a strict set of criteria to obtain putatively neutral loci ( Text S9 ) . The prior distributions over model parameters was defined by a product of Gamma distributions using the default setting chosen by Gronau et al . [19] . Markov Chain was run for 100 , 000 burn-in iterations , after which parameter values were sampled for 200 , 000 iterations every 10 iterations , resulting in a total of 20 , 001 samples from the approximate posterior . Convergence was inspected manually for each run . We conditioned inference on the population phylogeny based upon the neighbor-joining tree constructed from the genome-wide distance matrix described above ( Fig . 4A ) . We also constructed models under a ‘regional domestication’ scenario , in which each dog lineage originated from a wolf lineage from the same geographic region , i . e . Basenji from Israeli wolf , Boxer from Croatian wolf , and Dingo from Chinese wolf . We assessed models in which the branch ancestral to dogs was sister to a particular extant wolf population , or one of internal branches in the wolf clade . In addition , we investigated the sensitivity of parameter estimates to choice of locus length , number of loci , intra-locus recombination , distance from coding exons , and selection on linked sites . For more details , see Text S9 . | The process of dog domestication is still poorly understood , largely because no studies thus far have leveraged deeply sequenced whole genomes from wolves and dogs to simultaneously evaluate support for the proposed source regions: East Asia , the Middle East , and Europe . To investigate dog origins , we sequence three wolf genomes from the putative centers of origin , two basal dog breeds ( Basenji and Dingo ) , and a golden jackal as an outgroup . We find that none of the wolf lineages from the hypothesized domestication centers is supported as the source lineage for dogs , and that dogs and wolves diverged 11 , 000–16 , 000 years ago in a process involving extensive admixture and that was followed by a bottleneck in wolves . In addition , we investigate the amylase ( AMY2B ) gene family expansion in dogs , which has recently been suggested as being critical to domestication in response to increased dietary starch . We find standing variation in AMY2B copy number in wolves and show that some breeds , such as Dingo and Husky , lack the AMY2B expansion . This suggests that , at the beginning of the domestication process , dogs may have been characterized by a more carnivorous diet than their modern day counterparts , a diet held in common with early hunter-gatherers . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genome",
"sequencing",
"genetics",
"population",
"genetics",
"comparative",
"genomics",
"biology",
"genomics"
] | 2014 | Genome Sequencing Highlights the Dynamic Early History of Dogs |
Members of the mammalian tick-borne flavivirus group , including tick-borne encephalitis virus , are responsible for at least 10 , 000 clinical cases of tick-borne encephalitis each year . To attempt to explain the long-term maintenance of members of this group , we followed Ornithodoros parkeri , O . sonrai , and O . tartakovskyi for >2 , 900 days after they had been exposed to Karshi virus , a member of the mammalian tick-borne flavivirus group . Ticks were exposed to Karshi virus either by allowing them to feed on viremic suckling mice or by intracoelomic inoculation . The ticks were then allowed to feed individually on suckling mice after various periods of extrinsic incubation to determine their ability to transmit virus by bite and to determine how long the ticks would remain infectious . The ticks remained efficient vectors of Karshi virus , even when tested >2 , 900 d after their initial exposure to virus , including those ticks exposed to Karshi virus either orally or by inoculation . Ornithodoros spp . ticks were able to transmit Karshi virus for >2 , 900 days ( nearly 8 years ) after a single exposure to a viremic mouse . Therefore , these ticks may serve as a long-term maintenance mechanism for Karshi virus and potentially other members of the mammalian tick-borne flavivirus group .
Karshi virus is a member of the mammalian tick-borne flavivirus group ( genus Flavivirus , family Flaviviridae ) [1] . Members of this group include tick-borne encephalitis virus ( including subtypes Central European encephalitis virus ( CEEV ) and Russian spring-summer encephalitis virus ( RSSEV ) , Omsk hemorrhagic fever virus , Langat virus ( LGTV ) , Alkhurma hemorrhagic fever virus , Kyasanur Forest disease virus ( KFDV ) , Powassan virus ( POWV ) , Royal Farm virus , Karshi virus , Gadgets Gully virus , and Louping ill virus [1 , 2] . This group of viruses , also known as the TBEV serocomplex [1 , 3] , are responsible for at least 10 , 000 clinical cases of tick-borne encephalitis each year [4] . A second group of tick-borne flaviviruses is known as the seabird tick-borne flavivirus group [5] . Although a member of the mammalian tick-borne flavivirus group , Karshi virus is not known to cause disease in humans [5] . However , its close relationship to both POWV and KFDV indicated that it should be capable of causing disease in humans [1 , 2] . The natural transmission cycle of the mammalian tick-borne flavivirus group involves ixodid ticks and rodents , with Ixodes ricinus and I . persulcatus being the principal vectors of CEEV and RSSEV viruses , respectively [6 , 7] . This cycle is essentially identical to that for the Lyme disease spirochete , Borrelia burgdorferi , in I . scapularis . In the Lyme disease cycle , the mouse , Peromyscus leucopus , remains infectious for several months [8] . Therefore , once a mouse becomes infected by being fed upon by an infectious nymphal tick , it would continue to expose larval and nymphal ticks to the spirochete for months . However , because viremias in rodents exposed to members of the mammalian tick-borne flavivirus group are transient , often lasting only a few days [9 , 10] , the timing of nymphal and larval attachment becomes critical . If infectious nymphal ticks attach too early in the season , the viremia in the rodent will have ended prior to the attachment of the larval ticks . Unlike these Ixodid ( hard ) ticks that normally attach for 2–13 days to complete a blood meal and only feed once during the larval , nymphal , and adult stages [11] , members of the genus Ornithodoros attach and complete feeding usually within 10–30 min and most complete feeding within an hour [12] . Also , these ticks will feed multiple times both as nymphs and as adults , often live in rodent borrows , and can live about 20 years [12 , 13] . Previous studies indicate that Ornithodoros spp . ticks are able to become infected and transmit members of the mammalian tick-borne flavivirus group [14–16] as well as other pathogens [12] . Because of their long life span and repeated feedings , they can remain infectious for an extended period of time . Ornithodoros tholozani were shown to transmit Borrelia persica ( a causative agent of relapsing fever ) for at least 13 years after a single exposure [13] and field-collected O . turicata were able to transmit B . recurrentis ( repsorted as Spirochaeta recurrentis ) for at least 6 . 5 years [17] . In addition , many Ornithodoros spp . ticks are considered to be nidicolous , i . e . , living in close association with their vertebrate hosts such as living in rodent burrows [18] . Onithodoros sonrai are found in burrows of many rodent genera in Senegal and western Africa [19]; O . tartakovskyi , which is widely distributed in central Asia from Iran to the Xinjiang Province in western China are found in burrows of various rodent species , but primarily the great gerbil , Rhombomys opimus , [20 , 21]; and O . parkeri found in the western portions of the United States and Canada , is associated with numerous rodent species , but primarily prairie dogs [22 , 23] . To determine the potential for these ticks to serve as a long-term maintenance mechanism for these viruses , we evaluated the potential for O . sonrai , O . parkeri , and O . tartakovskyi ticks to transmit Karshi virus over an extended period of time .
We used three species of Ornithodoros ticks . These included a laboratory colony of O . sonrai derived from wild-caught specimens excavated from mammal burrows in the Bandia Forest of Senegal in 1989 [15] . No virus was detected upon examination of parental ticks from this colony . Georgia Southern University provided a colony of O . parkeri derived from specimens captured in Spicer City , CA , in 1965 . The National Institute of Allergy and Infectious Diseases provided a laboratory colony of O . tartakovskyi . All three colonies were maintained as described by Durden et al . [24] . We used the U2-2247 strain of Karshi virus . It had been passaged once in Vero cells and once in suckling mice before use in these experiments . Serial dilutions of blood , brain , and tick samples were tested for virus by plaque assay on confluent monolayers of 2- to 3-d-old primary chicken embryo cells or by subcutaneous inoculation into 2- to 4-d-old suckling mice . The identity of the original virus , and virus recovered from ticks and mice , was confirmed by a Karshi-specific quantitative real-time Real Time- polymerase chain reaction ( PCR ) assay and by direct sequencing of the PCR products [16 , 25] . One-day-old suckling mice ( BALB/c strain ) were inoculated intraperitoneally with 106 . 3 suckling mouse lethal dose50 ( SMLD50 ) units of Karshi virus . Two or 3 days after inoculation , a Karshi virus-inoculated mouse was placed in a cage containing ~50 O . sonrai , O . parkeri , or O . tartakovskyi ticks at various stages of development ( larvae through adult , but predominately early nymphs ) . After the ticks had been allowed to attach to the mouse for about 5 min , the mouse was removed and a second virus-inoculated mouse was added to the cage . This was repeated for up to three mice for each species of tick used in this study . The ticks were allowed to feed on the virus-inoculated mouse for about 2 h . At that time , those ticks that had attached and did not feed were removed and discarded . Each mouse was then euthanized with CO2 and blood was collected by cardiac puncture . Blood was mixed 1:10 in diluent ( Medium 199 with Earle’s salts containing 10% heat-inactivated fetal bovine serum and 5 μg of amphotericin B , 50 μg of gentamicin , 100 units of penicillin , and 100 μg of streptomycin per ml and 0 . 075% NaHCO3 ) and frozen at -70°C until tested to determine the viremia at the time of tick feeding . The engorged ticks were placed in a cage maintained at room temperature ( ~20°C ) until tested for either infection or for the ability to transmit virus by bite . For each species , some of the ticks that had not attached to a virus-inoculated mouse were inoculated intracoelomically with 104 SMLD50 ( 107 . 5 SMLD50/ml ) of the same virus strain that had been used to infect the mice [26] . These inoculated ticks were treated in the same manner as the engorged ticks , except that the inoculated O . parkeri were maintained in an incubator maintained at 26°C rather than at ambient air temperature . To determine transmission rates , virus-exposed ticks were allowed to feed for up to 2 hours on naive suckling mice ( either BALBc or Swiss Webster ) individually , i . e . , one tick per mouse . These suckling mice were marked by subcutaneous inoculation of India ink , returned to their dam , and then monitored daily over the next 21 d for signs of viral infection . Each litter contained one or two suckling mice that were either unexposed to ticks or were fed upon by a tick from the uninfected colony to serve as negative controls . Moribund mice were euthanized with CO2 , and brain samples were obtained from a subset of them and then triturated ( 1:10 ) in diluent and frozen at -70°C until tested for virus . In most of the tick transmission trials , ticks were caged individually in plastic vials ( 12 ml , about half filled with washed sea sand ) after feeding on the mice . Many of these same ticks were tested multiple times over the following 8 years for their ability to transmit virus by bite . Research was conducted under an IACUC approved protocol in compliance with the Animal Welfare Act , PHS Policy , and other Federal statutes and regulations relating to animals and experiments involving animals . The facility where this research was conducted is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care , International and adheres to principles stated in the Guide for the Care and Use of Laboratory Animals , National Research Council , 2011 . The USAMRIID IACUC approved these studies .
Viremias in the suckling mice at the time of the tick feedings ranged from 106 . 5 to 106 . 7 SMLD50/ml . When allowed to feed on a susceptible mouse ≤94 days after the initial blood meal , transmission was very inefficient , with none of 17 ticks transmitting virus to the mice ( Table 1 ) . However , when tested ≥105 days after the initial feeding , at least 60% of the ticks that had fed on a mouse with a viremia about 106 . 5 SMLD50/ml transmitted virus , regardless of tick species , including several ticks that failed to transmit virus when allowed to feed at days 59–94 after virus exposure . When ticks that had transmitted virus on one occasion were allowed to feed on a second mouse at some point in the future , nearly all of them ( 86% , n = 14 ) transmitted each time they were allowed to feed . Each of the species transmitted virus the last time it was tested , and all species transmitted virus for at least 2 , 000 days ( Table 1 ) . Data for each transmission attempt is provided in S1 Table . For both O . sonrai and O . tartakovskyi , five of six ticks transmitted virus by bite when tested 43 days after inoculation with Karshi virus ( Table 2 ) . However , all 34 ticks ( eight O . parkeri , 11 O . sonrai , and 15 O . tartakovskyi ) tested at ≥64 days after inoculation transmitted Karshi virus by bite . Data for each transmission attempt is provided in S2 Table . These 34 ticks took a total of 43 blood meals from susceptible mice and transmitted virus in each case ( Table 2 ) . Individuals in each species transmitted virus the last time that species was tested , with the final transmission occurring >2 , 100 days after the tick had initially been inoculated with Karshi virus .
Ornithodoros spp . ticks were able to transmit Karshi virus for >2 , 900 days ( nearly 8 years ) after a single exposure to a viremic mouse . Therefore , these ticks may serve as a long-term maintenance mechanism for Karshi virus and potentially other members of the mammalian tick-borne flavivirus group . This study was a continuation of a study [16] that examined the potential for these Ornithodoros ticks to transmit Karshi virus , but that original study only followed the tick for 3 years . Traditionally , viruses in the mammalian tick-borne flavivirus group have been associated with ixodid ticks , I . ricinis and I . persulcatus in Europe and Asia , respectively [6 , 7] and with I . cookei and I . scapularis in the Americas [27] . Larval and nymphal ticks are exposed to virus when overwintering infected nymphal ticks feed on naïve rodents in the spring . These ticks can also be infected by co-feeding with an infected tick [28] , regardless of the immune status of the rodent [29] . However , transmission by co-feeding on an immune rodent was only about 10% as efficient as co-feeding on an immunologically naïve rodent when the two ticks were not immediately collocated [29] . Given the relatively short period of viremia for these viruses in their rodent hosts [9 , 10] , one could hypothesize that this cycle would be too inefficient to maintain these viruses for many years in the same location . However , if a rodent became infected after being fed upon by an infectious tick and then went back to its burrow , it could potentially expose many of the Ornithodoros ticks living in that burrow . When that rodent died , or was killed by a predator , the burrow would remain vacant until discovered by a new rodent . Individual Ornithodoros ticks can remain viable for up to 4 years between feedings [13 , 30 , 31] and can survive for 10–20 years [13 , 32–34] . In addition , this study observed transmission of Karshi virus for up to 8 years post infection . Thus , ticks present in the vacant rodent burrow could remain a source of virus for many years . When a new rodent entered that burrow and was fed upon by the infected Ornithodoros ticks , the rodent would become infected and all the ixodid ticks present on that rodent exposed to virus . These ixodid ticks could then spread the virus to other rodents and to larger mammals including humans . Ornithodoros ticks have a wide distribution , with species found in much of the range of the mammalian tick-borne flaviviruses [35 , 36] . However , there are regions where members of this virus complex are found , but for which members of the genus Ornithodoros have not been described , i . e . , the northeastern US for Powasson virus and deer tick virus , and parts of the northern range of the mammalian tick-borne flaviviruses in Eurasia . Therefore , other methods must exist for the perpetuation of these viruses in those areas . Experimental studies on members of the mammalian tick-borne flavivirus group have focused on ixodid ticks . However , several members of this and the closely related seabird tick-borne flaviviruses group have been isolated from naturally occurring Ornithodoros ticks . These include Karshi virus [37] , KFDV [38] , Alkhurma hemorrhagic fever virus [39] , Meaban virus [40] , and Saumarez Reef virus [41] . Therefore , the susceptibility of O . parkeri , O . sonrai , and O . tartakovskyi to infection with Karshi virus; their ability to transmit this virus for extended periods ( at least 2 , 905 days ) ; their long life span; and the isolation of several members of both the mammalian and seabird tick-borne flavivirus groups from Ornithodoros ticks indicate that Ornithodoros species should be studied as potential long-term reservoir hosts for members of the tick-borne flavivirus groups . | Members of the mammalian tick-borne flavivirus group , including tick-borne encephalitis virus , remain a significant cause of human disease and are responsible for at least 10 , 000 clinical cases of tick-borne encephalitis each year . One of the principal questions in their epidemiology is how they persist from year to year in a given area . To attempt to explain the long-term maintenance of members of this group , we exposed Ornithodoros parkeri , O . sonrai , and O . tartakovskyi ticks to Karshi virus , a member of the mammalian tick-borne flavivirus group . Ticks were exposed to Karshi virus either by allowing them to feed on viremic suckling mice or by intracoelomic inoculation . To determine their ability to maintain the virus for an extended period of time and to transmit Karshi virus , ticks were allowed to feed individually on suckling mice after various periods of extrinsic incubation . Ticks exposed to Karshi virus , either orally or by inoculation , remained efficient vectors of Karshi virus , even when tested >2 , 900 days ( approximately 8 years ) after their initial exposure to virus . Therefore , these ticks may serve as a long-term maintenance mechanism for Karshi virus and potentially other members of the mammalian tick-borne flavivirus group . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Experimental Transmission of Karshi (Mammalian Tick-Borne Flavivirus Group) Virus by Ornithodoros Ticks >2,900 Days after Initial Virus Exposure Supports the Role of Soft Ticks as a Long-Term Maintenance Mechanism for Certain Flaviviruses |
Faithful meiotic chromosome segregation and fertility require meiotic recombination between homologous chromosomes rather than the equally available sister chromatid , a bias that in Saccharomyces cerevisiae depends on the meiotic kinase , Mek1 . Mek1 is thought to mediate repair template bias by specifically suppressing sister-directed repair . Instead , we found that when Mek1 persists on closely paired ( synapsed ) homologues , DNA repair is severely delayed , suggesting that Mek1 suppresses any proximal repair template . Accordingly , Mek1 is excluded from synapsed homologues in wild-type cells . Exclusion requires the AAA+-ATPase Pch2 and is directly coupled to synaptonemal complex assembly . Stage-specific depletion experiments further demonstrate that DNA repair in the context of synapsed homologues requires Rad54 , a repair factor inhibited by Mek1 . These data indicate that the sister template is distinguished from the homologue primarily by its closer proximity to inhibitory Mek1 activity . We propose that once pairing or synapsis juxtaposes homologues , exclusion of Mek1 is necessary to avoid suppression of all templates and accelerate repair progression .
Meiosis is a specialized cell division that produces haploid gametes from diploid progenitors and is essential for sexual reproduction . The reduction in ploidy is achieved by a unique chromosome division phase ( meiosis I ) that segregates homologous chromosomes ( homologues ) . Errors in this process are a leading cause of infertility , miscarriages , and birth defects in humans [1] . Proper meiosis I chromosome segregation in most organisms requires that each homologue pair be linked by at least one crossover . Crossover formation occurs during the extended prophase preceding meiosis I and is promoted by the programmed induction of DNA double-strand breaks ( DSBs ) . Resection of these breaks exposes single-stranded DNA tails that invade a donor template for repair . A subset of strand-invasion reactions subsequently matures to form double Holliday junctions , which are generally resolved as crossovers [2] . To promote linkages between homologues , meiotic DSB repair is strongly biased toward using the homologue rather than the physically more proximal sister chromatid [3 , 4] . Available evidence , stemming mostly from studies in the budding yeast Saccharomyces cerevisiae , suggests that homologue bias results primarily from suppression of repair from the sister template . In yeast , this barrier to sister repair is mediated to a large extent by the chromosomal kinase Mek1 , a meiosis-specific orthologue of mammalian CHK2 kinase that is recruited to the axial element structures of meiotic chromosomes upon DSB formation [3 , 5] . Mek1 recruitment requires the phosphorylation of the chromosome axis protein Hop1 on threonine 318 ( T318 ) by the checkpoint kinases Mec1 ( ATR ) and Tel1 ( ATM ) [6] . Chromosomal recruitment leads to the dimerization and activation of Mek1 [7] . Mek1 , in turn , phosphorylates a variety of targets , including the repair factors Rad54 and Rdh54 , as well as histone H3 [8 , 9] . Phosphorylation of Rad54 inhibits its interactions with the recombinase Rad51 and is thought to help suppress sister-targeted repair along with additional Mek1 targets that remain to be identified [8] . In addition , Rad51 is kept inactive by its meiosis-specific inhibitor Hed1 , which further biases repair towards the homologue [10–12] . Current models suggest that a DNA “tentacle” formed by the assembly of Rad51 and the meiotic recombinase Dmc1 on one end of a DSB interprets the suppressive signal established by Mek1 , leading to preferred repair engagement with the homologue [3 , 4 , 13–15] . For homologue bias to be established , the DSB repair machinery must be able to distinguish homologue and sister templates . A probable mechanism included implicitly or explicitly in many models of homologue bias is that the sister chromatid is identified either by spatial proximity and/or through the cohesive linkages resulting from DNA replication [3 , 4 , 13–15] . As a result , the sister is subject to Mek1-dependent repair suppression , whereas the generally spatially more distant or unlinked homologue is not . An extension of the spatial proximity model is that if the homologue were to be transported within the range of Mek1 activity , it would also become suppressed as a template . Indeed , experimental hyperactivation of Mek1 also delays interhomologue repair [16] , suggesting that Mek1-dependent repair suppression does not inherently distinguish between sister chromatids and homologous chromosomes . The close alignment of homologous chromosomes is an essential part of meiotic prophase that in many organisms culminates in the assembly of the synaptonemal complex ( SC ) . The SC is a conserved tripartite structure that assembles along the entire length of paired homologues during meiotic prophase [17] . In S . cerevisiae , SC assembly ( synapsis ) initiates at sites of crossover designation and centromeres [18–20] and involves the progressive deposition of Zip1 , an extended coiled-coil protein that aligns homologous chromosomes at a fixed distance [21] . The function of the SC remains obscure . The SC is thought to stabilize pairing interactions , but homologues often remain co-aligned , albeit at a greater distance , in the absence of Zip1 [22] . Recent experiments have hinted at a signaling role for the SC . In several organisms , SC assembly is associated with a loss of chromosomal proteins , most notably yeast Hop1 and the orthologous HORMAD proteins in mouse [23–26] , as well as the yeast DSB regulators Rec114 and Mei4 , which require Hop1 for recruitment [27–29] . As DSB levels are elevated in SC mutants , these observations have led to the model that the SC acts as a feedback signal to suppress DSB formation on chromosomes that have engaged in crossover repair [10 , 30] , although some evidence suggests that this suppression is not absolute [31 , 32] . Given that Mek1 recruitment also depends on chromosomal Hop1 , the synapsis-associated loss of Hop1 would also be expected to affect Mek1 binding . Unexpectedly , however , Mek1 was reported to persist on synapsed chromosomes [33] . Here , we reinvestigated the chromosomal dynamics of Mek1 and its role in regulating meiotic DSB repair . We demonstrate that Mek1 is in fact eliminated from synapsing chromosomes and that removal requires Zip1-mediated recruitment of the AAA+-ATPase Pch2 . Moreover , we show that DSB repair on synapsed chromosomes requires the function of Rad54 , a target of Mek1-dependent inhibition . Importantly , failure to remove Mek1 from synapsed chromosomes leads to delays in DSB repair , indicating that Mek1 must be inactivated on fully engaged chromosomes to ensure timely completion of meiotic DSB repair .
To investigate the dynamics of Mek1 binding to meiotic chromosomes , we analyzed chromosome spreads using a functional Mek1-GFP construct . Nuclei were staged based on the progressive deposition of Zip1 , marking the assembly of the SC along meiotic chromosomes . An NDT80 deletion was used to prevent SC disassembly and Mek1 degradation due to exit from meiotic prophase [32 , 34] . Mek1 foci were abundant on chromosomes prior to the association of Zip1 ( Fig 1A ) . However , in contrast to a previous report , which detected many apparent Mek1 foci on synapsed chromosomes [33] , we observed a notable loss of chromosomal Mek1 from regions of extended Zip1 staining , such that Mek1-GFP foci were nearly undetectable when all chromosomes had assembled an SC ( Fig 1A ) . The reason for this discrepancy is unclear but may be related to differences in strain background . The loss of chromosomal Mek1 signal was confirmed using a polyclonal antibody against Mek1 ( S1A Fig ) , and was not due to a drop in Mek1 protein levels during meiotic prophase [34] . Examination of nuclei with partially assembled SC indicated that the disappearance of Mek1 foci was directly correlated with SC deposition even on individual chromosomes . As SC formation is relatively rapid in wild-type cells , we confirmed this observation in a zip3Δ mutant , in which chromosome synapsis is delayed and limited [18 , 20] . Like in the wild-type situation , Mek1-GFP signal was strongly reduced in synapsed regions but persisted on unsynapsed chromosomes in a zip3Δ mutant ( Fig 1B ) . These data indicate that chromosome synapsis coincides with a loss of chromosomal Mek1 . The disappearance of Mek1 from chromosomes was mirrored by a loss of Mek1-dependent chromatin marks . Phosphorylation of histone H3 T11 requires Mek1 activity [9] . Immunostaining using an antibody specific for H3-pT11 revealed numerous foci on unsynapsed chromosomes but a near complete absence once chromosomes were synapsed ( Fig 1C ) , implying that Mek1 is not active on synapsed chromosomes . To support this observation , we analyzed H3-pT11 by western blotting in a meiotic time course . Cells were blocked at the end of prophase using an ndt80Δ mutation to avoid secondary effects from Mek1 inactivation after prophase [38 , 39] . H3-pT11 first became detectable at 3 h after meiotic entry ( Fig 1D ) , corresponding to the time of DSB induction . This timing correlated well with the phosphorylation of other meiotic checkpoint targets , including Zip1 , Zip3 and Sae2 . H2A-pS129 accumulated earlier presumably because of its role in premeiotic DNA replication [40] . Consistent with the analysis of chromosome spreads , H3-pT11 signal disappeared 5 h after meiotic induction , when most cells in the culture were completing SC formation ( Fig 1E ) . The disappearance of H3-pT11 is in contrast to the other tested checkpoint targets , which remained phosphorylated during SC formation ( Fig 1D and 1E ) . Intriguingly , the SC-associated loss of Mek1 appeared to occur irrespective of persistent DNA repair intermediates . zip3Δ mutants are severely defective in DSB repair [18 , 41] and accumulate abundant repair foci marked by the Rad51 recombinase ( S1B Fig ) . However , whereas Mek1 signal was largely restricted to unsynapsed regions in zip3Δ mutants , Rad51 foci were abundantly detectable on both unsynapsed and synapsed chromosomes . These data indicate that , at least in the absence of ZIP3 , completed chromosomal DNA repair is not a prerequisite for the loss of Mek1 from synapsed chromosomes . To test whether the SC is responsible for the loss of Mek1 , we removed Zip1 from meiotic chromosomes . To circumvent potential pleiotropic effects of earlier roles of Zip1 in centromere pairing and DSB repair [42] , we used the “anchor-away” technique [37] to conditionally deplete Zip1 from chromosomes that had already assembled SCs . In this technique , proteins tagged with the FRB domain of human mTOR are actively depleted from the nucleus after rapamycin addition due to interaction with a cytoplasmic anchor ( a ribosomal protein fused to FKBP12; Fig 1F ) . Zip1 was quantitatively depleted from meiotic chromosomes within 2 h of rapamycin addition ( S1C Fig ) . Nuclear depletion of Zip1-FRB throughout meiosis caused defects in sporulation and spore viability approximating the zip1Δ mutant , whereas untagged control strains treated with rapamycin retained wild-type spore viability ( S1A and S1B Table ) . Strikingly , specific removal of Zip1 starting at the 6 h time point , when the vast majority of nuclei have fully synapsed chromosomes , caused rapid reaccumulation of Mek1 on chromosomes ( 17/20 nuclei; Fig 1G ) . We conclude that Zip1 assembly on chromosomes promotes the removal of Mek1 and is required to maintain Mek1 exclusion from synapsed chromosomes . Chromosomal recruitment and activation of Mek1 requires the phosphorylation of Hop1-T318 [6] , which may be subject to Zip1-dependent regulation . Consistent with this notion , the phosphorylation-dependent slower migrating forms of Hop1 disappear at the time of SC extension ( Fig 2A ) [40] . To more directly test the role of Hop1-T318 , we raised a polyclonal antibody that specifically recognizes the phosphorylated form of this residue ( Figs 2A and S2A ) . Immunofluorescence analysis revealed that , similar to Mek1 , Hop1-pT318 foci were abundantly present in early prophase but disappeared coincident with SC assembly ( Fig 2B and 2C ) . Moreover , we observed an increased accumulation of Hop1-pT318 signal in cell extracts and on meiotic chromosomes after Zip1 was depleted from the nucleus by anchor-away compared to the undepleted controls ( Fig 2D and 2E ) . These data are consistent with a model whereby the disappearance of Mek1 from synapsed chromosomes is the result of a loss of Hop1-pT318 epitopes . Loss of at least some Hop1-pT318 epitopes is likely a secondary consequence of the reduced binding of Hop1 to synapsed chromosomes ( S2B Fig ) [23 , 25] . Indeed , Hop1 re-accumulates on chromosomes after depletion of Zip1 ( Fig 2F and 2G ) . Hop1 removal from synapsing chromosomes requires the SC-bound AAA+-ATPase Pch2 [24 , 43 , 44] and super-resolution microscopy of the SC in pch2Δ mutants revealed an over-enrichment of Hop1 in two parallel tracts along the length of the lateral elements ( Fig 3A ) . This effect was specific for Hop1 , as the staining patterns of other SC lateral and central element components with respect to Zip1 appeared similar in wild type and pch2Δ mutants ( S3A–S3C Fig ) . Significantly , Hop1-pT318 and Mek1 foci were visible on fully synapsed chromosomes in pch2Δ mutants ( Fig 3B and 3C ) , which are almost never seen in wild-type cells . Chromosomal accumulation of Mek1 occurred independently of the ndt80Δ arrest ( Fig 3D ) . Furthermore , phosphorylated Hop1 was abundant in pch2Δ whole-cell extracts ( Fig 3E ) . These data indicate that Pch2 is responsible for the loss of Hop1-pT318 and Mek1 from synapsing chromosomes . The persistence of Hop1 on synapsed chromosomes in the pch2Δ mutant is associated with unusually distinct parallel tracts of DAPI-stained chromatin along the lengths of chromosomes , a conformation only occasionally observed in short stretches on synapsed wild-type chromosomes ( Fig 3F ) . Previous analyses had shown that PCH2 is required for establishing separate domains of Zip1 and Hop1 along chromosomes , which fail to be formed in pch2Δ mutants [43 , 44] . We speculate that the distinctive parallel organization of chromosomes observed in pch2Δ mutants is another reflection of this altered chromosome structure , although we currently do not know whether the chromosomal persistence of Hop1 or Mek1 is responsible for this chromosome conformation in pch2Δ mutants . Our data suggest that removal of Mek1 depends on a Pch2-associated function during synapsis . Further analysis identified a non-null allele of ZIP1 that assembles SC but fails to recruit Pch2 to synapsed chromosomes . Cells lacking a leucine-zipper in the coiled-coil region of Zip1 ( zip1-4LA ) [45] exhibited overall wild-type SC structure but lost all SC-associated Pch2 staining ( Figs 3G and S3D–S3F ) . By contrast , the nucleolar pool of Pch2 , which is independent of ZIP1 [43] , persisted in these mutants . Consistent with the failure to recruit Pch2 to the SC , zip1-4LA mutants retained large amounts of Hop1 on synapsed chromosomes ( Fig 3A ) and accumulated high levels of phosphorylated Hop1 and Mek1 ( Fig 3B , 3C and 3E ) . Furthermore , as seen upon loss of PCH2 , nuclear spreads of zip1-4LA mutants exhibited distinctly parallel DAPI tracks ( Fig 3F ) . We note that the Pch2-mediated checkpoint , which specifically involves the nucleolar pool of Pch2 [43] , remains active in zip1-4LA mutants [45] . These observations suggest that the Zip1-mediated recruitment or stabilization of Pch2 couples SC assembly to the removal of Mek1 . We investigated whether the SC-associated loss of Hop1-pT318 is mediated by dephosphorylation in addition to Hop1 removal . PP4 protein phosphatase , comprising the catalytic subunit Pph3 and the cofactor Psy2 , negatively regulates Hop1 phosphorylation [35 , 46] . To specifically interrogate the role of PP4 in Hop1 dephosphorylation during chromosome synapsis , we conditionally depleted the PP4-cofactor Psy2-FRB by anchor-away at the time of full synapsis . Nuclear depletion of Psy2 caused a modest accumulation of Hop1-pT318 signal in cell extracts ( Fig 4A ) and an increase in Hop1-pT318 focus number ( Fig 4B ) , indicating that PP4 contributes to the removal of Hop1-pT318 . Unlike in pch2Δ mutants , the increases in Hop1-pT318 signal were not associated with an increase in chromosomal Hop1 levels ( S4A and S4B Fig ) . We do note , however , that the Hop1-pT318 signals often appeared at sites of discontinuity in Zip1 staining ( Fig 4B ) , perhaps reflecting sites where Hop1 persists on synapsed chromosomes . These findings suggest that PP4 acts in parallel to Pch2 in eliminating Hop1-pT318 ( and thus Mek1 ) . The reappearance of Hop1-pT318 foci upon PP4 depletion also presented a puzzle , as it implied an increasing number of unrepaired DSBs on synapsed chromosomes . DSB formation , a prerequisite for Hop1 phosphorylation and Mek1 recruitment [6 , 47] , is thought to be largely shut down upon homologue engagement and synapsis [10 , 28 , 30 , 48 , 49] , although several groups have reported continued presence of DSBs in ndt80 mutants [30–32] . To test for the presence of unrepaired DSBs , we analyzed Rad51 focus number upon Psy2-FRB depletion . Nuclei with fully synapsed chromosomes displayed very few Rad51 foci when Psy2 was present ( Fig 4C ) . By contrast , Psy2-FRB depletion led to a significant increase in Rad51 focus number on synapsed chromosomes that matched Hop1-pT318 accumulation ( Fig 4C ) , suggesting an increased presence of DSBs . The accumulating Rad51 foci may reflect DSB repair intermediates that became destabilized upon PP4 depletion . Alternatively , they may represent continued DSB formation on synapsed chromosomes in the absence of PP4 activity . This latter possibility would imply that DSB formation continues on synapsed chromosomes . To begin to distinguish between these possibilities , we first asked whether DSB formation can be restored upon removal of the SC , which would indicate that DSB suppression associated with synapsis is reversible . We depleted Zip1-FRB by anchor-away and used immunofluorescence analysis of Rad51 to monitor DSB levels ( Fig 5A ) . Zip1 depletion led to a significant increase in steady-state focus number of Rad51 ( Fig 5A and 5B ) . This effect is not observed in untagged control cells ( S5A Fig ) and is mirrored by an increase in steady-state focus numbers of the single-stranded DNA-binding protein RPA ( S5B Fig ) . Importantly , co-depletion of Zip1 and an essential DSB factor , Mer2 , did not lead to an increase in Rad51 foci ( Fig 5B ) . This outcome was not due to non-specific disruption of Mer2 by the FRB tag , because Mer2-FRB strains accumulated near wild-type levels of Rad51 foci prior to synapsis and produced fully viable spores in the absence of rapamycin ( S5C Fig and S1A and S1B Table ) . These data indicate that new DSBs form in a Mer2-dependent manner after SC depletion . We used physical assays at several endogenous DSB hotspots to monitor the occurrence of new DSBs upon depletion of Zip1 [30 , 50] . Electrophoretic separation of restriction-digested genomic DNA followed by Southern analysis allows detection of the larger unbroken DNA ( parental size ) as well as the faster migrating DSB fragments . DSB fragments reappeared at the ERG1 hotspot in the ZIP1-FRB strain but not in the untagged control strain following rapamycin addition ( Fig 5C and 5D ) . These DSBs were absent when Mer2 was co-depleted ( Fig 5D and S6A Fig ) , indicating that they represent newly formed DSBs and are not the result of destabilized repair intermediates . DSB signal may be further increased due to the loss of Zip1 repair functions upon depletion [42] . A similar increase in DSBs was also observed at the YIL094c hotspot after Zip1 nuclear depletion ( Fig 5E and S6B Fig ) . However , Zip1 nuclear depletion did not lead to significant DSB reappearance at the YGR279c or the YCR047c hotspot ( Fig 5F and S6E Fig ) . Thus , although the increase in DSBs after Zip1 nuclear depletion is consistent with the notion that Zip1 prevents the formation of new DSBs on fully synapsed chromosomes [30] , our data suggest that this suppression may occur in a locus-specific manner . We note that following Zip1 depletion , the DSB bands at several hotspots migrated at a higher molecular weight than DSB fragments observed in early prophase ( Fig 5C and S6B–S6D Fig ) , suggesting that processing of DSB ends is altered in this situation . Given that depletion of PP4 led to an increase in Rad51 foci even in the presence of Zip1 and that previous studies have reported continued presence of DSBs in ndt80 mutants [30–32] , we asked whether some DSB formation is maintained when chromosomes appear fully synapsed in late prophase . To test this possibility , we depleted DSB repair factors from synapsed chromosomes to trap newly formed DSBs . We chose Rad54 , which promotes Rad51-dependent DSB repair , and Rdh54 , a Rad54-like protein that activates the meiosis-specific recombinase Dmc1 . Dmc1 and Rdh54 are required for homologue-directed repair in meiosis [51 , 52] , whereas Rad54 is inhibited by Mek1 to suppress intersister repair [8] . We reasoned since Mek1 is nearly absent on synapsed chromosomes , Rad54 may become active in this situation . We used anchor-away to deplete Rdh54-FRB and Rad54-FRB from synapsed chromosomes ( Fig 5G and 5H ) . No increase in Rad51 focus number was observed upon removal of Rdh54 ( Fig 5H ) , although nuclear depletion of Rdh54-FRB throughout meiosis caused an expected reduction in sporulation efficiency , indicating effective depletion ( S1A Table ) . By contrast , Rad54 removal led to a strong increase in Rad51 focus number on synapsed chromosomes ( Fig 5G and 5H ) . Although this finding may indicate that DSB formation continues on synapsed chromosomes , previous studies indicated that Rad51 also associates with undamaged DNA in the absence of Rad54 activity [53] . To address this possibility , we co-depleted a DSB-cofactor Mer2 or the DSB-inducing enzyme Spo11 with Rad54-FRB . Co-depletion of either factor significantly reduced Rad51 focus formation on synapsed chromosomes ( Fig 5H ) . These data strongly suggest that DSB formation continues even when chromosomes appear fully synapsed , and that DSB turnover depends on Rad54 . Southern analysis indicated that DSB accumulation on synapsed chromosomes upon Rad54 depletion is locus-dependent . The accumulation of unrepaired DSBs was apparent at the YGR279c and YCR047c DSB hotspots ( Fig 5F and S6D–S6F Fig ) , whereas the DSB signal at the YIL094c and ERG1 hotspots did not increase substantially above background ( Figs 5D , 5E and S6B ) . Interestingly , these patterns of DSB accumulation are opposite to the patterns observed upon Zip1 depletion ( Figs 5D–5F and S6D–S6F ) . Thus , these differences may reflect the varying propensities of different genomic regions to synapse or differential dependence on ZIP1 function for DSB repair . Alternatively , individual hotspots may differ in their dependence on Hop1/Mek1 for DSB formation and/or repair . In contrast to the slower-migrating DSB fragments after nuclear depletion of Zip1 , the DSB fragments that appeared at the YGR279c and the YCR047c locus after nuclear depletion of Rad54 were faster migrating compared to DSBs in early prophase ( S6D and S6F Fig; compare DSB pattern at T = 3 h to rapamycin-treated sample in Rad54-FRB ) . This migration pattern is consistent with hyperresection of DSBs ends and is typically observed when strand-invasion activity is blocked [54] . Despite the accumulation of Rad51 foci in Rad54-depleted nuclei , we observed no defect in SC structure ( Fig 5G ) and no increase in Hop1-pT318 focus number , overall Hop1 phosphorylation , or total chromosomal Hop1 signal upon Rad54 depletion ( S7A–S7C Fig ) . This behavior is in stark contrast to the commensurate increase in Rad51 and Hop1-pT318 foci upon depletion of Zip1 ( Figs 2D , 5A and 5B ) or PP4 ( Fig 4A–4C ) . These observations support the model that Zip1-dependent Hop1 removal and PP4 activity collaborate to prevent Hop1-T318 phosphorylation on synapsed chromosomes . We conclude that unrepaired DSBs do not lead to Mek1 recruitment when chromosomes appear fully synapsed . The loss of Mek1 activity upon SC formation suggests that DSB repair on already synapsed chromosomes may not be constrained by homologue bias . To test this possibility , we investigated the formation of intersister ( IS ) and interhomologue ( IH ) double Holliday junction ( dHJ ) intermediates over time in ndt80Δ mutants at two DSB loci , HIS4-LEU2 and GAT1 . Engineered restriction site polymorphisms surrounding these DSB sites permit the separation of IS and IH repair intermediates by two-dimensional gel electrophoresis [30 , 55] ( Fig 6A ) . As ndt80Δ mutants accumulate unresolved dHJs , analysis of IS and IH dHJs at a given time point will provide the cumulative average of template bias up until that time point . Analysis of the HIS4-LEU2 hotspot revealed a strong IH bias that persisted over time ( S8A Fig ) , consistent with previous results [56] . GAT1 reproducibly exhibited a weaker IH bias ( IH:IS ~1 . 5:1; Fig 6B and S8B Fig ) than other strong DSB hotspots ( IH:IS ~4:1 ) [10 , 56] , but still substantially higher than the IH:IS ~1:9 template bias observed for mitotic DSB repair [57] . Notably , the cumulative IH:IS ratio at GAT1 became progressively lower ( Fig 6B and S8B Fig ) consistent with decreased IH bias at later time points . These data support the notion that , at least at the GAT1 locus , meiotic repair constraints are relaxed after chromosomes are fully synapsed . Because technical difficulties precluded us from analyzing IH bias at additional loci , we do not know to what extent this effect extends to other DSB hotspots . A major mechanism of establishing homologue bias is to make repair from the sister chromatid more difficult [13 , 58] . One conceptually simple way to achieve this goal is to establish a Mek1-dependent “zone” of repair suppression , such that spatially proximal sequences ( i . e . , the sister ) cannot easily be used as repair templates [13] . If so , then removal of Mek1 may be necessary once chromosomes are aligned , as alignment would also place the homologue in this zone of repair suppression , thereby rendering repair from all templates equally difficult ( see Fig 7 ) . This model predicts that unrepaired DSBs should accumulate in cells that fail to remove Mek1 from synapsed chromosomes . Indeed , we observed an accumulation of Rad51 foci on fully synapsed chromosomes of pch2Δ mutants ( Fig 6C ) . Rad51 accumulation occurred independently of the ndt80Δ-mediated prophase arrest ( S8C Fig ) and is consistent with previous observations showing a delay in DSB repair in these mutants [44 , 59] . To test whether the increased Rad51 foci on synapsed chromosomes are due to the persistence of Mek1 activity , we used an allele of Mek1 ( mek1-as ) that can be conditionally inactivated upon addition of a small molecule inhibitor ( 1-NA-PP1 ) [60] . Addition of the inhibitor after chromosomes were synapsed led to the disappearance of Rad51 foci in pch2Δ mek1-as mutants , whereas the foci persisted in untreated control cells ( Fig 6D and 6E ) , suggesting rapid repair of DSBs once Mek1 was inactivated . To confirm these results , we performed Southern analysis at the ERG1 and YCR047c DSB hotspots . Consistent with the persistence of Rad51 foci , pch2Δ mutants accumulated DSBs at both hotspots ( Fig 6F and S8D Fig ) . The persistent DSBs differed in their processing from DSBs formed in early prophase , similar to what was observed upon Zip1 depletion ( S8E Fig ) . Importantly , the DSB bands were lost at both hotspots upon inactivation of Mek1 ( Fig 6F and S8D Fig ) . Together , these results indicate that one function of the SC is to prevent Mek1 association with synapsed chromosomes in order to allow rapid DSB repair following homologue engagement .
For repair template bias to be established , cells must be able to distinguish sister chromatids from homologous chromosomes . Our data point to a simple mechanism , whereby the primary determinant distinguishing sister from homologue is the spatial distance of the respective template from DSB-associated Mek1 activity ( Fig 7 ) . This model is in line with current models of template choice [3 , 4 , 13–15] , which propose that a Mek1-dependent inhibitory domain suppresses repair progression from the proximal sister template , while the generally more distant homologue escapes this suppression . It is further supported by the observation that hyperactivation of Mek1 also delays interhomologue repair [16] . We argue , however , that a consequence of this simple setup is that once homologous chromosomes pair and establish close juxtaposition , Mek1 must be inactivated , so as not to place the homologue in the inhibitory domain and thus render all possible repair templates unfavorable . The stochastic nature of chromosome pairing would require this inactivation to be coupled to the behavior of individual chromosomes . One prediction emerging from this model is that Mek1 activity along chromosomes must be spatially and temporally restricted , a notion supported by our experiments . In addition , Mek1 recruitment must be dynamic , as the genomic distribution of DSBs varies from cell to cell . Accordingly , Hop1 distribution is highly stereotyped and DSB-independent [27] , whereas Mek1 recruitment is coupled to DSB induction [6 , 47] . The model that Mek1-dependent suppression of DSB repair is not inherently selective for the sister can also explain why DSBs persist in pch2Δ mutants . Chromosome pairing is unaffected in pch2Δ mutants [43] , implying functional interhomologue repair interactions . However , repair completion may be suppressed because Mek1 activity persists on these chromosomes . This model may also explain why both crossover and non-crossover formation is delayed in pch2Δ mutants while the formation of single-end invasion intermediates occurs with wild-type kinetics [44] . Mek1 has been suggested to promote homologue bias in part by sequestering one DSB end in a quiescent state [14 , 61] . Perhaps persistent Mek1 activity hinders use of the sequestered end for completion of repair , thereby equally affecting crossover and non-crossover repair . Alternatively , the SC structure may create a situation that renders interhomologue repair structurally difficult , while Mek1 activity hinders repair with the sister template in pch2Δ mutants . Intriguingly , despite the severe repair delay , pch2Δ mutants display a wild-type level of spore viability [43 , 50] . This result implies that Mek1 suppression of DSB repair can be overcome with time and supports the notion that meiotic template choice is not absolute but rather the consequence of a kinetic barrier to repair [13] . We speculate that the presence of Mek1 may also contribute to the accumulation of Rad51 foci when PP4 or Zip1 are depleted from synapsed chromosomes ( Figs 3C and 4A ) . In both experiments , Mek1 was bound to chromosomes that were allowed to fully pair prior to experimental manipulation ( Zip1-FRB , Psy2-FRB ) , creating a situation similar to what is observed in pch2Δ mutants . Thus , although PP4 and Zip1 clearly have additional roles in recombination [35 , 42] , the presence of Mek1 may further impair DSB turnover in these situations . The loss of Mek1 upon chromosome synapsis implies that meiotic repair constraints become progressively relaxed at late stages of meiotic prophase , such that repair perhaps transitions into a mitotic-like state . One likely consequence of this transition is that at least some of the DSB repair on synapsed chromosomes depends on the mitotic DSB repair factor Rad54 , which presumably promotes Rad51-dependent repair . Moreover , as Rad54 activity mediates the disassembly of Rad51 filaments [62] , it may also promote release of the second DSB end , which is thought to be held in a quiescent state by Rad51 [14] . The loss of Hop1 and Mek1 from synapsed chromosomes may also cause a down-regulation of Dmc1 activity , as Dmc1 no longer promotes meiotic DSB repair in the absence of either Hop1 or Mek1 [14] . Such loss of activity may explain why the DSBs that persist on synapsed chromosomes upon Rad54 depletion are not repaired and why depletion of the Dmc1-cofactor Rdh54 had little effect . The successive implementation of repair constraints may be particularly important for the repair of DSBs in regions that lack an allelic sequence for repair , such as inversions or deletions , which , in fact , are repaired efficiently using the sister [13] . Chromosome synapsis is not dependent on sequence homology [63] and can thus spread into such regions from sites of SC nucleation . Conversely , constitutive binding of Zip1 , as observed at yeast centromeres [64] , may constitutively prevent the recruitment of Mek1 and thus activation of meiotic template bias . Indeed , deletion of Zip1 leads to an increase in interhomologue recombination specifically around centromeres in the absence of increased DSB formation [65] , consistent with the model that centromeric DSBs are primarily repaired from the sister . Our results complement a growing body of evidence that identifies the SC as a macromolecular signaling conduit . By extending out from sites of crossover designation , the SC may communicate successful engagement in crossover repair to the rest of the chromosome and trigger a profound switch in meiotic chromosome behavior , including the remodeling of meiotic chromosome structure and the dampening of further DSB activity [10 , 28 , 30 , 44 , 66] . Our work adds to this list the relaxation of meiotic repair constraints as a result of the SC-dependent removal of Mek1 . Work in mice suggests that SC-dependent changes in chromosome structure and DSB activity are conserved [26 , 67] . It remains to be determined whether the same is true for the loss of repair constraints . Like in yeast , phosphorylation of HORMAD proteins is limited to unsynapsed chromosome axes in mice [68] , and synapsis leads to strong TRIP13/Pch2-dependent depletion of chromosomal HORMAD proteins [26] . However , higher eukaryotes do not encode a clear Mek1 orthologue . Although CHK2 kinase could conceivably fulfill the role of Mek1 in these organisms , mouse CHK2 was recently shown to be required for checkpoint function without having a direct role in repair [69] . However , a role for the SC in regulating repair pathway choice is apparent in Caenorhabditis elegans , as partial depletion of the SC central region structure leads to increased interhomologue crossover events [70 , 71] . Although synapsis initiates independently of meiotic recombination in this organism [72] , the change in repair parameters is associated with altered axial compaction [70] , which may be functionally related to the altered DAPI patterns apparent in yeast pch2Δ mutants ( Fig 3 ) . Ultimately , the transition in meiotic recombination mediated by the SC likely has at least two functions . First , it may preserve the pattern of crossover distribution by limiting the formation of additional crossovers [73] . Second , it minimizes the risk of aberrant repair events by restricting DSB numbers and by promoting the rapid repair of the DSBs that do form . Importantly , by executing this transition in cis , this feedback is robust to the inherently stochastic nature of chromosome pairing and meiotic crossover formation , and allows chromosomes to respond individually in a shared nuclear environment .
Antibody production was approved by the University Welfare Committee of New York University . All yeast strains used are in the SK1 background except strains AM2981 and K303 ( S3 Fig ) , which are in the BR1919-8B background . Genotypes are listed in S2 Table . Epitope tags and gene deletions were made by standard PCR-based transformations , except in the case of ZIP1-FRB . For construction of ZIP1-FRB , a previously published internally tagged ZIP1-GFP::URA3 plasmid [74] was used and GFP replaced with the FRB sequence before integration at the ZIP1 locus . URA3 along with the wild-type ZIP1 sequences was looped out on 5-FOA and a clone with a single copy of ZIP1-FRB was selected for further analysis . Cells were grown in liquid YPD culture at 23°C for 24 h and diluted at A600 0 . 3 into presporulation media ( BYTA; 50 mM sodium phthalate-buffered , 1% yeast extract , 2% tryptone and 1% acetate ) . The cells were grown in BYTA for 16 h at 30°C , washed twice in water and resuspended in sporulation media ( 0 . 3% potassium acetate ) at A600 2 . 0 to induce meiosis at 30°C . FACS analysis was used for all experiments to assay duplication of the genome and confirm synchronous meiotic initiation . Experiments to measure sporulation efficiency and spore viability were set up as synchronous meiosis as above and kept at 30°C in liquid sporulation media for 24 h . The anchor away technique was used to conditionally deplete proteins from the nucleus upon addition of rapamycin [37] . Rapamycin was added at a final concentration of 1 μM to the meiotic cultures at either meiotic induction ( T = 0 h ) or during pachynema ( T = 6 h ) except for depletion of Spo11-FRB or Mer2-FRB , where 2 μM rapamycin was added to the cells . mek1-as1 [60] was conditionally inactivated by addition of the ATP analog , 1-NA-PP1 ( Cayman Chemicals ) , at a final concentration of 2 μM , to the meiotic cultures during pachynema ( T = 6 h ) . 1-D gel analysis was performed as described in [75]; 2-D gel analysis of the dHJs was performed as described in [55] . Briefly , 15 mL samples were collected for the different time points and treated with 0 . 1% sodium azide . The cells were resuspended in 1 mg/mL Trioxsalen ( Sigma ) and the DNA was UV-crosslinked as described [35 , 76] . DNA was extracted and digested with the appropriate enzyme and then separated by two-dimensional gel electrophoresis . The DNA was transferred onto a ZetaProbe membrane ( Biorad ) by capillary transfer and detected by Southern hybridization . Probes for detection of dHJs at the HIS4-LEU2 DSB locus are described [55] . A probe to assay the YCR047c locus is described [50] . Probes for GAT1 and ERG1 loci were amplified from genomic DNA with primers- 5′-caataagcaggtggagttgctgcg-3′ , 5′-aaagatccaaagcccaccagattg-3′ and 5′-ggcagcaacatatctcaaggcc-3′ and 5′-tcaatgtagcctgagattgtggcg-3′ respectively . Primer pairs 5′ -attgtgcctgtaaccgaactgc-3′ and 5′ -agtggacgtagaaagaggagc-3′ , 5′ -ttcctcgttcgtgacactactc-3′ and 5′ -tagctgccaaacccattctgc-3′ were used to generate the probes for YIL094c and YGR279c DSB hotspots , respectively . 32P-dCTP was incorporated into the probe using a Prime-It random labeling kit ( Agilent ) . The Southern hybridization blot was exposed on a Fuji imaging screen and detected using a Typhoon FLA 9000 ( GE ) . Hybridization signal was quantified using ImageJ software ( http://imagej . nih . gov/ij/ ) . Antibodies against phosphorylated Hop1 peptides ( KLH-conjugated peptides: [H]- CKKLGNLLNS-pS-QASIQP -[NH2] and [H]- CKKQASIQP-pT-QFVSNNP -[NH2] ) were raised in rabbits by Covance . The serum was affinity purified with the respective phospho-peptide , followed by adsorption against the unphosphorylated peptide using a SulfoLink Immobilization kit ( Thermo Fisher Scientific ) . Affinity-purified pT318-Hop1 antibody was used at 1:100 for western analysis and 1:50 for immunofluorescence . The anti-Pch2 antibody was raised against the recombinant N-terminal 300 amino acids purified from Escherichia coli . An open-reading frame of the truncated Pch2 was PCR-amplified and inserted into the pET15b plasmid ( Novagen ) , in which the N-terminus of the PCH2 gene was tagged with 6x-Histidine . His-Pch2 protein was affinity-purified using a nickel resin as described by the manufacturers and used for immunization ( MBL Co . Ltd ) . Rabbit anti-Hop1 antibody ( kindly provided by N . Hollingsworth ) was used at 1:10 , 000 for western analysis or 1:500 for immunofluorescence , rabbit anti-phospho-H3T11 ( Millipore ) and anti-Rfa2 antibodies ( kindly provided by S . Brill ) were used at 1:100 for immunofluorescence . Goat anti-Zip1 ( Santa Cruz , SC-48716 ) was used at 1:200 , goat anti-Zip1 ( Santa Cruz , SC-15632 ) was used at 1:500 , rabbit anti-Rad51 ( Santa Cruz , SC-33626 ) was first pre-absorbed to rad51Δ meiotic spheroplasts and then used at 1:200 , and rat anti-HA ( Roche-11867431001 ) was used at 1:200 . Secondary fluorescent-conjugated antibodies were obtained from Jackson Laboratory and were used for immunofluorescence after pre-absorption to yeast spheroplasts . HRP-conjugated secondary antibodies from Pierce were used for western analysis . Meiotic cells were collected at various time points , treated with 200 mM Tris pH7 . 5/20 mM DTT for 2 min at room temperature and then spheroplasted in 2% potassium acetate/ 1 M Sorbitol/ 0 . 13 μg/μL zymolyase T100 at 30°C . The spheroplasts were rinsed and resuspended in ice-cold 0 . 1 M MES pH6 . 4/ 1 mM EDTA/ 0 . 5 mM MgCl2/ 1 M Sorbitol . Two volumes of fixative ( 3% para-formaldehyde/ 3 . 4% sucrose ) were added to the cells on a clean glass slide ( soaked in ethanol and air-dried ) followed by four volumes of 1% lipsol . The slide was tilted to mix the contents . Four additional volumes of the fixative were added to the slide and the samples were spread with a clean glass rod . After spreading was completed , slides were rinsed in 0 . 4% Photoflo ( Kodak ) , dried overnight and stored at -80°C . Images were collected on a Deltavision Elite imaging system ( GE ) equipped with an Olympus 100X lens/1 . 40 NA UPLSAPO PSF oil immersion lens and an InsightSSI Solid State Illumination module . Images were captured using an Evolve 512 EMCCD camera in the conventional mode and analyzed using softWoRx 5 . 0 software . Structured illumination microscopy was carried out on an OMX Blaze 3D-SIM super-resolution microscope equipped with a 6-line SSI Solid State Illumination module , 100X lens/1 . 40 NA UPLSAPO PSF oil immersion lens ( Olympus ) and three EVOLVE EMCCD cameras ( housed at the Bio-imaging Resource Center , Rockefeller University ) . Super-resolution images for Fig 3 were collected on a Deltavision OMX V4 equipped with a 60X/1 . 42NA PLAPON oil immersion lens ( Olympus ) . 100mW solid-state lasers were used along with three PCO sCMOS cameras for detection . Structured illumination reconstructions were carried out in softWoRx 6 . 1 . Scatterplots were generated using the Graphpad program in Prism and statistical significance was assessed using a Mann-Whitney test . | Chromosome segregation errors during meiosis may cause infertility , fetal loss , or birth defects . To avoid meiotic chromosome segregation errors , recombination-mediated linkages are established between previously unattached homologous chromosomes . Such recombination events initiate with breaks in the DNA , but how these breaks are preferentially repaired using the distal homologous chromosome , rather than the physically more proximal sister chromatid of similar sequence , is not well understood . Meiotic repair-template bias in the budding yeast depends on the function of Mek1 , a meiosis-specific protein kinase . Previous models suggested that Mek1 activity creates repair-template bias by suppressing repair with the sister chromatid . We found that Mek1 localizes on meiotic chromosomes until the homologues pair and closely align . Removal of Mek1 requires the assembly of a conserved zipper-like structure between meiotic chromosomes , known as the synaptonemal complex . DNA break repair is delayed in mutants in which Mek1 persists on closely aligned homologues . These findings suggest that persistent Mek1 activity can suppress repair from all templates , and that one function of the synaptonemal complex is to remove this activity from chromosomes . Our findings build on previous models to propose that Mek1 activity creates a local zone of repair suppression that is normally avoided by the spatially distant homologous chromosome to promote repair-template bias . | [
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"chromosom... | 2016 | Chromosome Synapsis Alleviates Mek1-Dependent Suppression of Meiotic DNA Repair |
Through an analysis of polymorphism within and divergence between species , we can hope to learn about the distribution of selective effects of mutations in the genome , changes in the fitness landscape that occur over time , and the location of sites involved in key adaptations that distinguish modern-day species . We introduce a novel method for the analysis of variation in selection pressures within and between species , spatially along the genome and temporally between lineages . We model codon evolution explicitly using a joint population genetics-phylogenetics approach that we developed for the construction of multiallelic models with mutation , selection , and drift . Our approach has the advantage of performing direct inference on coding sequences , inferring ancestral states probabilistically , utilizing allele frequency information , and generalizing to multiple species . We use a Bayesian sliding window model for intragenic variation in selection coefficients that efficiently combines information across sites and captures spatial clustering within the genome . To demonstrate the utility of the method , we infer selective pressures acting in Drosophila melanogaster and D . simulans from polymorphism and divergence data for 100 X-linked coding regions .
The role of adaptation versus alternative , non-adaptive forces in shaping the diversity of life within and between species lies at the heart of many questions in biology [1]–[3] . Consequently , detecting the genetic signature of natural selection in patterns of polymorphism and divergence across multiple species has become a major goal of evolutionary biology [4] , [5] . From analyses of polymorphism within and divergence between species , we hope to learn about the distribution of selection coefficients acting on mutations in the genome [e . g . 6]–[8] , in particular the frequency and strength of positive selection [9]–[12] , changes in the fitness landscape over time [13] , and the specific sites in the genome that underlie adaptive phenotypes [14] , [15] . Polymorphism and divergence offer complementary angles on the evolutionary process . The McDonald-Kreitman ( MK ) test [16] exploits this contrast to detect adaptation where divergence or polymorphism data alone might not allow one to do so , owing to variation in selection coefficients within a gene . If adaptive change occurs at a limited number of sites in an otherwise constrained gene , deleterious mutations might limit the relative rate of non-synonymous to synonymous substitution , DN/DS , to a value much less than 1 , and thereby swamp the signal of adaptation . Yet an excess DN/DS ratio compared to the relative rate of non-synonymous to synonymous polymorphism , PN/PS , may still reveal a surplus of non-synonymous substitution compared to polymorphism , indicative of adaptive change . Therefore the MK test is a test of the null hypothesis , under the neutral theory [3] , [17] , that the odds ratio ( DN PS ) / ( DS PN ) equals one; a DN/DS ratio significantly greater than PN/PS is indicative of adaptive evolution between the two species . Several model-based interpretations of the MK test have been proposed [10] , [18] , [19] , of which the Poisson random field ( PRF ) approach is most widely used [18] , [20] , [21] . Rooted in diffusion theory , PRF does not in its native form model variation in selection coefficients within a gene except for a class of inviable mutants ( but see [22]–[24] ) . Arguably , this sets a high threshold for detecting adaptive change , because the net effect of selection at variable sites must be adaptive change . If , as one might expect in a functional protein-coding gene , weakly deleterious mutations provide the backdrop to adaptive change through a significant contribution to polymorphism , they will inflate the PN/PS ratio , and thereby raise the threshold that the DN/DS ratio must exceed for adaptation to be detected [19] , [25] , [26] . Perhaps this explains in part why scans of the human or yeast genome have not found a clear excess of genes that evolve under positive directional selection compared to what is expected by chance [21] , [27] , [28] . The mathematical conveniences of diffusion theory , particularly the infinite sites model of mutation , make PRF simple and attractive to use . But they also make it difficult to extend to scenarios requiring multiple alleles , multiple species , sophisticated mutation models , probabilistic inference of ancestral states and variable selection pressures . Methods to detect fine-scale variation in selection pressures such as codeml [29] , [30] and omegaMap [31] exist but exploit respectively divergence and polymorphism data alone . The aim of this paper is to develop a method for directly analyzing coding sequence data within and between species in order to ( i ) infer the distribution of selection coefficients within species ( ii ) contrast that distribution between species ( iii ) detect variation in selection coefficients within genes . There are two main novel aspects to the method . First , we develop a combined population genetics-phylogenetics model of codon evolution that predicts patterns of polymorphism within species and divergence between species ( Figure S1 ) . Second , we use a Bayesian sliding window approach [31] , [32] to model intragenic variation in selection coefficients . We demonstrate our approach with an analysis of 100 X-linked coding regions surveyed in Drosophila melanogaster and D . simulans , using D . yakuba as an outgroup [33] . The key parameter of the model is the population-scaled selection coefficient , γ = 2PNes , where P is the ploidy ( P = 1 . 5 for the Drosophila X chromosome ) , Ne is the effective population size and fitness is defined relative to the ancestral allele so that s is the fitness advantage of any derived allele encoding an amino acid different to the ancestral allele . Assuming no dominance effect , homozygotes for the beneficial allele have fitness advantage 2s . Stop codons are assumed inviable . The mutation model is that of Hasegawa , Kishino and Yano [34] , adapted for codons . The model parameters are the transition:transversion ratio κ and the population-scaled mutation rate θ = 2PNeμ , where μ is the mutation rate per generation . Over long timescales , the phylogenetic substitution rate for this population genetics model converges to that of Nielsen and Yang [29] , the model underlying codeml [29]–[30] , where their parameter for the DN/DS ratio , ω , is related to the population-scaled selection coefficient , γ , through the equation [35] .
To infer the distribution of selection coefficients , also known as the distribution of fitness effects [4] ( DFE ) , we estimated the frequency of codons at which non-synonymous mutations fall into one of twelve categories defined by the selection coefficient , γ . The categories encompass the range of selective effects from strongly beneficial ( 100 , 50 ) through moderately beneficial ( 10 , 5 ) , weakly beneficial ( 1 ) , neutral ( 0 ) , weakly deleterious ( −1 ) , moderately deleterious ( −5 , −10 ) and strongly deleterious ( −50 , −100 ) to what is effectively inviable ( −500 ) . Classifying selection coefficients this way allowed us to estimate the relative frequencies of selection coefficients ( the DFE ) without making assumptions about the shape of the distribution . We estimated the DFE independently for each of the three lineages in the unrooted phylogeny . Figure 1A shows the inferred DFE for D . melanogaster and D . simulans , color-coded by selection coefficient . We do not present the results of the analysis of selection for the D . yakuba lineage because it was based on a single sequence , the reference genome [36] . The DFE gives the frequency with which new non-synonymous mutations occur . For both D . melanogaster and D . simulans , the vast majority of new non-synonymous mutations ( 81% and 71% respectively ) have strongly deleterious fitness consequences , to the extent that they are effectively inviable ( γ = −500 ) . Thus , most sites are essentially completely constrained in the amino acid that they encode . Mutations with less severe deleterious effects are progressively less common for γ = −100 , −50 , −10 and −5 . There is an increase in the frequency of weakly selected and neutral mutations , with for 6 . 1% and 3 . 8% of new mutations in the two lineages respectively . Moderately beneficial mutations are less common −1 . 5% and 3 . 0% of new mutations have γ = 5 or 10 in the two lineages – while strongly beneficial mutations ( γ = 50 , 100 ) are the rarest of all with a combined frequency of 0 . 2% and 0 . 3% . Interestingly , we found that , with 99% posterior probability , at least 0 . 7% of newly arising non-synonymous mutations in D . melanogaster ( and 1 . 9% in D . simulans ) were moderately or strongly beneficial . The DFE is strikingly similar in the two lineages , with a slight tendency towards stronger selective effects in D . simulans , excluding the inviable class . The rate at which mutations fix , relative to their neutral expectation , is given by . Consequently , the DFE of amino acid substitutions ( Figure 1B ) is enriched for beneficial mutations and greatly depleted of deleterious mutations . In both D . melanogaster and D . simulans , moderately and strongly beneficial mutations dominate the substitution process ( 80% and 91% of substitutions in the two lineages respectively ) , despite their rarity among mutations . The DFE of amino acid substitutions is similar for both lineages , albeit with a somewhat greater contribution from weakly beneficial , neutral and weakly deleterious mutations in D . melanogaster . Smith and Eyre-Walker [10] classified amino acid substitutions into neutral substitutions expected under drift ( which we label D0 ) and an excess of beneficial mutations driven by positive selection ( which we label A+ ) , assuming that deleterious mutations cannot fix and beneficial mutations contribute negligibly to polymorphism . Since we relax those assumptions , we can break down substitutions further into a class of beneficial mutations that would have fixed merely by drift ( D+ ) and a class of deleterious mutations that fixed in spite of selection ( D– ) . Figure 2 shows the frequency of each type of substitution . The vast majority of substitutions −77% in D . melanogaster and 86% in D . simulans – were beneficial and driven by selection . This finding corresponds well to estimates obtained by other methods for these two lineages [33] . In total 88% and 95% of substitutions were beneficial and driven by drift or selection . Just 4 . 2% and 1 . 7% of substitutions were deleterious , as expected almost all weakly so ( γ = −1 ) . Other parameters shared across genes are reported in Table 1 . To account for variation in synonymous diversity between loci , we fitted a log-normal distribution to the population-scaled mutation rates θ with parameters μθ and σθ . The estimates of these parameters yield a mean of θ = 31 . 7 per kilobase and a standard deviation of 13 . 2 . The estimated branch length , T , was considerably longer for D . melanogaster than D . simulans ( 3 . 60 versus 1 . 48 PNe generations ) . Assuming the same generation length and mutation rate per generation , this suggests the D . simulans population has been larger on average than the D . melanogaster population since they split , which is consistent with the propensity towards stronger selection in the DFE . The transition:transversion ratio κ was similar in D . melanogaster and D . simulans ( 2 . 66 and 2 . 38 respectively ) . A smoothing parameter , p , for intragenic variation in selection coefficients was estimated independently for each lineage . The inverse of mean window length , p was estimated to be 0 . 0105 in D . melanogaster and 0 . 0277 in D . simulans , which corresponds to mean window lengths of 96 and 36 codons respectively . This difference may reflect the response of the smoothing parameter to the larger number of polymorphic sites in D . simulans , which means there is more information available . The inferred DFE is influenced somewhat by the sliding window length , and this is illustrated in Figure S2 . In the extreme cases that p = 1 and p = 0 , windows correspond to single codons or whole genes respectively; we refer to these two models as sitewise and genewise . Under the sitewise model , we tend to infer weaker selection in the DFE of non-synonymous mutations and amino acid substitutions . The DFE under the genewise model is rather more similar to the sliding window model , except there is an even greater frequency of effectively inviable mutations ( γ = −500 ) . The proportion of substitutions that were beneficial and driven by positive selection ( the A+ class ) is robust to window length , but under the sitewise model , there is a smaller fraction of neutral and deleterious mutations driven by drift ( the D0 and D– classes ) . As the 95% credible intervals for the smoothing parameters excluded p = 1 and p = 0 for both D . melanogaster and D . simulans , we can conclude that the data support the sliding window model over both the sitewise and genewise models . While our model does not account for linkage disequilibrium and demographic change , these are known to have shaped patterns of genetic diversity in D . melanogaster and D . simulans ( e . g . , [33] , [37] ) , and can influence the inference of selection from allele frequency information [8] , [38] , [39] . Text S6 reports the results of simulations [40] that we performed to investigate the effects of these forces using demographic scenarios and recombination rates estimated for Drosophila [33] , [37] . We found that the demographic changes may cause slight underestimation of the frequency of moderately beneficial mutations in D . simulans , but the overall effect was weak , indicating robustness to this model violation . We found that the low levels of linkage disequilibrium observed in D . melanogaster and D . simulans led to no additional bias beyond that induced by the demographic change ( Figure S6 ) . In addition to estimating the frequency of selection coefficients across all codons ( the DFE ) , our method yields codon-specific posterior probabilities for each selection coefficient , allowing the signal of selection to be localized . At a particular codon , there are a number of ways to summarize the distribution of selection coefficients including the probability of positive selection , the probability of viability , and the mean selection coefficient given that the codon is viable . Whole gene versions of these summary statistics can be calculated by taking the mean across codons . Figure 3 shows the evidence for positive selection across genes and sites , where genes are ordered horizontally according to the rank of the posterior probability of positive selection per gene . Much of the variability in the evidence for positive selection at the whole gene level can be understood in terms of the entries of the McDonald-Kreitman table ( Figure 3A ) . The ratio of the relative number of non-synonymous to synonymous substitutions ( DN/DS ) , and the corresponding quantity for polymorphisms ( PN/PS ) are both strongly correlated with the probability of positive selection per gene ( Spearman rank correlation coefficients of 0 . 81 and 0 . 72 respectively in D . melanogaster , 0 . 70 and 0 . 75 respectively in D . simulans ) . Surprisingly however , the odds ratio underlying the MK test , ( DN PS ) / ( DS PN ) , was uncorrelated with the probability of positive selection ( Spearman rank correlations of 0 . 06 in D . melanogaster and −0 . 09 in D . simulans ) . Of the three statistics summarizing the distribution of selection coefficients per gene , the largest correlation was between the probability of positive selection and the mean selection coefficient conditional on viability ( Spearman rank correlations of 0 . 92 and 0 . 91 in D . melanogaster and D . simulans respectively ) , followed by the correlation between the mean selection coefficient conditional on viability and the probability of viability ( 0 . 15 and 0 . 43 ) , and lastly between the probability of positive selection and the probability of viability ( 0 . 15 and 0 . 26 ) . The relationship of these statistics and the odds ratio is shown in Figure 3B . A comparison of the probability of positive selection at the level of the whole gene versus the individual codon ( Figure 3C ) suggests that positive selection is not restricted to the few genes with the strongest signal of selection; rather it has affected sites in many genes , particularly in D . simulans , most of which are unexceptional by whole gene metrics . By using site-specific evidence for selection , we can look for unusual signatures of selection outside the usual dichotomy of adaptation versus constraint . For example , we can detect genes with a stark contrast in intragenic selection pressures owing to the occurrence of adaptation against the backdrop of widespread constraint . On the basis of evidence at the whole gene level , protein-coding gene CG32568 , of unknown function but highly expressed in adult male testes , exhibited the greatest degree of adaptation while CG3869 , the ubiquitously expressed mitochondrial assembly regulatory factor Marf , exhibited the greatest degree of constraint . Based on evidence at the level of individual codons , CG1824 , a ubiquitously expressed gene of unknown function , exhibited the starkest contrast in selection pressures between codons in D . melanogaster . Figure 4 illustrates intragenic variation in the posterior probability of positive selection for these three genes , annotated by the positions of synonymous and non-synonymous substitutions and polymorphisms . The complete absence of non-synonymous polymorphism or substitution in CG3869 ( Figure 4A ) , in conjunction with considerable synonymous diversity , results in strong evidence against positive selection throughout the gene . CG1824 ( Figure 4B ) is similarly conserved for most of its length with two exceptions . A Val→Ile polymorphism in D . melanogaster results in a small peak in the posterior probability of positive selection at position 13 , associated with a slight increase in the probability of positive selection at nearby sites owing to the sliding window model . While there is a 23% probability that this polymorphism , which coincidentally has sample frequency 23% , is positively selected , it may simply be a neutral ( Pr = 31% ) or deleterious ( Pr = 46% ) mutation that has reached appreciable frequency by drift . At position 112 there has been a Ser→His substitution in the D . simulans lineage that provides considerably greater evidence for the action of positive selection ( Pr = 95% ) . Again , there is a slight increase in the probability of positive selection at nearby sites as a consequence of the sliding window model , but in the absence of other non-synonymous diversity nearby , the effect decays rapidly . On balance , the evidence is in favor of positive selection at the non-synonymous substitution in D . simulans but against positive selection at the non-synonymous polymorphism in D . melanogaster because the former has a posterior probability greater than 50% and the latter does not . We use a 50% threshold for concluding that positive selection has acted because the prior probability is specified by the DFE that we explicitly estimated across all sites ( rather than making strong prior assumptions about the relative frequency of beneficial , neutral and deleterious mutations ) . The fact that positively selected sites are estimated to be very rare in the DFE means that our prior probability of positive selection is very low , demanding considerable evidence to the contrary in order to surpass the threshold of 50% posterior probability . The frequency of non-synonymous polymorphisms influences the evidence for positive selection , as illustrated by Figure S3 . While the evidence for positive selection generally increases with the frequency of a derived non-synonymous mutation , in D . melanogaster this alone was barely sufficient to surpass a 50% probability of positive selection even with derived allele frequencies of 75% or more . In D . simulans , however , a non-synonymous derived allele frequency exceeding 75% provided more compelling evidence of positive selection . The reasons for these differences are multifarious and include the observation that the estimated DFE has a tendency towards stronger selection in D . simulans . Non-synonymous substitutions provide altogether stronger evidence for positive selection , and the large number in CG32568 in both D . melanogaster and D . simulans lineages contribute to the strong signal of adaptation ( Figure 4C ) . Their abundance also raises the background probability of positive selection in CG32568 for both species as a result of the sliding window model . Figures S4 and S5 offer an alternative visualization of the codon-by-codon posterior distribution of selection coefficients in D . melanogaster and D . simulans respectively for CG32790 , a transcription factor of unknown function that is expressed more or less ubiquitously , CG1824 and CG32568 . The sliding window model is designed to detect local correlation structure in selection coefficients and to infer the scale over which the selection regime varies spatially along the genome . It was found to fit the data better than either the sitewise or genewise models on the basis that the 95% credible intervals exclude p = 1 and p = 0 ( Figure S2D ) . The influence of the sliding window model was visually apparent in the local estimates of selection coefficients within individual genes ( Figure 4 ) . Figure 5 shows the spatial correlation in the posterior distribution of selection coefficients aggregated over all genes , up to a maximum distance of 220 codons . With the exception of the inviable sites ( γ = −500 ) , which were assumed to occur independently of the sliding window , the posterior probability distribution of selection coefficients is highly correlated for adjacent codons . The magnitude of the spatial correlation is greatest for strongly deleterious mutations , and weakest for strongly beneficial mutations , suggesting that regions of constraint tend to be longer than regions of adaptation . As the distance between codons increases , the correlation decreases initially smoothly , and then more erratically as the number of pairs of codons involved in the calculation decreases . The spatial correlation tails off more rapidly in D . simulans , as expected from its shorter mean window length of 36 versus 96 codons . Even at distances of 220 codons , there is still substantial correlation in the posterior probabilities for each selection class , indicating that distant sites within the same gene are substantially more similar in selection profile than sites in different genes . The selection coefficients in the different Drosophila lineages were assumed independent of one another , yet an appreciable correlation in the posterior probability distribution of selection coefficients was detectable between sites across D . melanogaster and D . simulans ( Table 2 ) . By comparing the correlation in the distribution of selection coefficients between the two species , we can examine how the selection regime has changed over evolutionary time ( Figure S7 ) . For selection coefficients γmel and γsim , a positive correlation in the posterior probabilities indicates an excess of sites ( purple triangles ) . A particularly large positive correlation is seen for strongly deleterious mutations , suggesting that sites strongly constrained in one species tend to be strongly constrained in both . There is a corresponding deficit of sites strongly deleterious in one species but not the other , as evidenced by negative correlation coefficients ( orange triangles ) . For concordant selection coefficients ( both positive or both negative across species ) , an excess of sites was observed for which the magnitude of selection was greater in D . simulans , consistent with other evidence for a larger effective population size in that lineage [33] . Among discordant selection coefficients , there was a small excess of sites weakly beneficial in D . melanogaster yet deleterious in D . simulans . The cause of this pattern is unclear , but see [41] for similar observations .
Our method has a number of advantages over predominantly population genetics-based approaches [18] , [20] , [38] , [39] , [42] , [43] . By fitting a complex , multi-parameter mutation model with repeat and back mutation , coding sequences can be directly analyzed without pooling alleles or discarding codons with more than two alleles , and discarding allele frequency information . Ancestral states are inferred probabilistically instead of by parsimony , thereby accounting for uncertainty [44] . In the analysis of polymorphism data , the advantage over phylogenetic methods [29] , [30] , [45]–[47] is the bottom-up model that accounts for the expected contrast between short-term and long-term evolutionary processes [16] . This is important because top-down applications of phylogenetic models to polymorphism data [31] , [35] can give the misleading impression of a relaxation of functional constraint in contemporary diversity [48] , [49] . In turn , the advantage of the sliding window model is that it allows inference of fine-scale variation in selection pressures by combining information across adjacent sites for statistical efficiency , but in a way that adapts to the local signal of variation in selection coefficients . The distribution of fitness effects ( DFE ) is of direct interest in describing the selection regime experienced by a species . Moreover , it is important to estimate the DFE rather than making prior assumptions about its shape , as it has a strong influence on local inference of selection within genes [50] . Other methods that use allele frequency information to estimate the DFE have assumed parametric forms for the distribution , such as a gamma distribution for deleterious mutations [38] , or a reflected gamma distribution [6] or normal distribution for beneficial and deleterious mutations [8] . Initial technical problems in fitting a normal and other standard distributions to the DFE by MCMC led us to switch to a discrete , non-parametric distribution defined by the relative frequency of twelve fitness classes ranging from strongly beneficial to strongly deleterious and effectively inviable . The resulting DFE estimated for the Drosophila coding regions looked quite unlike commonly used parametric forms ( Figure 1 ) , which may explain the difficulty in fitting . Application of the method to other datasets will determine whether the form of the DFE is a peculiarity of the Drosophila data or more widespread . We made a number of simplifying assumptions in our model , amongst them that the population size is constant , that sites are independent , and that synonymous mutations are neutral . Keightley and Eyre-Walker [38] , [39] and Boyko et al [6] have made advances in the co-estimation of selection and demographic change from allele frequencies . Key to their approaches is the use of computational techniques to obtain the distribution of allele frequencies when the population size changes . Presently , those techniques rely on the assumption of biallelic loci . Since the development of multiallelic models was one of our goals , a similar approach is currently out of our reach . As no method can hope to encompass all aspects of the evolutionary process , perhaps not even all the important ones , it seems reasonable to use simulations [40] in conjunction with our method to test robustness to departures from modeling assumptions . For the data analyzed in this paper , simulations suggested that demographic changes may cause slight underestimation of the frequency of moderately beneficial mutations in D . simulans . The assumption of independence between sites is equivalent to assuming that sites , even adjacent sites , are completely unlinked . In fact the assumption is stronger than that since it also implies that there will be no effect of Hill-Robertson interference caused by selection acting at other loci [51] . Although the assumption of independence between sites is common in the analysis of allele frequency information [6] , [8] , [15] , [42] , [53] , [38] , it is of concern because selection at linked sites can skew allele frequencies at synonymous sites and may lead to false inference of selection [42] . By conducting simulations that model linkage disequilibrium [37] , we were able to test the robustness of our conclusions to this assumption under recombination rates estimated for Drosophila [37] . Recombination rates are relatively high in the genes analyzed here . Perhaps as a result , simulations suggested that linkage did not have a large effect on our inference of the DFE . This conclusion is consistent with other investigations [8] . The classification of mutations as either non-synonymous or synonymous is a useful proxy for predicting whether mutations are likely to have a functional effect or not . However , in Drosophila it is well known that synonymous mutations are not strictly neutral [52] . In particular , there can be selection between codons encoding the same amino acid , thought to be attributable to differences in the efficiency of translation , mediated by the abundance of different tRNAs . The excess number of synonymous substitutions on the D . melanogaster lineage has been attributed to the relaxation of constraint on codon usage as a result of a reduction in the effective population size [33] , implying that the difference in the branch lengths of the D . melanogaster and D . simulans lineages ( Table 1 ) is accounted for primarily by a change in effective population size , but secondarily by the reduction in constraint on synonymous diversity in D . melanogaster . In the future , it may be possible to incorporate differences in the fitness of synonymous mutations into our multiallelic model . Another simplification made during inference is to measure fitness relative to the ancestral allele . A widespread convenience common to NY98 and PRF [29] , [18] , measuring fitness relative to the ancestor avoids estimating selection coefficients for every possible allele , most of which go unobserved . However , it has some peculiar consequences that are often overlooked . Under positive selection ( γ>0 ) , the ancestral allele is always disfavored , creating a continual drive for innovation . One could characterize such a model as recurrent directional selection because , as in shift models [54] , the selection regime switches upon fixation , setting up an arms race-like scenario . Under negative selection ( γ<0 ) , when derived alleles are disfavored , the behavior of the model is also peculiar . Were a mildly deleterious allele to fix by drift ( in spite of selection ) , then upon fixation the selection regime would switch and rather than the back mutation restoring fitness as one might expect , it would erode it further . The convenience of models of recurrent selection has made them popular for inference and thus a natural starting point for our work . Nonetheless , it would be interesting to see what effect relaxing this assumption has on inference of selection parameters .
We use three steps to combine a population genetics model of the distribution of allele frequencies in a population or species with a phylogenetic model of the substitution process between species . The first step is to modify the stationary distribution of allele frequencies in the population by conditioning on the identity of the ancestral allele . Let f be a vector of the frequencies of K alleles at a site ( typically , K = 4 nucleotides , 20 amino acids or 61 non stop codons ) , where . To condition the stationary distribution , , on the identity of the ancestral allele , A , we use Bayes' rule ( 1 ) where is the probability that allele A is ancestral given f , and is the unconditional probability that A is ancestral . The second step is to integrate over uncertainty in the population allele frequencies in order to obtain the conditional likelihood for a sample given the identity of the ancestral allele . Let x be a vector of the number of times each allele was observed at a particular site in a sample of size n , so that . Then ( 2 ) where is an appropriate sampling distribution; for example the multinomial distribution when alleles are sampled at random from the population with replacement . The third step is to sum over uncertainty in the identity of the ancestral allele of all modern populations and ancestral populations in order to calculate a joint likelihood for the observed data . On the phylogenetic tree relating our populations of interest , the tips represent modern populations that were sampled directly , and the internal nodes represent ancestral populations that were not . Felsenstein's pruning algorithm [55] makes calculation of the phylogenetic likelihood straightforward , by separating the computation into manageable chunks . The algorithm traverses the tree from tips to root , calculating , defined as the likelihood of the data observed in all populations descended from node k , conditional on ancestral allele sk at node k . For node k whose immediate descendants are nodes i and j , ( 3a ) where vi is the length of the branch separating node i from its ancestor , and is the phylogenetic transition probability from allele sk to si along that branch . The joint likelihood is calculated as ( 3b ) where is the probability that allele s0 is ancestral . In the standard phylogenetic setting , is defined at the tips to equal 1 if the sequence corresponding to that tip has allele sk , and 0 otherwise [55] . In our setting , where multiple sequences may have been sampled from the population represented by a tip , we define ( 3c ) where is the vector of allele sample frequencies in population k and the right hand formula is specified by Equation 2 . Our extended pruning algorithm incorporates uncertainty in the ancestral state of modern populations at the tips of the tree . Thus it would differ from Felsenstein's algorithm even when there was a single sequence for each tip because we account for the possibility that the sequence may contain derived as well as ancestral alleles . In this section we construct a combined population genetics-phylogenetics model with parent independent mutation and selection ( PIMS ) as the basis for an approximation to more general mutation in the next section . In parent-independent mutation , any allele can mutate to any other allele and the mutation rate is dependent only on the destination allele . The rate of mutation to allele i is μi per generation . The Wright-Dirichlet distribution is the solution to the stationary distribution of allele frequencies in a diffusion model with PIMS , assuming that fitness effects and mutation rates are small relative to the effective population size Ne [56] , [57] . In our notation , ( 4 ) where is the population fitness as a function of f , is its population-scaled counterpart , is the population-scaled rate of mutation to allele i , and P is the ploidy . For tractability of inference and computation , we concentrate on models with two fitness classes , which we refer to as hot-or-not models . In the hot-or-not model , alleles belonging to the favored ( hot ) class have selective advantage s over other alleles; in a codon model , the two classes can be defined according to the amino acid encoded . In the hot-or-not model , the Wright-Dirichlet distribution simplifies to ( 5 ) where is the population-scaled selection coefficient , Fi is the total frequency of alleles encoding the same amino acid as allele i , and H represents an allele belonging to the hot class . We use the time-reversibility property to equate the probability that allele A is ancestral to the fixation probability , which for analytic tractability we approximate as the low-mutation limit [58] ( 6 ) We assume recurrent selection , in which the hot class comprises derived alleles encoding amino acids different to that encoded by the ancestral allele . Consequently , the sign of the population-scaled selection coefficient γ represents the selective advantage of mutations relative to the ancestral allele . From Equation 1 , ( 7 ) where is the multivariate beta function and is the confluent hypergeometric function . Assuming random sampling according to the multinomial distribution we use Equation 2 to obtain the conditional likelihood ( 8 ) where XA and ΘA are the total number of copies and total mutation rate for alleles encoding the same amino acid as the ancestral codon , and Θ is the total mutation rate across all alleles ( see Text S1 for a full derivation ) . The phylogenetic substitution rate specified by the population genetic model is well approximated by taking the limit that the initial frequency of a derived allele tends to zero [18] , [35] so that for , ( 9 ) The diagonal elements of the phylogenetic rate matrix are defined so that the rows sum to zero . Time is measured in units of PNe generations . At equilibrium , the allele frequencies are ; that they are independent of γ is a consequence of the recurrent selection model . The phylogenetic substitution matrix required by the extended pruning algorithm ( Equation 3 ) is obtained by exponentiating the rate matrix using standard numerical techniques , so that . In this section we utilize our PIMS model to approximate a general model of parent-dependent mutation with selection ( PDMS ) , in which the mutation rate can differ between every pair of alleles . The approximation to PDMS that we take exploits the observations that ( 1 ) the conditional likelihood is dependent on the ancestral allele and ( 2 ) the ancestral allele will often be the genetic background upon which new mutations arise . Therefore we can modify the mutation rates in the likelihood formula ( Equation 8 ) to suit the allelic state of the ancestral allele , re-weighting the rates to depend on the ancestral background . In Text S2 we detail the approach . Briefly , we match the rates for a parent-independent and a parent-dependent model by using average mutation probabilities , in which we calculate the expected probability of mutation from the ancestral allele A to every other allele , averaging over the coalescent time between two individuals in a neutral population . We use our parent-dependent approximation to implement a codon-based analog to the HKY85 model [34] . In a codon-based HKY85 model the alleles are the K = 61 non stop codons , and the population-scaled mutation rate for is ( 10 ) where C normalizes the rate matrix so that the expected mutation rate is θ/2 per PNe generations . The diagonal elements of the matrix are defined so that the rows sum to zero . Over phylogenetic timescales , the substitution process for this population genetic model converges to the Nielsen and Yang model [29] commonly used for analyses of selection . The phylogenetic substitution process has stationary distribution π and ( following Equation 9 ) rate matrix ( 11 ) where is equal to the DN/DS rate parameter that they call ω . Owing to the approximations made in the development of likelihood functions for PIMS and PDMS models , we wished to evaluate the performance of this multiallelic selection model in a number of scenarios and over a range of parameter values . In Text S3 and Figure S8 we use simulations to examine the effect of the definition of allelic ancestry in the multiallelic setting on the accuracy of the approximate likelihood . In Text S4 and Figure S9 we test the performance of the approximate likelihood for inference over a range of parameter values: θ = 0 . 02–0 . 2 , κ = 0 . 05–20 and γ drawn from a normal distribution centered on zero with a standard deviation of 10 . For the analysis of intragenic variation in selection pressure , we adopted a sliding window model similar to that used by omegaMap [31] . In the sliding window model of omegaMap , it is assumed that there are contiguous blocks or windows within the locus , such that all non-synonymous mutations arising within the window share the same selection coefficient . We modify this approach by allowing , with some probability , the non-synonymous mutations at any site to possess a selection coefficient different to that of the window . We model the distribution of selection coefficients , also known as the distribution of fitness effects ( DFE ) [4] using a discrete range of values of γ . We define two classes of selection coefficient , G1 and G2 , containing Γ1 and Γ2 levels of γ each . The first class provides values of γ that the window as a whole may take , and the second class provides values of γ that individual codons may take independently of the window within which they are situated . We specified Γ1 = 11 , G1 = {−100 , −50 , −10 , −5 , −1 , 0 , 1 , 5 , 10 , 50 , 100} , which encompasses the spectrum of fitness effects from strongly deleterious , through moderately and weakly deleterious , neutral , weakly and moderately beneficial to strongly beneficial . We specified Γ2 = 1 , G2 = {−500} , a strength of selection that corresponds effectively to inviability . The rationale for this approach was to allow individual sites within a window to be inviable , while maintaining a spatial dependency at viable sites . The DFE is then given by the vectors λ1 and λ2 , which together sum to 1 . λ1i is the probability that a codon takes on the selection coefficient of its window , and the window has selection coefficient G1i . λ2i is the probability that a codon takes on a selection coefficient different to its window , and that selection coefficient is G2i . is the total probability that a codon takes on the selection coefficient of its window . is the total probability that a codon takes on a selection coefficient different from its window . The length of windows is geometrically distributed and controlled by the smoothing parameter p , which is the probability that one window ends and another begins between a pair of adjacent codons . The average length of a window is 1/p . When p is smaller , windows are longer , which leads to greater smoothing in the estimates of variation in selection coefficients along the gene . At one extreme , p = 0 , there is a single window per locus . Sites may be viable or inviable; those that are viable share the same selection coefficient . This “genewise” model , is equivalent to that used in the standard PRF [18] , [20] . At the other extreme , p = 1 , every codon has its own independent γ . This “sitewise” model features frequently in approaches based on the site frequency spectrum ( although these tend to be based on nucleotides rather than codons ) [e . g . 8] , [38] , [39] . Both genewise and sitewise models have been implemented in codeml [29] , [30] . We analyzed the 100 X-linked coding sequences of Drosophila melanogaster and Drosophila simulans [33] . We include the Drosophila yakuba reference sequence [36] in the analysis to help attribute substitutions to the melanogaster or simulans branches . Each locus corresponds to a single exon from a single gene . The average length of coding sequence per locus was 630 base pairs . We parameterized each of the three branches of the unrooted phylogeny separately . Employing the multiallelic model ( codon-based HKY85 with selection ) , we estimated the distribution of fitness effects λ , the sliding window smoothing parameter p , the transition:transversion ratio κ and the branch length T for each . For each locus we also estimated a branch-specific mutation rate θ and branch- and site-specific selection coefficients γ . Our approach was Bayesian . For the DFE , we employed a symmetric Dirichlet prior with parameter α = 1 for the prior on λ = {λ1 , λ2} . This distribution is equivalent to a -dimensional uniform distribution subject to the constraint that the elements of λ sum to 1 . In other words , no fitness class is preferred over any other fitness class . In this sense the prior is uninformative . For the sliding window smoothing parameter p , we assumed a uniform distribution on the interval ( 0 , 1 ) . For a locus of length L codons , this prior gives equal probability to the number of windows between 1 and L . We employed improper log-uniform priors on κ and T , which are uninformative regarding the scale of the parameters in the sense that the prior probability is equal for every order of magnitude . For the branch- and locus-specific mutation rate θ we employed a log-normal prior distribution with mean μθ and variance on the logarithmic scale , which allows variability in θ to be modeled while sharing some information across branches and loci . For the hyperparameters , we assumed an improper uniform prior on μ which is uninformative as to the order of magnitude of θ , and a log-normal prior distribution on with mean 0 and variance 4 which imposes some constraint on the variability of θ across branches and loci in the event that the data are weakly or not informative . We obtained a sample from the joint posterior distribution of all the parameters using Markov chain Monte Carlo ( MCMC ) , the details of which are described in Text S5 . Briefly , we ran two chains for 2 , 000 , 000 iterations each , recording the parameters at intervals of 40 iterations . After removing a burn-in of 20 , 000 iterations , the chains were visually compared for convergence and merged . Point estimates were calculated using the posterior mean , and 95% credible intervals were calculated as the ( 2 . 5% , 97 . 5% ) quantiles of the posterior distribution . The rate of substitution , relative to neutrality , of mutations with population-scaled selection coefficient γ is . Therefore in the distribution of fitness effects of amino acid substitutions , the frequency of selection coefficient Gi , where is ( 12 ) For γ>0 , ω is greater than 1 , so there is an excess of amino acid substitution relative to neutrality [10] . Hence for beneficial mutations we attribute a proportion to the action of positive selection ( class A+ ) , and the remaining proportion , which we would have expected under neutrality , we attribute to drift ( class D+ ) . The fixation of neutral mutations is attributable to drift ( class D0 ) . Likewise , the fixation of deleterious mutations , which occurs at a lower rate than expected under neutrality , is attributable to drift acting in spite of purifying selection ( class D– ) . Source code and executables for the software , gammaMap , are available online at www . danielwilson . me . uk . | Species differ genetically , and the way in which they vary is informative about the workings of natural selection: the proportion of the genome subject to selection , the degree to which selection has conserved function versus favoring novel forms , and the location of genes responsible for evolutionarily important adaptations that explain differences in biology between the species . Individuals also vary within species , and that variation provides a snapshot of the process of evolution , a snapshot that is useful for contrasting recent versus long-term evolution and for understanding the role of mutations that are destined to be lost from the population . However , existing methods tend to use only one of these sources of information . We have developed a tool to analyze variation within and between species jointly that is able to detect fine-scale differences in the action of natural selection within genes . By applying this method to 100 genes surveyed in three species of fruit fly , we show that we can detect fine-scale variation in selection pressures within genes as well as changes between species . | [
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"g... | 2011 | A Population Genetics-Phylogenetics Approach to Inferring Natural Selection in Coding Sequences |
Malaria is initiated when the mosquito introduces sporozoites into the skin of a mammalian host . To successfully continue the infection , sporozoites must invade blood vessels in the dermis and be transported to the liver . A significant number of sporozoites , however , may enter lymphatic vessels in the skin or remain in the skin long after the mosquito bite . We have used fluorescence microscopy of Plasmodium berghei sporozoites expressing a fluorescent protein to evaluate the kinetics of sporozoite disappearance from the skin . Sporozoites injected into immunized mice were rapidly immobilized , did not appear to invade dermal blood vessels and became morphologically degraded within several hours . Strikingly , mosquitoes introduced significantly fewer sporozoites into immunized than into non-immunized mice , presumably by formation of an immune complex between soluble sporozoite antigens in the mosquito saliva and homologous host antibodies at the proboscis tip . These results indicate that protective antibodies directed against sporozoites may function both by reducing the numbers of sporozoites injected into immunized hosts and by inhibiting the movement of injected sporozoites into dermal blood vessels .
It is now widely accepted that most if not all malaria sporozoites are deposited by mosquitoes into the avascular tissues of their mammalian hosts , from where their motility allows them to enter blood vessels for their subsequent journey to the liver and invasion of hepatocytes [1]–[6] . An early study [7] had shown rapid transfer of mosquito-injected sporozoites into the blood but failed to prove direct inoculation into the bloodstream , concluding instead that sporozoites were inoculated “into the tissues or directly into the blood vessels” . In our initial study , which we did by allowing Plasmodium berghei-infected mosquitoes to feed on mice and then extirpating the feeding site at various times post-feeding , we concluded that the first wave of sporozoites entered the blood circulation at around 15 min after feeding [1] . However , our approach did not allow us to determine the subsequent rate at which sporozoites continued to leave the skin or whether significant numbers of sporozoites remained in the skin for longer periods of time . We reported subsequently “that in addition to these early emigrants from the skin , substantially more sporozoites take a significantly longer time to enter the blood” and that “the relatively long time during which many sporozoites continue to migrate within the skin ( for at least 30 min ) was striking” [3] . This was confirmed and extended with P . berghei [5] and still later with P . yoelii [6] , this latter study reporting that sporozoites inoculated by the mosquito are released from the skin into the blood circulation in a trickle extending for hours after the mosquito bite . We decided to examine sporozoite deposition and the kinetics of sporozoite disappearance from tissues on which mosquitoes had fed , both in immunologically naïve mice and in mice that had been immunized against sporozoites . We had previously counted numbers of sporozoites injected into ear pinnae and abdominal tissue by direct fluorescence microscopy quantification of sporozoites expressing red fluorescent protein [8] . For the current study , we used the same procedure to count numbers of sporozoites in biopsy specimens taken at various times after mosquito injection into control and immunized mice to establish the kinetics of these sporozoites within the skin . Strikingly , mosquitoes deposited fewer sporozoites in mice that had been actively or passively immunized against sporozoites . Similar results were obtained when immobilized mosquitoes were allowed to release saliva and sporozoites into drops of media on microscope slides . We provide evidence that these results are due to formation of an immune complex that reduces release of some sporozoites from the tip of the mosquito proboscis . Those sporozoites that were successfully injected into immunized mice were rapidly immobilized , did not appear to invade dermal blood vessels and became morphologically degraded within several hours .
A summary of numbers of sporozoites visualized at the bite site on the ear pinna after feedings by individual mosquitoes is presented as a scatter plot in Fig . 1 . After mosquitoes fed on non-immunized ( control ) mice , we found a median of 53 . 5 sporozoites in biopsy specimens taken from the bite site immediately after feeding . We observed no significant differences in the numbers of these sporozoites compared with numbers found in biopsy specimens taken 1 h post feeding . Indeed , contrary to expectations , the median was greater at 1 h ( median = 136 ) ; this was due to several high “outliers” observed in this 1 h group , resulting in an upwards skewing of the median . Thus , the variance within each of the groups was too great to permit detection of any significant differences between the medians for 0 vs . 1 h . However , there was a greater than 57% reduction in the number of sporozoites found 2 h post-feeding , compared with 0 h; this was highly significant; P = 0 . 001 . Numbers of sporozoites remaining at the bite site after 2 h appeared to stabilize . In vivo gliding motility of sporozoites was high at 0 and 1 h , with a reduction observed at 2 h; no motility seen at 3 h and beyond . This was observed in mice that had been examined only once at either 1 , 2 , 3 or 6 h; thus the reduction in sporozoite motility over time was not due to sporozoite damage caused by excessive illumination during fluorescence microscopy . Immunized mice at 0 h had a median number of sporozoites that was less than 45% of the median number deposited in non-immunized 0 h controls; this reduction was highly significant; P<0 . 001 . Motility of sporozoites in immunized mice ceased within min after deposition by mosquitoes . Counts of sporozoites injected into immunized mice were unreliable beyond 2 h due to deterioration and fragmentation of sporozoites ( Fig . 2 ) . Immunization status of mice was verified by ELISA , with use of a multiple antigen peptide specific for the P . berghei CSP for capture of anti-CSP antibodies in mouse sera . The geometric mean ELISA titer of sera taken from immunized mice on the day prior to challenge was 17 , 065 compared with <80 for sera from non-immunized controls . No immunized mice developed parasitemia after challenge by bite of individual mosquitoes , whereas 60% of the paired , non-immunized control mice developed parasitemia under the same conditions . To test the correlation between numbers of sporozoites left at the bite site and whether the mice subsequently developed blood infections , we performed an additional study in which we allowed infected mosquitoes to bite non-immunized mice ( 1 mosquito per mouse ) and assessed numbers of sporozoites within the ear at 1 h . We then followed the mice with daily Giemsa smears to assess patency . Seven of 18 mice ( 39% ) developed blood infections , with a mean prepatent period of 6 . 7 days . The numbers of sporozoites found in 1 h biopsy specimens from these positive mice ( median = 253 ) was significantly higher than the numbers of sporozoites found in 1-h biopsy specimens from the negative mice ( median = 11 ) ; P = 0 . 0001 . A summary of numbers of sporozoites visualized at the bite site on the ventral abdomen ( sum of the sporozoites found in the skin plus underlying tissues ) after feedings by individual mosquitoes is presented as a scatter plot in Fig . 3 . After mosquitoes fed on non-immunized ( control ) mice , we found a median of 57 sporozoites in biopsy specimens taken immediately after feeding . At 1 h there was a 37% reduction in numbers of sporozoites found in biopsy specimens but this reduction was not statistically significant . The numbers of sporozoites remaining at the bite site at 2 h ( median = 16 ) was reduced by 72% compared with 0-time; P = 0 . 001 . Numbers of sporozoites remaining at the bite site at 3 and 6 h appeared to stabilize , with no significant differences in the median number of sporozoites beyond the numbers seen in the 2 h biopsy specimens . As with the ear pinnae , in vivo gliding motility of sporozoites in abdominal skin was high at 0 and 1 h , with a reduction observed at 2 h and no motility seen at 3 h and beyond . Actively immunized mice ( status verified by ELISA ) at 0-time had a 92% reduction in numbers of sporozoites deposited within abdominal tissues compared with their non-immunized 0-time control counterparts; P<0 . 001 . Numbers of sporozoites seen in abdominal biopsy specimens from immunized mice seemed to stabilize beyond 0-time . As with the ear , however , counts of sporozoites injected into immunized mice were unreliable beyond 2 h due to deterioration and fragmentation of sporozoites . Motility of sporozoites within the abdominal skin of immunized mice ceased within min after deposition by mosquitoes . To assess whether the reduced number of sporozoites deposited in actively immunized mice could be attributed to antibodies alone , we repeated the above studies with mice that had received passive IV transfer of a monoclonal antibody ( MoAb 3D11 ) , which is directed against the repeat region of the CS protein of P . berghei sporozoites . Control mice received either PBS or no injection . Challenge by bite of infected mosquitoes was delayed for 24 h to allow the MoAb to permeate avascular skin tissue . A summary of numbers of sporozoites visualized at the bite sites after feedings by individual mosquitoes is presented as a scatter plot in Fig . 4 . After mosquitoes fed on the ear pinnae of control mice , we found a median of 51 sporozoites in biopsy specimens taken immediately after feeding; there was a 54% reduction in numbers of sporozoites injected into passively immunized mice; P = 0 . 005 . After mosquitoes fed on the ventral abdomen of control mice , we found a median of 33 sporozoites in biopsy specimens taken immediately after feeding; there was a 54 . 5% reduction in numbers of sporozoites injected into passively immunized mice; P = 0 . 005 . No immunized mice developed parasitemia after challenge by bite of individual mosquitoes , whereas 39% of the paired , non-immunized control mice developed parasitemia under the same conditions . As an additional negative control we passively immunized some mice in the same manner with MoAb NYS1 , which is directed against the repeat region of the CS protein of P . yoelii sporozoites [9] and challenged these mice by bite of mosquitoes infected with P . berghei sporozoites ( N = 8 , with 2 ear bite sites and 2 abdominal bite sites for each mouse ) . There was no significant difference in numbers of sporozoites deposited in the ear pinnae or abdominal tissues of mice passively immunized with this heterologous antibody vs . non-immunized control mice , as determined by ANOVA ( P>0 . 2 ) . Mice received an intravenous injection of FITC-conjugated MoAb 3D11and were challenged by mosquito bite 24 h later . When fed upon by mosquitoes with wild-type non-fluorescent P . berghei sporozoites ( with soluble P . berghei CS protein in their saliva ) , green densities were visualized in mouse tissue close to the tip of the proboscis ( Fig . 5 , upper panel and Video S1 ) . This was interpreted as formation of an immune complex ( IC ) at the distal end of the proboscis . When mice were fed upon by mosquitoes with P . yoelii sporozoites ( with soluble P . yoelii CS protein in their saliva ) , no such green density could be observed as a result of contact with the heterologous anti-P . berghei antibodies ( Fig . 5 , lower panel and Video S2 ) . Similarly , when mice were fed upon by non-infected mosquitoes , no green density could be observed in these negative controls ( Video S3 ) . To further assess the nature of these green densities , we took biopsy specimens of the ear , and probed these with FITC-conjugated Protein A or Protein A/G . The results ( Fig . 6 ) showed that these conjugates were specifically observed only in areas in which saliva and sporozoites had been deposited and only when the homologous combination of P . berghei sporozoites and 3D11 had been used , thus indicating the IC nature of these densities . No such focal FITC staining was seen in biopsy specimens from mice that had not been passively immunized with 3D11 or from passively immunized mice that had been challenged with heterologous P . yoelii sporozoites . To further assess the nature of the apparent obstruction of sporozoite release by mosquitoes into a milieu of homologous antibodies , we did additional studies in which individual mosquitoes infected with fluorescent P . berghei sporozoites were immobilized on glass slides and we documented direct release of saliva and sporozoites into drops of medium containing either FITC-conjugated 3D11 or FITC-conjugated BSA ( negative control ) . Sporozoites were consistently released freely from the distal end of the proboscis into control medium containing FITC-conjugated BSA ( Fig . 7A and Video S4 ) . When the infected mosquitoes were allowed to release saliva into the same medium containing FITC-conjugated 3D11 , we observed that some of this medium was sucked back into the distal end of the proboscis and there was a relative stasis both of sporozoite transit through the salivary duct and release into the medium ( Fig . 7B and Videos S5 and S6 ) . We made counts of sporozoites released into these media by individual mosquitoes ( n = 20 mosquitoes ) . Because the data were not normally distributed , they were log transformed and an unpaired t-test was performed . The median number of sporozoites found was 86 in the BSA-containing control medium , and 21 in the MoAb 3D11-containing medium ( P<0 . 01 ) .
These results have confirmed previous findings that in addition to those sporozoites that move from the skin into the blood within minutes after deposition by mosquitoes [1] , substantially more sporozoites take a significantly longer time to enter the blood , remaining , instead , in the skin for long periods of time [3] , [5] , [6] . In the current study , when we allowed infected mosquitoes to feed on the ear pinna of mice for 3 min , we were unable to detect any significant differences in numbers of sporozoites found within skin biopsy specimens taken immediately after feeding vs . 1 h post-feeding . Our earlier studies had shown that some P . yoelii sporozoites deposited into the ear pinna by mosquitoes begin to move into the blood within 15 min [1] . For this , we used a highly sensitive detection method ( development of parasitemia ) that can detect even a single sporozoite leaving the skin and subsequently traveling through the blood to infect the liver and initiate a blood infection; thus , this previous study was able to detect the earliest emigrants from the skin . Furthermore , intravital microscopy studies have clearly shown that some sporozoites move from avascular tissue to enter the blood circulation during the first hour after deposition by mosquitoes [3] , [5] . Yet , our current study was unable to find any statistical difference between numbers of sporozoites found in the ear pinna at 0 vs . 1 h after feeding . These results imply that the numbers of sporozoites leaving the ear pinna within 1 h was relatively small and was undetectable within the background of the high variance we observed in numbers of sporozoites injected by individual , infected mosquitoes ( Fig . 1: 0 vs . 1 h controls ) . When we evaluated the numbers of sporozoites remaining in the skin at later times after feeding , we found a significant loss of 57% of these late emigrants by 2 h compared with 0-time ( P<0 . 001 ) . Ultimately , there was a halt to this emigration , and we found a stabilization of sporozoite numbers within the ear in subsequent observations made beyond 2 h post-feeding . This correlates with our intravital microscopy observations of sporozoites within the ear pinna , which showed that sporozoite motility in the skin ceases within 2–3 h after mosquito inoculation of sporozoites . Amino et al . [5] , who also performed a microscopic assessment of fluorescent P . berghei sporozoites injected into the ear pinna by individual mosquitoes , reported that ∼50% of the inoculated sporozoites left an observed portion of the bite site to invade either blood or lymphatic vessels within 1 h . Our observations that on the order of half the inoculated sporozoites ultimately remain in the skin of the ear pinna are similar to those reported by these authors [5] , although differences in the rate of sporozoite departure from this site likely reflect differences in our methodologies . Both reports serve to quantify our earlier qualitative observations that large numbers of sporozoites remain in the skin after injection by mosquitoes [3] . Our comparative studies with individual mosquitoes feeding on the ventral abdomen showed substantially fewer sporozoites initially deposited per mosquito compared with numbers deposited in the ear , just as we had previously reported [8] . As we had concluded , this is likely the result of greater numbers of sporozoites being re-ingested by feeding mosquitoes , associated with more blood being re-ingested by mosquitoes feeding on the more heavily vascularized abdominal tissue [8] . Furthermore , mosquitoes feeding on heavily vascularized skin require fewer probes before they make contact with a source of blood; thus , less saliva and fewer sporozoites are likely to be delivered during this reduced probing time . In the current study , we found the numbers of sporozoites in biopsy specimens of abdominal tissues to be reduced by 37% at 1 h compared with 0-time specimens but this was not statistically significant . When we evaluated the numbers of sporozoites remaining in abdominal tissues at later times , we found a significant loss of 72% of sporozoites by 2 h compared with 0-time ( P<0 . 001 ) . There was a stabilization of sporozoite numbers within abdominal tissues in subsequent observations made beyond 2 h post-feeding . Our results differ considerably from those of Yamauchi et al . ( [6] , Fig . 2C ) with P . yoelii , who reported that only on the order of 5% of sporozoites injected by mosquitoes into the back left the site within 1 h and were demonstrable as liver-stage exoerythrocytic forms ( EEF ) . Whether this was due to differences in species or in technique or due to the fact that we measured loss of sporozoites from the skin , whereas Yamauchi et al . [6] counted only those sporozoites that transformed into EEF remains to be determined . Our overall results for ear and abdomen showed that emigration of mosquito-deposited sporozoites was restricted largely to a 2 h period that correlated with the ability of these sporozoites to move within the skin; sporozoites that have not left the skin within this period appear destined to remain there . These results differ from those reported by Yamauchi et al . [6] , who stated that , “infective sporozoites inoculated by the mosquito are released from the skin into the blood circulation in a trickle extending for hours after the mosquito bite . ” Most of the experiments reported by these researchers were done with syringe-injected sporozoites , so they are not directly comparable to our own studies , which restricted itself to analysis of the kinetics of sporozoites injected by mosquitoes . It has been well established that not all mosquitoes with salivary gland infections successfully transmit malaria to mammalian hosts [10] . In a previous study , we showed that some of this could be explained by a failure of ∼10% of the infected mosquitoes to transmit any sporozoites into the skin [8] . We now show that even mosquitoes injecting relatively few sporozoites into the skin are significantly more likely to fail to induce a blood infection . Mice that developed parasitemia subsequent to a bite on the ear pinna by individual mosquitoes had a median of 253 sporozoites found in the skin , whereas mice that failed to develop parasitemia had a median of only 11 sporozoites delivered ( P = 0 . 0001 ) . Because the biopsy specimens were taken 1 h after the bite , it is likely that some sporozoites had already left the bite site by this time; thus , numbers of sporozoites actually delivered had obviously been higher at 0-time . Nevertheless , we show elsewhere in our study that numbers of sporozoites leaving the ear within 1 h after the mosquito bite were relatively low . That mosquitoes delivering small numbers of sporozoites tend not to induce blood infections may be due to the relatively large percentage of sporozoites that never leave the skin . Mosquito-delivered sporozoites may either remain in the skin or exit it via dermal blood vessels [3] , [5] or lymph vessels [5] . If comparatively few sporozoites are delivered into the skin in the first place , there is a greater chance that none will be successful in reaching the liver to continue the cycle leading to parasitemia . Sporozoites injected by mosquitoes into mice that had been actively or passively immunized against sporozoites rapidly lost their motility as shown by intravital microscopy , as previously observed [3] . Because none of these mice developed a patent blood infection , it is presumed that none of the sporozoites successfully reached the liver and invaded hepatocytes from the blood . Our observation that fewer P . berghei sporozoites were deposited by mosquitoes into mice actively or passively immunized against P . berghei was unexpected . This was not seen when mice had been passively immunized with a heterologous MoAb directed against the repeat region of the P . yoelii CS protein . The simplest hypothesis to explain this phenomenon is that sporozoites were partially obstructed from leaving the proboscis by an immune complex formed by soluble CS protein released by sporozoites into the saliva [11]–[13] interacting with homologous anti-CS protein antibodies at the bite site . In support of this hypothesis , we observed an apparent precipitant reaction at the distal end of the proboscis during intravital fluorescence microscopy of P . berghei-infected mosquitoes feeding upon mice that had been passively immunized with antibodies against P . berghei sporozoites; these reaction sites were associated with specific binding of Protein A or A/G to sporozoites and to precipitated matter in the immediate vicinity of mosquito-deposited sporozoites , further implying the presence of IC at these sites [14] , [15] . No such precipitant reactions or specific binding of Protein A or A/G was observed when we tested non-infected mosquitoes or mosquitoes infected with heterologous P . yoelii sporozoites . Additional evidence for interference with mosquito delivery of sporozoites by homologous antibodies at the tip of the proboscis was furnished by studies in which we directly observed sporozoites being ejected from the proboscis into a drop of medium containing FITC-conjugated BSA or 3D11 . When the fluid contained antibodies directed against homologous sporozoites , there was clear interference with ejection of the sporozoites , and significantly fewer were delivered into the drops of medium . It has been recognized that pre-erythrocytic immunity can act at different times and sites: by sporozoite-immobilizing antibodies in avascular tissue that block sporozoites from reaching blood vessels that can carry them to the liver [3] , by antibodies in the circulation that may prevent IV-injected sporozoites from invading the liver [16] and by CD4+ and CD8+ T-cells that can act against infected hepatocytes [17] , [18] . We now propose an additional immune effector mechanism , namely , that mosquitoes inject significantly fewer sporozoites into immunized hosts in the first place . The flow of sporozoites released in saliva from the proboscis is already restricted to a limited number of sporozoites under normal circumstances [8] , [19]; further restriction by release of this saliva into an obstacle formed by an immune complex precipitate could additionally reduce the number of injected sporozoites , thus contributing to the protective effect of pre-erythrocytic immunity . Whether this also occurs in semi-immune humans in an endemic area remains to be determined . It has long been recognized that the size of a sporozoite inoculum may influence the nature of the subsequent blood infection in human malaria ( rev . in [20] ) . Thus , an antibody-mediated reduction in numbers of sporozoites injected into semi-immune humans might play a role in limiting the intensity of the ensuing blood infection in combination with a subsequent immune response against blood stages of the parasite .
Anopheles stephensi mosquitoes were infected with a clone of the rodent malaria parasite , P . berghei , whose sporozoites constitutively express RedStar , an improved red fluorescent protein [21] . For some studies we used mosquitoes infected with wild-type P . berghei ( strain NK65 ) or P . yoelii ( strain 17NXL ) , neither of whose sporozoites express fluorescent protein . We used standard protocols for infecting and maintaining mosquitoes [22] , which were infected by feeding upon gametocyte-carrying 6–8 wk-old Swiss-Webster mice ( Taconic Farms Inc . , Germantown , NY ) . Our protocols for maintenance and use of experimental animals were approved by the Institutional Animal Care and Use Committee at New York University School of Medicine , and our animal facility is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ( Rockville , MD ) . Mosquitoes were used for sporozoite transmission studies 18 days after the infective blood meal . Prior to use of infected mosquitoes for feedings observed by intravital microscopy , live , intact mosquitoes were examined by fluorescence microscopy to establish that they had salivary gland infections [23] , [24]; mosquitoes found to be negative were discarded . For these and other sporozoite transmission studies , mosquitoes fed on BALB/c mice anesthetized by IP injection of ketamine ( 50 mg/kg ) plus xylazine ( 10 mg/kg ) and acepromazine ( 1 . 7 mg/kg ) , and placed on a warming tray . To restrict the area of sporozoite deposition for more efficient counting of sporozoites , the dorsal aspect of one ear pinna was partially masked with tape so that only its edge ( 8–10 mm long and 2–3 mm wide ) was accessible to a feeding mosquito . Mosquitoes , previously selected for having positive salivary gland infections , were kept individually in plastic feeding tubes 2 . 5 cm in length and with an inside diameter of 1 . 5 cm; one end of the tube was covered with netting through which the mosquito was able to feed and the other end was closed with a screw-cap . Each mosquito was allowed to probe and feed on the ear through the netting for 3 min from the time that probing was first observed . At appropriate times after each feeding , the fed-upon region of the ear plus the taped adjacent area ∼2 . 5 mm beyond this was excised . This biopsy specimen was separated into dorsal and ventral leaflets with fine forceps [25] , after which each leaflet was mounted under a coverslip and examined by fluorescence microscopy to count sporozoites and record their distribution [8] . Biopsy specimens were taken either immediately after feeding or at intervals of 1 , 2 , 3 or 6 h after feeding . Parallel studies were done with mice that had been actively or passively immunized against sporozoites . Fed-upon mice were kept for up to 14 days to obtain blood smears from the tip of the tail; smears were stained with Giemsa and observed by bright field microscopy to detect patent blood infections . This is an extremely sensitive way to establish whether even a single sporozoite has left the skin to develop further in the liver and establish a blood infection . Mosquitoes were allowed to feed on mice anesthetized as above . Hair was removed from an area of the ventral abdomen with a razor blade . Anesthetized mice were placed on a warming tray , ventral side facing up , and a portion of the abdominal skin was masked with tape that had a 4 mm-diameter hole punched into it . Mosquitoes placed individually in plastic feeding tubes , as above , were allowed to probe and feed through the hole in the tape for 3 min from the time that probing was first observed . Immediately after feeding , the periphery of the feeding circle was marked and the tape was removed . At appropriate times after each feeding , the full depth of skin of a circle 6 mm in diameter , centered around the bite site , was removed with a skin punch device ( 6 mm Miltex Biopsy Punch ) while mice were under deep anesthesia . Then , an underlying circle of peritoneal muscle wall was removed and both incisions were closed [8] . Both biopsy specimens were mounted under cover-slips and viewed through a fluorescence stereoscopic microscope to count sporozoites , as above . We added the numbers found in the skin to the numbers found in peritoneal musculature to obtain the total numbers of sporozoites remaining in the ventral abdomen after mosquito feedings [8] . Biopsy specimens were taken either immediately after feeding or at intervals of 1 , 2 , 3 or 6 h after feeding . Parallel studies were done with mice that had been actively or passively immunized against sporozoites . For follow-up information , mice , were maintained for blood smears , as above , to detect patent blood infections . Sporozoites for immunization were first purified on a DEAE ion-exchange column [26] in order to distinguish between effects due to anti-sporozoite vs . anti-saliva immunity . Purified sporozoites were irradiated within a gamma irradiator ( MDS Nordion Gammacell® 1000 Elite ) to a central dose of ±12049 cGy and a minimum dose of ±10266 cGy . Mice received an IV injection of 50 , 000 irradiated sporozoites , with 2 subsequent booster injections of 10 , 000 irradiated sporozoites , each , at 15 days intervals . They were challenged by mosquito bite 15 days after the second boost . Serum from immunized mice was taken the day prior to challenge to assess levels of anti-sporozoite antibodies . Some mice were passively immunized by IV injection of 320 µg per mouse of MoAb 3D11 , directed against the repeat region of P . berghei CS protein [3] or with MoAb NYS1 , directed against the repeat region of P . yoelii CS protein [9] . All mice were challenged by bite of infected mosquitoes 24 h after antibody transfer . Parallel challenges were done on non-immunized control mice . For these studies MoAb 3D11 was conjugated with FITC prior to injection . For this , 150 µl of 1 mg/ml FITC solution ( Fluorescein Isothiocyanate; Sigma , St . Louis , MO ) was added to a 2 mg/ml 3D11 solution ( or BSA for negative controls ) and incubated at room temperature for 1 h . Unbound FITC was then separated from the conjugate with a PD-10 gel filtration column loaded with Sephadex G-25 M ( Amersham Biosciences , Piscataway , NJ , USA ) . Mice were passively immunized by IV-injection of 150 µg per mouse of the conjugates . On the next day , the ear pinnae of these mice were fed upon by mosquitoes infected with P . berghei or P . yoelii sporozoites ( none of these sporozoites expressing fluorescent protein ) or by non-infected mosquitoes , while intravital fluorescence videomicroscopy observations were made of the feedings , with particular focus on the site of injection of saliva from the distal end of the proboscis . Subsequent to these observations , biopsy specimens were taken from the fed-upon ears and probed with FITC-conjugated Protein A or Protein A/G ( Pierce Biotechnology IL , USA; 20 µg/mL ) . To study ejection of saliva and sporozoites by mosquitoes into media , we used a modification of the method of Frischknecht et al . ( 2004 ) [27] . The feeding stylets of individual mosquitoes immobilized on a microscope slide were positioned under a 13×13 mm coverslip and the mosquito was allowed to salivate into 5 µl of RPMI medium containing 1 mg/µl of either FITC-conjugated BSA ( control ) or FITC-conjugated MoAb 3D11 . Salivation was observed for 10 min by videomicroscopy with the Leica MZ16FA fluorescence stereoscopic microscope , after which counts were made of the sporozoites released into the media . Antibody titers were determined by enzyme-linked immunosorbent assay ( ELISA ) , using the P . berghei-specific B4 multiple Ag peptide ( MAP ) as antigen ( 4 branch MAPS , each branch with 3 repeats of DPPPPNPN ) from AnaSpec , Inc . , San Jose , CA ) [28] . Peroxidase-labeled anti-mouse IgG was used as a secondary antibody and 2 , 2′-azinobis ( 3-ethylbenthiazolinesulfonic acid ) ( ABTS ) as the substrate . The end point was measured as the highest dilution of serum having a delta O . D . greater than the mean+3 standard deviations obtained with non-immune sera . Results were expressed as geometric mean titers . For counting of sporozoites , we used a Leica MZ16FA fluorescence stereoscopic microscope with a 2 . 0× stereoscopic objective lens . Illumination for fluorescence studies was with an EXFO X-Cite 120 F1 illumination system and with a DsRED filter set , restricting illumination to 515–556 nm ( peak = 545 ) and signal emission to 590 nm . Observations of remnant sporozoites and Protein A or A/G staining in the skin of control and immunized animals were performed using a Leica Inverted Laser Scanning Confocal Microscope ( Model Number TCS SP2 AOBS ) and Leica LCS Software . Each image is the average projection superimposed stacks of individual focal plane images . The total thickness of the stack varies depending upon the position of the sporozoites in the biopsy specimen . Intravital videomicroscopy was done with a Leica DMI 4000B inverted fluorescence microscope with a 10× objective lens . Illumination for fluorescence studies was with a CTR4000 illumination system and with a dual Green/Red filter set , restricting illumination to 480–500 nm ( peak = 490 ) and 560–590 nm ( peak = 575 ) and signal emission to 505 and 600 nm . Images were acquired with a Leica DFC300 FX digital camera and saved as digital files for further analysis and processing . We used Leica Application Suite software ( LAS V2 . 7 . 1 ) for documentation and analysis . For 3D reconstruction and volume rendering , raw 3D data set were processed using Imaris 6 . 1 . 5 ( BitPlane , 2008 ) software . A Gaussian filter was used for noise reduction on the average projection and Iso-surface objects were created on an intensity value on a per channel basis . Surfaces were colored in green for the green channel ( FITC-conjugated Protein A ) and red for the red channel ( P . berghei Red Star ) . Green surfaces were attributed a 50% transparency in order to visualize double staining . The numbers of sporozoites injected by mosquitoes did not follow a Normal distribution but were highly skewed with a clear floor effect . However , when log-transformed [ln ( spz count+1 ) ] , the data sufficiently approximated a Normal distribution that allowed the use of parametric tests . Analysis of variance ( ANOVA ) examined the effects of site ( abdomen vs . ear ) , immunization status ( control vs . immunized ) and time ( 0 to 6 h ) , as appropriate; Student's t-test ( unpaired , 2-tailed ) compared means when only two experimental groups were considered . The analyses were performed using SPSS 15 . 0 for Windows ( SPSS Inc . , 2006 ) and GraphPad Prism Version 5 software ( San Diego , California . | Malaria is initiated by a mosquito injecting malaria sporozoites into the skin . To successfully continue the infection , sporozoites must then invade blood vessels in skin for transportation to the liver . However , the majority of these injected sporozoites are unable to reach the blood . The numbers of sporozoites that successfully invade the blood may influence the characteristics of the subsequent clinical malaria infection . We studied this by microscopy with fluorescent sporozoites of the rodent malaria parasite Plasmodium berghei injected into mice by mosquitoes . Sporozoites introduced into mice that have been immunized against sporozoites become immobilized and cannot reach the blood; those that remain at the bite site become degraded within several hours . Strikingly , mosquitoes introduce significantly fewer sporozoites into skin of immunized mice . These findings indicate that antibodies directed against sporozoites seem to function both by reducing the numbers of sporozoites injected into immunized hosts in the first place and then by inhibiting the movement of the injected sporozoites into the bloodstream . | [
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] | 2009 | Kinetics of Mosquito-Injected Plasmodium Sporozoites in Mice: Fewer Sporozoites Are Injected into Sporozoite-Immunized Mice |
Killer cell immunoglobulin-like receptors ( KIRs ) influence both innate and adaptive immunity . But while the role of KIRs in NK-mediated innate immunity is well-documented , the impact of KIRs on the T cell response in human disease is not known . Here we test the hypothesis that an individual's KIR genotype affects the efficiency of their HLA class I-mediated antiviral immune response and the outcome of viral infection . We show that , in two unrelated viral infections , hepatitis C virus and human T lymphotropic virus type 1 , possession of the KIR2DL2 gene enhanced both protective and detrimental HLA class I-restricted anti-viral immunity . These results reveal a novel role for inhibitory KIRs . We conclude that inhibitory KIRs , in synergy with T cells , are a major determinant of the outcome of persistent viral infection .
Killer cell immunoglobulin-like receptors ( KIRs ) are a family of transmembrane proteins that are expressed on natural killer ( NK ) cells and subsets of T cells [1]–[3] . They bind HLA class I molecules and have activatory and inhibitory isoforms [4] . KIRs contribute directly and indirectly to antiviral immunity . Directly , KIRs on NK cells sense the loss of HLA class I molecules from the cell surface and trigger NK-mediated cytolysis . Indirectly , NK cells regulate adaptive immunity via crosstalk with dendritic cells and by the production of chemokines and cytokines [5]–[7] . HLA class I molecules can be grouped into allotypes with similar KIR binding properties [8] . For example , KIR2DL2 binds group C1 HLA-C molecules which have asparagine at residue 80 , and , with a weaker affinity , group C2 molecules which have a lysine at position 80 [9] . Early research on KIRs investigated NK-mediated protection by studying disease associations with KIRs in the context of their HLA class I ligands [10]–[11] . There is now compelling evidence that KIRs also regulate adaptive immunity [5]–[7] , but it is not known whether this has a significant impact on the response to infection in vivo . Differences between human KIRs and their mouse functional homologues ( the Ly49 receptors ) and the paucity of KIR allele-specific antibodies have hindered work on the role of KIRs in controlling adaptive immune responses . Here we used immunogenetics to investigate whether KIR genotype modulates HLA-mediated anti-viral protection in vivo . We focussed on HLA class I alleles which have previously been associated with disease outcome and investigated whether these effects were altered by the KIR background . We studied 4 well-documented HLA class I allele-disease associations in two viral infections: human T lymphotropic virus type 1 ( HTLV-1 ) and hepatitis C virus ( HCV ) . HTLV-1 is a persistent retrovirus that infects 10–20 million people worldwide . Most infected individuals remain lifelong asymptomatic carriers ( ACs ) . However , approximately 10% of infected individuals develop associated diseases including HTLV-1-associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) , an inflammatory disease of the central nervous system that results in progressive paralysis . It is poorly understood why some individuals remain asymptomatic whereas others develop disease , but one strong correlate of disease is the proviral load , which is significantly higher in HAM/TSP patients than in ACs [12] . We have previously shown that HLA-A*02 and C*08 are each associated with both a reduced risk of HAM/TSP and a reduced proviral load in ACs and that HLA-B*54 is associated with an increased prevalence of HAM/TSP and an increased proviral load in HAM/TSP patients [13]–[14] . HCV is among the most widespread viral infections , with 170 million infected people worldwide . As in HTLV-1 infection , the outcome of HCV infection is heterogeneous: the virus persists in approximately 70% of infected individuals while the rest clear the infection spontaneously . Chronic HCV infection can cause serious liver damage including cirrhosis and hepatocellular carcinoma [15] . The origins of this heterogeneity are not completely understood but several genetic determinants have been identified , including HLA-B*57 which is associated with spontaneous clearance in several cohorts [16]–[19] . The aim of this study was to test the hypothesis that KIR genotype determines the efficiency of HLA class I-mediated anti-viral immunity . We tested this hypothesis for 4 HLA class I associations: HLA-C*08 , A*02 and B*54 in HTLV-1 infection and B*57 in HCV infection . We show , using multiple independent measures , that for both HCV and HTLV-1 , possession of the KIR2DL2 gene enhanced HLA class I-restricted immunity .
In the cohort from Southern Japan , HLA-C*08 was associated with a significantly reduced odds of developing HAM/TSP ( OR = 0 . 47 , p = 0 . 03 , OR<1 indicates a protective effect while OR>1 indicates a detrimental effect ) [14] . We investigated the impact of KIRs on this protective effect by stratifying the cohort by KIR genotype . Of the KIR studied , one particular KIR , KIR2DL2 , had a noticeable interaction with C*08 ( Table 1 and Figure 1 ) . We found that the C*08 protective effect was weakened and no longer statistically significant in the subset of individuals who were KIR2DL2- ( OR = 0 . 67 , p = 0 . 4 ) but enhanced in KIR2DL2+ individuals ( OR = 0 . 16 , p = 0 . 02 ) . There were more KIR2DL2- individuals than KIR2DL2+ individuals so the absence of significance in the KIR2DL2- individuals was not simply due to reduced cohort size . Similarly , HLA-B*54 , which is associated with a significantly increased risk of HAM/TSP ( OR = 3 . 11 , p = 0 . 0009 ) , had a weakened impact on disease risk in the absence of KIR2DL2 ( OR = 1 . 70 , p = 0 . 2 ) but an enhanced impact in the presence of KIR2DL2 ( OR = 12 . 05 , p = 0 . 004 ) . Again , the absence of a significant effect of B*54 in KIR2DL2- individuals was not attributable to a loss of power . In contrast , although HLA-A*02 was associated with a reduced risk of HAM/TSP , there was no significant additional impact of KIR genotype which could not be attributed to power . As an independent test of the observation that KIR2DL2 enhanced the effect of both protective and detrimental HLA class I alleles in HTLV-1 infection , we investigated the interaction between HLA class I alleles , KIR2DL2 and HTLV-1 proviral load ( pvl ) . We investigated pvl in ACs and HAM/TSP patients separately , so any observed impact on pvl is independent of the impact on disease status . C*08 has previously been associated with a low pvl in ACs ( difference in log10 pvl between C*08+ and C*08- Δ = −0 . 33 , p = 0 . 05 ) ; again , this effect was weakened in KIR2DL2- individuals ( Δ = −0 . 29 p = 0 . 2 ) but enhanced in KIR2DL2+ individuals ( Δ = −0 . 66 p = 0 . 07 ) ; Table 1 . Similarly , HLA-B*54 , which is associated with a high pvl in HAM/TSP patients ( Δ = +0 . 24 p = 0 . 01 ) showed a weakened effect in the absence of KIR2DL2 ( Δ = +0 . 22 , p = 0 . 05 ) but an enhanced effect in the presence of KIR2DL2 ( Δ = +0 . 42 , p = 0 . 01 ) . Two previous observations on HTLV-1 immunogenetics have , until now , remained unexplained . Firstly , although C*08 has been associated with a low pvl in ACs it has no detectable impact on pvl in HAM/TSP patients; similarly , B*54 , which was associated with a high pvl in HAM/TSP patients , had no impact on pvl in ACs [13]–[14] . Why some HLA class I alleles apparently “cease working” in some populations was unknown . We hypothesised that the lack of the expected C*08 and B*54 effects in HAM/TSP patients and ACs respectively was due to a low frequency of KIR2DL2 in these groups and that the decrease or increase in pvl due to C*08 or B*54 respectively would be manifest only in KIR2DL2+ individuals . Consistent with this hypothesis we found that the frequency of KIR2DL2 carriage in the groups that did not show the expected effect of HLA genotype on pvl was approximately half that of the groups in which HLA-associated effects were observed ( prevalence of KIR2DL2+ amongst B*54+ individuals: 12 . 5% in ACs vs 29 . 5% in HAM/TSPs; prevalence of KIR2DL2+ amongst C*08+ individuals: 18 . 2% in HAM/TSPs vs 27 . 8% in ACs; Table 1 ) . The small numbers of individuals in the stratified cohorts ( HAM/TSP KIR2DL2+: C*08+ N = 4 , C*08- N = 50 . AC KIR2DL2+: B*54+ N = 3 , B*54- N = 45 ) precluded a reliable test for an impact of HLA on pvl in KIR2DL2+ individuals . However , in the larger of these groups there was a significant impact; i . e C*08 was associated with a significant reduction in pvl in KIR2DL2+ individuals ( Δ = -0 . 86 , p = 0 . 005 ) . This provides , for the first time , a plausible explanation for the reported observation [14] that the B*54 effect on pvl was not manifest in ACs and the C*08 effect on pvl was not manifest in HAM/TSP patients . We recently reported that in HTLV-1 infection , HLA class I molecules that bind peptides from the virus protein HBZ are associated with a reduced risk of HAM/TSP and , independently , a reduced pvl [20] . In the same study we showed , using IFNg ELISpot , chromium release and CD107 staining , that HBZ-specific CD8+ T cells were present and functional in fresh PBMC from infected individuals . We therefore investigated the interaction between KIR2DL2 and the protective effect of binding HBZ . We used epitope prediction software [21] to predict the strength of binding of HBZ peptides to an individual's HLA-A and B molecules . We found that ACs had HLA-A and -B molecules that are predicted to bind HBZ significantly more strongly than those in HAM/TSP patients ( median difference 12% , p = 0 . 00005 ) [20] and that this effect was stronger in KIR2DL2+ individuals ( median difference 25% , p = 0 . 00006 ) than in KIR2DL2- individuals ( median difference 7% , p = 0 . 06 ) ; Figure 2a . We reasoned that this difference in HBZ binding between ACs and HAM/TSP patients was due to HLA-A*02 and B*54 , which differ in their HBZ peptide-binding affinities [20] and are associated with different outcomes in HTLV-1 infection . We therefore removed all individuals with A*02 or B*54 from the cohort and repeated the analysis . Surprisingly , we still found the same pattern: possession of HLA molecules that bind HBZ strongly was significantly associated with remaining asymptomatic ( median difference 10% , p = 0 . 04 ) and this effect was strengthened in KIR2DL2+ individuals ( median difference 23% , p = 0 . 02 ) but not in KIR2DL2- individuals ( median difference 3% , p = 0 . 2 ) ; Figure 2b . This demonstrates that the protective effect of binding HBZ peptides by multiple HLA class I molecules , both A and B , is enhanced by KIR2DL2 . As previously reported , HLA-B*57 was associated with significantly decreased odds of chronic infection ( OR = 0 . 571 , p = 0 . 02 ) . This protective effect was enhanced in the presence of KIR2DL2 ( OR = 0 . 40 , p = 0 . 007 ) but weakened in the absence of KIR2DL2 ( OR = 0 . 83 , p = 0 . 6 ) ( Table 2 ) . Furthermore , the impact of B*57 was strongest in KIR2DL2 homozygote individuals ( OR = 0 . 21 , p = 0 . 02 ) , weaker in KIR2DL2 heterozygote individuals ( OR = 0 . 48 , p = 0 . 07 ) and absent in KIR2DL2-negative individuals ( OR = 0 . 83 , p = 0 . 6 ) . KIR2DL2 enhanced the association between B*57 and spontaneous clearance independently and with similar strength in both African Americans and Caucasians ( Table S1 in Text S1 ) . Next we investigated the impact of B*57 and KIR2DL2 on HCV viral load . We only considered patients with chronic infection , so any observed impact on viral load is independent of the impact on viral clearance . This analysis was possible in two cohorts: MHCS and ALIVE . We found that B*57 was associated with reduced chronic HCV viral load , particularly in the MHCS cohort ( MHCS: difference in log10 VL Δ = −3 . 1 p = 0 . 0003; ALIVE: Δ = −0 . 18 p = 0 . 3 . Combined p = 0 . 0006 ) . Consistent with our observations in HTLV-1 infection , this reduction was enhanced in the presence of KIR2DL2 ( MHCS: Δ = −4 . 5 p<0 . 0001 , ALIVE: Δ = −0 . 46 p = 0 . 05 . Combined p = 0 . 00003 ) but weakened in the absence of KIR2DL2 ( MHCS Δ = −1 . 64 p = 0 . 2 , ALIVE Δ = +0 . 32 p = 0 . 3 . Combined p = 0 . 7 ) ; Table 2 and Figure 3 . In MHCS ( but not ALIVE ) we also observed a progressive effect with KIR2DL2 copy number ( 2 copies: Δ = −6 . 5 , p = 0 . 0005 . 1 copy: Δ = −4 . 1 p = 0 . 001 . 0 copies Δ = −1 . 6 p = 0 . 2 ) ; the number of homozygous individuals are too small to draw firm conclusions but this progressive effect is consistent with our other observations . These data show that KIR2DL2 enhances both protective and detrimental HLA class I associations . Therefore , KIR2DL2 would not be predicted to have a significant net impact across all HLA class I molecules . That is , possession of KIR2DL2 ( alone or with its C1 ligand ) without a particular protective or detrimental HLA allele , would not be expected to be significantly protective or detrimental . This prediction was verified ( Table S2 in Text S1 ) . There is strong linkage disequilibrium among the KIR genes and among the HLA class I genes . Analysis of the linked genes indicates that the primary genes driving the observed associations are most likely to be KIR2DL2 in combination with HLA-B*54 , C*08 and B*57 rather than individual linked KIR , multiple stimulatory linked KIRs or linked HLA class I genes ( sections 3 and 4 in Text S1 ) . We cannot rule out an effect of linkage between KIR2DL2 and neighbouring loci outside the KIR genes . However , there is little evidence of significant linkage between KIRs and even the next closest gene cluster , the LILR [22] . Furthermore , we observed the same effect of KIR2DL2 in three different populations ( Japanese , African-American and Caucasian ) so a putative linked locus driving the effect would have to be linked to KIR2DL2 in all three populations . Although HLA-C*08 , as a group C1 molecule , is expected to bind KIR2DL2 , the most frequent subtype in our cohort ( Cw*0801 , 88% ) binds KIR2DL2 very weakly ( comparable to background [23] ) , furthermore HLA-B*54 and HLA-B*57 are not expected to bind KIR2DL2 and the most frequent subtypes in our cohorts ( B*5401 and B*5701 ) have been shown not to bind KIR2DL2 [9] , [23] . Finally , KIR2DL2 enhanced the protective effect of binding HBZ peptides by multiple HLA-A and –B molecules . With the exception of B*4601 and B*7301 ( which were not responsible for the enhancement , data not shown ) KIR2DL2 is not thought to bind HLA-A and B molecules and has been shown not to bind 29 HLA-A and 54 HLA-B allotypes [9] . We therefore hypothesised that the effect of KIR2DL2 on HLA class I-mediated immunity we have observed is not attributable to KIR2DL2 directly binding the HLA molecule whose effect is enhanced . To test this hypothesis we first investigated whether the other group C1 alleles had the same effect as C*08 in HTLV-1 infection . Grouping all the C1 alleles we found no significant association between C1 and decreased risk of HAM/TSP either in the whole cohort or in KIR2DL2+ individuals . Similarly , there was no relation between pvl and C1 in either ACs or HAM/TSP patients . Analysis of the individual C1 alleles confirmed the hypothesis that the C*08 effect we observed was not exhibited by other group C1 alleles ( Tables S4 and S5 ) . HLA-B*54 , a group Bw6 HLA allele , is not known to bind any KIR molecule . We therefore tested whether the observed B*54 effect was attributable to C*01 , which is in linkage disequilibrium with B*54 and which encodes molecules that bind KIR2DL2 . This analysis suggested that B*54 , not C*01 , was the gene driving the observed detrimental effect on HTLV-1 outcome ( section 5 . 2 in Text S1 ) . This result , and the observation that no other C1 allele shows “B*54-like” behaviour , indicate that , as postulated , the interaction between B*54 and KIR2DL2 cannot be explained by direct KIR-HLA binding . The most frequent B*57 allele in our cohort is B*5701 , which does not bind KIR2DL2 [9] . There are therefore two ways in which the observed interaction between KIR2DL2 and HLA-B*57 could be attributed to “classical” KIR-HLA binding: either KIR2DL2 might bind a class I HLA molecule whose encoding gene is linked to HLA-B*57 , or the effect might be due to KIR3DL1/S1 , which does bind B*57 . Analysis of both these possibilities indicated that they did not explain the KIR2DL2-B*57 effect ( section 5 . 3 and 5 . 4 in Text S1 ) . Therefore , as hypothesised , the enhancement of C*08 , B*54 and B*57–restricted immunity by KIR2DL2 is not explained by direct binding between the respective HLA molecules and KIR2DL2 . Instead , we suggest that KIR2DL2 binds its HLA-C ligands and indirectly modulates C*08 , B*54 and B*57–restricted T cells . Consistent with this , we found some evidence that KIR2DL2 enhanced HLA Class I effects more strongly when it's stronger C1 ligands are present ( section 6 in Text S1 ) . It seems unlikely that KIR2DL2 behaves fundamentally differently to other inhibitory KIRs . The effect of KIR2DL2 may be most apparent because KIR2DL2 is present at informative frequencies and its C1 and C2 ligands are ubiquitous . We addressed the role of other inhibitory KIRs in 3 ways . We studied the effect of individual inhibitory KIRs ( section 4 . 1 in Text S1 ) , we investigated whether the number of inhibitory KIR:ligands had a cumulative effect ( section 7 in Text S1 ) and we examined the role of the group A KIR haplotypes which are dominated by inhibitory KIRs but do not contain KIR2DL2 ( section 8 in Text S1 ) . We found little evidence that the other inhibitory KIRs enhanced HLA class I-mediated immunity but this may be due to small cohort sizes and masking by the dominant KIR2DL2 effect . We found no evidence that activating KIR were enhancing HLA class I-restricted immunity . Haplotype B , the more activatory KIR haplotype , enhanced HLA class I associations but this was only true if the haplotype contained KIR2DL2 ( section 8 in Text S1 ) . We found no evidence that the cumulative presence of activating KIR enhanced HLA class I restricted immunity ( section 4 . 3 in Text S1 ) . And , as far as it was possible to separate KIR2DL2 and KIR2DS2 , which are in tight linkage disequilibrium , the enhancement of HLA class I restricted immunity appeared to be attributable to KIR2DL2 rather than KIR2DS2 ( section 4 . 2 in Text S1 ) .
We show that KIR2DL2 enhanced several independent HLA class I-mediated effects in two unrelated viral infections . In HTLV-1 infection , KIR2DL2 enhanced the protective and detrimental effects of HLA-C*08 and B*54 respectively on disease status . KIR2DL2 also enhanced the association between C*08 and low proviral load in ACs and between B*54 and high proviral load in HAM/TSP patients . Additionally , KIR2DL2 enhanced the protective effect of HBZ binding by multiple HLA molecules . Strikingly , on stratifying by KIR2DL2 , we observed , for the first time , a protective effect of C*08 on pvl in HAM/TSP patients and explained the lack of impact of B*54 on pvl in ACs . In HCV infection , KIR2DL2 enhanced the protective effect of B*57 on spontaneous clearance and the association between B*57 and low viral load in chronic carriers; for both clearance and viral load a progressive effect with KIR2DL2 copy number was observed . This progressive effect is consistent with reports of an association between KIR gene copy number and the frequency of cell-surface expression of the respective KIR molecule [24]–[25] . There are two mechanisms by which KIR2DL2 could act: it could enhance either NK-mediated or T cell-mediated immunity . That is , NK cell killing of virus-infected cells could be altered by KIR2DL2 expression or , alternatively , the virus-specific CD8+ T cell response could be modified by KIR2DL2 expression ( on NK cells or T cells ) . Two observations indicate that it is the T cell response that is more likely to be enhanced . First , strong binding of HBZ viral peptides via multiple different HLA-A and B molecules was associated with asymptomatic status [20] and this protective effect was enhanced by KIR2DL2 . KIR2DL2 is not known to bind HLA-A or–B molecules ( with the exception of B*4601 and B*7301 ) [9] , [23] , [26] so it is unlikely that the enhancement of the protective effect of binding HBZ by KIR2DL2 is due to direct binding between KIR2DL2 and HBZ peptide in the context of HLA-A and –B molecules . Furthermore , although NK cells exhibit peptide dependence [27] , it is hard to reconcile protein-specificity via multiple HLA molecules with an NK cell-mediated mechanism . Second , the KIR2DL2 enhancement could not be explained by binding between KIR2DL2 and any of the 3 HLA class I molecules investigated . One further observation also suggests a T cell-mediated mechanism . Two protective genotypes in HCV infection that are postulated to operate via innate immune mechanisms [28] ( namely a SNP upstream of IL28B and KIR2DL3-HLA-C1 ) had no impact on viral load in chronic infection [10] , [29] . The authors hypothesised that this was because innate barriers offer little protection once overcome . In contrast , the KIR2DL2/B*57 effect that we report here had a significant impact on viral load: again , this is perhaps more consistent with adaptive immunity . Our results indicate that KIR2DL2 enhances HLA class 1-restricted CD8+ T cell-mediated adaptive immunity . KIRs on both NK cells and CD8+ T cells have been reported to shape adaptive immunity [5]-[7] . Of particular interest are reports [30]–[33] that inhibitory KIRs on CD8+ T cells promote the survival of a subset of memory phenotype CD8+ αβ T cells with enhanced cytolytic potential ( Tm1 [34] ) by reducing activation-induced cell death . Ugolini et al suggested that this phenomenon helps maintain specific CD8+ T lymphocytes during chronic viral infection [30] . Tm1 cells have been described in both HTLV-1 and HCV infections , where they constitute a minority of virus-specific CD8+ T cells but the majority of perforin-bright cells [35]–[36] . Consistent with our findings , these studies showed that the HLA molecule that restricts the T cell whose survival is promoted was independent of the HLA-C molecules that ligated the KIR [30] , [34] . We postulate that , in the face of chronic antigen stimulation , protective T cells survive longer if they carry KIR2DL2 and therefore exert stronger protection . Likewise , T cells restricted by HLA alleles associated with increased disease susceptibility also survive for longer in the presence of KIR2DL2 and so are more detrimental ( Figure 4 ) . Hence , KIR2DL2 enhances both protective and detrimental HLA class I associations . Alternatively , it is known that NK cells kill activated T cells and that this killing is reduced by inhibitory KIR [37]–[38] . So again , T cells restricted by protective and detrimental HLA class I molecules may survive longer in the presence of inhibitory KIR and thus the protective and detrimental associations would be enhanced . Ugolini et al proposed that inhibitory KIRs promote T cell survival by increasing the activation threshold of T cells . This may explain why the HLA-A*02 protective effect in HTLV-1 is not significantly enhanced by KIR2DL2 . A*02 molecules bind peptides significantly more strongly than other alleles ( section 9 in Text S1 ) and the immunodominant HTLV-1 peptide Tax 11-19 is bound exceptionally strongly . Therefore , even if the T cell activation threshold were increased , the strength of signalling may remain above the threshold and consequently the A*02 protective effect cannot be enhanced . Why does KIR2DL2 enhance T cell responses whereas the other inhibitory KIRs apparently do not ? The effect of KIR2DL2 may be most apparent because KIR2DL2 is present at informative frequencies and its C1 and C2 ligands are ubiquitous; i . e . unlike the other KIR every individual carries a KIR2DL2 ligand . It will be important to determine whether inhibitory KIRs play a similar role in enhancing CD8+ and possibly CD4+ T cell-mediated immunity to other pathogens and in autoimmune disease . KIR-expressing virus-specific CD8+ T cells have been reported in other chronic infections including HIV-1 , CMV and EBV [39]-[41] . Furthermore , in HIV-1 infection , high expression alleles of an inhibitory KIR , KIR3DL1 , in the context of HLA-Bw4I have been associated with slow progression to AIDS [11] . In order to explain protection by an inhibitory KIR the authors proposed a model based on NK cell development . Our results suggest an alternative explanation , i . e . that KIR3DL1 enhances protective HLA-B-restricted responses to HIV-1 . In contrast to previous studies of KIR genotype , which investigated the antiviral action of NK cells , we investigated the impact of KIRs on HLA class I-mediated antiviral immunity . We find a clear and consistent effect of KIR2DL2 . The effect sizes are striking: KIR2DL2 homozygotes with B*57 are almost 5 times more likely to clear HCV infection spontaneously; if they fail to clear the virus they have a viral load that is reduced by 6 . 5 logs . Until now , the advantages offered by inhibitory KIRs in virus infections have been unclear . Our data support an alternative role in which inhibitory KIRs enhance both beneficial and detrimental T cell-mediated immunity in persistent viral infection .
The HTLV-1 cohort has been approved by the following committees: 1 ) St . Mary's Local Research Ethics Committee , 1995: title “The immunology and virology of the treatment of HTLV-1-associated inflammatory disease” . Approval reference number: EC3108 . 2 ) Kagoshima University Hospital Clinical Research Ethics Committee: 27th May 1999 . Title: “Investigation of HAM pathomechanism: relationship between host genetic background and clinical status of HTLV-1 infection” . Approval reference number: 22 . All samples were taken under written informed consent . The HCV cohort consisted of four sub-cohorts: AIDS Link to Intravenous Experience ( ALIVE , N = 262 ) [42] , Multicenter Hemophilia Cohort Study ( MHCS , N = 320 ) [43] , Hemophilia Growth and Development Study ( HGDS , N = 110 ) [44] and a UK cohort ( N = 341 ) [10] . 251 individuals were excluded due to incomplete information . The cohort had 257 resolved and 525 chronic patients . HLA class I associations in three of these 4 cohorts ( ALIVE , MHCS and HGDS ) have previously been reported [18] . The HTLV-1 cohort ( N = 431 ) [14] consists of individuals recruited in Kagoshima Japan . All individuals were of Japanese ethnic origin and resided in Kagoshima Prefecture , Japan . The cohort had 229 HAM/TSP patients and 202 asymptomatic carriers . HLA genotyping of the HCV and HTLV-1 cohorts was performed in previous studies [10] , [14] , [18] . KIR genotyping of the HCV cohort ( but not the HTLV-1 cohort ) was performed previously [10] . The binding strength of HLA class I molecules to viral proteins was assessed using epitope prediction software . Prediction of T cell class I epitopes is now highly accurate and algorithms can achieve accuracy of up to 94% [51] . In this study we use the epitope prediction software Metaserver [21] ( http://web . bioinformatics . ic . ac . uk/metaserver ) . We used the rank measure technique [52] in which the strength of an allele's preference for a particular protein is quantified by ranking the strength of binding of the top binding peptide from the protein of interest amongst the strength of binding of peptides from the entire proteome to that allele . Specifically , we split the HTLV-1 proteome into overlapping nonamers offset by a single amino acid and predicted a binding affinity score for each nonomer . For each allele we rank all nonamers from the proteome from the weakest to strongest predicted binding scores . This produces a list of rank values for each protein to that particular allele that quantifies the binding relationship between that allele and the protein . We then invert the rank so the bigger 1/rank , the stronger the preference of the allele for the protein; the logarithm of this measure is plotted on the y axis of Figure 2 as “HBZ binding score” . Each individual therefore contributes up to 4 values ( alleles for which no predictive algorithms were available were excluded from the analysis ) . Binding scores were compared between ACs and HAM/TSP patients using the Wilcoxon rank sum test and reported both as separate p values for HLA-A and B molecules and combined ( since we found no evidence to reject the null hypothesis that the HBZ binding score of an individual's A and B molecules was independent , spearman correlation = 0 . 05 p = 0 . 5 ) . The median difference in binding score is the median of the difference of average HBZ binding between ACs and HAM/TSP patients expressed as a percent of the AC binding score for HLA-A and –B molecules . | Hepatitis C virus ( HCV ) and human T-cell leukemia virus ( HTLV-I ) infect millions of people worldwide . Some HCV-infected individuals spontaneously clear the virus and many HTLV-1-infected people remain asymptomatic; however , in both cases the infection can lead to serious illness such as cancer . The factors which determine outcome are still elusive . We have found that a gene that encodes a receptor ( KIR2DL2 ) enhances both protective and detrimental HLA class I-mediated immunity to HCV and HTLV-1 . Strikingly , although KIRs are primarily associated with innate immunity , our observations suggest that they also have a major impact on the efficiency of the adaptive immune response . This work helps to explain why one individual infected with a virus remains healthy but another , infected with the same virus develops disease; it also helps to explain why particular HLA class I molecules do not always protect or cause susceptibility as expected . Interestingly , the impact of the KIR is entirely context dependent: if an HLA class I molecule is protective then protection is enhanced , but in the context of a detrimental HLA then susceptibility is enhanced . This study reveals a novel role for inhibitory KIRs in adaptive immunity . | [
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] | 2011 | KIR2DL2 Enhances Protective and Detrimental HLA Class I-Mediated Immunity in Chronic Viral Infection |
The connection between chromatin nuclear organization and gene activity is vividly illustrated by the observation that transcriptional coregulation of certain genes appears to be directly influenced by their spatial proximity . This fact poses the more general question of whether it is at all feasible that the numerous genes that are coregulated on a given chromosome , especially those at large genomic distances , might become proximate inside the nucleus . This problem is studied here using steered molecular dynamics simulations in order to enforce the colocalization of thousands of knowledge-based gene sequences on a model for the gene-rich human chromosome 19 . Remarkably , it is found that most ( ) gene pairs can be brought simultaneously into contact . This is made possible by the low degree of intra-chromosome entanglement and the large number of cliques in the gene coregulatory network . A clique is a set of genes coregulated all together as a group . The constrained conformations for the model chromosome 19 are further shown to be organized in spatial macrodomains that are similar to those inferred from recent HiC measurements . The findings indicate that gene coregulation and colocalization are largely compatible and that this relationship can be exploited to draft the overall spatial organization of the chromosome in vivo . The more general validity and implications of these findings could be investigated by applying to other eukaryotic chromosomes the general and transferable computational strategy introduced here .
The advent of innovative fluorescence-based techniques has provided an unprecedented insight into the organization of eukaryotic chromosomes during various phases of the cell cycle [1] , [2] . A notable example is given by the demonstration - based on imaging techniques - that when the tightly packed mitotic chromosomes enter interphase they swell and occupy specific nuclear regions , aptly termed “territories” [1] . More recently , the salient local and global spatial properties of chromatin fibers inside these territories have been addressed by the so-called “chromosome conformation capture” techniques [3]–[7] , which allow for probing the cis/trans contact propensity of various chromosomal loci . The recent systematic application of these experimental techniques is providing increasing evidence that chromosomes are organized in functionally-heterogeneous macrodomains with different molecular and genetic composition [6] , [8] , [9] . Several efforts are being spent to clarify the functionally-oriented implications of such chromosomal organization . Towards this goal , some of us have recently carried out a comprehensive bioinformatic survey of data gathered in more than 20 , 000 gene expression profiles measured for several cell lines in different human tissues [10] . It was thus established that genes can be grouped into large clusters based on significant pairwise correlations ( mutual information ) of their expression patterns . In addition , the matrix of pairwise gene expression correlations displayed features qualitatively similar to the matrix of pairwise gene contacts inferred from the HiC [6] . Furthermore , for various model organisms , specific sets of genes that are systematically coexpressed were shown to be in spatial contact too [11]–[13] . A chief example is provided by the human gene , an base pairs-long region on human chromosome 9 . This gene , during virus infection , induces colocalization and coexpression of distant bound genomic loci [14] . While not all sets of coexpressed or coregulated genes are expected to be nearby in space [15] , several arguments and model calculations have consistently indicated that the simultaneous colocalization of multiple genes can occur with appreciable probability even when the genes are far apart along a chromosome and in the presence of a crowded nuclear environment [16] , [17] . Indeed , it has been argued that the cooperative colocalization of various genes can provide a very efficient means for achieving their functional coregulation [18] , [19] . These considerations motivated the present numerical study where a knowledge-based coarse-grained model of eukaryotic chromosome 19 is used to ascertain whether the large number of coregulated gene pairs on a given chromosome can be actually colocalized in space . The analysis therefore complements recent efforts through which the organization of model chromatin fibers was investigated by bringing distant regions into contact by using attractive interactions , which either mimicked the effect of transcription factories [17] or 5C-based distance restraints [20] . Our investigation , is carried out for human chromosome 19 ( Chr19 ) . This chromosome , which is typically located at the nucleus center [6] , was chosen because it has the highest gene density and extensive gene expression data are available for it . By analysing the mutual information content of thousands of such expression profiles we identify hundreds of coregulated gene pairs for Chr19 . These coregulated gene pairs are next mapped onto a previously-validated model for interphase chromosomes ( where the chromatin filament is coarse-grained at a resolution of ) and their pairwise colocalization is enforced using a steered molecular dynamics scheme . The protocol is applied to various initial chromosome configurations where the degree of entanglement is comparable to that expected for chromosomes in vivo ( based on the crumpled-globule interpretation of HiC data [6] , [21] ) or much higher ( as in equilibrated polymer chains ) . Further terms of comparisons were obtained by randomizing the positions or pairings of the loci to be colocalized . Notably , for initial chromosome conformations with low entanglement , it is found that most ( ) of the coregulated gene pairs can indeed be brought into contact and this promotes the formation of spatial macrodomains similar to those inferred from HiC measurements of human chromosome 19 . The percentage of satisfied colocalization constraints , and the macrodomain similarity is dramatically reduced when the initial chromosome arrangements are significantly entangled and when the coregulatory network is changed by suppressing the numerous native coregulatory cliques , that are groups of genes all mutually coregulated . The observed compliance of the model chromosomes towards the gene colocalization demonstrates that bringing into simultaneous spatial proximity most of the thousands of coregulated gene pairs for Chr19 is physically viable . The findings are therefore consistent with the hypothesis that coregulated genes are likely to be in contact too . This conclusion is further supported by the fact that the spatial macrodomains found in the constrained , steered conformations of Chr19 are well-consistent with those inferred from Hi-C data .
A number of experimental studies have given the consensual indication that various sets of coregulated genes tend to be nearby in space , even if they are at a large genomic distance ( reviewed in Ref . [12] ) . Because gene colocalization is not necessary in principle to achieve gene coregulation or coexpression ( the latter can , for instance , be induced by controlled hormone addition [15] ) it is not clear whether there exists a general connection between gene coregulation and gene colocalization and what would be the general biological implications . In particular , two such important ramifications regard the interplay of chromosome conformational arrangement and gene expression or regulation . The first issue relates to the entanglement of the long and densely packed chromatin filaments: is their arrangement too intricate to allow for the simultaneous colocalization of all ( or most ) pairs of coregulated genes ? Secondly , in case there exists a strong association between gene coexpression and colocalization , is it at all feasible to use gene coexpression data as distance restraints to pin down viable chromosome conformations ? To make progress on these standing issues we developed and used a knowledge based numerical approach to investigate the gene coregulation–colocalization relationship in human Chr19 using a coarse-grained chromosome model . Chr19 which is long , was chosen because it has the highest gene density compared to other chromosomes [22] . This property reflects , in turn , in the possibility to use publicly available gene expression data to derive knowledge based colocalization constraints that cover extensively Chr19 . To this purpose we started by considering expression measurements for probe sets for Chr19 . As customary we shall hereafter refer to the probe sets simply as genes . By analysing this large pool of data using the approach described in the Materials and Methods section , we singled out 1 , 487 pairs of genes which , according to the high mutual information content of their expression profiles , are deemed to be significantly coregulated [23] . Notably , the selected pairs of genes are typically far apart along the chromosome contour . The median genomic separation of the midpoints of the coregulated genes is as large as . To clarify whether , and to what extent , the coregulated gene pairs can be simultaneously colocalized we used a coarse-grained model for chromatin filaments that has been previously shown to be capable of accounting for the fractal-like organization observed for eukaryotic chromosomes [6] , [21] , [24]–[28] . Specifically , we adopted the model of Ref . [21] where chromatin is described as a homogeneous chain of beads with effective diameter equal to and persistence length equal to . Accordingly , Chr19 is described as a chain of beads , for a total contour length of . To mimic inter-chromosome interactions in the dense nuclear environment , we considered a system where six copies of Chr19 are placed in a cubic simulation box ( with periodic boundary conditions ) of side equal to . The overall system density is therefore , which corresponds to a 10% volume fraction . Such density matches the typical genomic one in human cell nuclei ( in a nucleus that is in diameter [21] ) . To mimic the mitotic state , each model chromosome was initially prepared in an elongated solenoidal-like configuration [21] , and the six copies were placed in a random , but non-overlapping arrangement inside the cubic simulation box as shown in Fig . 1A . To remove any excessive intra-chain strain of the orderly designed mitotic arrangement , the model chromosomes of Fig . 1A were briefly evolved with an unbiased MD protocol . The resulting relaxed mitotic configuration is shown in Fig . 1B . This mitotic arrangement was further evolved for a much longer simulation time , roughly corresponding to hours in “real-time” [21] , to obtain the fully decondensed arrangement shown in Fig . 1C . Such configuration exhibits the same power-law decay of contact probabilities versus genomic separation as observed in HiC experiments [6] , [29] , see inset of Fig . 1C . The model system therefore aptly reproduces the salient experimentally-observed features of interphase chromosomes . After setting up the mitotic and interphase systems , we next applied a steered molecular dynamics protocol to each of them ( see Materials and Methods ) to promote the spatial proximity of regions corresponding to coregulated gene pairs . The compliance of the two systems to the steering protocol is illustrated in Fig . 2 which shows the increase of the percentage of target gene pairs that are successfully colocalized . It is striking to observe that for both system it is possible to simultaneously colocalize a very high fraction of the target pairs , namely 80% of them ( averaged over the six chromosome copies ) . The conformations reached at the end of the steering protocol are shown in the right panels of Fig . 2 . Considering the relatively-high density of the simulated system of chromosomes and that most of the coregulated pairs lie at large genomic distances , the results point to an unexpectedly high degree of plasticity of the mitotic and interphase conformations , which is presumably ascribable to their fractal-like metric properties which keeps at a minimum the entanglement of the chromatin fiber [6] , [21] , [24]–[28] , [30] . A second noteworthy feature of the results of Fig . 2 emerges considering the diversity of the sources used to derive the knowledge-based coregulation data . In fact , granted the validity of the coregulation–colocalization hypothesis , one might have envisaged a priori that the chromosomal configurations corresponding to different tissues or experimental conditions would be so heterogeneous that it would be impossible to satisfy the cumulated set of colocalization constraints . By contrast , the results of Fig . 2 demonstrate a posteriori , that the set of pairwise colocalization constraints are largely mutually compatible because most of them can be simultaneously satisfied . The findings are therefore not only consistent with the coregulation–colocalization hypothesis but , based on such hypothesis , also suggest that the conformations adopted by a chromosome in various conditions can share a common underlying pattern of colocalized genes . To further characterize the overall organization of the steered conformations shown in Fig . 2 we identified their spatial macrodomains and compared them with those inferred from the analysis of HiC data collected by Dixon et al . [9] . In both cases , the starting point of the analysis was the construction of the chromosome contact map with a resolution , which is commensurate with both the experimental resolution ( ) and the bead equivalent contour length ( ) . The HiC-data based contact map was derived from the contact enrichment values reported by Dixon et al . [9] while the simulation-based one was computed from the bead pairwise distances at the end of the steering protocol ( averaged over the six chromosome copies ) , see Materials and Methods . Both matrices are shown in Fig . 3 . A clustering analysis of the contact maps was next used to subdivide Chr19 into up to ten spatial macrodomains , each spanning an uninterrupted chromosome stretch , and with the proviso that one domain should cover the centromere . For both maps the consensus domain boundaries were well-captured by the subdivision into eight spatial domains , see Fig . S1 . The corresponding macrodomain partitions are overlaid on the contact maps of Fig . 3 . The good consistency of the domains found using HiC-based and steered-MD contacts maps is visually conveyed by the matching colored regions in the schematic chromosome partitioning of Fig . 3 . It is interesting to notice that the two domain subdivisions consistently indicate larger domains for the upper arm . Quantitatively , the overlap of the two subdivisions is , which has a -value smaller than . This means that random partitions of the chromosome into eight domains ( one always being the centromere ) yields overlaps in less than of the cases , see Fig . S2 . The quantitative comparison therefore indicates a statistically-significant consistency of the spatial macrodomains arising in the steered chromosome conformations and those inferred from experimental data . Besides the previous considerations , the results of Fig . 2 prompt the question of whether , and to what extent the feasibility to colocalize a significant fraction of the coregulated gene pairs depends on distinctive chromosomal features , such as the spatial arrangement of the mitotic and decondensed states or the network of coregulated genes . To address these issues we re-applied the steering protocol starting from 3 different initial conditions , which correspond to specifically designed variants of the model chromosomes . Specifically , the three systems are: As for the native network of target gene pairs , we report on the properties measured at the end of the steering protocol after averaging them over the six chromosome copies in the simulation cell . We stress that the three variants are prepared so to preserve the native overall density , number of coregulated genes and also the number of coregulated pairs to which a selected gene takes part to . They nevertheless present major differences which allow for probing the impact of different system properties on gene “colocalizability” . In particular , the random-walk-like arrangement has a much higher degree of intra- and inter-chain entanglement than all other arrangements , as illustrated by the much wider distribution of gene pairwise distances in the initial configuration , see Fig . 5 . For randomly-paired and randomly-repositioned genes , instead , the distributions of genomic distances of the target genes to be paired is similar to the native one . This is clearly shown by the distributions in Fig . 5 . However , the same figure clarifies that the two randomized cases differ markedly from the native one for the clustering coefficient . The clustering coefficient captures the degree of cooperativity of the ( putative ) coregulatory network in that it measures how frequently two genes that are both coregulated with a third one , are themselves coregulated too . The inspection of the rightmost graphs in Fig . 5 therefore indicates that the clustering coefficient distribution of the randomly-paired system is shifted towards much smaller values than the others , which all inherit the native pairings network . This fact indicates that the clustering coefficient of the native network is significantly larger than random . This implies that genes can frequently interact concertedly in groups of three or more . The results of the steering protocol applied to the three system variants are shown in Fig . 6 . The data indicate that: ( i ) for random-walk-like chromosomes only a minute fraction ( ) of the target contacts can be satisfied; ( ii ) for randomly-paired genes about of the gene pairs can be colocalized , while ( iii ) for randomly-repositioned genes about of the gene pairs can be colocalized , similarly to the native case ( Fig . 2 ) . These findings provide valuable clues for interpreting the high degree of “colocalizability” of coregulated genes observed in Fig . 2 for the mitotic and interphase arrangements . In particular , the very low asymptotic value of the percentage of successfully colocalized gene pairs for the random-walk-like system clarifies that the low intra- and inter-chromosome entanglement of both the mitotic and decondensed configurations is crucial for bringing into contact the coregulated gene pairs . Furthermore , the comparison of the randomly-paired and randomly-repositioned gene cases shows that the connectivity properties of the native coregulatory network appear even more important than the detailed positioning of the coregulated genes along the chromosomes . In fact , the randomly-repositioned genes – which retain the same clustering coefficient of the native coregulatory graph – have the same high degree of colocalizability of the native system . By converse , the low clustering coefficient of the randomly-paired gene case – corresponding to a significant disruption of the original network – reflects in an appreciably lower value of percentage of successfully colocalized gene pairs . It is also worth noticing that , in all cases , a significant fraction of gene pairs brought in contact are at large genomic distances ( ) , see Fig . S3 . Finally , the network randomization effects on the spatial organization of the steered conformations was addressed by measuring the overlap of their spatial macrodomains with those established from HiC data . We recall that for chromosome subdivisions into eight macrodomains , the native case overlap was . For the randomized gene positions and randomized gene pairings we instead observe the lower values and 0 . 63 , respectively . These values clearly have a much lower statistical significance than the native case; their -values being respectively and , see Fig . S2 . Their non-significant similarity with the reference , HiC-data based macrodomain subdivisions underscores the randomized , non-native constraints result in appreciably-different , and less realistic , chromosomal features . Recent experimental advancements have provided unprecedented insight into the occurrence of concerted transcription of multiple genes . In particular , it was reported that the chromatin fiber can rearrange so that genes , concertedly transcribed upon activation , are found nearby in space too . Because of its important ramifications , the possible existence of a general relationship between gene coregulation and gene colocalization , the so called “gene-kissing” mechanism [11] , [12] , is a subject of very active research . This standing question was addressed here numerically by carrying out molecular dynamics simulations of a knowledge-based coarse-grained model of human chromosome 19 . The model consisted of a coarse-grained representation ( resolution ) of the chromatin fiber complemented by the knowledge-based information of the loci corresponding to ( ) coregulated gene pairs . These pairs were identified from the analysis of extensive sets of publicly-available gene expression profiles . To mimic the crowded nuclear environment , we considered a system where several copies of the model chromosome 19 were packed at typical nuclear densities . The colocalization of the coregulated gene pairs was finally imposed by applying a steered molecular dynamics protocol . It was found that most ( ) of the coregulated pairs could be colocalized in space when the steering protocol was applied to chromosomes initially prepared in mitotic-like and interphase-like arrangements , see Fig . 2 . Notably , the pattern of intra-chromosome contacts established for the steered conformations exhibited significant similarities with that of experimental contact propensities [6] , [7] of chromosome 19 . Furthermore , the overall chromosomal organization into spatial macrodomains showed significant similarities with that inferred from experimental HiC data . By converse , the percentage of colocalized target pairs decreased substantially ( or vanished altogether ) when the system was initially prepared in a random-walk like arrangement , or if the genes to be colocalized were randomly paired or displaced along the chromosome . Likewise , the macrodomain organization of these alternative systems was found to be much less similar to the HiC-data based one . The present findings allow to draw several conclusions . First , the data in Fig . 2 demonstrate that , even in a densely packed system of mitotic or interphase chromosomes it is physically feasible to achieve the simultaneous colocalization of a large number of pairs of loci that can be very far apart along a chromosome . This result is therefore well compatible with the gene coregulation–colocalization hypothesis . In fact , the findings can be read as adding support to the hypothesis in consideration of the fact that if no meaningful relationship existed between coregulation and colocalization one might have expected the unfeasibility of bringing into simultaneous contact so many coregulated pairs . The much poorer compliance of alternative systems ( random-walk-like chromosome conformations , randomized gene pairings and positions ) to the steering protocol provides valuable insight into the native chromosomal properties that allow for gene colocalization . The first and most important property is the low degree of entanglement that mitotic or interphase chromosomes are known to have compared to equilibrated polymer solutions of equivalent density [6] , [21] , [24]–[28] , [30] , [31] . The second property is that the number of gene cliques that is present in the native gene regulatory network of chromosome 19 is much higher than for the equivalent random network . In this respect it is worth pointing out that the atypically large number of cliques found in biological regulatory networks has also been observed and pointed out in different contexts and for a different set of chromosomes [32] . To further validate this conclusion we considered an additional target network for the steered-MD simulations . This network was obtained by a partial randomization of the native gene pairings and its average clustering coefficient was , which is intermediate to the native one ( ) and the fully-randomized case ( ) discussed previously . As shown in Fig . S4 , of the target colocalization constraints were satisfied . This value is intermediate between the native and fully-randomized case ( and , respectively ) and hence supports the existence of a meaningful correlation between gene colocalizability and the regulatory network cliquishness . In perspective , because the computational strategy employed here is formulated in a general and transferable way , it would be most interesting to apply it to other eukaryotic chromosomes for which extensive co-regulatory data is available . This could clarify the more general validity of the gene coregulation-colocalization relationship as well as the broader implications of using it ( possibly with other knowledge-based constraints [20] , [33] , [34] ) , for charting the spatial organization of eukaryotic chromosomes , and possibly of systems of chromosomes .
To identify the set of significantly coregulated gene pairs on Chr19 we processed a set of expression profiles of human probe sets measured in distinct microarray experiments . The gene expression profiles , which were all measured on HG-U133A Affymetrix chip , pertain to different human cell types and tissues in various experimental conditions . This extensive dataset was recently compiled and curated by some of us [10] starting from the public ArrayExpress database [35] . The analysis was restricted to the set of 1 , 278 probe sets which exclusively target a single sequence ( i . e . an uninterrupted stretch ) of chromosome 19 . Next , to perform a robust comparison between the differently normalized gene expression profiles we coarse-grained all expression levels to one of three discrete states only: low , medium and high , as done in Ref . [10] . For each possible probe set pair , and , we next computed the mutual information [10] content ( MI ) of the expression profiles: ( 1 ) where [] runs over the three coarse-grained expression levels for probe set [] . In Eq . 1 , is the joint probability that , in a given experiment , the expression levels and are respectively observed for probe sets and , while the quantities and are the probabilities to observe expression level [] for probe set [] ( marginal probabilities ) . The MI thus provides a statistically-founded measure of how the gene expression pattern for gene is predictable assuming the knowledge of another pattern ( or , vice versa ) . To single out the pairs of probe sets with statistically-significant coexpression we proceeded according to the procedure described below and summarized graphically in Fig . 7 . First , to account for the expected dependence of gene coregulation on genomic distance , we subdivided the probe set pairs in groups . The first , second , etc . group gathered pairs of probe sets whose central bases had a genomic distance falling in the intervals 0–4 Mb , 4–8 Mb , etc . Next , for each group we fitted the histogram of the pairwise MI values , with the analytical expression which is known to approximate well the distribution of MI values expected for two random variables ( expression of the two genes ) assuming possible distinct values ( low , medium and high ) [36] . In the previous expression is the mutual information and and are the free fitting parameters . The comparison with the reference , null distribution is used to define the Mutual Information threshold above which at most one false-positive entry is expected to occur . All probe set pairs exceeding this stringent MI threshold were retained ( see Fig . 7C ) . The number of selected pairs for each bin ranged from to , for a total of probe pairs . It should be noted that several of these pairs involve chromosome regions that are highly overlapping and are hence degenerate ( or nearly degenerate ) . To eliminate this redundancy , we grouped together the pairs of coregulated probe sets that assure the coregulation of regions , whose central beads are separated by less than ( which corresponds to the chromatin fiber statistical ( Kuhn ) length [21] ) . For each of these groups , we retained only the pair with the largest MI value . This filtering procedure returned 1 , 487 non-degenerate probe set pairs , that involved probe sets ( native case ) . As customary , the significant degree of coexpression of such pairs was deemed indicative of their coregulation [23] . A system of densely packed chromosomes was modelled at a resolution of . Specifically , we considered model chromosomes packed at the typical nuclear density of . Each of the six chromatin fibers was described as a chain of beads with diameter , which corresponds to the total contour length of human chromosome 19 . Each bead therefore represents base pairs [38] . The potential energy of each chain is written as , ( 2 ) where and run over the bead indices and the three terms correspond to the FENE chain-connectivity interaction [39] , the bending energy , and the repulsive pairwise Lennard-Jones interaction . The three energy terms are parametrized as in previous studies of coarse-grained chromosomes [21] , [29] . Specifically , ( 3 ) where is the distance of the centers of beads and , , and the thermal energy equals [39] . ensures the connectivity of the chain , i . e . the centers of two consecutive beads must be at a distance about equal to their diameter . The bending energy has instead the standard Kratky-Porod form ( discrete worm-like chain ) : ( 4 ) where . ensures that the chain of beads bends over contour lengths the size of the persistence length to model the experimental rigidity of the chromatin fiber [40] . Finally , the excluded volume interaction between distinct beads , including consecutive ones , corresponds to a purely repulsive Lennard-Jones potential: ( 5 ) This repulsive interaction controls the inter-chain excluded volume too: ( 6 ) where is the number of chains in solution and the index runs over the beads in chain . ensures that any two regions along the same chain or on different chains cannot pass through each other . In this way , intra- and inter-chain topology is preserved . The LAMMPS molecular dynamics software package [41] is used to integrate the system dynamics at constant temperature and volume . The integration time step was set equal to , where is the Lennard-Jones time and is the bead mass which was set equal to the LAMMPS default value . Periodic boundary conditions apply . The “native case” system was evolved from three different starting conditions shown in Fig . 1: mitotic , interphase and random arrangements , whereas the randomized cases systems were evolved from the mitotic one . To monitor the progress of the steered molecular dynamics simulations and to characterize the salient properties of the resulting configurations we computed two order parameters , namely the percentage of coregulated pairs that are colocalized and the clustering coefficient of the coregulated pair graph . The two parameters are defined hereafter . In the above expression , the sum runs over the coregulated pairs of genes , and which are in total ( i . e . 1 , 487 for each of the six chromosome copies ) , is the distance of their centers of mass . is the Heaviside step which takes a value of 1 if and 0 otherwise . is used to restrict the sum to those gene pairs that are at distance within the contact range , . This cutoff distance was chosen because it is about equal to the typical size of a “transcription factory” [19] . The clustering coefficient of the individual th node in the graph is defined as [44] , [45] ( 8 ) where is the number of neighbours of while is the number of distinct links between the neighbours of node . The clustering coefficient per node , , is clearly defined only for nodes with at least two neighbours . The clustering coefficient of the whole graph is obtained by averaging over all nodes with . The clustering coefficient provides a measure of the incidence of cliques of size ( “triangular linkages” ) in the graph . The overall spatial organization of Chr19 was encoded in a binary contact matrix , , with a resolution . The generic matrix entry takes on the value or according to whether the th and th 60kbp-long segments ( equivalent to beads ) are in spatial proximity or not . The recent high-resolution HiC measurements of Dixon et al . [9] were used to derive the experimental , reference contact map . Specifically , for every significant HiC entry ( i . e . normalized contact enrichment ) the corresponding contact-matrix elements were set equal to . The resulting HiC-based contact map is sparse in that only of its entries are non-zero . For an equal footing comparison , we next populated the theoretical contact maps by considering in spatial contacts ( entries equal to 1 ) only the top -strands ranked for increasing average distance . The distance average is taken over the six Chr19 copies at the end of the steering protocol . A clustering analysis of the contact maps was next used to subdivide Chr19 into up to ten spatial macrodomains . Each domain spans an uninterrupted stretch of the chromosome and one domain always matches the centromere region . Following the K-medoids clustering strategy [46] the optimal domain partitioning was identified by minimizing the total intra-domain dissimilarity . Quantitatively , the internal dissimilarity of one specific domain , covering the chain interval to is measured as: ( 9 ) where is the contact map and , which is the domain representative , is the element belonging to the – interval for which is minimum . Consistently with intuition , the dissimilarity score , , takes on small or large values if respectively many or few domain members are in contact with the representative . For a given number of domains , the optimal domain partitioning is the one that minimizes the sum of the scores for the domains . For a given number of domains , the consistency of the steered-MD and HiC-based subdivisions was measured by establishing a one-to-one correspondence of each domain in the two cases and next measuring the percentage of elements , , having identical domain assignment . The one-to-one domain correspondence was identified by exploring the combinatorial space of correspondences and picking the one yielding the largest value of . | Recent high-throughput experiments have shown that chromosome regions ( loci ) which accommodate specific sets of coregulated genes can be in close spatial proximity despite their possibly large sequence separation . The findings pose the question of whether gene coregulation and gene colocalization are related in general . Here , we tackle this problem using a knowledge-based coarse-grained model of human chromosome 19 . Specifically , we carry out steered molecular dynamics simulations to promote the colocalization of hundreds of gene pairs that are known to be significantly coregulated . We show that most ( ) of such pairs can be simultaneously colocalized . This result is , in turn , shown to depend on at least two distinctive chromosomal features: the remarkably low degree of intra-chain entanglement found in chromosomes inside the nucleus and the large number of cliques present in the gene coregulatory network . The results are therefore largely consistent with the coregulation-colocalization hypothesis . Furthermore , the model chromosome conformations obtained by applying the coregulation constraints are found to display spatial macrodomains that have significant similarities with those inferred from HiC measurements of human chromosome 19 . This finding suggests that suitable extensions of the present approach might be used to propose viable ensembles of eukaryotic chromosome conformations in vivo . | [
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] | 2013 | Colocalization of Coregulated Genes: A Steered Molecular Dynamics Study of Human Chromosome 19 |
The emerging pathogen Cryptococcus gattii causes life-threatening disease in immunocompetent and immunocompromised hosts . Of the four major molecular types ( VGI-VGIV ) , the molecular type VGIII has recently emerged as cause of disease in otherwise healthy individuals , prompting a need to investigate its population genetic structure to understand if there are potential genotype-dependent characteristics in its epidemiology , environmental niche ( s ) , host range and clinical features of disease . Multilocus sequence typing ( MLST ) of 122 clinical , environmental and veterinary C . gattii VGIII isolates from Australia , Colombia , Guatemala , Mexico , New Zealand , Paraguay , USA and Venezuela , and whole genome sequencing ( WGS ) of 60 isolates representing all established MLST types identified four divergent sub-populations . The majority of the isolates belong to two main clades , corresponding either to serotype B or C , indicating an ongoing species evolution . Both major clades included clinical , environmental and veterinary isolates . The C . gattii VGIII population was genetically highly diverse , with minor differences between countries , isolation source , serotype and mating type . Little to no recombination was found between the two major groups , serotype B and C , at the whole and mitochondrial genome level . C . gattii VGIII is widespread in the Americas , with sporadic cases occurring elsewhere , WGS revealed Mexico and USA as a likely origin of the serotype B VGIII population and Colombia as a possible origin of the serotype C VGIII population . Serotype B isolates are more virulent than serotype C isolates in a murine model of infection , causing predominantly pulmonary cryptococcosis . No specific link between genotype and virulence was observed . Antifungal susceptibility testing against six antifungal drugs revealed that serotype B isolates are more susceptible to azoles than serotype C isolates , highlighting the importance of strain typing to guide effective treatment to improve the disease outcome .
The encapsulated basidiomycetous yeast Cryptococcus gattii is the second most important etiological agent of cryptococcosis , next to its sibling species C . neoformans . Both species can cause central nervous system and pulmonary manifestations [1] . However , C . gattii has a more limited geographical distribution and is recovered less frequently [2 , 3] . Initially considered prevalent only in tropical and subtropical regions , C . gattii emerged in a temperate climatic zone on Vancouver Island , British Columbia , Canada in 1999 and has since extended to the Pacific Northwest and other locations within the USA [4 , 5] . Despite the fact that the global burden of C . gattii may still be unrecognized [3] , the increasing isolation of uncommon molecular types and extension of their geographic spread defines C . gattii as an emerging fungal pathogen . C . gattii is comprised of two serotypes , B and C . Among them , four major molecular types , ( VGI/AFLP4 , VGII/AFLP6 , VGIII/AFLP5 and VGIV/AFLP7 ) have been consistently recognized by various molecular techniques , including PCR-fingerprinting [6 , 7] , restriction fragment length polymorphism ( RFLP ) analysis [8] , amplified fragment length polymorphism ( AFLP ) analysis [9] and multilocus sequence typing ( MLST ) [10] . More recently , a fifth AFLP type ( AFLP10 ) was described based on a single isolate [11] . These molecular types have been proposed for some time to be recognized as either varieties [12] or , more recently , as distinct species [13] . However , for the purpose of the current study they will be treated as distinct molecular types . Among C . gattii , the molecular type VGII , serotype B , has caused most of the outbreak-related cases , via clonal dispersion of three sub-genotypes , VGIIa , VGIIb and VGIIc [14–17] . Independent cases of infections caused by the molecular types VGI , VGIII and VGIV have been reported less frequently . VGI is the most prevalent molecular type in Australia and Papua New Guinea , where it is considered endemic [18 , 19] . VGIII is found predominantly in clinical and environmental sources in Mexico , Colombia and the USA [20–24] , and VGIV has been mainly reported from India , African and a number of South American countries [8 , 25 , 26] . The number of C . gattii infections due to strains of the major molecular type VGIII is not only increasing in the endemic areas in South and Central America but also in the USA , where it is now a major cause of human and animal disease , with increasing cases reported from the Southeastern parts , especially California [17 , 27–29] . A total of 42 cases of human disease alone have been recorded since 2010 , with the obtained genotypes being highly similar to the ones seen in both veterinary and environmental isolates . In contrast to the above-mentioned clonal VGII population , VGIII isolates have been highly diverse as shown by MLST analysis . This diversity has been attributed to the existence of both mating types α and a amongst clinical , veterinary and environmental VGIII isolates , which provides indirect evidence of sexual reproduction and dynamic recombination [17 , 27] . Furthermore , in South America and India , C . gattii VGIII has been isolated from the environment , although a specific association between its occurrence in the environment in these countries and cases of cryptococcosis has yet to be explored [22 , 30 , 31] . In a recent study , however , VGIII isolates recovered from arboreal sources in Southern California were genetically related to those causing human cases in similar locations , suggesting that these environmental isolates were the source of human infections [32] . Besides the C . gattii VGIII infections reported amongst HIV positive patients from Southern California and Mexico [20 , 21 , 33] , the same molecular type has been recovered from immunocompetent human and veterinary patients . Several cases , including fatal infections , have been reported in patients without predisposing risk factors in Brazil , Colombia , Cuba , Mexico and the USA [17 , 23 , 24 , 34–39] . The emergence of C . gattii infections in immunocompetent patients is a source of public health concern as cryptococcosis is not usually suspected in this group and significant delays in diagnosis are associated with adverse outcomes . In apparently healthy hosts , cryptococcosis typically presents with cerebral involvement and is associated with more severe neurological sequelae , such as stroke , blindness , deafness and permanent neural deficits [37 , 39–41] . In addition , C . gattii VGIII isolates have been reported to be more susceptible to amphotericin B and 5-flucytosine than isolates of C . neoformans and other C . gattii molecular types [42 , 43] . Although azoles may have good in vitro activity against VGIII isolates , veterinary isolates of molecular type VGIII exhibited a wide range of minimum inhibitory concentrations ( MICs ) for fluconazole , with MICs as high as 32 μg/ml [28] . Previous studies have also indicated that VGIII is the most virulent molecular type in a Drosophila model of infection [44] . These differences have brought about the need to evaluate the epidemiology , disease transmission and virulence factors of this emerging pathogen . Therefore , the current study aimed to characterize C . gattii VGIII isolates genetically and phenotypically . In particular , to investigate the population genetic structure of VGIII isolates and to correlate geographic origin , source ( clinical , veterinary or environmental ) , virulence in a mouse model of infection , and antifungal susceptibility . The data obtained from these investigations established a clearer understanding of the epidemiology and pathogenicity of C . gattii VGIII , revealed distinct ancestral populations , and found that there is no specific link between virulence and genotype of the studied isolates on the whole or mitochondrial genome level .
One hundred twenty-two isolates of C . gattii molecular type VGIII from Australia ( n = 1 ) , Colombia ( n = 37 ) , Guatemala ( n = 1 ) , Mexico ( n = 14 ) , New Zealand ( n = 1 ) , Paraguay ( n = 1 ) , the USA ( n = 66 ) and Venezuela ( n = 1 ) , were studied . Isolates were stored at -80°C in glycerol in the Westmead Millennium Institute Culture Collection ( Australian Medical Mycology Culture Collection ) located at the Molecular Mycology Research Laboratory , Westmead Millennium Institute , The University of Sydney , Sydney Medical School , Sydney , Australia ( S1 Table ) . Among these isolates 56 were clinical , 38 veterinary and 28 of environmental origin . Standard strains of the four major molecular types of C . gattii , WM 179 ( VGI/AFLP4 , serotype B ) , WM 178 ( VGII/AFLP6 , serotype B ) , WM 175 ( VGIII/AFLP5 , serotype B ) and WM 779 ( VGIV/AFLP7 , serotype C ) were included as reference strains for the genotypic analysis [10] . The proposed type culture of the AFLP10 strain [13] was not included , as it was not publicly available at the time this study was undertaken . Isolates were cultured on Sabouraud dextrose agar and incubated for 48 h at 27°C prior to DNA extraction . Genomic DNA was extracted as described previously [45] . Restriction fragment length polymorphism ( RFLP ) analysis of the orotidine monophosphate pyrophosphorylase gene ( URA5 ) following double digestion with the enzymes Sau96I and HhaI ( New England BioLabs Inc ) was used to identify the major molecular type of the isolates as reported previously [8] . Mating type was determined by PCR using the primers MfαU and MfαL for mating type α and MFa2U and MFa2L for mating type a and the PCR and amplification conditions as described previously [46] . The serotype of all isolates was determined using RFLP analysis of the capsular polysaccharide gene ( CAP59 ) , digested with the enzyme AgeI ( New England BioLabs Inc ) , as described previously [47] . The serotype of 53 selected isolates ( 23 of serotype B and 30 of serotype C ) was in addition determined using the agglutination test CryptoChek ( Iatron Laboratories , Tokyo , Japan ) according to the manufacturer’s instructions ( S1 Table ) . In all instances , serotypes were concordant by both methods . Sequences of 360 bp of the partial region of the CAP59 gene , which is used to determine serotype by RFLP , were extracted from the isolates with Whole Genome Sequencing ( WGS ) and a maximum likelihood dendrogram of these sequences was constructed to show the clear separation between serotypes B and C isolates ( S1 Fig ) . The alignment and dendrogram were generated using the program MEGA 6 . 0 [48] . MLST typing was performed using the International Society of Human and Animal Mycology ( ISHAM ) consensus MLST scheme for C . neoformans and C . gattii , which includes seven genetic loci , CAP59 , GPD1 , LAC1 , PLB1 , SOD1 , and URA5 genes , and the IGS1 region , as described previously [10] . All loci were amplified independently and the obtained PCR products were purified and commercially sequenced by Macrogen Inc . , Seoul , Korea . Sequences were edited and contigs were assembled using Sequencher 5 . 3 ( Gene Codes Corporation , Ann Arbor , USA ) . Each unique sequence was assigned an allele type ( AT ) number and the seven allele types per strain were subsequently combined to give a unique sequence type ( ST ) according to the ISHAM consensus MLST database , accessible at http://mlst . mycologylab . org . Alignments were generated using the program MEGA 6 . 0 [48] . The dendrogram showing the genetic relationship between the isolates based on the maximum likelihood analysis of the seven concatenated loci was generated using the same program . Haplotype network analyses were performed using the software Network 4 . 6 . 1 . 3 ( Fluxus Technologies Ltd . , Suffolk , UK ) . The goeBURST algorithm using the PHILOVIZ software ( http://www . phyloviz . net ) was used to generate a minimum spanning tree of the concatenated sequences to visualize relatedness of the C . gattii isolates according to the source of isolation and serotype . The diagrams show when the STs differ in a single locus variant ( SLV ) , double locus variant ( DLV ) , and triple locus variant ( TLV ) . A clonal complex ( CC ) concept was adopted when SLV linkage with the founder ST was present [49] . To estimate the genetic diversity amongst the STs , Simpsons diversity index ( D ) was calculated for the whole population , as well as by country , source , serotype and mating type of the isolates . The length of each MLST locus , the number of alleles and their frequency were determined and the genetic diversity of the seven loci was estimated by calculating the average number of nucleotide diversity ( π ) and the number of polymorphic ( segregating ) sites ( S ) using the software DnaSP ver . 5 . 10 . 1 [50] . For comparison of the genetic diversity between the VGIII sub-populations , the index θ , which is the Weir's formulation of Wright's fixation index ( FST ) for population differentiation analysis , was calculated for each locus . FST values of >0 . 05 generally indicate little inter-population variance , and can range from 0 for identical populations to 1 for populations with no alleles in common . The Index of Association ( IA ) and rBarD were also calculated to test for recombination between and within the VGIII sub-populations . Since clonal reproduction can mask the effects of recombination , IA and rBarD were calculated using the clone-corrected data for each ST after removal of identical genotypes were removed ( haplotypes only ) . The values of both IA and rBarD are expected to be zero if populations are freely recombining and greater than zero if there is association between alleles . The rBarD statistic takes into consideration the number of loci tested and is considered a more robust measure of association . Values of FST , IA and rBarD were calculated using the program MultiLocus 1 . 3 [51] . The BEAST v1 . 8 . 3 software was used to perform the Bayesian molecular clock analysis of the VGIII MLST sequences [52] . The Tamura Nei model with invariable sites and gamma distribution ( TrNef + I + G ) used in BEAST was the best model selected from the Bayesian Information Criterion in the software jModelTest 2 . 1 . 7 [53] . The stepping-stone sampling marginal likelihood estimator available in MrBayes v3 . 2 software was used to infer the best-fitting clock model for the dataset [54] . A relaxed lognormal clock was applied to infer the time scale incorporating one internal node calibration of 8 . 5 million years as the time to most recent common ancestor for VGIII as already described [12] . A normal prior age distribution of 0 . 25 million years was used in the analysis . The XML file was generated in BEAUTI v1 . 8 . 3 with a run of 108 generations , 1 tree sampled per 1 , 000 generations , and a burn-in of 10% [52] . The LogCombiner v1 . 8 . 3 , distributed with BEAST , was used to combine the files of two independent runs applying a burn-in of 10% . The results were visualized using the Trace v1 . 6 . 0 software distributed with BEAST and showed that the effective sample size was higher than 200 in all analyses . The tree with the highest log clade credibility was selected in the software TreeAnotator v1 . 8 . 3 and the tree presenting the posterior mean and 95% confidence intervals of the time to most recent common ancestor was visualized in the FigTree v1 . 4 . 3 software ( http://tree . bio . ed . ac . uk/software/figtree/ ) . Sixty isolates , comprising 33 serotype B and 27 serotype C , representing the full diversity of the VGIII MLST genotypes , were selected for WGS . High quality DNA was extracted with the ZR Fungal/Bacterial DNA MiniPrep kit ( Catalog N° D6005; Zymo Research , Irvine , CA , USA ) following the instructions of the manufacturer . The samples were sequenced using Illumina HiSeq as previously described [16 , 55] . DNA samples were prepared for paired-end Illumina sequencing using the Kapa Biosystems Library Preparation with Standard PCR kit ( Catalog N° KK8232; Woburn , MA , USA ) protocol . Approximately 1μg of double-stranded DNA ( dsDNA ) was sheared using a Sonicman sonicator ( Brooks automation , Spokane , WA , USA ) to an average size of 650 bp and DNA libraries were prepared for sequencing as described by the manufacturer . Modified oligonucleotides ( Integrated DNA Technologies , Coralville , IA , USA ) with 8bp indexing capability [56] were substituted at the appropriate step . Libraries were quantified prior to sequencing with quantitative PCR ( qPCR ) on the ABI 7900HT ( Life Technologies Corporation , Carlsbad , CA , USA ) using the Kapa library quantification kit ( Catalog N° KK4835; Woburn , MA , USA ) . Libraries were sequenced to a read length of 100bp on the Illumina HiSeq system . WGS read files were deposited in the NCBI Sequence Read Archive under BioProject PRJNA289249 . All sequenced samples were assembled de novo using the SPAdes v2 . 5 . 0 assembler [57] . Read data for all genomes were aligned against the de novo assembly for sample WM 175 using Novoalign 3 . 00 . 03 ( Novocraft Technologies , Selangor , Malaysia ) . Single nucleotide polymorphisms ( SNPs ) were detected using the Genome Analysis Toolkit v2 . 4 ( GATK ) [58] . SNP calls were filtered using NASP ( http://tgennorth . github . io/NASP/ ) and had to meet the following criteria per SNP loci to be included in the final matrix: coverage of a minimum 10X and less than 10% variant allele calls . Additionally , reads that mapped to multiple locations within the genome were excluded from the analysis , as were positions located in an insertion or deletion site . The de novo assembly of sample WM 1814 was used as the reference strain for serotype C analyses; otherwise WM 175 was used as the reference strain . Additionally , one isolate of each of the other three C . gattii major molecular types ( VGI , VGII , and VGIV ) , previously sequenced by WGS , were included for phylogenetic analysis [15] . In total , three SNP matrices with different taxa were produced: ( i ) C . gattii molecular types VGI to VGIV; ( ii ) C . gattii molecular type VGIII only , and ( iii ) C . gattii major sub-populations ( serotype B and C ) together . Whole-genome SNP typing ( WGST ) was performed as previously described [16 , 59 , 60] for phylogenetic analysis in order to understand genetic relationships between isolates . To put the VGIII population in context with the other C . gattii major molecular types the genomes of the following C . gattii isolates were retrieved from GenBank: WM 179 and WM 276 representing molecular type VGI , WM 178 , WM 05 . 419 , WM 04 . 78 , WM 06 . 12 , WM 08 . 309 , CDCR265 , CDCR272 , B9816 and GT 11 . 7650 representing molecular type VGII , and WM 779 representing molecular type VGIV [15 , 60] . Maximum parsimony SNP trees were constructed using PAUP* v . 4 . 0b10 ( Sinauer Associates , Inc . , Sunderland , MA , USA ) and visualized using FigTree v . 1 . 3 . 1 ( http://tree . bio . ed . ac . uk/software/figtree/ ) . The C . gattii VGI to VGIV tree was not rooted . Additionally , the SNP matrix ( iii ) was examined for recombination using the phi test [61] . The neighbor-joining split tree network was drawn on the SNP matrix ( iii ) in order to visualize the existing recombination between samples using the program SplitsTree4 [62 , 63] with the uncorrected P-distance transformation . A maximum likelihood tree with 1 , 000 bootstrap generations was produced from SNP matrix ( ii ) using the TVM+ASC+G4 model in IQ-TREEv1 . 3 . 10 [64] . The tree was visualized using FigTree v1 . 3 . 1 . fineStrucutre analysis [65] was performed on the SNP matrix ( ii ) and ( iii ) in order to infer the population structure within VGIII as well as identify admixture events occurring between molecular types . Using the phylogenetic tree produced above , one representative from each clonal clade was selected and the SNP matrix was reduced to a pairwise similarity matrix using Chromopainter , which was run using the linkage model and assuming uniform rates of recombination per base pair of sequence . Populations were determined through fineStructure using the above-mentioned similarity matrix . Putative gene content comparison was performed using BLAST score ratio ( BSR ) analysis [66] , as previously described [15] . VGIII serotype B and serotype C predicted gene content differences were confirmed by alignment of sequence read data . Putative gene characterization of confirmed gene differences were translated into amino acid sequences and searched against the Pfam database ( http://pfam . xfam . org/ ) in order to identify potential protein functions . Additional characterization for selected putative genes were searched against the NCBI non-redundant protein database ( http://www . ncbi . nlm . nih . gov/RefSeq/ ) using blastp . In order to assess mitochondrial re-arrangements and mutations , four high-virulence and four low-virulence serotype B samples were assembled using SPAdes3 . 0 [67] . Mitochondrial genes of the highly virulent VGII CDCR265 strain from the Broad institute were used as reference sequences . Fifteen mitochondrial genes were used to identify the mitochondrial contigs in the eight genome assemblies using BLAT . Contigs containing the 15 mitochondrial genes were pulled from the assemblies and were aligned using progressiveMauve v2 . 3 . 1 [68] . Additionally , the 56kb contig containing the 15 mitochondrial genes from the isolate WM09 . 47 was used as a reference sequence for SNP analysis using NASP as described above . Based on the MLST results , 17 isolates were chosen to study the pathogenic potential of C . gattii molecular type VGIII in a murine model of pulmonary cryptococcosis . Female BALB/c mice , 6-weeks-old and weighing between 16 to 18 g , were inoculated intranasally with 105 yeast cells suspended in sterile saline . Prior to inoculation , the isolates were grown in Sabouraud dextrose agar at 37°C for 24 hours . Five mice per cryptococcal isolate studied were weighed and anesthetized intraperitoneally by injecting 0 . 03 to 0 . 04 ml of a combination of ketamine ( 44 mg/ml ) and midazolam ( 2 . 7 mg/ml ) ( 80 mg of ketamine and 5 mg of midazolam in a total volume of 1 . 8 ml ) , using an insulin syringe . Following induction of anesthesia , the mice were hung on a silk thread by their incisor teeth , so that the necks were fully extended . By using a pipette , 50 μl of inoculum was slowly instilled directly into each nostril . The well-studied C . gattii strains CDCR265 ( VGIIa , highly virulent ) and CDCR272 ( VGIIb , low virulence ) from the Vancouver Island outbreak were included as reference strains for comparison [69] . Five mice were also inoculated with sterile saline as an inoculation control . After inoculation , mice were placed in standard cages with access to water and food ad libitum and weighed and observed daily for signs of infection ( e . g . difficulty breathing , neurological signs , ruffled fur , lethargy , poor appetite ) , until the end point of 60 days . Affected mice were euthanized by CO2 ( 5% ) inhalation immediately upon observation of any signs of distress . Necropsy was performed and the brain and lungs were collected for macroscopic and histopathological examinations , to determine the presence of yeast and lesions . Blood collected aseptically from the heart using an insulin syringe was plated on Sabouraud dextrose agar to check for hematogenous dissemination . After harvesting tissues at necropsy , infected material was autoclaved and disposed of by incineration . To compare the virulence of selected isolates , survival curves for each isolate were graphed . Median survival times were obtained and differences in survival times were analyzed by the Log-rank ( Mantel-Cox ) test . Statistical analysis and plots were carried out using GraphPad Prism version 6 . 0b ( La Jolla , CA , USA ) . In all cases , p-values of <0 . 05 was considered statistically significant . Susceptibility testing was carried out using the Sensititre YeastOne plate ( Thermo Scientific , USA ) , which is a colorimetric microdilution test , following the manufacturer’s instructions . Briefly , isolates were grown on Sabouraud dextrose agar and incubated for 24 h at 27°C . Discreet yeast colonies were suspended with a swab into 5 ml of sterile water , adjusted to a density of 0 . 5 McFarland standard ( 1–5 × 106 cells/ml ) , and 20 μl aliquots were transferred into 11 ml of YeastOne inoculum broth for a final concentration of 1 . 5–8 × 103 CFU/ml . An aliquot of 100 μl of inoculum was placed in each well of the Sensititre YeastOne plate using a multichannel pipette . Plates were sealed , incubated at 35°C and read manually after 72 h of incubation . The reference strains of Candida krusei ATCC 6258 and Candida parapsilosis ATCC 22019 , were used as quality control . Controls were read after 24 h of incubation . Purity of the cell suspension and colony counts were determined by plating 10 μl of inocula on to Sabouraud dextrose agar . The range of drug concentrations tested by 2-fold serial dilutions was 0 . 125–8 μg/ml for amphotericin B; 0 . 125–256 μg/ml for fluconazole; 0 . 015–16 μg/ml for itraconazole; 0 . 008–8 μg/ml for voriconazole and posaconazole; and 0 . 06–64 μg/ml for 5-flucytosine . The range of minimum inhibitory concentrations ( MICs ) , MIC50 , MIC90 and geometric mean MICs of each antifungal drug were estimated . Epidemiologic cutoff values ( ECV ) , defined as the MIC value encompassing at least 95% of the wild-type distribution , were calculated for each antifungal drug . Significant differences in MICs between two groups of isolates were compared using a Mann-Whitney test . Group comparisons for MIC data included serotype B vs . serotype C; mating type α vs . mating type a; clinical vs . veterinary isolates; clinical vs . environmental isolates and veterinary vs . environmental isolates . All analyses were performed with GraphPad Prism version 6 . 0b ( La Jolla , CA , USA ) ; p-values <0 . 05 were considered significant . The virulence study was carried out in accordance with the protocol No . 4151-06-09 approved by the Westmead Hospital Animal Ethics Committee ( WHAEC ) adhering to Australian Code for the Care and Use of Animals for Scientific Purposes 8th Edition 2013 and the Animal Research Act New South Wales 1995 .
RFLP analysis of the URA5 gene identified 116 of the 122 isolates as molecular type VGIII and six isolates displayed the restriction pattern of the molecular type VGIV . In silico restriction and alignments of the URA5 sequences of these six isolates , revealed a single nucleotide polymorphism ( SNP ) in the position 528 , which is the restriction site of the enzyme Sau96I , resulting in misidentification of those isolates as VGIV [12] . However , as MLST analysis established that all 122 isolates were related , they were classified as molecular type VGIII . Serotype and mating type analysis identified 60 ( 49% ) isolates as B/α , 39 ( 32% ) as C/α , 15 ( 12% ) as B/a and 8 ( 7% ) as C/a . The obtained serotype data were confirmed based on CAP59 sequences extracted from the whole genome sequencing ( WGS ) data ( S1 Fig ) . Detailed descriptions of the mating and serotype results obtained for each isolate are provided in S1 Table . Among the 122 isolates , 55 sequence types ( STs ) were identified ( S1 Table ) . Of these , ST75 was the most frequent sequence type ( 21 serotype B isolates: five clinical and 15 veterinary isolates from the USA and one clinical isolate from Mexico ) , followed by ST79 ( 16 serotype C isolates: four clinical and nine environmental isolates from Colombia , two clinical isolates from Mexico and one from the USA ) . ST116 was the third most common sequence type , containing seven serotype B environmental isolates from Colombia . Of the remaining 52 STs , 37 were represented by a single isolate each , while 15 were represented by two to five isolates , with ST65 ( n = 2 ) and ST68 ( n = 2 ) , ST146 ( n = 3 ) and ST74 ( n = 5 ) , each identified in more than one country . ST65 , ST146 and ST74 each contained isolates from different source ( S1 Table ) . Based on maximum likelihood analysis and coalescence analysis of the seven concatenated MLST loci , C . gattii molecular type VGIII isolates separated into two major clusters or sub-populations corresponding mainly to serotype B and C ( Figs 1A , 2 , S1 Fig ) . These two sub-populations most likely correlate with the VGIIIa and VGIIIb lineages , respectively , that were recently described in independent MLST studies using a different MLST scheme [20 , 32] , based on the loci that both MLST schemes share ( GPD1 , LAC1 , PLB1 and IGS1 ) [10 , 20 , 32] . Among the serotype B isolates , WM 1811 and WM 1812 ( ST 99 ) , were identified as serotype C . However , because they shared most of the MLST alleles with the serotype B isolates , they were considered as such for the purpose of the analysis . Similarly , isolates WM 02 . 138 ( ST95 ) , WM 11 . 943 ( ST140 ) and WM 1663 ( ST94 ) were identified as serotype B , but were considered as serotype C for the analyses , as they clustered more closely with isolates of the later serotype . Isolates outside the two major clusters , namely the six isolates misidentified as VGIV by URA5-RFLP , mentioned previously , grouped into two additional small sub-populations , with each corresponding either to the serotype B ( n = 4 ) or C ( n = 2 ) ( Figs 1A , 1B and 2 ) . From these atypical strains , we deduced the presence of a novel serotype B , VGIII ancient lineage among C . gattii VGIII isolates , represented by three isolates from Colombia ( WM 2004 , WM 2041 and WM 2042 ( ST64 ) ) and one isolate from the USA ( WM 11 . 32 ( ST114 ) ) , reported herein for the first time . The coalescence analysis showed that these isolates diverged from the VGIII isolates around 1 . 91 to 6 . 53 million years ago . A second ancient lineage that diverged from VGIII isolates around 7 . 94 to 8 . 92 million years ago , represented by two serotype C isolates ( WM 1802 ( ST100 ) and WM 1804 ( ST101 ) ) from Mexico , likely corresponds to the previously described VGIIIc/AFLP10 lineage [11 , 32] , as these isolates share most of their MLST alleles with the published strain CBS11687 ( IHEM14941 = RV 63979 ) , with the exception of the SOD1 allele [11 , 13] . Both lineages appear to be basal to the VGIII clade ( Figs 1A , 1B and 2 ) . The sequences obtained for each allele type were deposited in GenBank under the following accession numbers: CAP59 ( JX840782—JX840787 ) , GPD1 ( JX840788—JX840795 ) , LAC1 ( JX840805—JX840821 ) , PLB1 ( JX840822—JX840832 ) , SOD1 ( JX840833—JX840840 ) , URA5 ( JX840841—JX840851 ) , and IGS1 ( JX840796—JX840804 ) ( S2 Table or at the INTERNATIONAL FUNGAL MLST DATABASE website at mlst . mycologylab . org ) . The VGIII population was genetically highly diverse ( D = 0 . 061 ) . There were no statistically significant differences among the groups with respect to the country of origin , isolate source ( clinical vs . veterinary vs . environmental ) , or mating type . Only minor differences were identified between serotypes , with serotype C isolates being slightly more diverse than those of serotype B ( D = 0 . 056 vs . 0 . 105 ( p < 0 . 05 ) ) ( S3 Table ) . Among the seven loci studied , LAC1 was the most informative locus with 17 alleles , 31 polymorphic sites over 477 bp , a nucleotide diversity of 0 . 895% , and a fixation index ( FST or θ ) of 0 . 5137 . Although SOD1 was represented by eight alleles , two more than CAP59 , it was the least informative locus with only 16 polymorphic sites over 713 bp and a nucleotide diversity of 0 . 205% ( Table 1 ) . Overall , the seven concatenated loci resulted in an alignment of 4 , 212 bp with 160 polymorphic sites . The high FST values of all seven loci indicate high genetic diversity between the VGIII sub-populations ( serotype B and serotype C clusters , respectively ) , indicating low genetic flow between them ( Table 1 ) . The low number of shared MLST alleles between serotype B and serotype C isolates ( S1 Table ) , and the high values obtained with the tests of linkage disequilibrium ( IA = 1 . 16184 and rBarD = 0 . 197630 ( p = 1 . 00 ) ) , further support low genetic flow within this population structure . Tests of linkage disequilibrium showed some recombination within each sub-population , with serotype B isolates ( IA = 0 . 478372 and rBarD = 0 . 0962771 ( p < 0 . 01 ) ) recombining less frequently than serotype C isolates ( IA = 0 . 342210 and rBarD = 0 . 0575250 ( p = 0 . 02 ) ) . Haplotype network analyses per locus revealed a low number of shared MLST alleles between sub-populations , indicating a low level of recombination ( Fig 3 ) . The low number of shared alleles between these two C . gattii VGIII sub-populations was also supported by the goeBURST analysis with the concatenated dataset ( Fig 4 ) . Overall , only the four atypical serotype C isolates from Mexico ( WM 1802 ( ST100 ) , WM 1804 ( ST101 ) , WM 1811 ( ST99 ) and WM 1812 ( ST99 ) ) , shared alleles , and were grouped in the serotype B cluster . This analysis also presented 11 clonal complexes ( CC ) ( i . e . 11 groups presenting single locus variant ( SLV ) ) with two of them , CC79 ( composed of ST79 , ST82 , ST80 , and ST65 in serotype C group ) and CC75 ( composed of ST75 , ST115 , ST138 , ST72 , ST143 , ST139 , and ST87 in the serotype B group ) , appearing to play an important role in the epidemiological distribution of the C . gattii VGIII population due to the wide geographical distribution of the CCs ( Fig 4 ) . Whole genomes from 60 C . gattii VGIII isolates , representing at least one isolate per ST identified in the MLST analysis , were sequenced . Whole genome sequencing ( WGS ) determined the presence of 572 , 268 SNPs with 514 , 098 SNPs being parsimony informative . Within the serotype B and serotype C major sub-populations , 88 , 337 and 79 , 945 SNPs were identified , respectively . Maximum parsimony analysis based on whole genome SNP typing ( WGST ) confirmed the same clustering of the VGIII isolates as obtained by MLST typing , although with much higher resolution ( Figs 1B and 5 ) . When the whole genomes of the other major molecular types of C . gattii , VGI , VGII , and VGIV [15] are included ( Fig 5 ) , WGST SNP data found 1 , 347 , 295 total SNPs with 1 , 055 , 552 of them being parsimonious SNPs , with a consistency index ( CI ) of 0 . 7934 . Neighbor-joining phylogenetic splits tree network analysis of the WGST SNP data clearly separated the major serotype B and serotype C clusters within VGIII and showed many phylogenetic splits within each sub-population ( Fig 6A ) . These findings indicate a shared genetic history , possibly including sexual recombination events within , but not between the two main serotype groups in the VGIII population , which largely contributes to the genetic diversity found within each serotype . When the Phi test for recombination was performed using the SNP data , the test indicated that recombination was present within each of the two major VGIII serotype groups ( p = 0 . 0 ) . fineStructure analysis showed that the serotype B isolates shared very few or no genomic regions with the serotype C isolates . However , within sub-populations , there are some shared genome regions , with serotype C isolates having a greater amount of shared regions than serotype B isolates , indicating a significant separation between the two major VGIII serotype groups , but at the same time also suggestive of some level of recombination within each of them ( Fig 7 ) . In addition fineStructure analysis indicates incomplete lineage sorting among the atypical strains , accounting for the maintenance of ancestral genome parts ( S2 Fig ) . The VGI genome contributed more to the genomes of the atypical VGIII serotype B strains ( WM 1802 , WM 1804 ) and the VGIV genome contributed stronger to the genomes of the atypical VGIII serotype C strains ( WM 2004 , WM 2041 , WM 2042 and WM 11 . 32 ) ( S2 Fig ) . If these ancestral groups are isolated ( genomically and geographically ) they would have very little recombination opportunity and little new variation , and therefore do not “share” their genome with others . Very few differences in gene content were found between serotype B and serotype C isolates using the BLAST Score Ratio ( BSR ) . An analysis of the presence/absence of genes in the VGIII sub-populations identified two gene clusters that were unique to the serotype C genomes , and one gene cluster that was unique to the serotype B genomes . However , all three gene clusters represented hypothetical proteins of unknown function . Both clusters identified in the serotype C isolates , did not have significant matches ( E values 7 . 00E-51 and 3 . 00E-109 ) , and although the cluster identified in the serotype B isolates matched with a H-N-H homing endonuclease ( E value 8 . 00E-79 ) , the amino acid identity was only 50% with 96% coverage . Based on previous findings , implicating changes in mitochondrial morphology and mitochondrial gene expression to an increased virulence in the Vancouver Island Outbreak VGII strains [70] , the mitochondrial genomes were bioinformatically extracted from the WGS data set of the 60 C . gattii VGIII isolates , representing at least one isolate of each ST identified in the above mentioned MLST analysis . Interestingly , the estimated mitochondrial genome size of C . gattii VGIII strains was 55 kb , which is much larger than the mitochondrial genome sizes of C . gattii VGII strains ( 34 . 7 kb ) [70] , C . neoformans var . grubii strains ( 24 kb ) and C . neoformans var . neoformans strains ( 32 kb ) [71] . Mitochondrial genome sequencing determined the presence of 577 SNPs with 415 SNPs being parsimony informative , with a consistency index ( CI ) of 0 . 36 . Neighbor joining phylogenetic splits tree network analysis of the mitochondrial genomes confirmed a similar but not identical topology for the two major VGIII clusters identified in the WGS analysis ( Fig 6B ) . No recombination between the two major clades obtained from the mitochondrial genomes was identified , Phi test ( p = 0 . 09032 ) . However , the Phi test for recombination using the mitochondrial SNP data indicated that recombination was present within each of the two major VGIII serotype groups , serotype B , 498 SNPs , with 320 SNPs being parsimony informative , Phi test ( p = 0 . 0032 ) , and serotype C , 333 SNPs , with 258 being parsimony informative , Phi test ( p = 0 . 000000143 ) , indicating possible sexual recombination events . Seventeen isolates , widely representative of the identified MLST genotypes , were studied in a mouse model of infection . Five were highly virulent and caused 100% mortality , while 12 did not kill any mice within 60 days of inoculation ( Table 2 , Fig 8 ) . Of the virulent isolates , WM 11 . 105 ( C/α , ST79 , a clinical isolate from Colombia ) was the most virulent , even more lethal than CDC R265 ( the highly virulent VGIIa reference strain from the Vancouver Island outbreak , which was used as a control [69] ) ( p = 0 . 0112 ) , followed by WM 2088 ( B/a , ST59 , a clinical isolate from Colombia ) , WM 11 . 139 ( B/a , ST143 , a veterinary isolate from the USA ) , WM 09 . 47 ( B/α , ST74 , a veterinary isolate from the USA ) and WM 11 . 118 ( B/α , a clinical isolate from Colombia ) , which was the least virulent ( p = 0 . 0025 ) . Pairwise comparison among the other virulent isolates showed no significant differences ( p >0 . 05 ) . Four of the five virulent isolates were serotype B while only one was serotype C . Of the four serotype B isolates , two human clinical isolates ( WM 11 . 139 and WM 2088 ) were mating type a and two veterinary isolates ( WM 09 . 47 and WM 11 . 139 ) were mating type α . No environmental isolates were virulent in this mouse model . Macroscopic examination after necropsy revealed multiple granulomata in the lungs of the mice infected with virulent cryptococcal isolates . In contrast , few or no granulomata were observed in lung tissue from mice that survived for at least 60 days post inoculation . Direct microscopy of the lung tissue suspensions stained with Indian ink revealed numerous cryptococci in the lung samples of all infected mice . Lung tissue burdens of cryptococci ( number of yeasts per gram ) did not differ significantly among the virulent isolates . Brains excised from these mice were macroscopically normal and brain suspensions were culture negative . Direct microscopy of India ink preparations of the brain suspensions revealed not more than four cryptococcal cells . It is possible these represent yeasts originating from cerebral or meningeal blood vessels . Histological examination of lung tissue from mice infected with the five virulent isolates revealed widespread location of cryptococci within the alveoli , interstitial tissue and the airways . C . gattii was recovered from the cardiac blood from 11 out of the 17 isolates inoculated into mice , indicative of dissemination of cryptococcal cells from the lungs to circulation ( Table 2 ) . Fig 9 shows multiple granulomata and numerous cryptococci in the lungs of mice infected with the most virulent C . gattii VGIII isolate WM 11 . 105 . Isolates with enhanced virulence caused significant weight loss during the course of infection ( Fig 10 ) . Antifungal susceptibilities of all VGIII isolates to amphotericin-B , 5-flucytosine , posaconazole , voriconazole , intraconazole , and fluconazole , were determined ( S1 Table ) . Minimum inhibitory concentrations ( MICs ) , MIC50 , MIC90 , geometric mean MICs and epidemiological cut-off values for all isolates are shown in Table 3 . One veterinary isolate from the USA ( WM 11 . 937 ) and two clinical isolates from Colombia ( WM 11 . 105 and WM 11 . 112 ) had high fluconazole MICs; the first isolate ( WM 11 . 937 ) with a MIC of 64 μg/ml and the last two ( WM 11 . 105 and WM 11 . 112 ) with MICs of 128 μg/ml . The comparison of MIC distributions for the tested drugs according to the serotype , mating type and source of the isolates is shown in S4 Table . Overall , serotype C isolates had statistically significant higher modal MICs and geometric mean MICs for posaconazole , voriconazole , itraconazole and fluconazole than serotype B isolates , but lower geometric mean MICs for 5-fluorocytosine ( p <0 . 05 ) ( Table 4 , S4 and S5 Tables ) . Statistically , environmental isolates were less susceptible to the tested antifungals , except for amphotericin-B , compared with clinical and veterinary isolates ( Table 5 and S4 Table ) . There was no significant difference in antifungal susceptibility profiles between mating type α or a isolates ( p > 0 . 05 ) ( S4 Table ) . Susceptibility to amphotericin-B did not vary significantly with the source of the isolates ( S4 Table ) . The epidemiological cut off values ( ECVs ) were the same as the MIC90 for the tested drugs , except for posaconazole , where the ECV was 0 . 25 μg/ml , compared with an MIC90 of 0 . 125 μg/ml ( Table 3 ) .
Taking into account the rising importance of VGIII as cause of clinical and veterinary infections [17 , 20–24 , 27–29] , their genotypic and phenotypic epidemiology has been under-investigated , compared with strains of molecular type VGII . Two MLST studies performed on a VGIII population from HIV positive patients from Southern California , identified two major molecular groups , VGIIIa and VGIIIb , that differed in virulence and fertility and a minor VGIIIc cluster represented by only one isolate [20 , 32] . Shortly after the first study was published , VGIII was found to predominate among the molecular types recovered from both human and veterinary samples outside the Pacific Northwest in the USA [17] , and amongst cats in California [28] . It was also identified as the second most common molecular type amongst human cases in Colombia [24] . Based on these limited reports , we conducted a VGIII population analysis of clinical , veterinary and environmental isolates from a broader geographic range , taking into account the previously reported endemic areas and sporadic cases [25] . Isolates were sampled from the USA , Colombia and Mexico , and single cases from Australia , Guatemala , New Zealand , Paraguay and Venezuela , to give a more comprehensive perspective of the epidemiology of the VGIII molecular type and to investigate possible correlations between genotype and virulence and antifungal susceptibility phenotypes . Although C . gattii VGIII has been recovered infrequently from human cases in Argentina , Guatemala [8] , Cuba [37] , Western Europe [72] and Korea [73 , 74] , and from the environment in Argentina [31] and India [30] , this molecular type remains an important cause of neglected cryptococcosis cases in Brazil [34 , 38] , Colombia [23 , 24] and Mexico [21 , 35] . In addition , the previously described endemic region of C . gattii VGIII is expanding beyond the borders of the US state of California , with an increase of both clinical and veterinary cases [17 , 28] . The high incidence of cryptococcosis caused by C . gattii VGIII beyond the tropical and subtropical areas , where it is considered to be endemic , and the emergence of this pathogen in more temperate regions traditionally considered of low risk for the acquisition of this infection are major clinical concerns . C . gattii , including VGIII is mostly isolated from patients without recognized immunologic defects and may be associated with worse clinical outcomes , complications such as permanent neurologic sequelae and the requirement for prolonged periods of antifungal treatment [37 , 39–41] . Failure to consider the diagnosis or delays in doing so in immunocompetent patients , result in failing to initiate the most appropriate therapy , and consequently increase morbidity and mortality . This is illustrated by recent cases of disseminated cryptococcosis caused by C . gattii VGIII , including two fatal cases reported from Cuba and the USA [37 , 39] . As also reported in the aforementioned studies , the herein studied VGIII population showed a high level of genetic diversity , with no geographic restriction of genotypes . This is at variance with observations made with VGII subtypes [14–17] . Not only were the VGIII isolates from the endemic areas of Colombia , Mexico and the USA closely related , but they also shared genotypes with most of the sporadic cases from around the world , namely ST68 ( found in New Zealand and the USA ) , and ST65 ( found in Venezuela and Colombia ) ( Figs 1 and 4 ) . Although only a single isolate from Guatemala was identified as ST96 , this genotype clustered very closely with isolates from Mexico ( Fig 1 ) . Similarly , a single isolate of ST144 found in Australia , clustered very closely with isolates from the USA ( Fig 1 ) . Interestingly , there was an association between clinical and veterinary genotypes and those identified from environmental samples , which present the natural reservoirs for C . gattii ( Fig 1 ) . Determination of the serotype of the isolates clearly revealed that the major MLST , WGST and mitochondrial genome clusters within VGIII correspond to either serotype B or serotype C , corresponding most likely to the subgroups VGIIIa and VGIIIb , respectively , which were designated previously , based exclusively on MLST genotypic clustering [20 , 32] . This discrimination between the serotypes of C . gattii VGIII has been demonstrated previously using Fourier transform infrared spectroscopy , which , in contrast to MLST , WGS and mitochondrial genome sequencing , characterizes phenotype instead of genotype [72] . Phylogenetic and coalescence analyses also revealed two more distant , but basal groups in the VGIII population , which interestingly , have been erroneously designated as VGIV following URA5-RLFP analysis , due to a SNP in the restriction site of Sau96I [12] . In addition , these two minor clusters did not share any of the MLST alleles with the major groups . Notably , they were additionally separated according to serotype , in spite of being represented by only a few isolates each ( Figs 1 and 2; S1 Table ) . Importantly , one of these minor/basal groups , which includes the serotype C isolates WM 1802 and WM 1804 from Mexico , is closely related to the previously described AFLP10 type , which differs in one of the seven MLST loci , the SOD1 locus having allele type ( AT ) AT51 in strain IHEM14941S compared to AT39 in the herein studied strains WM 1802 and WM 1804 [11 , 32] . This strain has recently been proposed as a distinct species among C . gattii ( C . decagattii ) [13] . The second minor group described in this study , which includes the serotype B isolates WM 2004 , WM 2041 and WM 2042 ( all ST64 ) from Colombia , and WM 11 . 32 ( ST114 ) from the USA , could similarly represent another new cryptic species , considering the species concept proposed by Hagen et al . [13] . However , DNA barcoding gap analysis , accounting for all four identified subgroups within the VGIII isolates combined , does not reveal a DNA barcoding gap ( S3A Fig ) . If the newly described minor serotype B subgroup ( WM 2004 , WM 2041 , WM 2042 and WM 11 . 32 ) is removed from this analysis , a DNA barcoding gap emerges between the major two serotype groups , B and C , and the minor serotype C subgroup ( WM 1802 and WM 1804 , similar to AFLP10 [13] ) ( S3B Fig ) . As such , the separation of the AFLP10 isolates from the VGIII isolates may be mistaken because of the small sample size . Depending on the number of isolates that are included in the phylogenetic analysis , the species borders can become blurred , indicating ongoing speciation events . Based solely on the MLST analysis serotype C isolates were slightly more diverse than serotype B isolates and there was a low gene flow between isolates of different serotypes , as reflected by population differentiation analysis ( Figs 1 , 2 and 3 ) . Tests of linkage disequilibrium showed additionally that serotype C isolates recombine more readily than serotype B isolates . These conclusions were also supported by the phylogenetic network and fineStructure analyses of the whole genomes and the mitochondrial genomes , showed almost no recombination between the two serotypes but recombination within each of the serotypes , with more sharing of genomic content amongst serotype C isolates ( Figs 6 and 7 ) . Identification of the two opposite mating types amongst both serotype B and serotype C isolates provides further evidence for sexual recombination within these VGIII sub-populations , which may contribute to their genetic diversity . The occurrence of well-supported sub-populations , which are separated geographically and in time , suggests that recombination and genetic exchange events are not occurring between the two major serotype specific groups of the molecular type VGIII and that this population is going through a process of expansion , divergence and perhaps speciation . MLST , maximum parsimony-based WGST , and coalescence analyses demonstrated that the two major VGIII sub-populations , serotypes B and C , which share minimal genetic diversity , likely originated from very distant ancestors in the VGIII endemic regions of Colombia , Mexico or the USA ( Figs 1A , 1B , 4 and 5 ) . The two distinct atypical populations link the two major VGIII populations ( serotype B and C ) specifically to the C . gattii lineages VGI and VGIV , confirming findings from comparative WGS studies , which also showed a closer link between VGI , VGIII and VGIV [75] ( Fig 5 ) . fineStructure analysis confirmed their ancestral role by indicating shared gene content between VGI and/or VGIV ( S2 Fig ) . This reflects also findings by Farrer at al . , that structural genome rearrangements are almost exclusive to the VGI , VGIII and VGIV lineages [75] . Given that atypical isolates may be under-sampled or misidentified as molecular type VGIV using traditional molecular methods ( i . e . URA5-RFLP ) , further studies are required to more accurately infer the ancestors of the VGIII population . Inclusion of the highly virulent reference strain of the VGIIa subtype ( CDCR265 ) [69] in the mouse model of infection permitted the recognition of VGIII isolates with enhanced or comparable virulence to the VGII Vancouver Island outbreak isolates and very similar diseases patterns ( Figs 8 , 9 and 10; Table 2 ) . Importantly , the mortality from infection with serotype B isolates was higher than that caused by serotype C isolates . Nevertheless , most of the isolates formed granulomata and direct microscopic examination revealed yeast cells in all lung sections similar to an isolate with increased virulence ( WM 11 . 105 ) ( Fig 9 ) . These findings indicate that as reported in infections mainly caused by VGIIa strains in British Columbia and the Pacific Northwest [36 , 76] , pulmonary cryptococcosis is the predominant clinical manifestation of C . gattii VGIII serotype B infections . Among the highly virulent serotype B isolates identified in this study , the veterinary isolate WM 09 . 47 shared the same genotype ( ST74 ) with the strain responsible for a fatal case of cryptococcosis reported in New Mexico in 2010 ( WM 11 . 935 , B7495 ) [39] and with a clinical isolate ( WM 1819 ) from Mexico recovered in 1990 . This finding suggests that the identification of certain genotypes may be indicative of increased virulence . It is conceivable that these virulent genotypes are circulating but are undocumented . Paradoxically , there were no specific whole or mitochondrial genome differences between low- and high-virulence isolates in the two major groups . This is similar to the findings by Ma et al . in 2009 , which showed also no correlation between the major genes coding for known virulence factors and the actual virulence in the VGII Vancouver Island Outbreak strains [70] , but identified changes in the mitochondrial morphology and mitochondrial gene expression as major factors of increased intracellular proliferation , corresponding to increased virulence . However , specific comparative mitochondrial genome analysis between high and low virulent VGIII strains conducted herein did not , like the WGS analysis , reveal any specific changes . Gene rearrangement analysis ( i . e . , progressive Mauve ) showed variation among the mitochondrial genomes of the strains included in the virulent study , not specific changes associated with either high or low virulent strains were found ( S4 Fig ) . The variation found within the mitochondrial genomes is in agreement with the observation made previously , attributing the fact that mitochondria are more recombinogenic than their nuclear counterparts , to the ability to change the mitochondrial phenotype [75] . As no specific whole genome or mitochondrial genome differences between high and low virulent have been found herein , the differences in virulence may therefore be related to phenotypic characteristics generated by differences in gene expression , for example , different rates of multiplication at 37°C , the ability to disseminate from the lung to the brain and other sites via the blood and to overcome the host immune response . Recovery of yeast cells from cardiac blood ( heart blood collected at time of euthanasia ) suggests that C . gattii VGIII can disseminate to the CNS , but that experimentally-inoculated mice die of cryptococcal pneumonia before establishment of meningoencephalitis , as previously found [77] . We suggest that the small number of yeasts detected by direct examination of brain tissue from mice infected with VGIII isolates represented yeasts within cerebral blood vessels , as there was no clinical or histopathological evidence for infection of the central nervous system . To date , many in vitro susceptibility studies have been performed on C . neoformans and C . gattii , to elucidate differences between species , serotypes and molecular types . Differences between serotypes have been mostly reported within C . neoformans , where there is a clear correspondence between serotype and molecular type . This close correspondence has not been reported within C . gattii , mainly because the serotype in this species is rarely identified and documented [11 , 28 , 42 , 43 , 78–81] . Because differences in antifungal susceptibility can influence therapeutic choices in the clinical setting , the findings of this study are of interest . Serotype C isolates were significantly less susceptible to azoles , especially fluconazole , than serotype B isolates ( Table 4 ) . In addition , irrespective of serotype , environmental isolates were slightly less susceptible to azoles and 5-fluorocytosine than clinical and veterinary isolates , indicating that their use in establishing ECVs may be misleading ( Table 5 ) . However , the association between the source of the isolates ( clinical , veterinary and environmental ) and antifungal susceptibility profiles remains unclear , as different findings have been reported elsewhere . In a previous Brazilian study , for instance , clinical isolates of C . neoformans were reported to be less susceptible to antifungal drugs than environmental isolates [80] , and in general , veterinary isolates of C . gattii collected worldwide were found to be less susceptible to antifungal drugs than clinical and environmental isolates [11] . Overall , C . gattii VGIII strains have been more susceptible to amphotericin B and 5-flucytosine than other C . gattii molecular types and C . neoformans [42 , 78 , 81] . In the present study ECVs of both these antifungal agents were higher than reported previously ( 0 . 5 μg/ml for amphotericin B and 4 μg/ml for 5-flucytosine ) . C . gattii has shown variable susceptibility to fluconazole and other azoles [28 , 43 , 79] . However , this study is the first to document high values of GM MICs and ECVs amongst C . gattii VGIII isolates , specifically for fluconazole and itraconazole , which are currently recommended as alternative induction therapy for pulmonary cryptococcosis [82] . All of these findings emphasize that recognition of serotype and molecular type in C . gattii isolates can identify isolates with acquired resistance mechanisms , based on the reported ECVs for each drug and may be relevant to the choice of the treatment regimen for a specific cryptococcal infection . Clinical studies correlating these parameters with responses to therapy and patient outcomes are required . In conclusion , the herein reported study of clinical , veterinary and environmental isolates from the main endemic areas in the world revealed a high genetic diversity within the C . gattii molecular type VGIII population . Two well-supported and divergent lineages were identified , corresponding to serotypes B and C . In addition distant ancestors within the molecular type that are represented by isolates from VGIII endemic areas were revealed in either Mexico or Colombia/USA , linking the two major VGIII populations to the other major molecular types within C . gattii , specifically to VGI and VGIV . The predominant clinical manifestation of C . gattii VGIII infections was pulmonary disease rather than meningitis or encephalitis . No specific associations between the WGS or mitochondrial genome and virulence have been observed . Antifungal susceptibility profiles differed according to serotype . The results of this study reinforce the notion that global cooperation is necessary to more accurately determine the prevalence of C . gattii infection and redefine endemic regions . Additionally , surveillance of antifungal susceptibility patterns and correlation with clinical outcomes is needed to optimize therapeutic guidelines and hence clinical outcomes . | Cryptococcus gattii , which is classically divided into four major molecular types ( VGI-VGIV ) , and two serotypes B and C , is the second most important cause of cryptococcosis . The rising incidence of human and animal cryptococcosis cases caused by molecular type VGIII highlights the need for increased vigilance . In this study , we characterized a large set of C . gattii VGIII isolates . Genetic analysis revealed four diverging sub-populations , which were primarily associated with serotype B or C , and very likely originated from endemic regions in Colombia , Mexico and the USA . Differences in virulence and antifungal susceptibility between serotypes may result in different disease outcomes since serotype B isolates were more virulent in mice than serotype C isolates , but serotype C isolates were less susceptible to azoles , the primary treatment for uncomplicated cryptococcosis . Identification of cryptococcal serotype and molecular type in clinical practice has the potential to guide treatment regimens and hence reduce morbidity and mortality in both sporadic cases and those associated with outbreaks . Our study significantly contributes to the understanding of the epidemiology , genetics and pathogenesis of Cryptococcus and cryptococcosis . | [
"Abstract",
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"america",... | 2016 | MLST and Whole-Genome-Based Population Analysis of Cryptococcus gattii VGIII Links Clinical, Veterinary and Environmental Strains, and Reveals Divergent Serotype Specific Sub-populations and Distant Ancestors |
Drug-target interaction ( DTI ) is the basis of drug discovery and design . It is time consuming and costly to determine DTI experimentally . Hence , it is necessary to develop computational methods for the prediction of potential DTI . Based on complex network theory , three supervised inference methods were developed here to predict DTI and used for drug repositioning , namely drug-based similarity inference ( DBSI ) , target-based similarity inference ( TBSI ) and network-based inference ( NBI ) . Among them , NBI performed best on four benchmark data sets . Then a drug-target network was created with NBI based on 12 , 483 FDA-approved and experimental drug-target binary links , and some new DTIs were further predicted . In vitro assays confirmed that five old drugs , namely montelukast , diclofenac , simvastatin , ketoconazole , and itraconazole , showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0 . 2 to 10 µM . Moreover , simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays . The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning .
Over the past decade , the rate of new chemical entities transferred to therapeutic agents has been significantly decreased [1] . Interestingly , this phenomenon is concurrent with the dominant assumption that the goal of drug discovery is to design exquisitely selective ligands against a single target . However , this ‘one gene , one drug , one disease’ paradigm was challenged in many cases , and the concept of polypharmacology was hence proposed for those drugs acting on multiple targets rather than one target [1] . For example , serotonin and serotonergic drugs not only bind to G protein-coupled receptors ( GPCRs ) such as 5-hydroxytryptamine receptors 1 , 2 and 4–7 ( 5-HT1 , 2 , 4–7 ) , but also might bind to an ion channel , i . e . 5-HT3 [2] , [3] . Such polypharmacological features of drugs enable us to understand drug side effects or find their new uses , namely drug repositioning [4] . Some good examples are thalidomide , sildenafil , bupropion and fluoxetine [4] , [5] . To date , several in silico methods have been developed to address the issues of drug-target interaction ( DTI ) prediction and drug repositioning [6]–[11] . The conventional methods can be either ligand-based or receptor-based . Ligand-based methods like quantitative structure-activity relationships ( QSAR ) and similarity search are very useful in this context . For example , Keiser et al . predicted new molecular targets for known drugs using chemical two-dimensional ( 2D ) structural similarity , namely similarity ensemble approach [6] , [7] . Twenty-three new DTIs were confirmed and five of which were potent with Ki values<100 nM . Recently , Humberto et al . developed a multi-target QSAR ( mt-QSAR ) classifier and built a web server for DTI prediction [8] . Receptor-based methods like reverse docking have also been applied in drug-target ( DT ) binding affinity prediction , DTI prediction and drug repositioning [9]–[11] . However , those methods could not be used for targets whose three-dimensional ( 3D ) structures are unknown . More recently , several network-based and phenotype-based methods were developed for such purposes . Yildirim et al . constructed a bipartite graph composed of US Food and Drug Administration ( FDA ) -approved drugs and proteins linked by DT binary associations [12] . This method quantitatively showed an overabundance of ‘follow-on’ drugs . Campillos et al . identified new DTIs using side-effect similarity [13] . They tested 20 of unexpected DTIs and validated 13 ones by in vitro binding assays . Iorio et al . predicted and validated new drug modes of action and drug repositioning from transcriptional responses [14] . Recently Butte group also reported two successful examples of drug repositioning based on public gene expression data [15] , [16] . Furthermore , Yamanishi et al . developed a bipartite graph learning method to predict DTI by integrating chemical and genomic spaces [17] . Though high overall predictive accuracy was obtained in Yamanishi's work , the sensitivity was anomaly low and the method was not validated experimentally . In this study , three inference methods were developed to predict new DTI: drug-based similarity inference ( DBSI ) , target-based similarity inference ( TBSI ) and network-based inference ( NBI ) , all derived from complex network theory [18]–[21] . Four benchmark data sets with known drugs targeting enzymes , ion channels , GPCRs , and nuclear receptors respectively , were used to assess the performance of the methods in comparison with literature reports . The best-performed method was then selected to create a drug-target network of FDA-approved and experimental drugs and to predict new DTIs subsequently . Some of the predictions were further validated by in vitro assays . This work would provide new powerful tools for DTI prediction and drug repositioning .
Four benchmark data sets were used to assess the performance of the methods . The data sets were named after four major drug targets , i . e . enzymes , ion channels , GPCRs , and nuclear receptors . At first , all known DTIs ( Table S1 ) involved in the data sets were used to generate a DT bipartite network ( Figure S1 ) , in which a drug ( circle ) and a target ( square ) were connected if the target was known to the drug according to experimental evidence . Figure 2 illustrated the receiver operating characteristic ( ROC ) curves calculated by the methods on the benchmark data sets using the 30 simulation times of 10-fold cross validation , from which it is easy to see that all methods performed well with high true positive rate ( TPR ) against low false positive rate ( FPR ) at any threshold . As shown in Figure 2 , NBI always gave the best TPR values at any FPR value , suggesting that the NBI method would have the highest predictive ability among them . The average area under ROC curve ( AUC ) values of NBI method by the 30 simulation times of 10-fold cross validation were 0 . 975±0 . 006 , 0 . 976±0 . 007 , 0 . 946±0 . 019 and 0 . 838±0 . 087 for enzymes , ion channels , GPCRs and nuclear receptors , respectively ( Table S2 ) . Figure S2 illustrated precision ( P ) as a function of predicted length ( L ) with different methods . For enzymes , ion channels and GPCRs , the curves from up to down were yielded for NBI ( dash curve in the figure ) , TBSI ( solid curve ) and DBSI ( dot dash curve ) subsequently , which coincided with the performance of AUC . For nuclear receptors , the relation of the three curves was not so regular as in the former three data sets , which suggested that data completeness [22] should be important for DTI prediction because there were only 90 DTI pairs in the nuclear receptor data set and the average of known targets for a drug was less than 2 ( Table S1 ) . Figure S3 illustrated recall ( R ) as a function of L with different methods . The R value from NBI was much better than those from TBSI and DBSI ( Table S3 ) . It should be highlighted that the R value is the most important parameter in DTI modeling . A low R value indicated the low ability of a model to recognize known DTIs from complex DT networks . At first , a DT bipartite network was constructed with known DTI data extracted from DrugBank [23] . As shown in Figure 3 , there were obviously polypharmacological features for many approved drugs . For example , the promiscuous drug NADH was connected with 95 proteins , while the promiscuous target α1A adrenergic receptor was linked with 52 drugs . This comprehensive mapping of pharmacological space enables us to predict new indications for old drugs by our methods . NBI method was then used to predict new DTI in the DT bipartite network . To test the feasibility of NBI on DrugBank , the performance was assessed by the 30 simulation times of 10-fold cross validation . As shown in Figure S4 , high AUC values of 0 . 865±0 . 009 and 0 . 849±0 . 012 were yielded with NBI for the approved drugs and the global data set containing approved and experimental drugs , respectively , which indicated that NBI method is valid for DrugBank . In order to validate the predictions experimentally , one enzyme , DPP-IV , and two receptors , ERα and ERβ , were selected as the targets , just because the drug screening systems of these targets are available in our laboratory . By applying NBI method on the global DrugBank database , all new potential drugs targeted with DPP-IV , ERα and ERβ were predicted . Nine purchasable old drugs were selected from top 50 recommended potential DPP-IV inhibitors ( Table S4 ) , whereas 31 purchasable old drugs were selected from top 80 recommended potential ER ligands ( Tables S5 and S6 ) for experimental assays . All the 40 old drugs were purchased and tested by in vitro assays accordingly . As shown in Figures 4 and 5 , one approved drug , i . e . montelukast , was identified from the 9 purchased compounds as an unreported DPP-IV inhibitor with half maximal inhibitory concentration ( IC50 ) = 9 . 79 µM . For ERα and ERβ , four approved drugs , namely diclofenac , simvastatin , ketoconazole , and itraconazole , were identified out of the 31 compounds as novel ER ligands with IC50 or half maximal effective concentration ( EC50 ) values less than 10 µM . Itraconazole was a dual-profile compound , which showed agonistic activity with EC50 of 200 nM on ERα but a higher antagonistic activity with IC50 of 280 nM on ERβ than tamoxifen , a classical anti-breast cancer drug . Moreover , the antiproliferative potencies of diclofenac , simvastatin , ketoconazole , and itraconazole were evaluated on human MDA-MB-231 breast cancer cell line by MTT assays . As shown in Figure 6 , simvastatin and ketoconazole showed potent antiproliferative activities with IC50 values of 1 . 49 µM and 8 . 95 µM , respectively . Network visualization of drug-target , target-disease and disease-gene associations could provide helpful information for discovery of new therapeutic indications or adverse effects of old drugs . As illustrated in Figure 7 , where disease-related genes and disorder-disease gene associations ( given in Table S7 ) were extracted from Online Mendelian Inheritance in Man ( OMIM ) Morbid Map [24] , it is easy to see polypharmacological effects of the five old drugs ( cyan ) . For example , simvastatin originally inhibits HMG-CoA reductase ( on-target labeled with red square box ) [23] , [25] , but it has more than 20 off-targets ( gray square box ) in Figure 7 [24] . In this study , simvastatin was validated to have antagonistic effects on ERβ with IC50 value at 3 . 12 µM and showed good antiproliferative activity on human MDA-MB-231 breast cancer cell line with IC50 value of 1 . 49 µM ( Figures 5 and 6 ) . Although some drugs act by binding to specific proteins , most of FDA-approved drugs were developed without knowledge of molecular mechanisms responsible for their indicated diseases . For example , ketoconazole inhibits the production of testosterone , and has been used by urologists to treat refractory bone pain and impending neurologic injury in patients with advanced metastatic prostate cancer [26] , [27] , but the molecular mechanism is unknown . In this study , ketoconazole was found to selectively inhibit ERβ with IC50 value of 0 . 79 µM and showed good antiproliferative activity on human MDA-MB-231 breast cancer cell line with IC50 value of 8 . 95 µM , which indicated that ketoconazole may have more broad-spectrum anti-cancer indications with therapeutic effects of breast cancer in clinic .
In this study , three supervised inference methods , i . e . DBSI , TBSI and NBI , were developed to predict new DTI . Excellent performance was obtained for these methods on four benchmark data sets , which outperformed some methods reported elsewhere [17] , [28] , [29] . The essential difference of the three methods is the definition of similarity . DBSI is based on chemical 2D structural similarity , and TBSI is based on genomic sequence similarity , whereas NBI is only based on DT bipartite network topology similarity ( Figure 1 ) . The worse AUC values of DBSI on the benchmark data sets indicated that the prediction based on chemical structure similarity alone was poor ( Figure 2 ) . This may be caused by the redundancy in the similarity . For example , in the enzyme data set , though chemical structure similarity can present drug similarity very accurately , similar structures without binding to enzymes should be redundant to reduce the predictive accuracy . There is a similar redundancy problem in TBSI . Although NBI is the simplest one for ignoring structural information of drugs and targets , the prediction is the most reliable ( see box plot in Figure S5 ) . And NBI only used DTI topology network similarity for inferring new potential DTI , which did not need any 3D structural information of targets and drugs . Therefore , NBI performed better than DBSI , TBSI and other reverse docking methods [10] , [11] . Recently , Hansen et al . created four features from gene-drug network and built a logistic classifier for drug-gene association prediction [30] . Although high predictive performance were obtained , an inherent problem in Hansen's work is that the negative drug-gene pairs were randomly constructed ( selected on the basis of unknown drug-gene associations ) , which easily brought noise in a logistic classifier building by the inaccurate negative sample selection . Yamanishi et al . predicted new DTIs by integration of chemical and genomic spaces . Reasonable AUC value was obtained , but the R values were extremely poor , only 0 . 574 , 0 . 271 , 0 . 234 and 0 . 148 for enzymes , ion channels , GPCRs , and nuclear receptors respectively [17] , and the predicted results were not validated experimentally . Compared with those reported methods , NBI only used the simple DT association information and yielded high predictive performance ( R more than 0 . 9 , Table S3 ) . Chiang and Butte developed a guilt-by-association method for disease-gene association prediction and drug repositioning [31] . This method only used gene-disease linkage information . In present study , NBI takes fully advantage of the labeled and unlabeled information encoded in the full DT network topology ( Figure 1 ) , thereby simultaneously exploiting both topological and functional modularity . Usually there are two major methods for DTI prediction and drug repositioning: traditional drug discovery method , in which new drugs or hits are predicted for a certain target; and chemical biology method , where new potential targets are predicted for a given drug or chemical [17] . In this study , NBI method inherited the advantages of both methods . It can prioritize candidate drugs for a given target or prioritize candidate targets for a given drug simultaneously by personal recommendation [18] , [19] . With matrix transposition , we could also prioritize new potential targets for a given drug . As shown in Figure S6 , the high performance was yielded for our three methods in prediction of new candidate targets for a given drug , and NBI exhibited the highest predictive accuracy . Therefore , NBI could be a powerful tool in drug repositioning . Since NBI only utilized known DTI information , for a new drug without known target information in the training set , NBI could not predict targets for this new drug . This is a weakness of the method . However , potential targets of a new drug can be predicted by integrating DBSI , TBSI and NBI together . We are actively developing new network inference method by integrating drugs , proteins and phenotype features based on diffusion theory [32] . Our methods could also be used in prediction of other biological networks , such as protein-protein interactions , drug-gene , gene-disease , and drug-disease networks , by integrating additional similarity measures among diseases , genes , and drugs [33]–[35] . Montelukast , antagonist of cysteinyl leukotriene 1 receptor , was marketed in the US and other countries by Merck with the brand name Singulair® . Although Langlois et al . reported that montelukast regulates eosinophil protease activity through a leukotriene-independent mechanism recently [36] , there is no report about its binding with DPP-IV so far . Herein , montelukast was predicted and validated as a new DPP-IV inhibitor with IC50 = 9 . 79 µM . Recently , Faul et al . found that oral administration of montelukast could change the weak level of Insulin in small scale clinical experiment [37] . Therefore , it is reasonable to deduce that montelukast might have new potential indication in anti-diabetic treatment via inhibiting DPP-IV ( Figure 7 ) . Comparing the structural similarity between montelukast and sitagliptin , a classical DPP-IV inhibitor , the Tanimoto similarity based on MACCS keys [38] was only 0 . 38 , which confirmed that NBI could successfully predict novel structural skeleton molecules for a given target . Diclofenac is an acetic acid nonsteroidal antiinflammatory drug ( NSAID ) with analgesic and antipyretic properties , and widely used to treat pain , dysmenorrhea , ocular inflammation , and so on . In the past decades , the anti-inflammatory effects of diclofenac were thought to be linked with inhibition of both leukocyte migration and cyclooxygenase ( COX-1 and COX-2 ) , leading to the peripheral inhibition of prostaglandin synthesis [23] . Herein , we reported that diclofenac targeted ERα and ERβ with IC50 values of 7 . 59 and 2 . 32 µM , respectively for the first time ( Figure 4 ) . There were a few similar examples to show NSAIDs targeting nuclear receptors recently . Zhou et al . reported that sulindac could induce apoptosis by binding to retinoid X receptor α ( RXRα ) [39] , while Lehmann et al . found that indomethacin could activate the peroxisome proliferator-activated receptors α and β [40] . There were also several reports to show that oral administration of ER ligands had neuroprotective and anti-inflammatory effects [41] . Since ERα and ERβ are widely expressed in several tissues including central nervous system , cardiovascular system , gastrointestinal system , and immune system [42] , therefore the anti-inflammatory and neuroprotective effects of diclofenac might be resulted from the novel biological pathways of inhibition to ERα and ERβ ( Figure 7 ) . Simvastatin , the methylated form of lovastatin , is an antilipemic agent which inhibits HMG-CoA reductase [23] . Here we identified that simvastatin could inhibit ERβ with IC50 = 3 . 12 µM . There is some evidence to support our finding . For example , Wolozin et al . reported that simvastatin was associated with a strong reduction in the incidence of dementia , Alzheimer's disease ( AD ) and Parkinson's disease ( PD ) [43] , [44]; several studies proved that estrogen treatment was effective in many neurodegenerative disease models [41] , [45]; and statins were also found to have inhibitory effects on the proliferation of human breast cancer cells [46] . Therefore , the strong reduction in the incidence of dementia and PD and the inhibitory effects of the proliferation of human breast cancer cells could be explained by the potential novel biological pathway of inhibition to ERβ by simvastatin in Figure 7 . Ketoconazole and Itraconazole , as 14-α demethylase ( CYP51A1 ) inhibitors , are synthetic antifungal drugs [23] and could be used to treat refractory bone pain and neurologic injury in patients with advanced metastatic prostate cancer [26] , [27] . In this study , both drugs were identified to bind to ERα and ERβ with IC50 or EC50 value less than 1 µM ( Figure 5 ) . 14-α demethylase and ER did not share any common features in structures or functions , but they were deduced to have the same ligands by NBI method . The data showed that the therapeutic effect of ketoconazole in prostate cancer could be explained by the selective inhibition of ERβ by ketoconazole . In last decades , tissue- or subtype-selective ER modulators ( SERM ) showed great advantages in clinic due to less adverse side effects [47] , [48] . As shown in Figure 4 , ketoconazole selectively inhibit ERβ with IC50 = 0 . 79 µM , and it did not show any antagonistic or agonistic activity to ERα . However , itraconazole was a dual-profile compound , which showed agonistic activity on ERα but a higher antagonistic activity on ERβ than the classical anti-breast cancer drug tamoxifen ( Figure 5 ) . Both ketoconazole and itraconazole could serve as leads for the discovery of novel oral SERM .
Denoting the drug set as and target set as , the DTI can be described as a bipartite DT graph , where . A link is drawn between and when the drug is associated with the target . The DT bipartite network can be presented by an adjacent matrix , where if and is linked , otherwise . To test the performance of the methods , 10-fold cross-validation approach was applied and each result was yielded by recalculating 30 times . For each data set , all the DTIs were randomly divided into 10 parts with equal size . Each part was taken in turn as the test set , while the remaining nine parts were served as the training set . With the randomly splitting , some targets ( or drugs ) may be just in the test set and the corresponding links without any information in the training set could not be predicted with the NBI method . Such links were not considered in the performance assessment . Three parameters , AUC , precision ( P ) and recall ( R ) , were calculated to assess the performance . The AUC value is obtained by calculating ranking score , which can be denoted as , where is the length of the recommendation list . And the average ranking score of the links in the test set is: , where is the test set . And the AUC value is just equal to . Since the links in the test set are actual DTIs , a good algorithm is expected to give good prediction for them , thus leading to large AUC . P can be obtained from , where is the number of true positive predictions in the top drugs in the recommendation list of target . And R is defined as , where is the number of target 's missing links . Large P and R mean that more links in the gold standard interactions are predicted out . Considering all DTI as known information , we calculated the recommendation list with top predictive scores via NBI method for all data sets . With the score ranking from high to low , the drugs in the topside of the list should be more likely to interact with the given targets , and the corresponding new DTIs were predicted . The full predicted lists of all data sets mentioned above are free available online: http://www . lmmd . org/database/dti/ . | Study of drug-target interaction is an important topic toward elucidation of protein functions and understanding of molecular mechanisms inside cells . Traditional methods to predict new targets for known drugs were based on small molecules , protein targets or phenotype features . Here , we proposed a network-based inference ( NBI ) method which only used drug-target bipartite network topology similarity to infer new targets for known drugs . The performance of NBI outperformed the drug-based similarity inference and target-based similarity inference methods as well as other published methods . Via the NBI method five old drugs , namely montelukast , diclofenac , simvastatin , ketoconazole , and itraconazole , were identified to have polypharmacological effects on human estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration from submicromolar to micromolar by in vitro assays . Moreover , simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays . The results indicated that the drug-target bipartite network-based inference method could be a useful tool for fishing novel drug-target interactions in molecular polypharmacological space . | [
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... | 2012 | Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference |
The peptidoglycan ( PG ) cell wall is a peptide cross-linked glycan polymer essential for bacterial division and maintenance of cell shape and hydrostatic pressure . Bacteria in the Chlamydiales were long thought to lack PG until recent advances in PG labeling technologies revealed the presence of this critical cell wall component in Chlamydia trachomatis . In this study , we utilize bio-orthogonal D-amino acid dipeptide probes combined with super-resolution microscopy to demonstrate that four pathogenic Chlamydiae species each possess a ≤ 140 nm wide PG ring limited to the division plane during the replicative phase of their developmental cycles . Assembly of this PG ring is rapid , processive , and linked to the bacterial actin-like protein , MreB . Both MreB polymerization and PG biosynthesis occur only in the intracellular form of pathogenic Chlamydia and are required for cell enlargement , division , and transition between the microbe’s developmental forms . Our kinetic , molecular , and biochemical analyses suggest that the development of this limited , transient , PG ring structure is the result of pathoadaptation by Chlamydia to an intracellular niche within its vertebrate host .
Chlamydia is an obligate intracellular pathogen and the single most prominent cause of bacterial sexually transmitted infections and infectious blindness worldwide . Frequently referred to as the ‘silent epidemic’ , chlamydial infections are often asymptomatic , which results in a lengthy delay between infection and the onset of disease symptoms[1] . Approximately 1 . 4 million Chlamydia infections are reported in the United States annually[2 , 3] and an estimated 90 million individuals are believed to be infected globally[4] . Untreated chlamydial genital infections can result in cervicitis , pelvic inflammatory disease , and ectopic pregnancy in women and urethritis in men . Chlamydia has undergone a lengthy ( >700 million year ) adaptation to an intracellular environment in addition to its more recent co-evolution with humans and other vertebrate hosts [5] . As a result , pathogenic chlamydial species possess significantly smaller genomes compared to those of extracellular pathogens , free-living microbes , or environmental chlamydiae[5 , 6] . Chlamydia exhibit a distinctive , biphasic life cycle wherein they alternate between an infectious but non-replicative elementary body ( EB ) and a non-infectious but replicative reticulate body ( RB ) . Under certain conditions Chlamydia can differentiate into an aberrant , metabolically active but non-replicative form . These ‘aberrant bodies’ form when RBs are exposed to stressors , such as nutrient deprivation and certain antibiotics that inhibit peptidoglycan ( PG ) cell wall biosynthesis . Aberrant bodies exhibit a state akin to metabolic stasis that can last for days , enhancing persistence of the microbe in both human and animal hosts . When the stress is released , aberrant bodies differentiate back to RBs and normal bacterial replication continues . PG is a critical cell wall component of nearly all bacteria . It is comprised of a β- ( 1 , 4 ) linked N-acetylglucosamine ( GlcNAc ) and N-acetylmuramic acid ( MurNAc ) disaccharide backbone and a pentapeptide stem , i . e . a muropeptide . In Gram negative and some Gram positive bacteria , the peptide stem consists of L-alanine , D-glutamate , meso-diaminopimelic acid , and a dipeptide of D-alanine-D-alanine ( DA—DA ) ( Fig 1a ) . Once synthesis of the major structural component of PG ( lipid II ) is completed in the bacterial cytoplasm , it is flipped into the periplasm where PG assembly proceeds . Sugar moieties of the PG are initially polymerized , resulting in assembly of the nascent PG strand ( Fig 1a ) . This step is quickly followed by cross-linking of the stem peptides from multiple strands into a structure that in the vast majority of bacteria covers the entire bacterium as a mesh-like sacculus . PG is required for cell growth and division and provides the bacterium a defined , structurally rigid and species-specific shape [7] . The unique composition of PG makes it an excellent marker for detection of bacteria by the human immune system . Indeed , PG is one of the major pathogen-associated molecular patterns ( PAMPs ) recognized by innate immune receptors [8] . Despite the long recognized susceptibility of pathogenic chlamydial species to common anti-PG agents such as penicillin and D-cycloserine ( DCS ) , until recently , Chlamydia was thought to lack PG[9 , 10] . James Moulder summed up the seemingly conflicting physiological [11–13] and biochemical [14–21] findings as the ‘chlamydial anomaly’ [22] . This paradox deepened further after the genome of Chlamydia trachomatis was sequenced and found to possess almost all of the genes of the PG biosynthesis pathway [23] . Numerous studies have since shown that the vast majority of the proteins encoded by these genes are functional in vitro or when expressed in E . coli[24–29] . The chlamydial genome appears to lack only a few PG synthesis genes , such as glutamate / alanine racemases and transglycosylases , which are essential for making PG subunits and polymerizing these subunits into PG chains , respectively [23] ( Fig 1a ) . In addition the Chlamydia genome lacks a gene encoding FtsZ , a cytoskeletal cell division initiation protein that organizes numerous PG biosynthetic enzymes around the cell division plane in almost all bacteria[7] . Recently , a method was developed to label bacterial PG using the inherent promiscuity of PG biosynthetic enzymes for tagged , fluorescent D-amino acid ( FDAA ) probes as substrate analogs[30 , 31] . Our work with the next generation of bioorthogonally tagged D-amino acid dipeptide ( DAAD ) probes that mimic DA—DA during PG synthesis ( Fig 1a and 1b ) revealed PG in C . trachomatis[9] , providing the first direct evidence of its existence in these organisms . Another study confirmed the presence of PG in an environmental strain of Parachlamydiaceae , the amoebae-symbiont , Protochlamydia amoebophila[6 , 10] , providing evidence that both related phyla synthesize PG . Strikingly , while PG in P . amoebophila forms a typical , cell-encompassing sacculus , PG in C . trachomatis forms only a distinct ring-like band at its mid-cell . Despite these advances , many questions remain concerning the function of PG in Chlamydiae and the significance of its ring-like structure . Through the use of 3D super resolution structured illumination microscopy ( SIM ) and clickable DAADs we define the ring-like PG structure of C . trachomatis as a ≤ 140 nm wide , dynamic ring that forms immediately after the previous cell division and follows cell constriction at the division septum . We show that this limited PG ring is also present in other pathogenic chlamydial species; C . muridarum , C . caviae , and C . psittaci . Formation of the ring is non-uniform and directly linked to the bacterial actin-like cytoskeletal protein MreB . When MreB polymerization is inhibited the PG ring is rapidly and non-uniformly turned over , suggesting competition between two coordinated , but separable processes: MreB-linked PG synthesis and an unknown turnover mechanism . We propose a reshaping model to explain how this narrow PG ring facilitates both cell enlargement and division . We also propose that Chlamydia limits the timing of PG ring assembly and dissociation to the intracellular replicative phase , allowing the pathogen to moderate its detection by the host immune system . These results suggest that the absence of a PG sacculus by pathogenic Chlamydia is the result of pathoadaptation to its intracellular niche within vertebrate hosts .
Despite the demonstration of a conventional PG sacculus in P . amoebophila[10] , similar PG isolation and labeling techniques ( e . g . using FDAAs ) proved unsuccessful in studies of pathogenic Chlamydia[9 , 16 , 19] . We hypothesized that distinct morphological differences in PG configuration and structure might exist between pathogenic Chlamydia and the distantly related environmental endosymbionts . In our previous study we used DAADs ( Fig 1b ) with diffraction-limited conventional fluorescence microscopy to show that labeling of PG is constrained to a thin band in C . trachomatis[9] . This is in stark contrast to the uniform labeling of cell periphery observed in all other PG-containing bacteria that produce peripheral PG sacculi[9] . 3D super-resolution microscopy confirmed that PG from C . trachomatis grown in the presence of DAADs for several generations formed as a narrow ring ( Fig 2 ) . This unique PG localization is common to the Chlamydia genus , as the DAAD-labeled PG of three evolutionarily representative , pathogenic , veterinary chlamydial species , C . muridarum , C . caviae , and C . psittaci , also localized to a single , ring-like structure ( Fig 2 ) and none of these species had peripheral PG labeling . In contrast , DAAD labeling of the coccus-shaped Staphylococcus aureus , which synthesizes PG predominantly at the septum to produce a sacculus[30] , resulted in uniform cell surface labeling ( S1 Fig ) . Thus , we conclude that the restriction of DAAD labeling to a narrow ring at mid-cell is a unique and defining characteristic of pathogenic chlamydial species . Evasion of the immune response is a powerful evolutionary driver for many bacterial pathogens . Because the human innate immune system recognizes and responds to PG [8] , we hypothesized that Chlamydiaceae may limit PG synthesis to where and when it is absolutely needed , i . e . the division site of actively replicating cells . Since the immune system is most likely to interact with the infectious , extracellular EBs , we reasoned that Chlamydia may exclude PG from EBs and restrict its synthesis to the metabolically active intracellular RBs , as has been suggested previously[19 , 21 , 32] . This hypothesis is bolstered by the fact that the labeling of chlamydial PG was previously shown to occur no earlier than 8 hpi , coinciding with the EB to RB transition of C . trachomatis [9] . The use of asynchronous infections allow for the visualization of Chlamydia at various stages of the developmental cycle simultaneously , and the diameters of MOMP-labeled cells can be used to distinguish between RBs ( ≥ 1 . 0 μm diameter ) and EBs ( ~0 . 3 μm diameter ) . When asynchronous infections are carried out in the presence of PG-labeling probe EDA-DA , PG labeling localizes to chlamydial RBs and appears absent in the much smaller , non-replicative EBs ( Fig 3a ) . Given that the EBs present at 22 hpi may be noninfectious and thereby not accurately reflect the labeling potential in normal , healthy EBs , we conducted additional labeling experiments for extended durations ( 40 hrs ) , allowing enough time for completion of the Chlamydia developmental cycle . By 40 hpi , the vast majority of RBs containing labeled PG within mature inclusions have differentiated back into infectious EBs . In mature inclusions there was no detectable PG label in these smaller , MOMP-labeled particles ( Fig 3b ) . We also made use of a simple , biological separation method for establishing whether Chlamydia EBs retained labeled PG after transitioning from replicating RBs . We infected cells with C . trachomatis for 18 hours and then incubated them in the presence of a DAAD ( 4 mM ) for an additional 25 hours . This method allowed labeling of the bacteria in the presence of the DAAD for an entire developmental cycle , i . e . RB to EB . Chlamydia EBs were then harvested from mature inclusions 43 hours post infection ( hpi ) and used to infect a fresh cell monolayer . DAAD ( 4 mM ) was present during all steps of collection and infection . Infected cells were then fixed at 3 hpi to ensure that only EBs were observable ( RBs are incapable of invading host cells[16] ) and that invading EBs did not have sufficient time to differentiate into RBs ( EB to RB transition for C . trachomatis L2 strain BU/434 occurs ~8 hpi ) . None of the newly invading chlamydial EBs ( identified by MOMP labeling ) contained labeled PG ( Fig 3c ) . These results suggest that EBs do not synthesize PG nor do they retain any PG synthesized during the RB stage . The use of confocal microscopy in our previous study[9] severely limited resolution of the PG rings . In order to better characterize the localization of PG rings within chlamydial RBs , we utilized SIM microscopy to examine fluorescently labeled PG in intracellular RBs labeled for MOMP . We found that due to the compact nature of RBs within chlamydial inclusions , defining individual cell boundaries in 3D SIM projections was often challenging ( Fig 4a ) . We decided to augment our visual characterization by also examining individual imaging planes ( as opposed to rendered 3D projections ) . By eliminating foreground and background fluorescence contamination ( Fig 4b–4e , S2 Fig , S1 Video ) we were able to generate additional images that clearly delineate individual chlamydial RBs , and thereby more accurately report localization of their PG ( S3 Fig , S2 , S3 and S4 Videos ) . Detailed , high-resolution SIM analysis of DAAD-labeled PG in C . trachomatis at 18 hpi , when the majority of Chlamydia within inclusions are in the actively growing/dividing RB phase , revealed the following features: 1 ) PG rings were confined to mid-cell: in dividing bacteria in which a membrane invagination was clearly visible , PG localized to the division septum ( Fig 4a and 4b and S3a–S3d Fig ) ; 2 ) PG ring diameter ( x , in Fig 4f ) of individual cells within a single inclusion was variable but often correlated with the diameter of the mid-cell , i . e . smaller rings were present in MOMP-labeled RBs that showed clear mid-cell constrictions ( Fig 4a–4e and S3a–S3d Fig ) ; 3 ) while ring diameter and ring thickness were variable ( x , and z in Fig 4f ) , the apparent PG ring width ( y , in Fig 4f ) was relatively constant with an average of 138 . 6 ± 18 . 6 nm ( n = 25 ) ; 4 ) for a subset of bacteria , mid-cell rings were entirely absent and instead replaced by small , full discs between two adjacent RBs , measuring 202 . 7 ± 24 . 2 nm in diameter at their widest points ( n = 25 , Fig 4c–4e and S3f–S3h Fig; arrows ) ; and 5 ) instances of single RBs containing multiple PG rings and asymmetric localization of these rings between two/three cells apparently undergoing asymmetric division ( Fig 4g–4k , S3e and S3f Fig ) . In rod-shaped bacteria , assembly of the complete division complex ( and accompanying septal PG ) occurs at the mid-cell just prior to the initiation of cell division , and only after a lengthy cell elongation phase[7] . Similarly , the model coccus-shaped organism , Staphylococcus aureus , undergoes a period of peripheral growth before onset of septal PG synthesis [33] . In contrast , although C . trachomatis strain L2/434 has a generation time of ~ 2 . 5 h to 3 h[34 , 35] , experiments with long DAAD incubation times ( e . g . 18 h ) revealed complete septal PG rings in the vast majority of RBs present within the developing inclusion ( Fig 4a and 4b , S3 Fig ) . This suggests that synthesis and assembly of the septal PG begins unusually early in the cell cycle compared to other bacteria . In order to investigate the dynamics of PG ring formation , we varied the time that RBs were exposed to DAADs . Pulse labeling experiments on actively dividing RBs ( 18 hpi ) confirmed that complete PG rings were detectable with as little as 10 min of DAAD labeling ( Fig 5a ) , indicating that the basic PG ring assembly is also rapid . Although there was no significant change in the overall ring structure ( i . e . a relatively constant ≤ 140 nm PG ring width ) after labeling pulses longer than 10 min , the DAAD signal intensity gradually increased with increasing pulse duration . We later quantified this , allowing us to conduct the first kinetic analysis on the uptake and transport of any substrate by intracellular Chlamydia . The close proximity of individual RBs within chlamydial inclusions and the difficulty in comparing fluorescence intensities of PG rings in dissimilar spatial orientations make single cell PG signal quantification practically impossible . Therefore , as a general quantitative approach , we tracked average epifluorescence values for individual inclusions using maximum intensity projections and the MOMP signal to normalize the average cell density per inclusion . We utilized an asynchronous infection model , allowing us to view a broad range of inclusion sizes ( representing various stages of maturation / development ) at each given time point . The range of inclusion sizes was similar for all time points , and the average inclusion size did not significantly differ between time points analyzed . Measurement of new DAAD incorporation over 2 h ( from 18 to 20 hpi ) showed that while mean MOMP fluorescence remained relatively constant ( Fig 5b ) , mean PG fluorescence per inclusion gradually increased over the same time span ( Fig 5c ) . No correlation was detected between inclusion size and average pixel intensity for either labeled PG or the major chlamydial outer membrane protein ( MOMP ) ( S4a–S4c Fig ) , indicating that asynchronous chlamydial infections did not significantly affect the experimental outcome . Together , these results indicate that DAADs are continuously incorporated into one basic PG ring structure over several hours ( Fig 5b and 5c ) during the replicative phase of the chlamydial developmental cycle . Rapid , yet persistent chlamydial PG ring assembly would require tight spatiotemporal coordination of new PG incorporation along the ring . We investigated the degree to which these mechanism ( s ) are coordinated by the chlamydial cell division machinery . The bacterial tubulin-like protein FtsZ plays an essential role in cell division by assembling the division apparatus at the correct division plane in almost all bacteria , chloroplasts , some mitochondria , and even some archea[7 , 36–38] . Chlamydiae[9 , 10] and its distant relative Planctomyces[39 , 40] constitute the only two phyla of PG-containing bacteria that lack an FtsZ homolog[41] . Studies now suggest that in the absence of FtsZ , MreB may act as the division plane organizer in Chlamydia , since both MreB and the septal organizer RodZ localize to the division septum[42–44] . MreB is an actin-like protein that controls the cylindrical nature of rod-shaped bacteria by forming dynamic patches traveling around the circumference of the side walls . Since DAADs labeled chlamydial PG at the division septum , and chlamydial MreB has previously been shown to localize to the division septum [44] , it is a logical candidate for facilitating PG ring assembly . By combining anti-chlamydial MreB antibody[44] with DAADs and 3D-SIM imaging , we found that in RBs chlamydial MreB is patchy , similar to the localization patterns reported in other bacteria [45–47] , and that these MreB patches appeared to co-localize with PG rings ( Fig 6a ) . Inhibition of filament formation with MreB depolymerizing agents , A22 and MP265[48 , 49] , resulted in a loss of MreB labeling in normal chlamydial RBs ( S5a Fig ) , a reduction in the abundance of MreB patches in DCS-induced aberrant bodies ( S5a Fig ) , and inhibition of DAAD incorporation ( Fig 6b ) . No labeled MreB was visible in the metabolically inactive chlamydial EBs ( S5b Fig ) as previously reported[44] . These results strongly suggest that MreB polymerization is required for new PG incorporation along the septal PG ring structure in Chlamydiae . Because MreB moves circumferentially along the lateral walls of many bacterial species [45–47] , we hypothesized that MreB could be similarly dynamic along the chlamydial PG ring . We reasoned that we could infer MreB dynamics by labeling its downstream product , namely newly incorporated PG , with DAADs . Though indirect , a brief DAAD pulse would record the movement of MreB on the PG ring in the form of a fluorescent trace of new DAAD incorporation . A short ( five minute ) pulse with DAAD resulted in non-uniform , patchy PG trace distinguishable by epifluorescence ( S6a Fig ) and more clearly discernable at SIM resolution ( Fig 6c ) . Anti-MreB signal partly co-localized with short EDA-DA pulses and the remaining signal was interspersed between PG patches , appearing to complement the newly forming PG arcs ( S6b Fig ) . The lack of complete co-localization of MreB patches and PG patches indicates that we may lack the instrumental sensitivity to follow these rapid processes with ideal spatiotemporal resolution , however , these observations are consistent with the hypothesis that dynamic MreB patches facilitate incorporation of new PG along the septal plane , eventually completing a full PG ring in ~10 min ( Fig 5a ) . If A22 successfully blocks MreB-dependent PG incorporation in Chlamydia , we predict that inhibition of MreB polymerization should result in the accumulation of incomplete PG muropeptides , as is the case in E . coli [38] . Because classical analytical techniques[16 , 17 , 19 , 21] are inadequate to detect chlamydial PG in lysates of infected cells , we employed a new , highly sensitive , immunodetection approach [50] . Chlamydial PG fragments are detected by the host cell via the intracellular NOD2 receptor , which recognizes N-acetylmuramic acid ( MurNAc ) present on muropeptide breakdown products of bacterial PG [50] . When coupled with mass spectrometry , this then can be used to measure the relative abundance of chlamydial PG muropeptides[50] . In order to determine if incomplete PG muropeptides accumulate when MreB polymerization is inhibited , we infected HeLa cells with Chlamydia for 18 hours followed by A22 ( 75 μM ) treatment for 2 hours . Lysates from infected cells treated with A22 resulted in a significant increase in NOD2 signaling when compared to untreated ( no A22 ) lysates from Chlamydia-infected cells ( S7a Fig ) . Analysis of A22-treated and untreated cell lysates by Mass and Enhanced Product Ion ( EPI ) scans revealed an increase in products of ~477 , 494 , and 666 mass units ( mu ) ( S7b and S7c Fig ) . These peaks correspond to a degradation product of chlamydial muramyl dipeptide ( MDP , S7d Fig ) and the free species of chlamydial MDP and MTP , respectively [50] . Subsequent analysis via tandem mass spectrometry ( MS/MS ) confirmed that the breakdown products of the 477 mu peak were identical to those of an MDP fragment from C . trachomatis ( S7d Fig ) and column retention times for 494 , 666 , 653 peaks correspond to those observed previously for chlamydial MDP , MTP , and the cross-linked PG fragment [50] . In addition , we found that the relative abundance of a cross-linked , PG degradation product of 653 mu ( described previously , [50] ) decreased significantly in A22-treated samples ( S7c Fig ) . As the structures for the UDP- precursors of chlamydial lipid I synthesis have not yet been identified / characterized , we cannot definitely state whether the accumulation of MDP and MTP in A22-treated cells is the result of PG degradation , incomplete lipid I synthesis , or both . In an attempt to ascertain whether these fluctuations in muropeptide abundance resulting from A22-treatment were similar to those seen when PG biosynthesis is directly inhibited , we examined samples collected from Chlamydia-infected cells that had been treated with DCS , a D-Ala-D-Ala ligase inhibitor that arrests PG biosynthesis ( Fig 1 ) . We found that , similar to A22 , DCS treatment resulted in an increase in the abundance of both chlamydial MDP and MTP and a decrease in 653 muropeptide species ( S7c Fig ) . In addition to blocking PG biosynthesis , inhibition of chlamydial MreB polymerization also affected Chlamydia replication and its interaction with host cells throughout its biphasic life cycle . Consistent with previous work [43 , 51] , we found that A22 inhibition of MreB after the EB to RB transition ( 8 hpi ) resulted in arrest of RB replication ( S8a and S8b Fig ) . A22-treated RBs also became slightly enlarged , though significantly less than aberrant bodies induced by exposure to antibiotics targeting various penicillin-binding proteins ( PBPs ) , i . e . β-lactams[43] . Unlike β-lactam induced aberrant bodies that still show strong DAAD labeling [9] , A22-induced aberrant bodies completely lacked detectable PG ( Fig 6b ) . This observation suggests an essential role for MreB as well as PG in maintaining chlamydial cell size and shape . When infected cells were treated with MreB inhibitors prior to the EB-RB transition ( < 8 hpi ) , some aberrant body formation ( ~ 4–6% ) was observable , but the vast majority of bacteria appeared to remain in an EB-like state of development , despite being intracellular for more than 16 hpi ( S8b , S8c and S8f Fig ) . Inhibition of the normal developmental cycle is reversible as these EB-sized Chlamydia appeared to differentiate into RB sized Chlamydia when the inhibitor was removed ( S8b and S8f Fig ) . This suggests that in addition to maintaining cell size and division , functional MreB may also be critical for the EB to RB transition . In order to establish how compounds A22 and MP265 affect the ability of Chlamydia to complete its developmental cycle , infected cells were treated for the indicated times with each compound , and EBs were collected at the termination of a normal chlamydial developmental cycle ( 44 hpi ) . Numbers of inclusion forming units ( IFUs ) were then obtained by re-infecting monolayers to establish the number of viable EBs present in each test group . We found that when Chlamydia-infected cells were treated with either A22 ( 75 μM ) nor MP265 ( 125 μM ) for brief ( one hour ) periods , the number of IFUs recovered was only slightly fewer than in our untreated control groups ( S8d Fig ) . This was the case when compounds were added early in infection ( 2 hpi ) when Chlamydia is in the EB developmental state or later in infection ( 18 hpi ) when EBs have all transitioned to replicative RBs ( compare UTD , 2–3 hpi and 18–19 hpi columns , S8d Fig ) . When compounds are added to infected cells 2 hpi or 18 hpi and left in the growth medium for the remainder of the developmental cycle ( 42 and 26 hours , respectively ) few if any IFUs are recoverable ( 2–44 hpi and 18–44 hpi columns , S8d Fig ) , as has been reported previously [43] . However , if compounds are added 2 hpi and removed after 8 hours ( at 10 hpi ) viable EBs are recoverable when harvested at 44 hpi ( 2–10 hpi ( 44 hpi ) columns , S8d Fig ) . In the A22-treated group , ~104 IFUs were recovered , whereas in the MP265-treated group ~107 IFUs were recovered . Compound A22 has previously been reported to exhibit off-target effects in other bacterial systems [49] , and we suspect that this may explain the difference in IFUs recovered between A22 and MP265 treatment groups . When IFUs were collected at 52 hpi ( as opposed to 44 hpi ) , the number of recovered IFUs increased substantially ( 2–10 hpi ( 52 hpi ) column , S8d Fig ) . By contrast , no similar increase in IFUs recovered was observed in MP265-treated groups that were allowed an additional 8 hours of recovery time , prior to EB harvesting . Consistent with the idea that polymerized MreB is a general facilitator of chlamydial growth and the biphasic developmental cycle , MreB inhibition also arrested inclusion formation/maturation as evidenced by the lack of discernable inclusion membrane protein A ( IncA ) labeling in the inclusion membrane upon A22 treatment , while DCS-treated bacteria still developed mature inclusions ( S8e Fig ) . Time course studies revealed that inclusion maturation continued to be suppressed when the MreB polymerization inhibitor was added as late as 12 hpi , approximately four hours after the EB-RB transition is known to occur [52] and ~2–4 hours before IncA can be detected at the inclusion membrane . In contrast , DCS-treated Chlamydia were capable of expressing IncA and incorporating it on the surface of their inclusions . When the inhibitor was removed as late as 10 hpi , chlamydial inclusions resumed normal maturation as evidenced by the appearance of IncA at the inclusion membrane with no aberrant bodies visible at 22 h ( S8f Fig ) . Taken together , these data suggest that MreB is a key regulator of chlamydial growth and development essential for the EB to RB transition ( ~8 hpi ) , cell enlargement and division , PG biosynthesis in RBs ( >8 hpi ) , maturation of inclusions ( >12 hpi ) , and differentiation of RBs back into EBs . DAAD incorporation into chlamydial PG is independent of the D , D-transpeptidation/carboxypeptidation activity of PBPs , as evidenced by the persistent and rapid labeling in ampicillin-treated aberrant bodies[9] . In the presence of β-lactam antibiotics ( ampicillin or piperacillin ) the PG signal followed irregular branches within the enlarged , chlamydial aberrant bodies and rarely exhibited complete ring morphology ( S9a and S9b Fig ) . Despite this abnormal localization , the PG remained susceptible to lysozyme , an enzyme that targets 1 , 4 β-linkages in the glycan polymer chain ( S9c Fig ) . Consistent with the link between chlamydial MreB and PG synthesis described above , the MreB signal co-localized along these PG branches as individual patches and , in some instances , extended structures ( S9d Fig ) . We also observed similarly patchy localization of MreB in DCS-induced aberrant bodies ( S9e Fig ) . These results , taken together , suggest that chlamydial MreB can still facilitate the formation of transglycosylated strings of nascent PG in the absence of D , D-transpeptidation/carboxypeptidation activity and an increase in cell size , but that PG cross-links are essential for the regulation of cell size and the initiation of cell division . Our data strongly suggest that chlamydial MreB ( in combination with PBPs ) is responsible for the assembly of a properly sized PG ring localized at mid-cell and the division septum in actively dividing bacteria . However , these data do not explain how PG rings follow the cell constriction . A degradation and/or reshaping mechanism must be acting on this structure , as we observe rings of different diameters but not a full division plane until the very end of cell division ( Fig 4 and S3 Fig ) . Based on our observation that the chlamydial PG biosynthesis machinery rapidly synthesizes and maintains a defined PG ring structure along the constriction plane , we reasoned that the older PG might be degraded homogenously around the ring as new PG is synthesized; we will refer to this as the ‘homogeneous model’ . Alternatively , new PG could be synthesized from the center of each PG ring band and push old PG outward towards the cell poles , as occurs in PG elongation of cocci and oval species , and be degraded at the edge of the DAAD-labeled ring band thereby restricting PG to the mid-cell ( S10a Fig ) . For simplicity , we will refer to this putative mechanism as the ‘bidirectional model’ . To explore this reshaping mechanism , we first measured how PG rings age . Chlamydia inclusions ( 18 hpi ) were labeled with a DAAD for one hour ( pulse ) after which cells were washed in fresh medium and then allowed to incubate in medium without probe ( chase ) . Loss of PG fluorescence signal from labeled chlamydial inclusions over time was then quantified following appropriate controls similar to earlier experiments ( S10b and S10c Fig ) . Average fluorescence pixel intensities of whole inclusions , calculated over a four hour chase , indicated that the mean PG signal per inclusion ( Average [Total PG signal per inclusion / area of inclusion] ) decreased by 50% over approximately three hours , while the mean cell density ( represented by Average [Total MOMP signal per inclusion / area of inclusion] ) did not appear to change ( Fig 7a and 7c ) . In contrast , a complementary analysis of the same data set showed that while the average integrated MOMP signal per inclusion ( Average [Total MOMP signal per inclusion] ) steadily increased ( due to an increase in the number of cells per inclusion during chases ) , the average integrated PG signals per inclusion ( Average [Total PG signal per inclusion] ) did not change ( Fig 7b and 7c ) . This suggests that the pulsed DAAD within an inclusion is distributed between daughter cells during the chase , though it is not currently possible to ascertain whether all of the free DAAD is fully incorporated into chlamydial PG during the hour-long pulse . Some amount of unincorporated DAAD may be simply retained by replicating RBs and incorporated into newly synthesized PG at later time points . Although the time required for mean PG signal reduction by 50% is close to the estimated doubling time of C . trachomatis strain L2/434 ( 2 . 5–3 hours ) [52] , the overall thickness of the rings did not appear to change with longer chase times , and PG ring width ( y , in Fig 4f ) remained relatively constant with an average width of ~156 . 9 nm ( sd 12 . 8 nm; n = 10 ) at t = 1 h , an average of 147 . 9 nm ( sd 20 . 7 nm; n = 19 ) at t = 3 h and an average of 155 . 9 nm ( sd 18 . 1 nm; n = 18 ) at t = 4 h , compared to the aforementioned 138 . 6 nm ± 18 . 6 nm at t = 0 . Inherent to the design of our pulse-chase experiment , if a bidirectional mechanism existed , older DAAD signal would split in two and become thinner at the edges as unlabeled new PG is incorporated in the middle ( S10a Fig ) . However , as we are currently operating at the limits of 3D structured illumination microscopy , future studies conducted at higher resolutions will be needed to definitely refute the bidirectional synthesis model . We previously showed that DAADs can substitute for natural DA—DA in C . trachomatis[9] and that aberrant bodies induced by β-lactam antibiotic inhibition of chlamydial periplasmic D , D-carboxypeptidases/transpeptidases ( i . e . PBPs ) are still capable of incorporating DAADs ( [9] , and S9a , S9b and S9d Fig ) . These results demonstrate that chlamydial PBP2 and PBP3 , which are both inhibited by ampicillin , are not required for DAAD incorporation and that incorporation of label into chlamydial PG likely occurs in the cytoplasm . The slow decrease of the PG signal and the distribution of the signal between the newly formed daughter cells observed during pulse-chase experiments ( Fig 7a and 7b ) could therefore be due to incorporation of DAADs trapped within the inclusions and/or the cytoplasm of chlamydial RBs . This continuous DAAD incorporation could obscure the true kinetics of the observed PG degradation mechanism . We reasoned that if incorporation of new material was arrested by inhibition of MreB , we could uncouple chlamydial PG synthesis from degradation . When RBs ( 18 hpi ) were pulsed with DAADs and chased with fresh medium containing A22 , the signal intensity of labeled PG rings began decreasing in a non-uniform manner within 15 minutes ( Fig 8a ) with no significant change in PG ring width , with an average width of 151 . 7 nm ( sd 19 . 9 nm; n = 17 ) at t = 15 min , an average of 153 . 9 nm ( sd 16 . 4 nm; n = 17 ) at t = 30 min and an average of 142 . 2 nm ( sd 19 . 6 nm; n = 9 ) at t = 45 min , compared to 156 . 7 nm ( sd 17 . 2 nm; n = 9 ) at t = 0 . At 30 minutes , all PG rings were almost completely dissociated with only punctate labeling present on a handful of RBs within any given inclusion . After one hour of A22 or MP265 treatment , no labeling of chlamydial PG was discernable ( A22 / MP265 chase ) compared to the control ( first chase ) ( Fig 8b ) . Taken together , our data indicate the presence of two separable and highly active mechanisms critical for the reshaping of the chlamydial PG ring as RBs constrict during cell division: MreB-dependent PG synthesis and an as yet uncharacterized degradation mechanism ( s ) . When cells were pulsed with DAADs and chased with fresh medium containing A22 ( resulting in complete loss of PG signal ) , subsequent removal of A22 and cell recovery in fresh medium ( in the absence of exogenously added DAAD or A22 , second chase ) , resulted in a striking restoration of PG ring signal in chlamydial RBs ( Fig 8a and 8b ) . This is indicative of relatively high concentrations of free ( and/or cytoplasmic PG precursor-bound ) DAADs being retained by the chlamydial cells/inclusions , despite media changes and wash steps subsequent to pulse-labeling . Since MreB inhibition is reversible ( S8 Fig ) , washing out A22 is expected to restart new PG synthesis and also incorporation of any residual DAADs remaining within cells/inclusions . However , when we conducted the second chase in the presence of very high concentrations of the native unlabeled dipeptide DA—DA ( second DA—DA chase ) , we observed a slight drop in PG signal recovery compared to when no native DAAD was added ( Fig 8b and 8c ) which may indicate that the extra DA-DA is competing with the unincorporated probe during the recovery . In contrast , when the second chase was done in the presence of DCS , an inhibitor of DA—DA synthesis , we observed an increase in PG signal recovery ( Fig 8b and 8c ) . This was expected , as depleting the cellular pool of the competing , native DA—DA would increase the frequency of DAAD incorporation . When the second chase was conducted in the presence of ampicillin , we observed only a slight decrease in signal recovery compared to the second cold chase ( Fig 8b and 8c ) , indicating that probe PG incorporation ( S9a and S9b Fig ) and degradation are independent of D , D-transpeptidation/D , D-carboxypeptidation activity , which are both inhibited by β-lactam antibiotics . The competition of the native DA-DA with DAADs is also evident in the decrease in EDA-DA labeling when our initial EDA-DA pulse is followed by a native DA-DA chase ( S11a Fig ) . We observed an even greater reduction of alkyne containing EDA-DA labeling when we chase with the alternative azide containing DAAD , ADA-DA ( S11b Fig ) , however , this reduction in fluorescence is most likely the result of these two PG incorporated DAADs within close proximity forming stable conjugates with each other via azide-alkyne [3+2] cycloaddition ( and not their corresponding fluorophores ) upon the initiation of the click chemistry reaction . Nevertheless , the retention of components of immunogenic PG ( DAADs ) , the absence of PG biosynthesis / retention in the extracellular form of Chlamydia , and the limiting of PG to a mid-cell ring often localizing to the division septum of its replicative form , all indicate that Chlamydia restricts its PG to where and when it is absolutely needed . In the context of an obligate , intracellular pathogen that has co-evolved with vertebrate hosts for hundreds of millions of years , this severe limitation on PG biosynthesis and maintenance is indicative of pathoadaptation by Chlamydia .
To better understand the role of PG in the life cycle of obligate , intracellular pathogenic Chlamydia , we characterized the assembly and maintenance of this critical cell wall component . Our observations show that four species of pathogenic Chlamydia maintain a distinctive PG ring limited to a small region at mid-cell and localizing to the septum in bacteria actively undergoing division . We refer to this structure as a unique and limited PG ring based on four major observations: Based on current detection techniques and data from previous publications , we conclude that these PG rings constitute the only PG cell wall structure in actively growing pathogenic Chlamydia . Regardless of the duration of labeling with DAADs , rings of relatively constant width represent the sole fluorescence trace of PG observed in the replicative form of pathogenic Chlamydia , as opposed to the uniform , peripheral labeling present in other bacteria with classical , cell-encompassing PG sacculi following prolonged labeling[9] . While our experimental approach limits labeling of PG to the fourth amino acid of the stem peptide , we surmise that position 4 is very stable , as no L , D transpeptidases , which would cleave the stem peptide at this position , appear to be encoded within the chlamydial genome[23] . Our observations also match immunofluorescence data obtained by researchers utilizing antisera raised primarily against mycobacterial PG , which gave structures very similar to the DAAD-labeled PG rings present exclusively in RBs[32] . Pathogenic Chlamydia lack a peripheral peptidoglycan layer when imaged via electron microscopy[13] , and sacculi have never been successfully purified from chlamydial cells[16 , 19] . The existence of a thin , lysozyme sensitive ( [9] and this work ) , MurNAc-containing [50] PG ring present only in RBs would account for the findings of past researchers , who each reported finding only trace evidence , if any , of PG in Chlamydia [14 , 15 , 18 , 20 , 32] . Additionally , the absence of PG labeling in EBs is consistent with the lack of NOD1 and NOD2 stimulatory PG fragments in lysates of chlamydial EBs , with only trace amounts of the PG-specific sugar , MurNAc , detectable in infected cell lysates after the EB-RB transition [50] . Our limited PG model is also supported by evolutionary analysis . We observed narrow PG ring structures in four different pathogenic chlamydial species that are evolutionarily representative of the Chlamydia genus . In contrast , Pilhofer et al . reported that Parachlamydia endosymbionts ( environmental Chlamydia-like bacteria represented by P . amoebophila ) possess a conventional , structurally supportive , shape-determining sacculus[10] . Simkania negevensis , a pathogenic Chlamydia-like bacterium associated with community-acquired pneumonia in humans[6 , 53] , does not appear to possess a PG sacculus . This may hint at a speciation event of ancestral Chlamydiae: the pathoadaptation of ancestral Chlamydia ( possibly also Simkania ) to their human/animal hosts may have resulted in spatially restricting PG to a bare minimum , a small ring , and temporally limiting the period of PG synthesis to actively replicating , intracellular RBs . Future studies examining the PG of other environmental and pathogenic Chlamydia species will shed light on the prevalence of this peculiar PG architecture throughout the Chlamydiae . We propose that pathogenic Chlamydia do not require a sacculus due to the osmotically stable environment in which they reside . It is noteworthy that E . coli , upon treatment with penicillin-based antibiotics under osmotically stable conditions does not lyse , and instead continues to expand , unable to divide ( similar to ampicillin-induced , chlamydial aberrant bodies ) . The genomes of some obligate extracellular ( Mycoplasma ) and intracellular ( Ehrlichia , anaplasma ) pathogens lack all or the vast majority of PG synthesis genes , indicating that PG ( and by extension , a sacculus ) is dispensable for the survival of obligate extracellular / intracellular pathogens . While this line of reasoning does not account for the presence of sacculi in some obligate intracellular pathogens such as Coxiella , these exceptions can often be explained when their various environments and lifecycles are considered . Rickettsiae , for example , are highly pleiomorphic organisms and generally have broad host ranges that include arthropods , potentially subjecting them to less osmotically stable conditions than those found within vertebrate hosts . Coxiella in particular is among the most resistant of the Rickettsiae to adverse environmental conditions ( i . e . osmotic stress ) indicating that one major function of a PG sacculus ( osmotic protection ) potentially remains essential to the organism . Assuming that Chlamydia has effectively dispensed with the need to withstand osmotic stresses by adapting to an obligate , intracellular niche , we speculate that PG would still be maintained by the organism i ) if it was essential for cell division , and/or ii ) if it acts as a signaling molecule to other microbes or to the host immune system , to the direct benefit of Chlamydia . Limiting , masking , or removing PG ( and other immunostimulatory PAMPS ) is likely an intrinsic adaptation of ancient , obligate intracellular pathogens . Such a reduction of its immunogenic profile is beneficial for a pathogen so long as this does not compromise core functions essential for its survival . As a logical extension of this model , the complete removal of PG from the organism would be ideal . However , while other pathogens ( such as Mycoplasma ) have developed cell division mechanisms that function in the absence of PG , Chlamydia has not . We propose that by limiting PG to the septum during its replicative growth phase , Chlamydia maintains a minimal PG synthesis activity essential for division while also minimizing its recognition by host innate immune receptors . PG fragments are potent signaling ligands for both NOD1 and NOD2 receptors of the innate immune system[54] , and limiting their abundance may significantly mitigate Chlamydia’s immunogenic profile . This hypothesis is supported by our finding that DAAD probes , unnatural substitutes for the PG D-Ala-D-Ala residue [9] , appear to be retained within RBs and distributed between the daughter RBs upon division . The recent discovery [50] that pathogenic Chlamydia incorporates glycine into the first position of the PG stem peptide further supports pathoadaptation by the microbe . This specific modification of the peptide chain has only been observed in the intracellular pathogen Mycobacterium leprae[55] and studies have shown that alterations at this amino acid position can decrease immunoadjuvant capacity[56] as well as significantly affect MDP recognition by the NOD2 receptor[57] . Our observations indicate that chlamydial PG rings exclusively localize to the apparent division plane of RBs . Upon the onset of division , the ring contracts with the outer membrane and immediately upon division PG appears to localize in polar patches . PG is only ever discernable in these polar patches or complete rings , which suggests that the new chlamydial division plane forms immediately after the previous division . These polar PG patches may act as cues for priming the new division plane , which would force it to form perpendicular to the previous one ( Fig 9a ) , similar to what occurs in other cocci [33] . Complete PG rings are also discernable prior to any apparent sign of constriction ( Fig 4 and S3 Fig ) . This observation may indicate that the chlamydial PG ring either must first mature before driving constriction or it may confer some additional utility to the organism , such as structural support or acting as a scaffold for the division and membrane constriction machinery . Additionally , while cell division following ring constriction most often occurs symmetrically , the occurrence of bacteria with asymmetric division planes may serve to normalize cell size or to initiate the RB-EB transition . The formation of incomplete PG rings upon short DAAD pulses , the patchy , partial colocalization of MreB on newly forming PG patches , the requirement for a functional MreB in order to synthesize and assemble PG rings , and the patchy and dynamic nature of MreB in other bacteria[45–47] all support a model in which new PG rings expand from polar patches with the aid of a mobile MreB upon completion of a cell division event ( Fig 9a ) . The tendency of newly incorporated PG signal trailing the MreB patches might be due to the incomplete saturation of DA-DA pools with our modified dipeptides . Alternatively , there might also be a threshold of DAAD-labeled PG subunits that need to be built into the edge ( s ) of growing PG arcs by the pioneering MreB before it can be detected by our imaging conditions . Either would prevent us from unambiguously visualizing complete colocalization of MreB patches with short pulse PG patches . Therefore we suspect that these process may occur too rapidly for us to accurately visualize with technology currently available . We speculate that the MreB-facilitated PG synthesis occurs in a directional manner around the septal ring , constantly and dynamically threading the new PG into the older , ring-shaped material . While MreB co-localization with PG strands in enlarged aberrant bodies upon β-lactam inhibition of PBPs is consistent with this model , this observation also shows that transglycosylation in Chlamydia can be uncoupled from D , D-transpeptidation/carboxypeptidation . D , D-transpeptidation/carboxypeptidation may still play a role in defining the proper ring shape for division , but not for nascent PG assembly . We believe that future studies utilizing fluorescent protein fusions of relevant chlamydial proteins , such as MreB , coupled with live-cell optimized PG labeling DAADs will allow for these models to be tested in real-time via time-lapse microscopy . We showed that inhibition of MreB arrests the EB to RB transition early in infection , the growth of RBs and the maturation of chlamydial inclusions at mid-infection , and the RB to EB transition late in infection . All of these phenotypes are reversible when the MreB inhibitor is removed . Therefore , we conclude that MreB is required not just for chlamydial replication , but also for cell differentiation and growth . There is a distinctive association between the spatiotemporal distribution of MreB , PG synthesis , and chlamydial cell growth . MreB and PG are both absent from chlamydial EBs and aberrant bodies induced by MreB inhibition . PG biosynthesis and cell division genes are both up-regulated upon initiation of the EB to RB transition[58] and we have shown that MreB localization and PG biosynthesis occur only after the transition . MreB and PG are both present and co-localize in normally growing RBs as well as in β-lactam induced aberrant bodies , which unlike aberrant bodies that form due to MreB inhibition , continue to grow at the same rate as healthy cells[59] while exhibiting long strands of nascent PG and patchy MreB ( S9 Fig ) . Similar cell enlargement occurs in DCS-induced aberrant bodies that appear to be able to assemble an uncross-linkable , yet still polymerized nascent PG [32] . DCS treatment results in the accumulation of degraded muropeptides and/or PG precursors in Chlamydia and incomplete muropeptides have been shown to incorporate into the PG of E . coli , following DCS treatment [60] . Our observations , as well as those of others[32] indicate that PG glycan chains in ampicillin and DCS-induced aberrant bodies still form , giving weight to the hypothesis that nascent PG biosynthesis is independent of cross-linked PG . In the case of DCS-treated cells , incomplete muropeptides can still transit to the periplasm and incorporate into newly forming PG glycan chains , but cannot form proper crosslinks[60] . This is in contrast to inhibition of MreB function in chlamydial RBs , which leads to arrest of cell growth and absence of PG synthesis . RBs lacking only D , D-transpeptidation/carboxypeptidation activity ( inhibited either directly by β-lactams or indirectly by DCS ) , continue to enlarge , synthesize and assemble nascent PG , but appear to lack the ability to regulate their size , likely due to poor coordination of proper cell division and the absence of cross-linked PG . This also indicates that chlamydial PG may have a cellular organizational role other than simply facilitating cell division . While our initial observations indicated chlamydial PG was comprised of relatively fixed and static rings[9] , this PG ring has proven to be highly and uniquely dynamic , as evidenced by its rapid assembly and , in the absence of functional MreB , disassembly . Other bacteria that grow from the mid-cell synthesize new PG at the septum , splitting the older septal PG and pushing it outwards toward the poles where it is eventually modified and/or degraded . Given that Chlamydia maintain PG as a ≤ 140 nm ring , we are currently unable to test this bidirectional model of PG reshaping in Chlamydia ( S10a Fig ) , as the width of the PG rings is currently at the resolution limits of SIM imaging systems . Assuming that A22 dispersion of MreB polymers does not significantly affect the activity of the PG degradation mechanism , we favor a model in which , as the cell grows and divides , the PG ring in pathogenic Chlamydia is constantly re-sculpted by coordinated , yet independent PG synthesis and degradation mechanisms ( Fig 9b ) . Our model for the maintenance of chlamydial PG rings predicts the presence of an equally dynamic and closely localized PG degradation mechanism accompanying MreB-facilitated PG synthesis ( Fig 9b ) . This degradation mechanism would likely include a yet uncharacterized lytic transglycosylase capable of cleaving the PG glycan chains and the chlamydial amidase , AmiA [61 , 62] , which cleaves the peptide stem from the N-acetylmuramic acid . Interestingly , the chlamydial amiA is expressed simultaneously with genes of the PG biosynthetic pathway[58] and the protein localizes diffusely in the periplasm[51] . The amidase encoded by C . pneumoniae possesses carboxypeptidase activity and lacks the regulatory domain present in the homologous E . coli protein[61] . A diffuse and constitutively active chlamydial AmiA possessing both amidase and carboxypeptidase activities could explain how a PG ring of constant width is maintained in normal RBs and how aberrant PG forms in β-lactam-induced aberrant bodies . When MreB is not functional , a diffuse and active AmiA would completely degrade older PG with no new PG to take its place . This could also explain the slight enlargement of RBs in the presence of A-22; in the absence of a strong PG belt , the RBs might expand slightly to reach the hydrostatic equilibrium within the inclusions . This MreB vs . PG degradation model also links MreB localization and movement to how , where , and when chlamydial PG will be made; directly by the incorporation of new PG material , and/or indirectly by down-regulating the activity of the degradation mechanism . While MreB co-localization with PG strands in enlarged aberrant bodies upon β-lactam inhibition of PBPs is consistent with this model , this observation also shows that transglycosylation in Chlamydia can be uncoupled from D , D-transpeptidation/carboxypeptidation . D , D-transpeptidation/carboxypeptidation may still play a role in defining the proper ring shape for division , but not for nascent PG assembly . In conclusion , pathogenic Chlamydiae lack a classical PG sacculus and limit PG synthesis to a narrow PG ring during the replicative phase of their developmental cycle . We propose that the expression of minimal quantities of immunostimulatory PG represents pathoadaptive evolution by Chlamydia . Immune evasion by the restriction or modification of PAMPs is well-documented in other pathogenic microbes [63] . The reduced PG ring present within pathogenic Chlamydia species is likely the product of their adaptation to an obligate , intracellular lifestyle in which a full PG sacculus is not required and may even be detrimental to bacterial survival . We propose that this PG ring is made and re-sculpted to facilitate cell division and growth by the interplay of MreB with a PG degradation mechanism . This study also established a direct visual link between nascent bacterial PG synthesis and a functional and dynamic MreB . With the availability of inducible promoters[64 , 65] , gene inactivation systems [66] and allelic exchange [67] in Chlamydia , DAADs will allow future studies to explore the models proposed in this work by controlling various aspects of PG biosynthesis and degradation in intracellular , pathogenic Chlamydia . Our observation that inhibition of MreB has global inhibitory effects throughout the chlamydial life cycle not only makes Chlamydia an attractive organism in which to study MreB , but also presents this protein as a promising anti-chlamydial target for future studies .
Clickable dipeptide PG probes ( EDA—DA and ADA—DA ) and MreB polymerization inhibitors ( A22 and MP265 ) were synthesized as previously described[9 , 49] . Alkynyl- and Azide-functionalized Alexa Fluor 488 , TAMRA-5-azide , and Click-iT Cell Reaction Buffer Kit were purchased from Invitrogen . Antibodies against chlamydial MreB and IncA were generously provided by Scott Hefty ( University of Kansas ) and Dan Rocky ( Oregon State University ) , respectively . The chlamydial RSGFP-expressing p2TK-SW2 plasmid was generously provided by Isabelle Derré ( University of Virginia ) . C . trachomatis serovar L2 strain 434/Bu was provided by H . Caldwell ( Rocky Mountain Laboratories ) . C . muridarum strains Nigg "M9"[68] , C . psittaci strain 6BC "BCRB"[69] , C . caviae strain GPIC "SP6" [70] are all clonal lab strains picked and expanded from single plaques . Chlamydial stocks were generated and asynchronous infections were performed as previously described[9] . Briefly , chlamydial EBs were harvested from infected L2 ( mouse fibroblast ) cells at 40 hpi and stored at -80°C until use . For infections , tissue culture-treated glass coverslips were placed in 24 well plates ( Costar ) and L2 cells were plated so as to reach ~70–80% confluence by the day infections were carried out . Cells were washed twice with warm Dulbecco's modified Eagle's medium ( DMEM ) , infected at a multiplicity of infection ( MOI ) of 1 with bacteria resuspended in DMEM , plates were rocked for two hours at 37°C 5% CO2 , unbound bacteria were subsequently removed , and medium was replaced with DMEM supplemented with 10% FBS ( HyClone ) , 1 × MEM Non-Essential Amino Acids Solution ( Sigma ) , and 0 . 2 μg ml−1 cycloheximide ( Sigma ) . Medium was supplemented with native/modified dipeptide molecules , antibiotics , and/or MreB inhibitors as noted in the text . For quantification of the toxicity of MreB-polymerization inhibitors , infections were carried out as described above utilizing the C . trachomatis serovar L2 strain 434/Bu transformed with the p2TK-SW2 plasmid [71] for the expression of RSGFP . Inhibitors were added at the time points / durations indicated in the text . At either 44 or 52 hpi , 1 ml of sucrose/phosphate glutamate buffer ( SPG ) was added to each well and cells were collected via scraping with glass beads . Resuspended cells were then subjected to sonication for brief ( ten second ) pulses , 1:10 dilutions were prepared and were immediately used to infect 96 well monolayers of L2 cells , which were seeded the previous day with 200 μl of 200 , 000 L2 cells/ml per well . Plates were then spun in an Eppendorf desktop centrifuge at 3000 rpm 35C for 1 hour and then allowed to incubate at 37°C 5% CO2 for 24 hours . Inclusions were counted in live cells via fluorescence microscopy and IFUs were calculated for each experimental group in biological triplicates . Experiments in which chlamydial PG was labeled with incorporated , clickable dipeptide probes ( DAADs ) were carried out as described previously[9] . Briefly , at designated time points post infection , treated coverslips containing Chlamydia-infected cells were washed first in warm DMEM , then with PBS , and fixed/ permeabilized with methanol for five minutes . Cells were then washed with PBS , further permeabilized with 0 . 5% Triton X for five minutes , and washed a final time with PBS . Coverslips were then blocked with 3% BSA ( in PBS ) and the click chemistry reaction was carried out utilizing the Click-iT Cell Reaction Buffer Kit ( Invitrogen ) with appropriate azide-modified fluorophores ( Alexa fluor 488/647 and TAMRA-5 ) . The addition of copper ( cupric sulfate ) results in the alkyne group on the dipeptide probe and the azide group on the fluorophore forming a stable triazole conjugate , thereby fluorescently labeling the PG in which the probe has been incorporated . Where indicated , chlamydial major outer membrane protein ( MOMP ) was labeled with anti-MOMP antibody ( LifeSpan Biosciences , 1:500 ) , chlamydial inclusions were labeled with anti-IncA antibody ( [72] , 1:500 ) and chlamydial MreB was labeled with anti-chlamydial MreB diluted 1:1 , 000 . Alexa fluor-conjugated anti-mouse , anti-goat , and anti-rabbit secondary antibodies ( Invitrogen ) were diluted 1:2 , 000 . L2 cells were infected with C . trachomatis serovar L2 strain 434/Bu for 16 hours . Infection medium was then removed , cells were washed once with pre-warmed DMEM , and new infection medium was added containing 4 mM EDA—DA ( pulse ) . After one hour , medium was removed , cells were washed twice with pre-warmed DMEM , and new medium was added containing a second probe , small molecule inhibitor , or neither ( chase ) . Cells were fixed , permeabilized at indicated time points , and labeling of chlamydial PG and MOMP was conducted , as described previously . For the pulse-chase experiment conducted in Fig 8 , a similar principle was followed , but with the following differences . After a 2 h EDA—DA ( 4 mM ) pulse , cells were washed and incubated in fresh medium containing MP265 ( 125 μM ) for an additional 1 h ( 1st Chase ) . At this point the cells were washed again and new medium ( that lacked both EDA—DA or MP265 ) was added containing either antibiotics , D-Ala—D-Ala , or neither ( 2nd Chase ) . All confocal imaging was conducted with a Zeiss 710 laser scanning microscope utilizing Zen 2012 ( Carl Zeiss ) software and all epifluorescence imaging was conducted by a Nikon Ti-E inverted fluorescence microscope equipped with a Plan Apo 60x/1 . 40 Oil Ph3 DM objective and a DAPI/GFP/Cy3/Cy5 filter cube and an Andor DU885 EMCCD camera . Settings were fixed at the beginning of image acquisition and for experiments in which different samples/time points were to be compared , the same parameters were applied for collecting and post processing all images taken . Deconvolution and maximum intensity projection ( when used ) was conducted utilizing AxioVision ( Carl Zeiss ) software employing the inverse filter setting or ImageJ , respectively . ImageJ was used for all subsequent image analysis . For generating the data presented for the kinetic analysis of the average fluorescence of chlamydial inclusions , confocal Z-stacks were taken using the 40x objective and maximum intensity projections were generated from those stacks and pixels assigned a brightness level between 0 and 255 . Basic intensity quantification was conducted by using the MOMP-labeling ( red ) channel to define the area which encompassed individual inclusions , and this was then used as an overlay to subsequently quantify the level of fluorescence present within each inclusion area present within the EDA—DA ( green ) channel . 3D SIM super-resolution microscopy was performed on a Delta Vision OMX microscope equipped with an Olympus 100X/1 . 40 Oil PSF objective and a Photometrics Cascade II EMCCD camera . The samples were excited with lasers at 405 nm , 488 nm , 561 nm , 642 nm and the emission was detected through 419 nm -465 nm , 500 nm -550 nm , 609 nm -654 nm , 665 nm -705 nm emission filters . The image processing was conducted by SoftWorx imaging software . Further image analysis was conducted via ImageJ including the measurements of PG ring widths . For PG width measurements , rings that were aligned perpendicular to the x , y axis ( for the maximum resolution ) were used . SIM imaging Z stacks of chlamydial inclusions were obtained via a Zeiss ELYRA PS . 1 utilizing Zen 2012 ( Carl Zeiss ) software for image processing . Muropeptide fragments of PG were isolated from Chlamydia-infected cells as previously described [50] . Two 175 cm2 flasks of confluent HeLa cells were either infected with C . trachomatis L2/434 at an MOI of 1 or mock infected and incubated for two hours at 37°C in 5% CO2 with rocking . Infection medium was then removed and replaced with DMEM with heat inactivated fetal bovine serum ( 10% ) , and cells incubated for an additional 16 hours . Medium was then removed and replaced with fresh DMEM ( 10% FBS ) with or without 75 μM compound A22 and cells were then allowed to incubate an additional two hours . Medium was removed , cells were washed once with warm DMEM ( to remove any residual A22 ) and then cells were harvested using glass beads , resuspended in DMEM , and sonicated at 40 amps with one-minute pulses , repeated five times . Supernatants were centrifuged at 4 , 000 g for five minutes to remove cellular debris , lysates were passed through a 0 . 22 μm filter , and then 3 kDa centrifugal filters ( Amicon UFC900324 ) at 4000 x g for one hour at 37°C . Flow-through fractions from the 3 kDa centrifugal filters were then heat inactivated at 95°C for six minutes and assayed for activity in an HEK NOD reporter cell line ( see below ) . The NOD signaling assay was conducted on lysates of Chlamydia-infected cells as previously described . Briefly , HEK cells overexpressing either the NOD1 or NOD2 receptors , as well as the NF-κB-SEAP reporter gene ( Invivogen , CA ) , were used to quantify the immunostimulatory potential from cell lysate fractions taken from a reverse phase C18 HPLC column . Twenty μl of cell lysate fractions from mock-infected and Chlamydia-infected cells ( in the presence or absence of compound A22 ) were added to ~5 x 104 HEK-Blue NOD1 or NOD2 cells in 96 well plates ( total reaction volume 200 μl/well ) and incubated for 24 hrs at 37°C . Secreted alkaline phosphatase ( SEAP ) was measured by adding 20 μl of the supernatant from lysate fraction-treated wells to 180 μl of QUANTI-blue substrate ( Invivogen ) in a separate 96 well microtiter plate . Untreated or uninfected cell supernatants were used as negative controls . The reaction was incubated at 37°C for 30 min and SEAP activity was assessed by reading OD at 650 nm . HPLC was carried out on filtered , Chlamydia-infected cell lysates ( and controls ) as previously described [50] . LCMS experiments were performed on an Agilent 1200 Series liquid chromatography system coupled to an AB Sciex Q-Trap 4000 mass spectrometer with a Turbo V electrospray ionization source , as previously described [50] . Data from control/experimental groups was analyzed and overlaid using Analyst Software ( v1 . 5 . 1 ) with all experiments and subsequent analysis conducted at least twice . | Pathogenic Chlamydia do not assemble their peptidoglycan ( PG ) cell wall in a classical , mesh-like sacculus , but instead apparently confine it to the mid-cell in the actively dividing , non-infectious form . We characterize the assembly and aging of this PG-ring and link its synthesis to MreB , an actin-like protein associated with lateral cell wall synthesis in bacteria . As PG is recognized by the host innate immune system , we hypothesize that the limited amount of PG synthesized by Chlamydia is an adaptation to the microbe’s intracellular lifestyle . | [
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"dis... | 2016 | Pathogenic Chlamydia Lack a Classical Sacculus but Synthesize a Narrow, Mid-cell Peptidoglycan Ring, Regulated by MreB, for Cell Division |
Transglutaminase ( TG ) catalyzes protein-protein crosslinking , which has important and diverse roles in vertebrates and invertebrates . Here we demonstrate that Drosophila TG crosslinks drosocrystallin , a peritrophic matrix protein , to form a stable fiber structure on the gut peritrophic matrix . RNA interference ( RNAi ) of the TG gene was highly lethal in flies and induced apoptosis of gut epithelial cells after oral infection with Pseudomonas entomophila . Moreover , AprA , a metalloprotease secreted by P . entomophila , digested non-crosslinked drosocrystallin fibers , but not drosocrystallin fibers crosslinked by TG . In vitro experiments using recombinant drosocrystallin and monalysin proteins demonstrated that monalysin , a pore-forming exotoxin of P . entomophila , was adsorbed on the crosslinked drosocrystallin fibers in the presence of P . entomophila culture supernatant . In addition , gut-specific TG-RNAi flies had a shorter lifespan than control flies after ingesting P . entomophila , whereas the lifespan after ingesting AprA-knockout P . entomophila was at control levels . We conclude that drosocrystallin fibers crosslinked by TG , but not non-crosslinked drosocrystallin fibers , form an important physical barrier against exotoxins of invading pathogenic microbes .
Gut epithelia are the first line of defense against invading microorganisms . Drosophila has several gut defense systems , including the production of antimicrobial peptides [1–3] and reactive oxygen species [4–6] , peritrophic matrix formation [7] , and stem cell activation for cell renewal [8 , 9] . Transglutaminase ( TG ) is involved in the regulation of antimicrobial peptide production and peritrophic matrix formation [10] . TG catalyzes the isopeptide bond formation between lysine and glutamine residues , and has diverse physiologic roles in vertebrates and invertebrates [11 , 12] . Drosophila TG is involved in cuticular formation [13] and hemolymph coagulation , which traps invading pathogens [14 , 15] . The concept of hemolymph coagulation in invertebrates as a part of the early innate immune system has been extended to vertebrates [14 , 15] . Recently , we reported that systemic and gut-specific TG-knockdown flies have a shorter lifespan than control flies , concomitant with severe apoptosis of cells in the gut epithelium [10] . Moreover , we found that TG crosslinks N-terminal Relish in the immune deficiency pathway to suppress antimicrobial peptide expression , thereby enabling immune tolerance against gut microbes . Further , RNA interference ( RNAi ) of the TG gene causes peritrophic matrix defects and penetration of dextran beads from the gut lumen ( endoperitrophic space ) into the ectoperitrophic space [10] . The peritrophic matrix in insects is a non-cellular sieve-like structure that lines the midgut epithelium , and comprises chitin fibrils and chitin-binding proteins [16] . This matrix has a role analogous to that of the mucosal layer of the vertebrate intestine , and is thought to support digestion and provide protection against abrasive food particles and enteric pathogens [17] . The drosocrystallin gene , which encodes a 52-kDa glycoprotein with Ca2+-binding ability , was originally identified in Drosophila eyes , but its function was not clear [18 , 19] . Drosocrystallin was recently reported to have an important role in protecting against entomopathogenic bacteria such as Pseudomonas entomophila [7] . Drosocrystallin expression is induced upon oral infection by bacteria , and the peritrophic matrix of drosocrystallin-knockout flies is more permeable than that of wild-type flies , demonstrating the essential role of drosocrystallin in peritrophic matrix formation [7] . Drosocrystallin is a secreted glycoprotein containing a chitin-binding R&R motif [20 , 21] . Cuticular chitin-binding proteins in horseshoe crabs are substrates for TG [22] , suggesting that drosocrystallin could be a potential TG substrate . Here we demonstrate that TG enhances the structural strength of the peritrophic matrix by crosslinking drosocrystallin fibers , and that the crosslinked drosocrystallin fibers , but not non-crosslinked drosocrystallin fibers , are essential for protection against exotoxins secreted by gut-invading pathogenic bacteria .
Wild-type drosocrystallin and a lysine-to-arginine substituted mutant ( KR ) were prepared in Escherichia coli . To examine the functional activity of these recombinants , we evaluated chitin-binding activity . Both recombinants clearly bound to chitin ( Fig 1A , left and right panels ) . The recombinants were then incubated with TG in the presence of monodansylcadaverine ( MDC ) or biotin pentylamine , an amino-substrate of TG . MDC was incorporated into these recombinants in a time-dependent manner ( Fig 1B , left panel ) . MDC was incorporated into the KR mutant at the same rate as in the wild-type recombinant ( Fig 1B , right panel ) , because the amino-substrate is incorporated in glutamine residues . The biotin pentylamine-incorporated recombinants were also detected using streptavidin , but incorporation was inhibited in the presence of a TG inhibitor , EDTA ( Fig 1C ) . In wild-type drosocrystallin , a protein band was observed on the stacking gel , but not in the KR mutant in the absence of MDC and EDTA ( Fig 2A ) , indicating that drosocrystallin is covalently crosslinked by TG to form homopolymers . To clarify the crosslinking profile of drosocrystallin in vivo , we generated systemic TG-knockdown flies ( Da>TG IR ) . Western blotting using anti-drosocrystallin antibody revealed a protein band on the stacking gel in the gut of control flies ( Da>+ ) , but not TG-knockdown flies ( Fig 2B ) , indicating that drosocrystallin was covalently crosslinked by TG in vivo . In sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) , the non-glycosylated wild-type recombinant expressed in E . coli and intact drosocrystallin in the gut had apparent molecular masses of 52 kDa and 75 kDa , respectively ( Fig 2A and 2B ) . Drosocrystallin is important for host defense against bacterial protease AprA secreted by P . entomophila [7] . Protease AprA is a metalloprotease important for local infection [2 , 23 , 24] . Wild-type drosocrystallin was degraded by adding the culture supernatant from P . entomophila in the absence of TG , but recombinant drosocrystallin crosslinked by TG was not degraded ( Fig 3A ) . In Drosophila , TG-mediated crosslinking of Fondue and hexamerin is important for trapping invading microbes in the hemocoel [14 , 25] . In the case of Trichoplusia ni , the chitin-binding proteins ( CBP1 and CBP2 ) and the insect intestinal mucin bind to chitin fibers on the peritrophic matrix , which protect the insect from food-derived digestive proteases [26–28] . These previous findings together with our results indicate that crosslinked drosocrystallin on the peritrophic matrix could act as a protective physical barrier against P . entomophila exotoxins . To identify the pathogenic protease ( s ) involved in the degradation of non-crosslinked drosocrystallin , the culture supernatant from P . entomophila was fractionated by gel filtration , and wild-type drosocrystallin was incubated with each fraction . The non-crosslinked recombinant was not detected in the fractions ( Nos . 18–36 ) by Western blotting , possibly due to proteolytic digestion ( Fig 3B ) . Metalloprotease AprA [2 , 23 , 24] and three proteases with unknown function , including MucD , C56 , and PMP , were identified from one fraction ( No . 27 ) by mass spectrometry , and AprA was the most abundant protease in this fraction ( Fig 3C ) . Therefore , we purified AprA from a fraction ( No . 26 ) , as described in the Materials and Methods . Wild-type drosocrystallin was completely degraded by the purified AprA ( Fig 3D ) . To confirm the involvement of AprA in the digestion of drosocrystallin , culture supernatant without AprA was prepared using AprA-knockout P . entomophila . The resulting supernatant did not significantly degrade wild-type drosocrystallin ( Fig 3E , upper and lower panels ) , clearly indicating that AprA is a key protease for the degradation of non-crosslinked drosocrystallin . TG-catalyzed fibers or a mesh formation of several proteins , such as proxin and stablin in horseshoe crabs , is important for wound healing and bacterial entrapment [29 , 30] . To determine whether drosocrystallin forms fibers or a mesh by protein-protein crosslinking of TG activity , wild-type drosocrystallin was incubated on cover glass under several conditions and observed by immunofluorescence microscopy ( Fig 4A ) . Interestingly , fiber-like structures were observed in the absence of TG , but the fibers were not detected in the presence of EDTA , indicating that Ca2+ induces non-covalent self-association of drosocrystallin ( Fig 4B ) . This finding is consistent with a previous finding that drosocrystallin exhibits Ca2+-binding ability [19] , and suggests that Ca2+ is required not only for TG activation , but also for non-covalent fiber formation of drosocrystallin . To clarify the importance of covalent crosslinking of drosocrystallin mediated by TG , the effect of culture supernatant including protease AprA from P . entomophila on the stability of drosocrystallin fibers was observed with or without active TG . In the absence of active TG , the fiber structure of wild-type drosocrystallin gradually collapsed in proportion to the amount of the culture supernatant from P . entomophila and the fluorescence intensity of the fibers decreased further ( Fig 4C ) . On the other hand , in the presence of active TG , the fiber structure and fluorescence intensity of drosocrystallin were not affected by culture supernatant of P . entomophila . These findings suggest that TG-mediated covalent crosslinking of drosocrystallin is required for the protection against proteolytic digestion . P . entomophila secretes another exotoxin , monalysin , that acts as a pore-forming toxin against cell membranes , causing host cell death [24] . To examine whether the crosslinked fibers of drosocrystallin protect against penetration of monalysin into the peritrophic matrix , crosslinked or non-crosslinked fibers of wild-type drosocrystallin were mixed with wild-type monalysin in the presence or absence of the culture supernatant , and both recombinants were observed by immunofluorescence microscopy . Wild-type monalysin colocalized with the crosslinked fibers of wild-type drosocrystallin ( Fig 4D , left panels of Pe sup + ) . In contrast , the non-crosslinked fibers in the absence of TG were degraded by proteases in the culture supernatant and wild-type monalysin did not colocalize with wild-type drosocrystallin ( Fig 4D , right panels of Pe sup + ) . In the absence of the culture supernatant , however , wild-type monalysin colocalized with both the TG-dependent crosslinked fibers and the Ca2+-induced non-covalent associated fibers of wild-type drosocrystallin ( Fig 4D , left and right panels of Pe sup − ) . These findings indicate that the non-covalent fiber formation of drosocrystallin leads to co-localization of monalysin and that the protease-resistant drosocrystallin fibers crosslinked by TG , but not non-crosslinked drosocrystallin fibers , trap monalysin released from P . entomophila in the presence of proteases such as metalloprotease AprA . The survival rate of flies ingesting P . entomophila was analyzed . No significant differences were observed between gut-specific TG-knockdown flies and control flies after ingesting a non-lethal pathogen , Erwinia carotovora carotovora 15 ( Ecc15 ) , but gut-specific TG-knockdown flies had a significantly shorter lifespan than control flies after ingesting P . entomophila , and the lifespan returned to the control level after ingesting AprA-knockout P . entomophila ( Fig 5A ) . In a previous study , we found that TG-induced dampening of the immune-eliciting signals in the gut and TG-RNAi ( NP1>TG IR ) -induced shortened lifespan occurred at least 7 days after eclosion [10] . Here , we confirmed that TG-RNAi itself did not affect the survival rate in the time span of ~5 days after eclosion ( Fig 5A , TG-RNAi + sucrose ) . These data indicate that TG is involved in host defense in the fly gut after infection with P . entomophila to preserve the proteolytic digestion of drosocrystallin . Protease AprA is a member of the metzincin superfamily and was originally identified in P . aeruginosa [31 , 32] . Flies injected with or ingesting P . entomophila have a shorter lifespan than untreated flies , because AprA facilitates bacterial survival by degrading antimicrobial peptides produced by host immunity [2] and activates monalysin by cleaving the N-terminal pro-peptide [24] . In the present study , we demonstrate for the first time that AprA is directly involved in degrading the peritrophic matrix protein drosocrystallin to shorten the lifespan of Drosophila . On the other hand , monalysin causes apoptosis of gut epithelial cells [7 , 24] . To determine the cause of the shortened lifespan of TG-RNAi flies after ingestion of wild-type P . entomophila , we evaluated gut epithelial cell death . In this experiment , 3 to 5-day old adult flies were used because of the negligible effect of commensal community on the survival rate of TG-RNAi flies ( Fig 5A ) . After ingesting P . entomophila , a significant number of dead cells was detected in the gut , and the ratio of cell death was increased in gut-specific TG-knockdown flies ( Fig 5B ) . These findings demonstrate that TG is essential for protection against pathogenic bacterial infection in the gut ( Fig 5C ) . Drosocrystallin non-covalently self-associated to form fiber-like structures in the presence of Ca2+ . Non-crosslinked fibers were not stable and were degraded more quickly by AprA than the crosslinked fibers . TG stabilized the drosocrystallin fibers through intermolecular crosslinking . Such a crosslinking reaction could "mask" potential proteolytic cleavage sites of AprA . Peritrophic matrix proteins , such as insect intestinal mucin and chitin-binding proteins containing multiple chitin-binding domains , are proposed to form a bridge-like structure on chitin fibers on the peritrophic matrix in T . ni [26 , 28] . There is no genetic evidence for involvement of these peritrophic matrix proteins with multiple chitin-binding domains in peritrophic matrix formation in Drosophila , but the TG-catalyzed crosslinked fibers could promote the formation of a rigid peritrophic matrix structure to protect against exotoxins . Importantly , flies die within 5 h after injection of purified AprA into the hemocoel [2] . Ingestion of a high concentration of AprA has little effect on fly survival , and thus AprA itself is not critical for the virulence of naturally infecting P . entomophila [2] . These findings suggest that the peritrophic matrix inhibits the penetration of AprA secreted by P . entomophila from the gut lumen into the ectoperitrophic space . In addition , fluorescein isothiocyanate-labeled dextran-feeding assays performed in our previous study indicated increased permeability of the peritrophic matrix in systemic TG-RNAi flies [2] . In the present paper , we did not examine the survival of gut-specific TG-knockdown flies after ingesting monalysin and/or AprA , but the TG-mediated crosslinking of drosocrystallin in the peritrophic matrix clearly reduced AprA-mediated peritrophic matrix damage and blocked the movement of monalysin and other virulent factor ( s ) from the endoperitrophic space into the ectoperitrophic space . The drosocrystallin fibers crosslinked by TG , but not non-crosslinked drosocrystallin fibers , appear to form an important physical barrier against exotoxins of invading pathogenic microbes in the Drosophila gut .
Flies were maintained on standard yeast medium at 25°C . Da-GAL4 and w1118 flies were obtained from the Bloomington Stock Center ( Bloomington , IL ) . NP1-GAL4 flies were obtained from the Drosophila Genetics Resource Center ( Kyoto , Japan ) . UAS-TG IR flies were obtained from Dr . Ryu Ueda at the National Institute of Genetics ( Mishima , Japan ) . Strain w1118 was used as a control strain . P . entomophila L48 [23] , P . entomophilaΔaprA [2] , and Ecc 15 were grown in Luria-Bertani ( LB ) medium for all experiments . Bacteria were grown at 29°C and allowed to reach the stationary phase . Cells were then concentrated at OD600 = 200 except when indicated . For oral infection , female flies were starved for 2 h at 29°C . Ecc 15 or P . entomophila ( OD600 = 200 ) was added to a filter disk ( Whatman ) that completely covered the surface of the standard fly medium , and the flies were placed on the medium . Flies were maintained at 29°C , and mortality was monitored at different time-points . To construct expression vectors , cDNA fragments were amplified by polymerase chain reaction ( PCR ) . An amplimer encoding the drosocrystallin-coding sequence without a putative signal sequence ( 1–60 ) was inserted into expression vector pET-22b ( Novagen ) between the NdeI and EcoRI sites . The construct was verified by DNA sequencing . The construct , which contained C-terminal His-tags , was expressed in the E . coli strain BL21 ( DE3 ) ( Novagen ) . Bacteria were cultured in LB medium , and expression was induced by the addition of isopropyl-β-D-thiogalactoside at a final concentration of 1 mM at 15°C for 24 h . Bacterial pellets were harvested by centrifugation and sonicated in 10 ml of 20 mM Tris-HCl , pH 8 . 0 , 200 mM NaCl containing 1% Nonidet P-40 and 1 mM phenylmethylsulfonylfluoride . After sonication , the supernatants were recovered by centrifugation and purified according to the manufacturer’s protocol using Ni-NTA agarose ( Qiagen ) . To produce protein insensitive to TG , all lysine residues of drosocrystallin were substituted with arginine by PCR-based site-directed mutagenesis . Each amino acid substitution was generated by PCR using specific 5’-phosphorylated primers . The lysine-substituted mutation was verified by DNA sequencing and expressed in BL21 ( DE3 ) /pLysS by the same method as used for the wild type . K35R-sense primer , GGTCCTCCAACCTTCAGCAG; K35R-antisense primer , TGGCTAGCTGGTTAAGATCG; K92 , 96 , 103R-sense primer , GGCGGCAGGAGGAGAGGCGCGATGGCGACCTGGTCAGGGGT; K92 , 96 , 103R-antisense primer , TGTCATCGCCAGTCAGCGAG; K135R-sense primer , GGCAGCGTCTGGATGAGCAG; K135R-antisense primer , TAGACACAATGGCATTGAAG; K470R-sense primer , GGAACTGGCCGAATTCGAGCTCCGTCGACAGGCTT; K470R-antisense primer , TAGAGCGACGTTCGGCACTG . The whole sequence encoding the TG gene was cloned into expression vector pET-22b . The construct was verified by DNA sequencing . The construct , which contained no tags , was expressed in the E . coli strain BL21 ( DE3 ) . Bacteria were cultured in LB medium , and expression was induced by the addition of isopropyl-β-D-thiogalactoside at a final concentration of 30 μM at 15°C for 24 h . Bacterial pellets were harvested by centrifugation and sonicated in 50 mM Tris-HCl , pH 8 . 8 , 50 mM NaCl , 10 mM dithiothreitol ( DTT ) , 2 mM EDTA , 10% glycerol , and 1% 3-[ ( 3-cholamidepropyl ) dimethylammonio]-1-propanesulphonate . After sonication , the supernatants were recovered by centrifugation . Then , buffer was exchanged with 50 mM Tris-HCl , pH 8 . 0 , 10 mM DTT , 1 mM tris ( 2-carboxyethyl ) phosphine , and 0 . 5 mM EDTA using a Sephadex G-25 Superfine column , and stored at -80°C before use . The whole sequence encoding the monalysin gene was generated by PCR with C-terminal HAT encoding primers and cloned into expression vector pET-15b . The construct was verified by DNA sequencing . The construct was expressed in the E . coli strain Rosseta-gami B . Bacteria were cultured in LB medium , and expression was induced by the addition of isopropyl-β-D-thiogalactoside at a final concentration of 0 . 1 mM at 18°C for 24 h . Bacterial pellets were harvested by centrifugation and sonication buffer ( 50 mM Tris-HCl , pH 8 . 0 , 500 mM NaCl , 1 mM phenylmethylsulfonylfluoride , 0 . 5 mM lysozyme ) was added and frozen . After thawing and sonicating the pellets , the supernatant was recovered by centrifugation . The supernatant was purified using HisTrap crude FF column ( 1 mL , GE Healthcare ) . After purification , the buffer was exchanged with PBS using Sephadex G-25 Superfine column . To prepare the polyclonal antibody , recombinant drosocrystallin without a putative signal sequence ( 61–472 ) was expressed in E . coli strain BL21 ( DE3 ) ( Novagen ) . An inclusion body containing the recombinant protein was isolated and subjected to SDS-PAGE under reducing conditions , and negatively stained . The protein band corresponding to the recombinant protein was excised from the gel band recovered by electroelution for immunization of rabbits ( MBL International ) . The polyclonal antibody was purified sequentially from the anti-serum using Protein A Sepharose CL-4B ( GE Healthcare ) and antigen-conjugated Affi-Gel 15 ( Bio-Rad Laboratories ) . The recombinant proteins were mixed with chitin in 50 mM Tris-HCl , pH 7 . 5 , and 150 mM NaCl , and incubated at 4°C for 30 min . Supernatants were separated by centrifugation and precipitates were washed with the same buffer . Proteins bound to chitin were eluted with 10% acetic acid . Eluted fractions ( 100 μL each ) were evaporated using a speed-vac ( Labconco ) . Input , bound , and unbound fractions were subjected to SDS-PAGE and detected by Coomassie brilliant blue staining . The relative intensity of each fraction compared to the input protein was calculated by ImageJ software . Recombinant proteins were incubated with TG in 50 mM Tris-HCl , pH 8 . 5 , containing 10 mM CaCl2 , 10 mM DTT , and 500 μM biotin pentylamine at 37°C for 1 h . Following the reaction , the aliquots were subjected to SDS-PAGE and electroblotted on a polyvinylidene difluoride membrane . After blocking with 20 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , and 5% dry milk , the membrane was incubated at room temperature for 1 h with the horseradish peroxidase-conjugated streptavidin diluted 1:1 , 000 with blocking buffer , followed by development with Chemi-Lumi One-Super reagent ( Nacalai Tesque ) . Recombinant proteins were incubated with TG in 50 mM Tris-HCl , pH 8 . 5 , 10 mM CaCl2 , 10 mM DTT , and 5 mM MDC at 37°C for the durations indicated in Fig 1B . Following the reaction , aliquots were subjected to SDS-PAGE and visualized by ultraviolet irradiation . Band intensity was calculated using ImageJ software . SDS-PAGE was performed in slab gels according to the method of Laemmli . Precision Plus protein standards ( Bio-Rad Laboratories ) were used to determine the apparent molecular masses . Protein bands were visualized by Coomassie brilliant blue staining . Samples were subjected to SDS-PAGE and transferred to a polyvinylidene difluoride membrane . After blocking with 5% dry milk , the membrane was incubated at room temperature for 1 h with the anti-drosocrystallin antibody and then with the secondary antibody ( horseradish peroxidase-conjugated goat anti-rabbit IgG; Bio-Rad Laboratories ) , followed by development with Chemi-Lumi One , Chemi-Lumi One-super ( Nacalai ) , or WesternBright Sirius ( Advansta ) . For detection of the His-tag , horseradish peroxidase-conjugated anti-6 × His tag antibody ( MBL International ) was used . Chemifluorescence was detected using an Omega Lum G fluorescence imager ( Aplegen ) or X-ray film . Wild-type drosocrystallin or the KR mutant was incubated with TG in 50 mM Tris-HCl , pH 8 . 5 , 10 mM CaCl2 , and 10 mM DTT at 37°C for 1 h . Following the reaction , samples were subjected to SDS-PAGE and detected by Western blotting using anti- 6 × His tag antibody . Guts from wild-type and TG-knockdown flies ( Da>TG IR ) were homogenized in 50 mM Tris-acetate , pH 7 . 5 , 1% Nonidet P-40 , and protein inhibitor cocktail ( Nacalai Tesque ) , and centrifuged at 15 , 000 rpm at 4°C for 15 min to collect the supernatant . The supernatant was precipitated by 10% trichloroacetic acid , subjected to SDS-PAGE , and detected by Western blotting using anti-drosocrystallin antibody . Quantification of dead cells was performed as follows: 4 h after ingestion of P . entomophila , the guts were dissected and stained with Hoechst 33342 ( 1:1 , 000 , Dojindo Molecular Technologies ) and propidium iodide ( 1:2 , 000 , Life Technologies ) . Pictures were obtained with a fluorescence microscope . From these pictures , 100 Hoechst 33342-stained nuclei , representing all nuclei , were randomly defined and the number of propidium iodide-positive nuclei , representing dead cells , was determined . Three parcels per gut were analyzed . Results represent the mean of 10 independent experiments . In this experiment , 3 to 5-day-old adult flies were used . Culture supernatant from the wild-type P . entomophila was fractionated using ÄKTA start with a HiPrep 16/60 Sephacryl S-100 HR column ( GE Healthcare ) . Each fraction was incubated with the wild-type recombinant . The sample from fraction No . 26 was dialyzed with 20 mM Tris-HCl , pH 7 . 5 , and applied to a DEAE Sepharose CL-6B column ( 1×2 cm ) . The flow-through fraction was applied to a CM Sepharose CL-6B column ( 1×2 cm ) . After washing with 20 mM Tris-HCl , pH 7 . 5 , the protein was eluted with a linear NaCl gradient ( 100–500 mM ) in the same buffer . One microgram of wild-type drosocrystallin in 50 mM Tris-HCl , pH 8 . 5 , and 10 mM DTT with 10 mM CaCl2 or 50 mM EDTA was placed on a coverslip and incubated at 37°C for 1 h with or without TG . Next , the culture supernatant from P . entomophila and/or HAT-tagged monalysin was added to the coverslip and incubated at 37°C for 1 h . After incubation , the proteins were fixed with 4% paraformaldehyde for 20 min , washed with 20 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , and blocked with 2% bovine serum albumin in the same buffer . The proteins were then incubated for 1 h with anti-His tag monoclonal antibody ( MBL International ) for wild-type drosocrystallin and the anti-HAT polyclonal antibodies ( GenScript ) for monalysin . For detection , CF488 or CF568-conjugated goat anti-mouse secondary antibody ( Biotium ) and CF568-conjugated goat anti-rabbit secondary antibody ( Biotium ) were used . The proteins were imaged with a ZOE fluorescence microscope ( Bio-Rad ) for detection of the structure of wild-type drosocrystallin or MZ10 F ( Leica ) for calculating the mean gray value . The mean gray value of the signal for wild-type drosocrystallin was calculated using ImageJ software . The sum of gray values in the protein-coated area was divided by the number of pixels . The mean gray value of uncoated-area was subtracted from the value of the protein-coated area . | Intestinal homeostasis is ensured by a subtle balance between bacteria and host immunity . Gut epithelial barriers , such as the mucus layer in mammals and the peritrophic matrix in invertebrates , have a protective function for the host , as they are impermeable to invading intestinal microbes . Here we found that , in the fly Drosophila melanogaster , transglutaminase ( TG ) , a molecular glue involved in protein-protein covalent bond formation , is essential for peritrophic matrix formation by converting the peritrophic protein drosocrystallin into a stable fiber-like structure and inhibition of pathogenic bacteria . Knockdown of the TG gene led to increased permeability of the peritrophic matrix and greatly increased the susceptibility to a toxic bacterial protease . TG contributes to form a stable fiber-like barrier on the peritrophic matrix and increase tolerance to pathogenic microorganisms . | [
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] | [] | 2015 | Crosslinking of a Peritrophic Matrix Protein Protects Gut Epithelia from Bacterial Exotoxins |
Salicylic acid ( SA ) is a key phytohormone that mediates a broad spectrum of resistance against a diverse range of viruses; however , the downstream pathway of SA governed antiviral immune response remains largely to be explored . Here , we identified an orchid protein containing A20 and AN1 zinc finger domains , designated Pha13 . Pha13 is up-regulated upon virus infection , and the transgenic monocot orchid and dicot Arabidopsis overexpressing orchid Pha13 conferred greater resistance to different viruses . In addition , our data showed that Arabidopsis homolog of Pha13 , AtSAP5 , is also involved in virus resistance . Pha13 and AtSAP5 are early induced by exogenous SA treatment , and participate in the expression of SA-mediated immune responsive genes , including the master regulator gene of plant immunity , NPR1 , as well as NPR1-independent virus defense genes . SA also induced the proteasome degradation of Pha13 . Functional domain analysis revealed that AN1 domain of Pha13 is involved in expression of orchid NPR1 through its AN1 domain , whereas dual A20/AN1 domains orchestrated the overall virus resistance . Subcellular localization analysis suggested that Pha13 can be found localized in the nucleus . Self-ubiquitination assay revealed that Pha13 confer E3 ligase activity , and the main E3 ligase activity was mapped to the A20 domain . Identification of Pha13 interacting proteins and substrate by yeast two-hybrid screening revealed mainly ubiquitin proteins . Further detailed biochemical analysis revealed that A20 domain of Pha13 binds to various polyubiquitin chains , suggesting that Pha13 may interact with multiple ubiquitinated proteins . Our findings revealed that Pha13 serves as an important regulatory hub in plant antiviral immunity , and uncover a delicate mode of immune regulation through the coordination of A20 and/or AN1 domains , as well as through the modulation of E3 ligase and ubiquitin chain binding activity of Pha13 .
The plant hormone salicylic acid ( SA ) plays a major role in triggering local and systemic immune response for combating a broad-spectrum of biotrophic pathogens including viruses [1–3] . SA is involved in innate immunity including pattern-triggered immunity ( PTI ) and effector-triggered immunity ( ETI ) to ward off invaders [1] . Plants trigger PTI as the first line of defense through recognition of conserved microbe-associated molecular patterns ( MAMPs ) by pattern-recognition receptors [4 , 5] . PTI is also involved in plant viral resistance , and virus dsRNA has been shown to serve as a MAMP [6–8] . To successfully infect plant hosts , pathogens utilize various effectors to compromise PTI [9] . However , plants have evolved resistance ( R ) proteins capable of detecting these effectors to trigger ETI , which is a second line of plant defense against viruses [9] . Of note , elevated SA concentration is also essential to establish systemic acquired resistance ( SAR ) to further protect plants from diverse pathogens [10] . In summary , SA plays an important role in the regulation of PTI , ETI , and SAR to ward off virus infection [1] . The importance of SA-induced immunity has led to multiple screens for genes involved in the SA signaling pathway using pathogenesis-related protein ( PR ) genes as markers . However , various unrelated studies have only identified a single genetic locus , npr1 [11–13] . NPR1 is conserved among plants and is a master regulator of the SA-induced plant immune signaling pathway [10] . NPR1 transcription is moderately induced ( 2–3 times ) upon pathogen or SA treatment [14 , 15] , and the post-translational regulation of NPR1 is essential to trigger immune response [16] . Even though plant NPR1 is seen to play important roles in regulating SA-induced plant immunity , SA-induced virus resistance still occurs in Arabidopsis npr1 mutants [17 , 18] , leading to questions about SA-induced NPR1-independent virus defense . In addition to PTI and ETI , gene silencing is also an important defense mechanism for combating viruses [19] . It is triggered by the presence of viral dsRNA , which is processed to small-interfering dsRNA ( siRNA ) by Dicer-like nuclease . The RNA dependent RNA polymerase ( RdR1 ) is responsible for the de novo synthesis of dsRNA to initiate secondary RNA silencing against viruses in plants [20–22] . RdR1 can be up-regulated after SA treatment [23] , and it is also dependent on NPR1 in Arabidopsis [24] . Yet , RdR1 may not completely resolve the mechanism of SA-induced virus defense since SA treatment can still trigger resistance to Tobacco mosaic virus in Nicotiana benthamiana with non-functional RdR1 [25] . Studies on diverse plants enable a broader understanding of comprehensive strategies applied by plants to cope with stresses . The Orchidaceae is among one of the largest family of flowering plants [26]; however , the slow growth and difficulty in regeneration has hampered the study of orchids . In order to facilitate gene functional studies in orchid , we previously developed a high-throughput Cymbidium mosaic virus-induced gene silencing system ( CymMV-VIGS ) [27] . Here , through VIGS screening of immunity related genes in orchids , we identified a regulator , Pha13 , which is involved in the SA immune signaling pathway . Transgenic monocot orchid and dicot Arabidopsis plants overexpressing Pha13 showed greatly enhanced resistance to different viruses . Transgenic Arabidopsis overexpressing Pha13 also enhanced plant resistance to Pseudomonas syringae pv . tomato DC30000 . Our detailed analysis revealed that Pha13 is regulated by SA and leads to transcriptional reprograming of massive numbers of immune responsive genes including the master regulator gene of plant immunity , NPR1 , as well as NPR1-independent virus defense genes . The AN1 domain was shown to be associated to the expression of orchid NPR1 , and both A20 and AN1 domains are required for virus resistance . Pha13 exhibits similar and distinctive biochemical features to other A20/AN1 proteins , including domains involved in E3 ligase and polyubiquitin chain binding activity , with known A20/AN1 proteins . Our findings revealed that Pha13 is conserved in mediating viral resistance among plants and serves as a pivotal regulatory hub in antiviral immunity .
Pha13 ( Orchidstra 2 . 0 database , http://orchidstra2 . abrc . sinica . edu . tw , accession number PATC148746 , the Pha13 coding sequence can be found in S1 File ) was identified here for the first time using the CymMV-VIGS system in Phalaenopsis aphrodite . Our data indicated that silencing Pha13 decreased the RNA of PhaPR1 ( Fig 1A ) . Amino acid sequence analysis revealed that Pha13 contained A20 and AN1 domains ( Fig 1B–1D ) and belonged to a fast emerging class of zinc-finger proteins ( ZFPs ) , termed stress associated proteins ( SAPs ) in plants [28] . To further address the interrelationship between Pha13 and known SA-related genes , we knocked down Pha13 RNAs by infiltration of agrobacterium carrying the 35S promoter driven hairpin RNA-expressing constructs of phpPha13 in P . aphrodite ( Fig 2A ) . Total RNAs extracted from agrobacterium-infiltrated plants were used to detect RNA expression of Pha13 , PhaNPR1 , and PhaPR1 by qRT-PCR . Knockdown of Pha13 RNA is correlated with decreased RNA levels of PhaNPR1 and PhaPR1 ( Fig 2A ) . Furthermore , we generated transient overexpression of Pha13 in P . aphrodite by infiltration of agrobacterium carrying the overexpression constructs ( pPha13-oe ) . Transient overexpression of Pha13 in P . aphrodite increased the RNA levels of PhaNPR1 and PhaPR1 ( Fig 2B ) . On the other hand , transient silencing of PhaNPR1 ( infiltration of agrobacterium carrying phpPhaNPR1 ) did not affect the RNA accumulation of Pha13 ( Fig 2C ) . This indicates that Pha13 affects the expression of PhaNPR1 but not vice versa . The results from our transient silencing and overexpression of Pha13 showed that Pha13 is involved in the SA-related plant immune pathway . Next , we tested whether SA or other plant hormones affect Pha13 transcription . Plant samples were collected at different time points up to 72 h after SA , jasmonic acid ( JA ) , and ethylene ( ET ) treatment , and analyzed for RNA of Pha13 and corresponding hormone-marker genes by qRT-PCR . Pha13 was induced by SA , JA , and ET at 1 h , 6 h , and 6 h post-treatment , respectively ( Fig 2D–2F ) . The results indicated that post-treatment expression of Pha13 was induced earlier in SA treatment , at 1 h post-treatment , compared to up regulation of PhaNPR1 at 24 h post-treatment ( Fig 2D ) . To determine whether Pha13 is involved in plant resistance to virus infection , we first analyzed Pha13 expression in mock- or CymMV-inoculated P . aphrodite by qRT-PCR . The results showed that Pha13 is induced by CymMV ( Fig 3A ) . Furthermore , we assayed the virus accumulation in transient silenced or overexpressed Pha13 in P . aphrodite . Transient knockdown of Pha13 increased CymMV accumulation in infected P . aphrodite ( Fig 3B ) ; overexpression of Pha13 in CymMV-infected P . aphrodite decreased CymMV accumulation ( Fig 3C ) . To validate the results , we generated transgenic P . equestris overexpressing Pha13 ( 35S::FLAG-Pha13 ) . Western blot using antiserum against Pha13 indicated that the overexpressed Pha13 could be detected in every individual asexually propagated progeny derived from transgenic T0 lines 27 , 29 and 30 ( Fig 3D ) . When we inoculated CymMV into 3 individual progenies derived from 3 transgenic lines , the results showed that CymMV decreased to a very low level in all progenies of the three transgenic lines as compared to the non-transgenic lines ( Fig 3E ) . To explore the gene ( s ) affected by Pha13 , we conducted microarray analysis of P . aphrodite overexpressing Pha13 ( pPha13-oe ) . Global gene expression analysis comparing microarray data of plants overexpressing Pha13 to the vector control 5 days post agroinfiltration revealed that overexpression of Pha13 affected the expression of 10639 genes ( S1A Fig ) . Gene ontology of the affected genes indicated that Pha13 is involved in cellular processes , metabolic processes , and single-organism processes ( S1B Fig ) . To verify the microarray data , we analyzed the RNA expression level by qRT-PCR of three genes PhaNPR1 , PhaRdR1 , and Glutaredoxin ( PhaGRX ) , that were up-regulated in the Pha13 overexpression plants ( S2 Fig ) . In our analysis of differentially expressed genes of plants overexpressing Pha13 , we observed several genes previously reported to be involved in plant resistance . Among them are SA-induced genes that are known to be NPR1-dependent and -independent ( S1 Table ) . We selected two genes , PhaRdR1 and PhaGRX , reported to be involved in plant resistance [29 , 30] and induced by SA , for further analysis . One of the selected genes , RdR1 is positively regulated by NPR1 [24] , whereas the regulation of GRX is irrelevant to NPR1 in Arabidopsis [31] . In orchids , PhaRdR1 and PhaGRX were both induced by SA and CymMV infection ( Fig 4A and 4B ) . We next investigated whether PhaNPR1 regulates PhaRdR1 and PhaGRX expression in P . aphrodite through transiently delivering PhaNPR1 hairpin RNA into P . aphrodite by agroinfiltration carrying phpPhaNPR1 . Our data for orchids is consistent with studies in other systems , in which transient silencing of PhaNPR1 can decrease the downstream marker genes , PhaPR1 and PhaRdR1 , whereas PhaGRX remain unchanged ( Fig 4C ) . To reveal whether PhaRdR1 or PhaGRX is involved in virus resistance , we transiently silenced the two genes in CymMV pre-infected P . aphrodite . Consistent with previous reports [29] , our data indicated that silencing PhaRdR1 ( phpPhaRdR1 ) increases CymMV accumulation ( Fig 4D ) . Although a previous report indicated that GRX is involved in plant defense against Botrytis cinerea infection [30] , its role in virus infection remains elusive . Our data indicated that silencing PhaGRX ( phpPhaGRX ) increased CymMV accumulation ( Fig 4E ) . Our data suggested that both PhaRdR1 and PhaGRX are involved in SA-induced virus resistance . To further analyze the relationship between Pha13 in the regulation of PhaRdR1 and PhaGRX , we performed transient silencing and overexpression assay of Pha13 in P . aphrodite . Transient silencing Pha13 did not affect the expression of PhaRdR1 or PhaGRX , while transiently overexpressing Pha13 increased the expression of PhaRdR1 and PhaGRX ( Fig 4F and 4G ) . Our data suggested that Pha13 positively regulates PhaRdR1 and PhaGRX expression . To analyze the function of A20 and AN1 domain in Pha13 , we overexpressed wild-type or mutant of Pha13 ( mutated A20 , and/or AN1 domain of Pha13 ) in healthy or CymMV pre-infected P . aphrodite . We substituted the conserved cysteine and histidine to glycine on A20 and/or AN1 of Pha13 ( Fig 1C and 1D ) to generate the A20 domain mutant ( Pha13A20m ) , AN1 domain mutant ( Pha13AN1m ) , and the A20 and AN1 domain double mutant ( Pha13A20mAN1m ) . The results indicated that overexpression of Pha13 A20 mutant ( Pha13A20m ) increased expression of PhaNPR1 , which is consistent with overexpression of Pha13 wild-type ( Fig 5A ) . The stable expression of Pha13 wild-type and mutants were confirmed by immunoblotting analysis ( S3 Fig ) . Overexpression of Pha13 AN1 mutant ( Pha13AN1m ) in P . aphrodite decreased the expression of PhaRdR1 , whereas the expression of PhaNPR1 and PhaGRX remained unchanged ( Fig 5A ) . Overexpression of Pha13 A20 and AN1 mutant ( Pha13A20mAN1m ) in P . aphrodite decreased the RNA levels of PhaRdR1 and PhaGRX , while the expression of PhaNPR1 remains unchanged ( Fig 5A ) . Overexpression of any Pha13 A20 and/or AN1 mutant resulted in increased accumulation of CymMV ( Fig 5B ) . These data are summarized in Fig 5C and S4 Fig . Our data suggests that Pha13 AN1 domain alone can affect the expression of PhaNPR1 , and both A20 and AN1 domains are required for regulation of PhaRdR1 , PhaGRX , and CymMV accumulation . A20-type zinc finger proteins have been reported to confer ubiquitin ligase activity [32] . Therefore , we analyzed whether Pha13 has self-ubiquitination E3 ligase activity . We purified His-tagged recombinant Pha13 ( Pha13-His ) from E . coli for self-ubiquitination E3 ligase activity analysis . Self-ubiquitination E3 ligase activity was observed in the presence of human E1 and E2 with Pha13-His ( Fig 6 ) by using anti-FLAG antibody to detect FLAG-ubiquitin . Furthermore , we also analyzed the self-ubiquitination E3 ligase activity of the A20 and/or AN1 domain . As shown in Fig 6 , E3 ligase activity of Pha13 was greatly reduced in A20 mutant ( Pha13A20m ) or A20/AN1 double mutant ( Pha13A20mAN1m ) . AN1 mutant ( Pha13AN1m ) showed higher self-ubiquitination E3 ligase activity than other domain mutants , but lower than the wild-type form of Pha13 ( Fig 6 ) . The results indicate that A20 domain of Pha13 plays a more important role in conferring E3 ligase activity . The data are summarized in S4 Fig . In addition , to search for Pha13 substrate or interacting proteins , we constructed a yeast two-hybrid ( Y2H ) library with RNA extracted from SA-treated P . aphrodite . In our initial screening , we identified 56 positive clones . After sequencing the clones , we found that the positive clones include 25 clones encoding ubiquitin , 4 clones encoding partial sequences of thioredoxin-like proteins , 3 clones encoding partial DNAJ-like proteins , and the rest of clones encode proteins of different identity that only appear once ( S2 Table ) . The full length of clones encoding proteins appearing more than once in our initial screen were subjected to further Y2H analysis . Only clones encoding ubiquitin showed positive interaction ( Fig 7A ) . To map the ubiquitin binding domain of Pha13 , a series of pha13 deletion mutants were generated and used for Y2H assay ( Fig 7A ) . The results indicated A20 domain is mainly involved in ubiquitin binding ( Fig 7A ) . To assay which type of ubiquitin chain binds to Pha13 , in vitro pull-down assays were performed by mixing recombinant Pha13 , A20 mutant ( Pha13A20m ) , or AtSAP5 ( positive control ) with commercially available linear polyubiquitin chains . After precipitation , the bounded linear polyubiquitin was detected by immunoblot with the anti-ubiquitin antibody . As shown in Fig 7B , Pha13 has the ability to bind to M1 , K6 , K11 , K29 , K33 , K48 and K63-linked ubiquitin chains , and A20 domain is mainly responsible for the binding . Our data indicates that Pha13 confers self-E3 ligase activity and ubiquitin chain binding activity . To analyze whether SA stimulate the ubiquitination of Pha13 or binding to ubiquitinated protein , immunoprecipitation ( IP ) assay was performed to precipitate the Pha13 and ubiquitinated Pha13 in H2O ( Mock ) and SA treated P . aphrodite using anti-Pha13 antibody . Then , we used anti-Pha13 or anti-ubiquitin antibody for time course determination of Pha13 and ubiquitinated Pha13 ( Fig 8A ) . We have repeated this experiments for more than 3 times with consistent results . Our data indicates that mock and SA treatment increased accumulation of the Pha13 . Compared to mock-treated P . aphrodite , decreased accumulation of Pha13 was observed in SA-treated P . aphrodite at each time point ( Fig 8A ) . When we used MG132 to inhibit 26S proteasome activity , the protein levels of Pha13 increased in SA treated- but not in mock treated-P . aphrodite ( Fig 8B ) . The results indicate that both mock and SA treatment increased the accumulation of Pha13; however , SA treatment may induce the degradation of Pha13 through 26S proteasome activity . In addition , in our assay using anti-ubiquitin antibody to detect the precipitated protein ( s ) , we detected a protein band with molecular weight of around 43 kD increased with time in both SA treated-P . aphrodite and mock treated-P . aphrodite . However , increased signals of the 43 kD protein band was detected more in SA treated- than in mock treated-P . aphrodite ( Fig 8A ) . The results indicate that SA treatment can have multiple effects on Pha13 at protein levels . Analysis of Pha13 amino acid sequence by the use of PredictProtein ( https://www . predictprotein . org/ ) ( Fig 1B ) revealed putative nuclear localization signal . We therefore wondered whether Pha13 localized to the nucleus . To observe the subcellular localization of Pha13 , we transiently expressed Pha13 fused to GFP either at the N- or C terminal , designated 35S::G-Pha13 and 35S::Pha13-G , respectively , in protoplasts isolated from P . aphrodite . Protoplasts were collected 24 h post-transfection and examined by confocal microscope . In about 50% of cells expressing G-Pha13 and Pha13-G , green fluorescence was observed exclusively in the nucleus ( S5 Fig ) . Nucleus-specific green fluorescence was not observed in the GFP control vector . Previously , phylogenetic analysis revealed that SAP proteins are evolutionarily conserved among plants [28] . To analyze whether Pha13 mediated plant immunity is conserved in plants , we transformed 35S promoter driven overexpression Pha13 ( pPha13-oe ) constructs into Arabidopsis ( Col-0 ) . Homozygous T3 plants derived from 3 T1 transgenic lines were selected for further disease resistance analysis . The overexpression of Pha13 was confirmed by qRT-PCR on the homozygote progenies ( Fig 9A ) . All Pha13 overexpression transgenic Arabidopsis displayed higher shoot fresh weight and longer radius of leaf than Arabidopsis wild-type ( S6A–S6C Fig ) and confer an early-flowering phenotype ( S6D and S6E Fig ) . We inoculated Tobacco rattle virus ( TRV ) , which does not cause symptoms on Col-0 , by agroinfiltration to four wild-type and four Pha13 overexpressing Arabidopsis . The results revealed that TRV accumulation is dramatically decreased in all three Pha13 overexpressing transgenic lines at 9 days post-inoculation ( dpi ) ( Fig 9B ) . In addition , we also mechanically inoculated Cucumber mosaic virus ( CMV ) to wild-type and Pha13 overexpressing Arabidopsis . The results showed that Pha13 overexpressing Arabidopsis greatly enhanced resistance to CMV ( Fig 9C and 9D ) . In addition to viruses , we also inoculated Pseudomonas syringae pv . tomato DC30000 ( PstDC3000 ) to five T3 progenies of At-pha13#1 , At-pha13#4 and At-pha13#15 . The three Arabidopsis Pha13 transgenic lines showed enhanced resistance to PstDC3000 compared to Arabidopsis wild-type at 3 dpi ( Fig 9E and 9F ) . Our phylogenetic analysis revealed that Pha13 is most related to Arabidopsis AtSAP5 ( S7 Fig ) . Therefore , we generated transgenic Arabidopsis ( Col-0 ) to overexpress AtSAP5 , and also generated RNAi lines to express hairpin RNA of AtSAP5 . The over- and down-expression of AtSAP5 were confirmed by qRT-PCR in two randomly-selected overexpression ( AtSAP5-oe-4 and AtSAP-oe-11 ) and RNAi ( AtSAP5-RNAi-3 and AtSAP5-RNAi-7 ) lines ( Fig 10A ) . No obvious difference in phenotype was observed among the transgenic plants and WT ( S8 Fig ) . We mechanically inoculated CMV to WT ( Col-0 ) , AtSAP5-oe-4 , AtSAP5-oe-11 , AtSAP5-RNAi-3 , and AtSAP5-RNAi-7 . Total RNA was extracted from CMV inoculated-WT , -AtSAP5-oe-4 , -AtSAP5-oe-11 , -AtSAP5-RNAi-3 and -AtSAP5-RNAi-7 , and used for qRT-PCR in the quantification of CMV . The results showed that CMV was detected in WT ( Fig 10B ) . In comparison , the level of CMV was below our accurate detection limit in AtSAP5-oe-4 and AtSAP5-oe-11 , whereas higher CMV accumulation was observed in AtSAP5-RNAi-3 and AtSAP5-RNAi-7 compared to WT ( Fig 10B ) . While similar disease symptom was observed on CMV infected-WT , -AtSAP5-RNAi-3 , and -AtSAP5-RNAi-7 , no disease symptom on CMV-infected AtSAP5-oe-4 and AtSAP5-oe-11 were observed ( Fig 10C ) . Our data indicates that AtSAP5 in Arabidopsis is also involved in virus resistance ( Fig 10B and 10C ) . To analyze whether AtSAP5 also plays similar role as Pha13 , total RNA was extracted from WT , AtSAP5-oe-4 , AtSAP5-oe-11 , AtSAP5-RNAi-3 and AtSAP5-RNAi-7 , with H2O ( Mock ) or SA treatment and used for the detection of AtSAP5 , NPR1 , PR1 , RdR1 and GRXC9 expression by qRT-PCR ( Fig 11 ) . Our analysis also revealed that SA treatment induced the expression of AtSAP5 at 1 and 6 hours post treatment ( hpt ) in WT ( Fig 10D ) . The expression level of NPR1 and RdR1 is similar in WT , AtSAP5-oe-4 , AtSAP5-oe-11 , AtSAP5-RNAi-3 and AtSAP5-RNAi-7 without treatment ( S9 Fig ) . The expression of PR1 and GRXC9 is below our accurate detection limit in WT , AtSAP5-oe-4 , AtSAP5-oe-11 , AtSAP5-RNAi-3 and AtSAP5-RNAi-7 without any treatment ( S9 Fig ) . With mock ( H2O ) treatment , higher expression of NPR1 and RdR1 was observed in AtSAP5-oe-4 and AtSAP-oe-11 ( Fig 11A and 11C ) as compared to H2O treated-WT at 1 hpt . With SA treatment , significantly higher expression of NPR1 ( 1 hpt ) , PR1 ( 1 hpt ) , and GRXC9 ( 1 and 6 hpt ) was observed in AtSAP5-oe-4 and AtSAP-oe-11 as compared to SA treated-WT ( Fig 11A , 11B and 11D ) . No decreased expression of NPR1 , PR1 , RdR1 and GRXC9 was observed in AtSAP5-oe-4 and AtSAP-oe-11 as compared to WT regardless of the treatment or time-point ( Fig 11A–11D ) . With mock ( H2O ) treatment , decreased expression of PR1 was observed in both AtSAP5-RNAi-3 and AtSAP5-RNAi-7 as compared to H2O treated-WT at 6 hpt ( Fig 11B ) . With SA treatment , decreased expression of NPR1 ( 6 hpt ) , PR1 ( 1 and 6 hpt ) and GRXC9 ( 1 hpt ) was observed in both AtSAP5-RNAi-3 and AtSAP5-RNAi-7 , and decreased RdR1 ( 1 and 6 hpt ) expression was observed in AtSAP5-RNAi-7 as compared to SA treated-WT ( Fig 11A–11D ) . No increased expression of NPR1 , PR1 , RdR1 and GRXC9 were observed in AtSAP5-RNAi-3 and AtSAP5-RNAi-7 as compared to WT regardless of the treatment or time point ( Fig 11A–11D ) . Collectively , our data using transgenic Arabidopsis overexpressing or silencing AtSAP5 suggests that AtSAP5 is involved in the expression of NPR1 and NPR1-independent genes .
In this report , we provided evidence indicating that an orchid SAP gene , Pha13 , serves pivotal roles in resistance to viruses through important but previously unidentified SA responsive transcriptional reprogramming of immune responsive gene ( s ) . First , our analysis revealed the striking similarities between Arabidopsis and orchids in plant immune responses . Counterparts of the SA-dependent plant immune responsive genes found in Arabidopsis including PR1 , NPR1 , RdR1 , and GRX were also identified in orchids ( i . e . PhaPR1 , PhaNPR1 , PhaRdR1 , and PhaGRX ) . Indeed , SA induces these orchid genes as they do in Arabidopsis counterparts ( Figs 2D and 4A ) . The dependency on PhaNPR1 for expression of PhaPR1 , PhaRdR1 , and PhaGRX are also similarly reported in Arabidopsis ( Fig 4C ) . Taking these results together we can see that this central immunity is conserved across plants . Furthermore , we also found that overexpression of Pha13 in engineered transgenic Arabidopsis conferred resistance to various viruses and bacteria ( Fig 9 ) . Similarly , a previous report indicated that overexpression of a rice SAP gene , OsSAP1 , in tobacco can enhance protection of plants against bacterial pathogen infection [33] . More importantly , we also demonstrated that Arabidopsis homolog of Pha13 , AtSAP5 , also play similar role in virus resistance and immune regulation ( Figs 10 and 11 ) . These findings together suggest that the downstream immune responsive pathways of SAPs are conserved among plants . Previous reports have suggested that NPR1 responds to SA signal and is regulated mainly at the post-translational level [10 , 16] . This mode of regulation allows plants to quickly respond to invading pathogens without undergoing transcription , which is particularly important in initial defense . Our study showed that RNA expression of Pha13 is induced earlier than PhaNPR1 during SA induction ( Fig 2D ) , and both silencing and overexpression of Pha13 affected the RNA accumulation of PhaNPR1 ( Fig 2A and 2B ) , whereas silencing PhaNPR1 did not affect the expression of Pha13 RNA in orchid ( Fig 2C ) . These results support the notion that Pha13 plays a role in immune regulation downstream of SA and upstream of PhaNPR1 ( Fig 5C ) . Interestingly , our data also indicated that in addition to participating in the expression of PhaNPR1 , Pha13 is also involved in the expression of PhaNPR1-independent immune defense genes ( Fig 5C ) . Overexpression of Pha13 in orchids affects at least 10639 genes ( S1A Fig ) including PhaNPR1 -dependent and–independent genes ( S1 Table ) , which suggest a broad spectrum of regulation by Pha13 . Although Pha13 is involved in SA responsive transcriptional reprogramming of immune responsive genes , our biochemical analysis suggests that Pha13 does not directly function as a transcriptional regulator; rather , Pha13 may regulate the transcription of immune responsive genes in an indirect manner . This is because our analysis indicates that Pha13 confers ubiquitin binding and E3 ligase activity , and both activities are not directly involved in transcriptional activity . Our data also suggests that SA participates in the regulation of Pha13 at the protein level , as our data indicates that while both mock and SA treatment increase the accumulation of Pha13 RNA and proteins ( Figs 2D and 8A ) ; only SA treatment can induce the degradation of Pha13 through 26S proteasome activity ( Fig 8B ) . In addition , an ubiquitinated protein band with molecular weight around 43 kD was immunoprecipitated with anti-Pha13 antibody in both SA treated-P . aphrodite and mock treated-P . aphrodite , and increased signals of the 43 kD protein band was detected in SA treated-P . aphrodite as compared to mock treated-P . aphrodite ( Fig 8A ) . Although the identity and function of the 43 kD protein has yet to be identified , our data suggested that SA also regulates Pha13 at the protein level . Proteins containing A20 and/or AN1 zinc finger domains are conserved among various organisms . Different numbers of A20/AN1 protein ( from 1 to 19 ) exist in different organisms including protists , fungi , animals , and plants [28 , 34] . The most well-known protein in this class , A20 , plays a pivotal role in negative regulating central immune transcription factor , NF-kB , in human . Human A20 binds to multiple signaling proteins ( substrates ) upstream of NF-kB to interfere with the function of the substrate or modulating the ubiquitination of different substrates to regulate NF-kB [35 , 36] . In addition to human A20 ( contains 7 A20 domain ) , AWP1 and ZN216 ( contain single A20 and AN1 domain ) are also reported to have redundant but distinct functions in regulating NF-kB [37 , 38] . Genetic studies have provided strong evidence indicating that A20/AN1 proteins are involved in abiotic stress in plant and known as stress associated proteins ( SAPs ) [28] . Among currently known plant SAPs , two SAP genes , Arabidopsis AtSAP9 and rice OsSAP1 , may be involved in plant immunity [33 , 39] . Overexpression of rice OsSAP1 in tobacco enhanced the plant resistance to bacterial pathogen infection [33] . Transgenic Arabidopsis overexpressing AtSAP9 decrease resistance to a non-host bacterial pathogen [39] . However , the role of OsSAP1 and AtSAP9 in plant immunity remains elusive . Human A20 and Rabex-5 ( guanine nucleotide exchange factor ) contain A20 domain but without AN1 domain; while ZNF216 and AWP1 more resemble Pha13 , AtSAP5 , and AtSAP9 , which contain both A20 and AN1 domains . In human A20 and Rabex-5 , the A20 domain exhibits both E3 ligase and ubiquitin binding ability [32 , 40] . However , only ubiquitin binding ability has been reported for ZNF216 and AWP1 [41 , 42] . In plants , AtSAP9 , AtSAP5 , TsSAP5 from wheat ( Triticum aestivum ) and Pha13 confer E3 ligase and/or ubiquitin binding ability ( Figs 6 and 7 ) [39 , 43–45] . Ubiquitin binding activity has been mapped to A20 domain on human A20 , Rabex-5 , ZNF216 , AWP1 , AtSAP5 and Pha13 ( Fig 7 ) [35 , 40–43] . The domain responsible for E3 ligase and ubiquitin binding ability of AtSAP9 and domain responsible for E3 ligase of TsSAP5 remain to be resolved; however , it has been demonstrated that A20 domain of AtSAP5 and Pha13 confer both E3 ligase and ubiquitin binding ability ( Figs 6 and 7 ) [43 , 44] . Human A20 bind K63 and M1 polyubiquitin chain through its fourth and seventh A20 domain . In plants , single A20 domain can bind to various polyubiquitins . AtSAP5 binds to M1 , K48 , and K63 polyubiqutin , with preference for K63 polyubiquitin; while AtSAP9 binds K48 and K63 polyubiquitin chain , and also with preference for K63 polyubiquitin [39 , 43] . In comparison to human A20 , AtSAP5 , and AtSAP9 , Pha13 has profound polyubiquitin binding ability . Our results revealed that Pha13 binds to M1 , K6 , K11 , k29 , k33 , K48 and K63 polyubiquitin chain , and prefers binding to M1 , K29 and K63 polyubiquitin ( Fig 7B ) . The ability for Pha13 to bind diverse polyubiquitin chains suggests that Pha13 may bind to a greater number of polyubiquitinated proteins . Our data indicated that Pha13 and human A20 share similar and distinct biochemical characteristics . Both Pha13 and human A20 exhibit E3 ligase and polyubiquitin chain binding activity . Human A20 binds to at least 10 substrates to regulate immunity through direct binding to proteins or ubiquitin chains on the proteins in regulating their ubiquitination status [46] . The profound binding activity of Pha13 to numerous different ubiquitin chains suggest that Pha13 may bind to multiple substrates in an even more elaborate regulation of plant resistance . In comparison to A20 domain , less is known about the biological and biochemical function of AN1 domain in either animals or plants . Notably , strong E3 ligase activity have been reported on AN1 domain of AtSAP5 [44]; however , our analysis indicated that most E3 ligase activity is conferred on the A20 but not on AN1 domain of the Pha13 ( Fig 6 ) . The result shows that A20 and AN1 domains may confer different functions in various SAPs . Collectively , we propose a model that , in the initial SA response , post-translational modification of PhaNPR1 quickly turns on immune response gene ( s ) ( PhaNPR1-dependent ) such as PhaPR1 ( Fig 2D ) . In addition , SA regulate Pha13 at both transcriptional and post-translational levels , and leads to broad transcriptional reprograming of immune responsive genes including PhaNPR1-dependent and PhaNPR1-independent genes . Our data suggests that SA mediates both NPR1 post-translational and Pha13-mediated plant immune response in a temporally controlled and functionally cooperative manner ( Fig 5C ) . Recently , a member of the CCCH zinc finger domain family , oxidation related zinc finger 1 , was reported to play a positive role in the SA dependent , NPR1-independent defense response against bacterial pathogen in Arabidopsis [47] . Although the biochemical properties of oxidation related zinc finger 1 remain elusive , the findings suggest that different zinc finger domain containing proteins may play an important role in SA mediated NPR1-indepdent immune pathway . In our study , we identified an ancient conserved immune regulator , Pha13 , in orchids that is crucial for SA–governed defense . SA regulates Pha13 at both transcriptional and post-translational levels . Moreover , transgenic Arabidopsis overexpressing orchid Pha13 also confers greater resistance to different pathogens ( Fig 9 ) , suggesting Pha13 regulated the downstream immune response pathway ( s ) that is conserved in both monocots and dicots . We also demonstrated that Arabidopsis homolog of Pha13 , AtSAP5 , also plays similar role in virus resistance and immune regulation ( Figs 10 and 11 ) . Our findings greatly enhanced the understanding in the regulation of the SA-mediated immune responses among plants , providing important information in the development of plant resistance to a broad-spectrum of pathogens .
The commercial orchid variety , Phalaenopsis aphrodite var . Formosa , was purchased from Taiwan Sugar Research Institute ( Tainan , Taiwan ) . P . aphrodite , P . equestris and transgenic P . equestris ( 35S∷FLAG-Pha13 ) plants were all first tested for two prevalent orchid viruses , Odontoglossum ringspot virus ( ORSV ) and CymMV , as detected by RT-PCR with primer pairs , ORSV-F/ORSV-R and CymMV-F/CymMV-R ( S3 Table ) , before maintaining in a greenhouse with a controlled 12-h photoperiod ( 200 μmol m-2s-2 ) at 25°C/25°C ( day/night ) . The Arabidopsis WT ( Col-0 ) and all transgenic Arabidopsis were maintained in a greenhouse with a controlled 12-h photoperiod ( 200 μmol m-2s-2 ) at 22°C/22°C ( day/night ) for ~four weeks before analysis . Cucumber mosaic virus isolate 20 was maintained in the Arabidopsis thaliana ( Col-0 ) as inoculation source in our study . The infectious clones of Cymbidium mosaic virus ( pCambia-CymMV ) and Tobacco rattle virus ( pTRV1 and pTRV2 ) were transformed into Agrobacterium tumefaciens C58C1 ( pTiB6S3ΔT ) H ( described below ) for inoculation of Orchid or Arabidopsis through agroinfiltration . E . coli strains BL21 was grown on Luria broth ( LB ) agar plates or in LB broth . Pseudomonas syringae pv . tomato DC30000 was grown in the King’s B medium ( 20 g/L proteose peptone , 1 . 5 g/L K2HPO4 , 10 ml glycerol , and 1 . 5 g/L MgSO4 •7H2O , pH 7 . 0 ) . Saccharomyces cerevisiae strain AH109 was grown on the YPAD ( Yeast extract 10 g/L , Peptone 20 g/L , Dextrose 20 g/L , and Adenine sulfate 0 . 4 g/L ) agar plate or YPAD broth . Sodium salicylate ( 50 mM ) ( Sigma ) , methyl jasmonate ( 45 μM ) ( Sigma ) , and aminocyclopropanecarboxylic acid ( 660 μM ) ( Sigma ) were directly rubbed on leaves of P . aphrodite by cotton swab . Leaf samples were collected at 0 h , 1 h , 3 h , 6 h , 12 h , 24 h , 48 h and 72 h after treatment . For Arabidopsis , 1 mM SA was sprayed on the leaves , and samples were collected at 1 h , 6 h , and 24 h after treatment . The protein domains of Pha13 were analyzed by use of PROSITE database of ExPASy Proteomics Server ( http://ca . expasy . org/ ) and Conserved Domain Database of NCBI database ( http://www . ncbi . nlm . nih . gov/ ) . The predicated nuclear localization signal of Pha13 was analyzed by use of PredictProtein ( https://www . predictprotein . org/ ) Phylogenetic analysis of Pha13 was conducted with 12 orchid A20/AN1 domain containing stress associated proteins ( SAPs ) and previously characterized 14 SAPs from Arabidopsis thaliana and 18 SAPs from Oryza sativa . The sequence alignment was performed by use of the Clustal W algorithm of the DNASTAR MegAlign software ( DNASTAR , WI , USA ) . An unrooted phylogenetic tree was constructed using the neighbor-joining method by MEGA5 with 1000 bootstrap replicates . The sequences of SAPs from orchids , A . thaliana , and O . sativa were obtained from the websites , Orchidstra database ( http://orchidstra2 . abrc . sinica . edu . tw/ ) , TAIR ( http://www . arabidopsis . org ) and the Rice Genome Annotation Project ( http://rice . plantbiology . msu . edu ) , respectively . The accession of each gene used for analysis is indicated in S7 Fig . Total RNA was extracted as described previously [27] . For semi-quantitative RT-PCR , 1 μg of total RNA and oligo ( dT ) primer were used to synthesize the cDNA . The PCR was performed using the gene-specific primer pairs ( S3 Table ) . The results of semi-quantitative RT-PCR were analyzed by ImageJ software for the relative quantification . For qRT-PCR , cDNA was synthesized from 500 ng of DNA-free RNA and oligo ( dT ) by use of PrimeScript RT Reagent Kit ( Perfect Real Time ) ( Takara Bio ) following the manufacturer’s instructions ( Takara Bio ) . The cDNA template was used for qPCR by use of SYBR Premix EX Taq II ( Ti RNase H Plus ) Kit ( Takara Bio ) in an ABI Prism 7500 sequence detection system ( Applied Biosystems ) . The PhaUbiquitin 10 or Actin was used as an internal quantification control . The primer pairs used in this study are listed in ( S3 Table ) . For construction of transient silencing vector of Pha13 , the oligonucleotide pair Pha13-hpRNA-F/Pha13-hpRNA-R ( S3 Table ) was used to generate the hairpin dsDNA fragments . The hairpin dsDNA fragments were cloned into the Gateway entry vector pENTR/D-TOPO ( Thermo Fisher-Scientific ) following the manufacturer’s instructions to generate pENTR-Pha13-hpRNA . Then , LR Gateway cloning reaction ( Thermo Fisher-Scientific ) was conducted to transfer the hairpin RNA fragments from pENTR-Pha13-hpRNA into 35S promoter driven pB7GWIWG2 ( I ) [48] to obtain phpPha13 . The method for construction of transient silencing vector of PhaNPR1 , PhaRdR1 , and PhaGRX was similar to that described above , except the oligonucleotide pairs PhaNPR1-hpRNA-F/PhaNPR1-hpRNA-R , PhaRdR1 -hpRNA-F/PhaRdR1-hpRNA-R , and PhaGRX-hpRNA-F/ PhaGRX-hpRNA-R ( S3 Table ) were used to generate the hairpin dsDNA fragments . For construction of virus-induced gene-silencing vector , plant RNA was used as a template to amplify the fragments of Pha13 by RT-PCR with the primer pair attB1-Pha13-F/attB2-Pha13-R ( S3 Table ) . The amplified fragments were cloned into the pCambia-CymMV-Gateway vector [27] by the BP Clonase II enzyme mix ( Thermo Fisher-Scientific ) to generate pCambia-CymMV-Pha13 . For construction of Pha13 transient overexpression vector , plant total RNA was used as a template to amplify the N terminal FLAG tagged of Pha13 by RT-PCR with the primer pairs FLAG-Pha13ORF-F/Pha13ORF-R ( S3 Table ) . The FLAG-Pha13 fragments were cloned into the Gateway entry vector pENTR/D-TOPO ( Thermo Fisher-Scientific ) following the manufacturer’s recommendations to generate pENTR-FLAG-Pha13 . Then , LR Gateway cloning reaction ( Thermo Fisher-Scientific ) was used to transfer the FLAG-Pha13 fragments from pENTR-FLAG-Pha13 into the 35S promoter driven overexpression vector , pK2GW7 [48] , to obtain pPha13-oe . For generation of A20 and/or AN1 mutant on pPha13-oe ( Fig 1C and 1D ) , site-directed mutagenesis was conducted by QuikChange Site-Directed Mutagenesis Kit ( Agilent Technologies ) . For A20 mutant , we substituted the conserved 3rd and 4th cysteine to glycine at A20 ( C29G and C32G ) . For AN1 mutant , we substituted the conserved 3rd cysteine and 1st histidine to glycine at AN1 ( C111G and H121G ) . The A20 mutated clone and AN1 mutated clone was designated pPha13A20m and pPha13AN1m , respectively . The A20 and AN1 double mutated clones was designated pPha13A20mAN1m . Primer pairs used for site directed mutagenesis are listed in S3 Table . For construction of overexpression vector to generate transgenic Phalaenopsis orchid , the FLAG-Pha13 fragment was transferred from pENTR-FLAG-Pha13 ( described above ) into binary vector , pH2GW7 [48] , to obtain pHPha13 . pHPha13 was used to generate transgenic P . equestris orchid using the method described by Hsing et al . [49] . For the construction of overexpression and RNAi vector of AtSAP5 , pAtSAP5-oe or phpAtSAP5-RNAi , we followed similar method as described above for the construction of pPha13-oe and phpPha13 , except the primers used to amplify the full length and the fragment of AtSAP5 were AtSAP5ORF-F/AtSAP5ORF-HA-R ( for overexpression vector , pAtSAP5-oe ) and AtSAP5-RNAi-F/AtSAP5-RNAi-R ( for RNAi vector , phpAtSAP5-RNAi; S3 Table ) . For the generation of transgenic Arabidopsis , the plants were transformed using the floral dip method with Agrobacterium tumefaciens strain GV3101 carrying the pPha13-oe , pAtSAP5-oe , or phpAtSAP5-RNAi to generate the transgenic plants . Agroinfiltration on orchid was conducted as previously described [27] , with modification . pCambia-CymMV , pB7GWIWG2 , pK2GW7 and their derivatives and were transformed into Agrobacterium tumefaciens C58C1 ( pTiB6S3ΔT ) H by electroporation . Briefly , A . tumefaciens strains were incubated at 28°C until reaching an OD600 to 1 . 0 . After centrifugation , 20 ml AB-MES medium ( 17 . 2 mM K2HPO4 , 8 . 3 mM NaH2PO4 , 18 . 7 mM NH4Cl , 2 mM KCl , 1 . 25 mM MgSO4 , 100 μM CaCl2 , 10 μM FeSO4 , 50 mM MES , 2% glucose ( w/v ) , pH 5 . 5 ) with 200 μm acetosyringone [50] was used to re-suspend the cells . After culturing overnight , 2 ml of infiltration medium containing 50% MS medium ( 1/2 MS salt supplemented with 0 . 5% sucrose ( w/v ) , pH 5 . 5 ) , 50% AB-MES and 200 μm acetosyringone [50] were used for infiltration . For the agroinfiltration on Arabidopsis , same method was used as described above except pTRV1 and pTRV2 were individually transformed into A . tumefaciens C58C1 ( pTiB6S3ΔT ) H by electroporation , and overnight culture of A . tumefaciens containing pTRV1 and pTRV2 was adjusted to OD600 of 0 . 5 and mixed at 1:1 ratio prior to infiltration . To assay the effect of Pha13 , PhaRdR1 , or PhaGRX in CymMV accumulation , we first inoculated CymMV in P . aphrodite . For inoculation of CymMV , agroinfiltration ( described above ) was performed to infiltrate A . tumefaciens carrying pCambia-CymMV in the leaf tip of P . aphrodite . The CymMV-infected P . aphrodite were maintained at least 14 days before further analysis . To assay the effect of transient silencing ( Pha13 , PhaRdR1 , or PhaGRX ) or transient overexpression ( Pha13 , or their derived mutants ) , A . tumefaciens carrying the control vector pB7GWIWG2 ( for silencing ) , pK2GW7 ( for overexpression ) , silencing vectors or overexpression vectors ( described above ) were infiltrated into the leaves . After agroinfiltration , a pair of disks ( 6 mm diameter ) were immediately ( defined as 0 dpi ) collected from both the control and assay vector infiltrated regions . After 5 dpi , another pair of disks were collected from the same infiltrated region . Total RNA extracted from the samples was used as a template to analyze the accumulation of CymMV by use of qRT-PCR . The ratio of CymMV accumulation at 0 dpi to 5 dpi was calculated for relative quantification . For TRV inoculation , agroinfiltration ( as described above ) was performed by infiltrating A . tumefaciens carrying pTRV1 or pTRV2 ( 1:1 ) to three leaves of four-week-old Arabidopsis by use of syringe . After 9 dpi , total three disks from three different distal leave were collected and quantified the accumulation of TRV by use of qRT-PCR . For inoculation with CMV , CMV-infected Arabidopsis leaves were ground with 0 . 01 M potassium phosphate buffer by mortar and pestle for use as the inoculation source . Four-week-old Arabidopsis leaves were inoculated mechanically ( pre-dusted with 300-mesh Carborundum ) with the CMV inoculation source . After 9 dpi , three disks from three different distal leave were collected and CMV accumulation was analyzed by use of qRT-PCR . Total RNA was extracted from leaves of P . aphrodite infiltrated with agrobacterium carrying vector ( pK2GW7 ) or pPha13-oe 5 days after infiltration . For microarray , 0 . 2 μg of total RNA was amplified by a Low Input Quick-Amp Labeling kit ( Agilent Technologies ) and labeled with Cy3 ( CyDye , Agilent Technologies , USA ) during the in vitro transcription process . An amount of 0 . 6 μg of Cy3-labled cRNA was fragmented at 60°C for 30 minutes . Corresponding fragmented labeled cRNA was then pooled and hybridized to Agilent P . aphrodite 8 × 60K Microarray ( Agilent design ID: 033620 ) [51] at 65°C for 17 hours . After washing and drying steps , the microarrays were scanned with an Agilent microarray scanner ( Agilent Technologies ) at 535 nm for Cy3 . The array image was analyzed by Feature Extraction software version 10 . 7 . 1 . 1 using the default setting . For the microarray analysis , data were analyzed from 3 biological repeats using GeneSpring ( Agilent Technologies , http://www . agilent . com ) . Pha13-responsive differentially expressed genes ( DEGs ) were identified based on significance compared to vector control ( unpaired t test P < 0 . 05 ) . Microarray data was deposited in the public repository GEO database ( https://www . ncbi . nlm . nih . gov/geo/ ) with accession number GSE93248 . Gene ontology ( GO ) classification was conducted with all the Pha13 -responsive DEGs by use of GO enrichment analysis algorithm of gene ontology database ( http://geneontology . org/ ) . All the Pha13-responsive DEGs were categorized into subcategories of biological process GO terms . Total proteins were extracted from leaves of transgenic orchid or leaves of orchids infiltrated with agrobacterium carrying vector ( pk2GW7 ) , overexpression clones of wild-type Pha13 ( pPha13-oe ) , or the respective A20 and/or AN1 mutant clone ( pPha13A20m , pPha13AN1m or pPha13A20mAN1m ) as previously described [52] with some modification . The boiled extraction buffer ( 4 M urea , 5% SDS , 15% glycerol , 100 mM Tris-HCl , pH 8 , with freshly added 2 mM phenylmethylsulfonyl fluoride , 2 mg mL-1 and 1X complete protease inhibitor [Roche] ) was used to extract the total proteins . The Pha13 or Pha13 A20 and/or AN1 domain mutant protein was analyzed by immunoblotting with antibody against Pha13 followed by HRP conjugated anti-rabbit antibodies ( Abcam ) . The protein level of tubulin ( loading control ) was analyzed by immunoblotting with antibody against tubulin followed by HRP conjugated goat anti-mouse antibodies ( GE Healthcare Life Sciences ) . Full-length Pha13 , or mutant cDNA were amplified by PCR with primer pairs , NdeI-Pha13-F/NotI-Pha13-R ( S3 Table ) , with previously described Pha13 overexpression or mutated clones as templates . PCR amplified gene fragments were cloned into the pET24b expression vector ( Novagen ) with fused C-terminal histidine tag ( His-tag ) to produce protein expression plasmids , pETPha13 , pETPha13A20m , pETPha13AN1m and pETPha13A20mAN1m . The constructed plasmids were transformed into Escherichia coli strain BL21 ( DE3 ) for protein expression . Bacteria were cultured at 37°C to an OD600 of 0 . 5 and transferred to 25°C for 1 . 5 hours for Pha13 , Pha13A20m , Pha13AN1m , and Pha13A20mAN1m protein induction . Protein induction was performed by addition of isopropylthio-β-galactoside ( IPTG; Sigma ) to a final concentration of 1 mM . His-tagged recombinant protein was purified by TALON Superflow ( GE Healthcare Life Sciences ) according to the manufacturer’s description . The elution was carried out with 250 mM imidazole ( Sigma ) . In vitro ubiquitination assays were performed as described [33] with modification . An amount of 3 μg purified His-tagged recombinant proteins ( described above ) were used for each ubiquitination reaction . Reactions were incubated at 30°C for 3 hours and analyzed by SDS-PAGE followed by immunoblot analysis . Blots were probed using anti-FLAG antibodies ( Sigma ) followed by HRP conjugated goat anti-mouse antibodies ( GE Healthcare Life Sciences ) . Full-length AtSAP5 cDNA was amplified by PCR with primer pairs , NdeI-AtSAP5-F/XhoI-AtSAP5-R ( S3 Table ) , and were cloned into pET24b following the same approach as the cloning of pETPha13 described above to generate the pETAtSAP5 . His-tagged AtSAP5 , Pha13 and Pha13A20m recombinant proteins were expressed by E . coli and purified with affinity resin . Purified recombinant proteins ( 60 μg ) were immobilized on magnetic beads ( 60 μl ) using Mag-beads Carboxyl Labeling kit ( Toolsbiotech ) following the manufacturer’s instructions . Recombinant proteins ( 6 μl ) conjugated magnetic beads were incubated with 4 μg of linear , K6 , K11 , K29 , K33 , K63-inked tetraubiquitin chains or K48-linked polyubiquitin ( Ub3-Ub7 ) chains ( BostonBiochem ) for 18 h at 4°C in pull-down buffer ( 20 mM Tris–HCl pH 7 . 5 , 150 mM NaCl , 10% glycerol , 1% Triton X-100 , 10 mM ZnSO4 , 0 . 5 mM DTT ) . After pull-down , the beads were washed three times with pull-down buffer . The pull-down proteins were boiled with SDS-sample buffer and analyzed by SDS-PAGE and immunoblotting with anti-ubiquitin antibody ( Research and Diagnostic Systems ) . The IP assay for Pha13 was performed using total proteins extracted from 0 . 25 g of Phalaenopsis orchid leaves with 200 ul immunoprecipitation buffer ( 50 mM Tris-pH 7 . 5 , 0 . 1% NP40 , 10 mM MgCl2 , 150 mM NaCl , 10 uM MG132 , 1X complete protease inhibitor [Roche] ) . The extract was centrifuged at 12000 ×g for 10 min at 4 °C . Then , the supernatants were transferred to the non-stick tube , and pre-cleared with protein A sepharose beads ( GE Healthcare Life Sciences ) 1 hours at 4 °C with gentle shaking . After , the extract was centrifuged at 1500 ×g for 2 min at 4 °C , the supernatant was further incubated with the anti-Pha13 antibodies for 2 hours at 4 °C with gentle shaking , followed by incubation with protein A sepharose beads ( GE Healthcare Life Sciences ) for 2 hours at 4 °C with gentle shaking . The beads were washed three times with ice-cold immunoprecipitation buffer and eluted by SDS-PAGE sampling buffer . The eluted proteins were analyzed by immunoblotting using the anti-Pha13 antibody and anti-ubiquitin antibody ( Research and Diagnostic Systems ) . For the degradation assay of Pha13 , the leaves of P . aphrodite were treated with H2O ( Mock ) or SA , and immediately followed by infiltration of DMSO or 40 uM MG132 . Total proteins were extracted from the treated samples at 3 hours post-treatment and followed by the IP assay using anti-Pha13 antibodies ( described above ) . The eluted proteins were analyzed by immunoblotting using the anti-Pha13 antibody . For construction of vectors used for subcellular localization analysis , the primer pairs , Pha13-ORF-F/Pha13-ORF-R and Pha13-ORF-F/Pha13-ORF-NONSTOP-R ( S3 Table ) were used to amplify 2 sets of Pha13 ORF ( with or without stop codon ) . All the amplified ORF fragments of Pha13 were cloned into the Gateway entry vector pCR 8/GW/TOPO Gateway ( Thermo Fisher-Scientific ) following the manufacturer’s recommendations to generate pCR8-Pha13 and pCR8-Pha13-NONSTOP . Then , LR Gateway cloning reaction ( Thermo Fisher-Scientific ) was used to transfer the ORF fragment of Pha13 from pCR8-Pha13 into p2FGW7 driven by 35S promoter [48] to obtain N-terminal GFP fused clones ( pG-Pha13 ) . To obtain C-terminal GFP-fused clones ( pPha13-G ) , we transferred pCR8-Pha13-NONSTOP into p2GWF7 . Protoplast isolation and transfection were as described [27] . Transformed protoplasts were detected for florescence signals by confocal microscopy ( Zeiss LSM 780 , plus ELYRA S . 1 ) with excitation at 488 nm and emission at 500 to 587 nm for GFP , and excitation at 543 nm and emission at 600 to 630 nm for mCherry . Full-length Pha13 cDNA was amplified by PCR with primer pairs , NdeI-Pha13-F/EcoRI-Pha13-R ( S3 Table ) . The fragments were cloned into the pGBK vector ( Clontech ) with fused N-terminal GAL4 DNA binding domain to produce pGBKPha13 plasmid as a bait vector . pGBKPha13 was transformed into AH109 yeast strain as competent cells for yeast two-hybrid cDNA library screening . Total RNA isolated from Phalaenopsis leaf tissue ( 24 hours after SA treatment ) was used for cDNA library construction . The cDNA library was generated with a Make Your Own “Mate & Plate” Library System ( Clontech ) following the user manual . Candidate yeast colonies were picked up PCR amplification with primer pairs , T7promoter/3’ ADprimer ( Clontech ) . Amplified DNA fragments were sequenced , and the sequences were used in a blast search on an orchid database ( http://orchidstra2 . abrc . sinica . edu . tw/ ) to identify corresponding genes . Full lengths of candidate genes were amplified with RT-PCR ( S3 Table ) and cloned into pGAD vector for further confirmation analysis . Yeast strains containing the appropriate bait and prey plasmids were cultured in liquid 2-dropout medium ( leucine- and tryptophan- ) overnight . The overnight yeast culture was diluted to an OD600 of 0 . 06 and spotted on selection plates ( containing histidine- , leucine- , tryptophan- and 5-bromo-4-chloro-3-indolyl-alpha-D-galactopyranoside ) for growth assay . Fragments of different N-terminus deletions of Pha13 were generated with restriction enzyme digestions of FseI ( NEB ) , ApaI ( NEB ) , SgrAI ( NEB ) , BtgI ( NEB ) , or PspXI ( NEB ) . Fragments of C-terminus deletions were generated by PCR amplification using primer pairs , Nd13Fs412F/EcoRI-Pha13-R , Nd13Ap361F/EcoRI-Pha13-R , Nd13Sg247F/ EcoRI-Pha13-R , Nd13Bt136F/EcoRI-Pha13-R , and Nd13Ps88F/EcoRI-Pha13-R ( S3 Table ) . Pha13 A20 domain was amplified by PCR with primer pairs , Nd13A20F/BH13A20R ( S3 Table ) . Pha13 AN1 domain was amplified with primer pairs , Nd13AN1F/BH13AN1R ( S3 Table ) . The yeast two-hybrid assay of the truncated Pha13 fragments were conducted as above . Four-week-old Arabidopsis were dipped in liquid suspension of 107 cfu/mL PstDC3000 in 10 mM MgSO4 containing 0 . 01% Silwet L-77 ( Lehle Seeds ) for 5 min . After inoculation , plants were kept at 100% relative humidity . For bacterial population quantification , three discs were collected from individual inoculated-plants after 3 days post-inoculation and grounded in sterile water . Serial dilution was performed and the King’s B medium containing 100 ug/ml rifampicin for colony counting . Data are presented as mean ± SD . The pair-wise t-test was performed to analyze the statistical significance between samples . The one-way analysis of variance ( ANOVA ) followed by Tukey’s test was performed to analyze the statistical significance for data of Pha13 expression level in different tissues and bacterial pathogen growth . In transient overexpression analysis , three plant replicates with expression level of at least 40% increase in Pha13 or the respective A20 and/or AN1 mutant , compared to vector control , were used for statistical analysis . Pha13 ( PATC148746 ) , PhaPR1 ( PATC126136 ) , PhaNPR1 ( PATC135791 ) , PhaRdR1; ( PATC143146 ) , PhaGRX ( PATC068819 ) , PhaUBQ10 ( PATC230548 ) , PhaJAZ1 ( PATC141437 ) , PhaACO2 ( PATC139319 ) , AtActin ( At3G18780 ) , AtSAP5 ( AT3G12630 ) , OsSAP3 ( LOC_Os01g56040 . 1 ) , OsSAP5 ( LOC_Os02g32840 . 1 ) . | The Salicylic acid ( SA ) -mediated plant immunity plays a major role against diverse pathogens including viruses . However , the underlying SA-mediated virus resistance pathway is not fully understood . Here , we identified the A20 and AN1 zinc finger domain containing protein , Pha13 , in Phalaenopsis aphrodite . We provided evidences that Pha13 and its Arabidopsis homolog , AtSAP5 , mediates the conserved immune response against diverse viruses in both orchid and Arabidopsis . SA affects Pha13 on both transcriptional and post-translational levels . In addition , Pha13 and AtSAP5 is involved in the expression of well-known master regulator , NPR1 , as well as NPR1-independent genes , and serve as a regulatory hub in the SA-mediated immune pathway . Biochemical analysis indicated that Pha13 confers both E3 ligase and ubiquitin binding activity , which mainly functions through the A20 domain . We also showed that Pha13 has the ability to bind various ubiquitin chains , suggesting that Pha13 may interact to multiple ubiquitinated substrates for the regulation of immune genes . In summary , Pha13 and AtSAP5 function in the previously unknown SA-mediated antiviral immune pathway . The diversified biochemical properties and distinct function of A20/AN1 domains revealed the sophisticated regulation of Pha13 in antiviral immunity . | [
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"... | 2018 | Plant A20/AN1 protein serves as the important hub to mediate antiviral immunity |
Globally , trachoma is the leading cause of infectious blindness . In Ethiopia , the overall Trachomatous Trichiasis ( TT ) surgical coverage is 41% . Identifying determinants for not utilizing TT surgery among TT patients is important to design and monitor effective intervention programs . Therefore , this study aimed to identify determinants for not utilizing TT surgery among TT patients in Mehalsayint District , North East Ethiopia . A community based unmatched case control study was employed from March 30 , 2017 to April 13 , 2017 . A total of 482 study participants ( 241 cases and 241 controls ) with age of ≥15 years were included in the study . The data were entered with Epi info version 7 . 2 software and exported to SPSS version 20 for analysis . Bivariate analysis was fitted to screen candidate variables with p<0 . 2 for the final model . Finally , multivariable logistic regression analysis was employed to identify significant factors ( p<0 . 05 ) for not utilizing TT surgery . Respondents’ age of 16–30 years ( AOR: 10 . 11; 95% CI: 2 . 72 , 37 . 59 ) and widowed respondents ( AOR: 0 . 40; 95% CI: 0 . 21 , 0 . 77 ) , time to reach the service ( AOR: 0 . 46; 95% CI: 0 . 24 , 0 . 87 ) , unavailability of TT surgeon ( AOR: 5 . 00; 95% CI: 1 . 16 , 21 . 38 ) , symptoms of trichiasis ( AOR: 7 . 49; 95% CI: 2 . 41 , 23 . 26 ) , duration of the problem ( AOR: 2 . 56; 95% CI: 1 . 44 , 4 . 54 ) , the affected eye ( AOR: 2 . 16; 95% CI: 1 . 23 , 3 . 80 ) , epilation practice ( AOR: 3 . 22; 95% CI: 1 . 84 , 5 . 64 ) , and place of TT surgery given ( AOR: 4 . 21; 95% CI: 2 . 48 , 7 . 14 ) were significant determinants for not utilizing TT surgical services . In this study , TT surgery against trachoma is very low and TT remains public health problem in the district . Being younger age and widowed , time taken to reach the service , absence of TT surgeon , symptoms of trichiasis , duration of problem , the affected eye , epilation practice , and service place were determinants for the inability of TT surgical services . The findings of this study would help in designing effective interventions to reduce trachoma in that district .
Globally , trachoma is the leading cause of infectious blindness of the eye [1] . It is caused by Chlamydia trachomatis that might effect in chronic inflammation of the eyelids . Based on WHO grading system for trachoma , the disease classifies into five grades . These are Trachomatous Inflammation-Follicular ( TF ) , which mostly requires topical treatment; Trachomatous Inflammation-Intense ( TI ) , which topical and systemic treatments are considered; Trachomatous Scarring ( TS ) -when scars are visible as in the tarsal conjunctiva and which may obscure tarsal blood vessels; Trachomatous Trichiasis ( TT ) -when an individual is referred for eyelid surgery; and Corneal Opacity-a stage during which a person is irreversibly blind [2] . At the third grade , the eyelids inflammation creates scarring of the conjunctiva that can consequently cause entropion trichiasis , resulting in interned eyelashes . The jailed eyelashes as well as other changes of the eye , such as corneal limbus and lacrimal function , harm the cornea causing severe pain , opacity of cornea and resulting vision loss [3] . Epidemiologically , worldwide , there are about 146 million active cases , 10 . 6 million with trichiasis and 5 . 9 million blind . Twenty-seven million cases of active trachoma and 3 . 8 million cases of trichiasis are found in 28 of the 46 countries in the WHO African Region , with 279 million estimated number population living in endemic areas [1] . In Ethiopia , around 1 . 2 million people are blind; about 2 . 7 million people are living with low vision; over 9 million children are affected by trachoma; and over 1 . 2 million adults are suffering from trachomatous trichiasis [4] . Low uptake of TT surgery has always been a concern for the success of the surgical services in trachoma-control strategy . Awkwardly , surgical coverage in affected communities of Africa is lower than 50% [5] . A review paper of Trichiasis surgical coverage in sub-Saharan Africa indicated that there was a fivefold variation between the lowest coverage ( 9% ) and the highest coverage ( 55% ) ; the mean was 30 . 4% ( ±SD 11 . 6% ) [6] . In Ethiopia , the overall coverage of trichiasis surgery was 41% [7] . The surgical treatment of trachomatous trichiasis is provided for free or subsidized in most trachoma endemic settings like Ethiopia . However , only 18–66% of TT patients attend for the service [8] . Various studies have examined why people with TT do not access the surgical services . Some of the reported causes of poor uptake of surgical services were unaware of how to access services , fear of surgery , burden of household tasks , indirect cost of surgery , longer walking distance , sex , family size , doubt surrounding the outcome , and absence of a companion and mildness of the condition [8–12] . To control and prevent this problem , international strategy had been created to eliminate trachoma as a blinding disease . The WHO-developed strategy is a combination of interventions known by ‘SAFE” . SAFE—stands for surgery for trichiasis ( interned eyelashes ) , antibiotics , facial cleanliness and environmental improvement [13] . In Ethiopia , by sharing the strategy , the national blindness and trachoma program was initiated in 1976 . Vision 2020 was launched in 2002 in Ethiopia . The Federal Ministry of Health has identified 2020 as the target for eliminating blinding trachoma as public health problem in Ethiopia . Moreover , the Amhara region had planned to eliminate blinding trachoma by 2015 which was not yet accomplished [14] . In Amhara region , 10% of the adult population was estimated to have TT . By 2008 , 404 health workers had been trained to perform TT surgery in Amhara Region . These individuals are usually stationed in larger health centers where they may perform TT surgery alongside their other duties . Furthermore , outreach surgical campaigns are periodically conducted [8] . However , despite these efforts , surgical services have been relatively low as compared with the need , 125 , 000 cases operated between 2001 and 2008 . Six hundred thousand of non-operated people with TT is estimated in the region [4] . At the study area , the Carter Center’s survey report stated that TT prevalence was 5 . 90% and the total backlogs were 2 , 665 of which 1 , 018 had received the surgical services and the remaining backlogs that didn’t get the service were 1 , 647 [15] . Fortunately , this study is relevant to support interventions towards clearing the backlogs of TT cases . In Amhara region , in support of primary eye care services including elimination of blinding trachoma , integrated eye care team was established and members of the team were assigned to play different roles towards TT surgical services . The health cadres assigned as Integrated Eye Care Workers ( IECWs ) are registered nurses or health officers with additional training to enable them perform trichiasis surgery . They are selected from public health facilities using set criteria as described in the trainees’ manual . They are the ones on whom everyone relies to clear the backlog of TT in the region by delivering surgical services . Program managers are zonal health departments’ and district health offices’ prevention of blindness focal persons , zonal program coordinators , and health centers’ heads . Health care providers are Integrated Eye Care Workers and TT surgery assistants . Health extension workers , kebele leaders and health development army volunteers are support staff . Quality control and monitoring and evaluation roles are given to zonal focal persons , The Carter Center staff ( trachoma training and supervisor officers and zonal program coordinators ) and mid-level eye care workers . The surgical services are given at static and outreach . This initiative has been working in collaboration to The Carter Center Ethiopia [16] . To reduce trachoma , several countries , including Ethiopia , have made considerable efforts to improve surgical services in the recent years . Unluckily , despite this increased service delivery , the number of operated cases was less than expected . This is due to a range of service and patient-specific barriers [17] . Despite that the magnitude of the problem is high , there is no similar study done in East Amhara sub region . Therefore , it is important to study the determining factors for not utilizing TT surgical services among trachomatous trichiasis patients in district . This study is aimed to identify determinants for not utilizing ( inability to use ) TT surgery among TT patients in Mehalsayint District , North East Ethiopia , so that strategies will be put in place to overcome the potential barriers .
This study used a case control study design with patients of untreated trichiasis being cases and those operated being controls . It was conducted in Mehalsayint district , 190 kilometers from Dessie , 675 kilometers East from Bahir Dar and 591 kilometers from Addis Abeba , the capital of Ethiopia . The district had a total population of 83 , 024 of which 40 , 848 ( 49 . 2% ) were men [18] . In the district there are five health centers and fourteen health posts . The TT prevalence of the district was 5 . 90% and the total backlogs were 2 , 665 of which 1 , 018 had received the surgical services and the remaining backlogs that didn’t get the service were 1 , 647 . The District had registered 902 new but not operated TT cases by house to house visits in 2016 [15] . All backlogs of the district were the target populations which were 2 , 665 obtained from Carter Center’s survey report . The study populations were all previously operated and non-operated TT patients registered in the service beneficiary registration book and in TT screened registration book in 2016 respectively living in the district . The previously operated TT patients were 1 , 018 and considered as controls and the non-operated TT patients were 902 and considered as cases . The study included both controls and cases whose ages were greater than 15 years and registered in 2015/16 . Structured and pretested questionnaire was employed for data collection . Data were collected from participants using systematic random sampling technique during March 30 , 2017 to April 13 , 2017 . Trachomatous trichiasis is defined as if at least one eye lash rubbing the eye ball or history of epilation secondary to trachoma [19] . Epilation is operationalized as removing of interned eyelashes mechanically from the eyes by local device ( locally known as “Worento” ) [20] . The sample size was calculated based on two population proportion formula using Epi Info version 7 . 2 . From previous case control studies done in North Ethiopia on determinants of uptake of surgical treatment for trachomatous trichiasis , the major reasons for not using trichiasis surgery by respondents were walking distance and symptoms of interned eye lash . Walking distance from the nearest health facility for < 30 minutes was 33 . 3% and for >30 minutes was 66 . 7% with OR of 0 . 44 ( 0 . 19 , 0 . 98 ) . Symptoms of the interned eye lash with persistent pain which was 29 . 7% and with no persistent pain was 70 . 3% with OR of 0 . 57 ( 0 . 32 , 0 . 99 ) [10] . The variables were selected based on the criteria of association , narrow confidence interval , and complete information . The district constitutes a total of five cluster health centers . Each health center encompasses two to five health posts . The operated and non-operated TT cases were registered and the registration was found in the five cluster health centers . Among the total of 1 , 018 operated and 902 non-operated and registered TT cases , 241 operated ( controls ) and 241 non-operated ( cases ) TT cases were selected using systematic random sampling technique from registration lists of each health center . Data were collected from a total of 482 study participants using pretested structured questionnaire by trained data collectors . Data were edited , coded and entered into Epi info version 7 . 2 software packages . It was then exported to Statistical Package for Social Sciences ( SPSS ) version 20 for analysis . Descriptive analysis was presented using frequency tables , figures , and percentages . In logistic regression , bivariate analysis model was fitted to screen candidate variables with p-value < 0 . 2 for the final model . To end , multivariable logistic regression analysis model , using backward stepwise method , was employed to identify significant factors for not utilizing TT surgical services . Hosmer and Lemshow goodness of fit test , with p-value>0 . 05 , was done to test model fitness . Adjusted odds ratio ( AOR ) with 95% CI and p-value of <0 . 05 was used to identify independent determinants for not utilizing TT Surgical services . This study was approved by Ethical Review Committee of College of Medicine and Health Sciences , Wollo University . Written field permits were obtained from South Wollo Zone Health Department and Mehalsayint District Health Office . All study subjects were adults . Verbal/oral consents were taken from the study participants . Since TT cases included in this study will not be stigmatized and even the issue is not sensitive , we preferred oral consents to save time . The consents were taken when interviewing the respondents . In general , it was done with respect to the principles of Declaration of Helsinki .
A total of 241 cases and 241 controls of trichiasis were interviewed . Respondents’ mean age was 53 . 87 ( ±SD , 14 . 04 ) years . Women constitute 153 ( 63 . 5% ) of cases and 149 ( 61 . 8% ) of controls in the study . The majority , 202 ( 83 . 8% ) of cases and 210 ( 87 . 1% ) of controls were unable to read and write . One hundred seventy six ( 72 . 6% ) of cases and 155 ( 64 . 3% ) of controls were married ( Table 1 ) . Among the total respondents , 137 ( 56 . 8% ) of cases and 212 ( 88% ) of controls had spent less than or equal to two hours to reach to the surgical services area from their homes . Conversely , 71 ( 29 . 5% ) of cases and 21 ( 8 . 7% ) of controls of the respondents have been reach in greater than two hours walk . One hundred ten ( 45 . 6% ) of cases faced difficulty of transportation . Twenty three ( 9 . 5% ) of cases and 3 ( 1 . 2% ) of controls went to surgery sites and returned home by absence of TT surgeon ( Table 2 ) . Of the total respondents , 86 ( 35 . 7% ) of cases and 124 ( 51 . 5% ) of controls had trichiasis on both eyes . One hundred sixty five ( 68 . 5% ) of cases and 190 ( 78 . 8% ) of controls were having eye illness for less than five years . Thirty one ( 12 . 9% ) of cases and five ( two percent ) of controls had no symptoms of trichiasis . Among the study subjects , 145 ( 60 . 2% ) of cases and 206 ( 85 . 5% ) of controls were practiced epilation . Of which , 93 ( 38 . 6% ) of cases and 142 ( 58 . 9% ) controls for more than one year . Among the study participants , 195 ( 80 . 9% ) of cases and 174 ( 72 . 2% ) of controls have minor trichiasis ( ≤ 5 lashes ) . Concerning the outcome of surgery , only 73 ( 30 . 3% ) of cases and 8 ( 3 . 3% ) of controls knew recurrence , whereas 157 ( 65% ) of cases and 232 ( 96% ) of controls knew good outcome . Almost all of cases and controls are volunteered to get the service if transport costs are covered , even if they get food and shelter service in free of charge for overnight until the dressing is removed ( Table 3 ) . As shown in Table 4 below , being lower age , widowed status , time to reach the service , missed opportunity due to absence of TT surgeon , being unaware of the problem , duration of the problem , the affected eye , epilation practice and lack of information about where the service is given were significant factors of not utilizing the surgical services . The younger ( 16–30 years ) age group was tenfold higher to not get the surgical services as compared to the elders ( > 60 years ) group ( AOR: 10 . 11 , 95% CI: 2 . 72 , 37 . 59 ) . Respondents that are separated and widowed were 59% and 60% less likely to not get TT surgical services as compared to those that are married ( AOR: 0 . 41 , 95% CI: 0 . 18 , 0 . 94; and AOR: 0 . 40 , 95% CI: 0 . 21 , 0 . 77 ) respectively . Respondents who went to surgery site and return by absence of TT surgeon were 5 times more likely not to get TT surgical services than those who did not miss the surgeon ( AOR: 5 . 00 , 95% CI: 1 . 16 , 21 . 38 ) . Respondents who travel < 2 hours walk from their home to reach to the service were 54% less likely to not go to TT surgical services as compared to those who had > 2 hours walk from their home to reach to the service ( AOR: 0 . 46 , 95% CI: 0 . 24 , 0 . 87 ) . Respondents who had not symptoms of trichiasis were 7 . 49 times more likely to not get TT surgery as compared to those who had symptoms of trichiasis ( AOR: 7 . 49 , 95% CI: 2 . 41 , 23 . 26 ) . Respondents who know the problem for > 5 years were 2 . 56 times more likely to not go to TT surgical services than those that know the problem for < 5 years ( AOR: 2 . 56 , 95% CI: 1 . 44 , 4 . 54 ) . Respondents who didn’t practice epilation were 3 . 22 times more likely to not get TT surgical services compared to those who practiced epilation ( AOR: 3 . 22 , 95% CI: 1 . 84 , 5 . 64 ) . Participants whose right eyes with the problem were 2 . 14 times more likely to not get the TT surgical services than participants whose both eyes affected ( AOR: 2 . 14 , 95% CI: 1 . 23 , 3 . 80 ) . Moreover , participants whose left eyes with the problem were 2 . 03 times more likely to not get the TT surgical services than participants whose both eyes affected ( AOR: 2 . 03 , 95% CI: 1 . 16 , 3 . 56 ) . Respondents who respond as “the service was given at health center” were 4 . 21 times more likely to not get TT surgery than those who know the service was given at health post ( AOR: 4 . 21 , 95% CI: 2 . 48 , 7 . 14 ) .
This study identified significant factors for no uptake of TT surgery . The determinants for not use of surgery were respondent’s younger age group and married respondents , long distance from the service , unavailability of TT surgeon , no symptoms of trichiasis , long time knowing the problem , single eye affected , no experience of epilation practice , and they know as “place of service is given at health center" . This finding revealed that the younger respondents were tenfold affected to not get the TT surgical services compared to the elders group ( > 60 years ) . This may be due to doubt by younger age group on the outcomes of surgery for their cosmetics reasons . In addition to the mildness of the condition , they had no severe pain and not to affect their vision . But , as age increases , probably severity of symptoms and disease progresses increase and they go to surgery to alleviate their pain as well as to prevent from suffering from blindness . This finding is in line with a study done in seventeen-outreach campaigns in Amhara Region [21] . In addition to the above reason , lack of time may be common for the young age groups than old patients . Because they may have greater childcare responsibilities , bear much of the responsibility for both agricultural and domestic work and are more likely to be the economically productive members of the family . They may also expect taking more time off work than is necessary after eye lid surgery . This finding revealed that married participants were more likely to not get the surgical services than their counterparts . This may be due to most married participants mistakenly believed that the surgical wound needs up to 2 months to heal . During this time they should avoid exposure to fire or smoke . Married participants have greater childcare responsibilities , agricultural and domestic work and no body supports the above activity and to prepare food for their family . If they exposed to fire or smoke , they believe the disease recurs . Furthermore , most married women have not the right to take surgery without the willingness of their husband . Attendants who had no symptoms of trichiasis were more likely to not get TT surgical services as compared to their counterparts . This may be due to most respondents in this category had severe form of trichiasis with effect on vision and severe discomfort . Patients with severe discomfort have problem on their vision secondary to the presence of lash on the cornea and the tearing . Such patients consider warranted surgery and want to take the trouble for treatment at present . If most patients reported ‘‘having symptoms” , this may reflect prioritization of eye care over work or farming [10 , 21 , 22] . This study shows that respondents who knew the problem for short period were better to go to TT surgical services than those that knew the problem for longer periods . This result contradicts with a study done in North Ethiopia , in which the uptake of trichiasis surgery increased with duration of illness [10] . This may be due to mostly the uptake of trichiasis surgery increases when the disease appears new . The uptake of surgery in those with the disease for short duration was good , possibly because severe symptoms might be developed when the disease starts and is not adapted and exposed to other means of local treatment . However , as the disease lasts greater than five years , they habituate the symptom and they utilize local treatment like epilation . The increased uptake of surgical treatment with persistent pain obligates them to deserve attention as the severity of pain is most likely associated with the severity of trichiasis grading and corneal opacity . The trend of seeking surgery at a later stage needs attention in that early surgery is likely to safeguard from troublesome effects . Respondents who didn’t practice epilation were more likely to not get TT surgical services when compared to those who practiced epilation . This may be due to clients who practice epilation perceived that the disease is severe and utilize surgery as a choice of treatment for long time . They tested as epilation was not permanent treatment and go to TT surgery site to get the service to relief from severe pain and to prevent from blindness . Whereas those respondents who did not practice epilation believe that the disease had no severe pain and did not affect their vision to initiate them for early surgery . Participants whose single eye had the problem were more likely to not get the TT surgical services than those whose both eyes affected . The uptake of surgery in those having bilateral TT was good . This may be due to severity of symptoms increases as the disease attacks both eyes . When both eyes were affected it was indicative of the severity of trichiasis grading and early corneal opacity . This finding is in line with a study done in Tanzania , individuals with bilateral trichiasis were more likely to have surgery than those with unilateral trichiasis [5] . Despite presence of free of charge trichiasis surgical treatment , most of study subjects were using other means of treatment like epilation . This could be due to lack of information , inaccessibility and misconception about trichiasis surgery treatment [22] . This study indicated that respondents who went to surgery site and return by absence of TT surgeon were more likely to not get TT surgery in other times than those who did not miss the Surgeon . This is similar with the study done in Tanzania and Ethiopia [5 , 21] . If patients had gone to the health facility for surgery but did not receive the operation due to the surgeon was not present at the time of visit , in such case the patient might lost hope and would not return for surgery . Respondents who said “the service is given at health center” were more likely to not get TT surgery than those who knew the service is given at health post . Surgical campaigns in health posts may be particularly effective at reaching larger numbers of patients in Ethiopia , though feasibility issues challenge the service delivery as it is a large country with very limited infrastructure and trained personnel [17 , 23] . Moreover , a walking distance to the service area for greater than an hour showed an association with decreased attendance for TT surgery in Amhara region [10] . Surgical campaigns in health posts are also likely to be particularly beneficial for reaching women and older people for whom transport , distance and lack of attendant were particular barriers [21] . The study shows that patients who live in the nearby health facility were better to have had surgery as compared to those that are distant . This is similar with a study done in North Ethiopia [10] . It may be due to those trichiasis patients even with the presence of post-surgical eye packs travel easily to their home after getting the service , which indicates as the service is near their home , awareness of utilizing the existing service improves . Our study is strong in that it provided operational recommendations based on the identified factors . However , it is limited in which the study uses TT cases that have been already screened by Health Extension Workers ( HEWs ) and Integrated Eye Care Workers ( IECWs ) and has not tried to search new cases because of cost and time . No follow up were conducted to ensure whether or not the patients have gone to the health facilities to get the surgical services based on the advice given to them when interviewed . Consequently , it may affect the result and recommendations given here . Prospective study is recommended to identify the root causes for not uptake of surgery treatment . The findings of this study have valuable policy implications for health programs scheme and interventions . Training and deploying of HEWs and Community-based screeners on house to house screening and awareness creation activities; expanding the service to health post level with campaign and outreach services; and posting the service days on different places may be important to reduce the problem . In other words , this findings supreme important to Mehalsayint district health office and the respective zonal health department , and partners to develop interventions programs against this neglected tropical disease . | Trachoma is the common ophthalmic infection and cause of blindness worldwide . It is caused by ocular infections with causative agent of Chlamydia trachomatis that might effect in chronic inflammation of the eyelids , which produces scarring of the conjunctiva that can consequently cause entropion trichiasis , resulting in interned eyelashes . The interned eyelashes as well as other changes of the eye , harm the cornea causing severe pain , corneal opacity and resulting vision loss . Over a million people in Ethiopia are estimated to have Trachomatous trichiasis ( TT ) . Trachomatous trichiasis surgery is the backbone treatment option . Though the provision of free surgical services in the country exists , utilization rates are very low . Identifying the determinants for not utilizing the service is mandatory to take measures towards surgical uptake . A total of 482 study participants ( 241 cases and 241 controls ) with age of ≥15 years were included in the study . The determinants for not use of surgical services were respondents in the younger age group ( 16–30 years ) and widowed participants , lengthy distance from the service , unavailability of TT surgeon , no trichiasis symptoms , long time knowing the problem , right/left eye affected , no experience of epilation practice , and participants who knew place of service was given at health center . | [
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"disease... | 2018 | Determinants for not utilizing trachomatous trichiasis surgery among trachomatous trichiasis patients in Mehalsayint District, North-East Ethiopia |
Trypanosoma cruzi ribosomal P proteins , P2β and P0 , induce high levels of antibodies in patients with chronic Chagas' disease Cardiomyopathy ( CCC ) . It is well known that these antibodies alter the beating rate of cardiomyocytes and provoke apoptosis by their interaction with β1-adrenergic and M2-muscarinic cardiac receptors . Based on these findings , we decided to study the cellular immune response to these proteins in CCC patients compared to non-infected individuals . We evaluated proliferation , presence of surface activation markers and cytokine production in peripheral blood mononuclear cells ( PBMC ) stimulated with P2β , the C-terminal portion of P0 ( CP0 ) proteins and T . cruzi lysate from CCC patients predominantly infected with TcVI lineage . PBMC from CCC patients cultured with P2β or CP0 proteins , failed to proliferate and express CD25 and HLA-DR on T cell populations . However , multiplex cytokine assays showed that these antigens triggered higher secretion of IL-10 , TNF-α and GM-CSF by PBMC as well as both CD4+ and CD8+ T cells subsets of CCC subjects . Upon T . cruzi lysate stimulation , PBMC from CCC patients not only proliferated but also became activated within the context of Th1 response . Interestingly , T . cruzi lysate was also able to induce the secretion of GM-CSF by CD4+ or CD8+ T cells . Our results showed that although the lack of PBMC proliferation in CCC patients in response to ribosomal P proteins , the detection of IL-10 , TNF-α and GM-CSF suggests that specific T cells could have both immunoregulatory and pro-inflammatory potential , which might modulate the immune response in Chagas' disease . Furthermore , it was possible to demonstrate for the first time that GM-CSF was produced by PBMC of CCC patients in response not only to recombinant ribosomal P proteins but also to parasite lysate , suggesting the value of this cytokine to evaluate T cells responses in T . cruzi infection .
Trypanosoma cruzi , the etiological agent of Chagas' disease , affects approximately 8–10 million people , and its infection is one of the major human health problems in Central and South America , being extended now to Europe ( especially Spain and Portugal ) , the United States , Canada , Japan and Australia [1] , [2] , [3] . Upon exposure to the parasite , the humoral and cellular immune responses elicited by the host , keep acute parasitemia under control [4] , [5] . However , approximately 30–40% of the infected individuals , several years after initial exposure , develop clinical symptoms of visceral damage , which may include cardiac lesions , digestive alterations or both manifestations ( cardiac plus digestive ) [5] . Chronic Chagas' disease Cardiomyopathy ( CCC ) , the most frequent and severe consequence of the chronic infection by T . cruzi , is manifested predominately as an arrhythmogenic cardiomyopathy [6]–[9] . Up to now , the mechanisms of the pathophysiology of Chagas' disease are not completely elucidated and two main hypotheses have been proposed . The first one is based on the inflammatory reaction elicited by the parasite leading to tissue damage , while the second argues for an autoreactive process resulting from an impaired immune response associated with molecular mimicry [10]–[13] . However , it is currently accepted that both mechanisms are not mutually exclusive and that Chagas' disease is the result of both , parasite persistence in the chronic phase and the presence of autoantibodies/self-reactive T cells to host molecules [14] , [15] . As supporting evidence for the autoimmune hypothesis , previous work in our laboratory demonstrated the presence of circulating antibodies against ribosomal P proteins of T . cruzi ( anti-P Abs ) with agonist-like properties on cardiac receptors in patients with CCC [16]–[24] . Those Abs predominantly recognized the C-terminal end of P2β ( peptide R13 , EEEDDDMGFGLFD ) or P0 proteins ( peptide P015 , EEEDDDDDFGMGALF ) , which bear structural similarity to the acidic motif , AESDE , located on the second extracellular loop of the cardiac receptor [19] , [20] , [22] . Several studies including patients with CCC as well as experiments performed in mice immunized with recombinant P2β or P0 protein demonstrated a correlation between the presence of anti-P Abs and cardiac disorders [21] , [22] . These findings were confirmed by the generation of anti-R13 monoclonal Ab , mAb 17 . 2 , which not only induce a dose-dependent increase on the beating frequency of rat cardiomyocytes in culture that is abolished by bisoprolol , a specific β1-adrenergic receptor antagonist [25] , but also provoke apoptosis in the murine cardiac cell line HL-1 by its long-lasting β1-AR stimulatory activity [24] . The humoral immune response against ribosomal P proteins has been largely studied in patients with CCC; however , little is known about their recognition by T cells . Most studies concerning the T cell immune response in Chagas' disease , have been performed using freshly isolated peripheral blood mononuclear cells ( PBMC ) but stimulated with epimastigote ( the replicative form found in the midgut of insect vectors ) or trypomastigote ( the infective form found in the bloodstream and other human extracellular fluids ) lysate [26]–[29] . Few investigations have been focused on the reactivity of T cells against purified antigens of the parasite [30]–[40] . To date , studies performed with recombinant parasite proteins , such as the cytoplasmatic repetitive antigen ( CRA ) , B13 , trans-sialidase , and paraflagellar rod proteins on PBMC and cruzipain on T cells lines revealed that patients with CCC produced significant amount of IFN-γ upon stimulation , which is in line with the typical pattern of inflammatory response described for T . cruzi lysate [34]–[40] . However , Lorena et al . also reported that the flagellar repetitive antigen ( FRA ) induced proliferation of PBMC by thymidine incorporation , but no difference was observed in IFN-γ and TNF-α secretion between patients with CCC and non-infected individuals [37] . The aim of this study was to analyze the cellular immune response developed in patients with CCC against T . cruzi ribosomal P proteins , knowing the existence of a cross-reactive component at the humoral level . The specificity of the response was analyzed by proliferation and cytokine production using multiplex technology because it allows to quantify a large spectrum of cytokines in the same cell culture supernatant . Results showed that T . cruzi ribosomal P proteins , specifically P2β and the C-terminal portion of P0 ( CP0 , 110 aa ) , did not induce the proliferation of PBMCs from CCC in a different manner than non-infected individuals . However , these antigens were able to induce the secretion of IL-10 , TNF-α and GM-CSF by PBMC as well as both CD4+ and CD8+ T cells in patients with CCC . Surprisingly , ribosomal P proteins did not stimulate but actually reduced the secretion of IFN-γ in cardiac patients . Furthermore , our results demonstrate for the first time that GM-CSF is produced in response not only to parasite lysate but also to ribosomal P proteins . These findings suggest that GM-CSF production could be included in the future to evaluate whole parasite and parasite protein specific T cell responses in Chagas' disease .
The research protocols followed the tenets of the Declaration of Helsinki and were approved by the Medical Ethics Committee of Ramos Mejía and Fernández Hospitals . All enrolled patients gave written informed consent , according to the guidelines of the Ethical Committee of the Hospitals , before blood collection and after the nature of the study was explained . Patient selection was conducted at the Cardiovascular Division of the Ramos Mejía and Fernández Hospitals , Buenos Aires , Argentina . Positive serology for Chagas' disease was determined by two or more tests ( indirect immunofluorescence , enzyme-linked immunosorbent assay [ELISA] , indirect hemagglutination , or complement fixation ) . Patients who had at least two of three tests were considered positive for Chagas' disease . Patients underwent a complete clinical and cardiologic examination that included medical history , physical examination , electrocardiogram ( ECG ) at rest , laboratory and chest X-ray analysis , and echo doppler cardiography evolution . The exclusion criteria included the presence of systemic arterial hypertension , diabetes mellitus , thyroid dysfunction , renal insufficiency , chronic obstructive pulmonary disease , hydroelectrolytic disorders , alcoholism , history suggesting coronary artery obstruction and rheumatic disease , and the impossibility of undergoing the examinations . The study population consisted of 27 patients who completed the screening protocol and were diagnosed with Chronic Chagas' disease Cardiomyopathy . Twenty non-infected individuals ( NI ) , within the same age range ( 30–70 years old ) and showing negative serological tests for Chagas' disease , were included as control group . Due to its predominant clonal proliferation , the T . cruzi species is composed by multiple strains showing extensive genetic diversity , which were recently grouped into 6 evolutionary lineages or discrete typing units ( DTUs ) known as TcI to TcVI [41] . Gluthatione S-transferase ( GST ) -fusion proteins bearing the entire TSSA from Sylvio X-10/1 strain ( henceforth TSSA Sy , representative of TSSA isoforms from DTU TcI parasites ) and CL Brener strain ( henceforth TSSA CL , representative of TSSA isoforms from DTUs TcII/TcV/TcVI parasites ) were expressed in Escherichia coli BL21 strain and purified as described [42] . Briefly , supernatants of bacterial cultures transformed with the indicated construct were induced for 3 h at 28°C with 0 . 250 mM isopropyl—β-D-thiogalactopyranoside , purified by glutathione-Sepharose chromatography and extensively dialyzed against PBS . The purity and integrity of GST-TSSA samples was assessed with silver-stained SDS-PAGE gels [42] . Whole antigenic lysate from T . cruzi epimastigotes was prepared as described previously [43] . Briefly , fresh epimastigotes ( CL Brener , DTU Tc VI ) cultured in a liquid medium ( liver infusion tryptose ) , were collected by centrifugation and washed three times with PBS . After centrifugation at 500xg during 5 min , the parasites were resuspended in lysis buffer ( PBS , EDTA 1 mM , β-mercaptoethanol 5 mM , 0 . 1% SDS and protease inhibitors cocktail ) and submitted to three cycles of freezing-thawing . The parasite lysate was diluted with PBS at 1 mg/ml , filter sterilized on 0 . 2 µm-pore-size membranes , assayed for protein concentration , aliquoted , and stored at −80°C until use . The T . cruzi recombinant proteins selected for this study were P2β-His and CP0-His; this last one corresponds to the C-terminal portion of P0 ( 110 aa ) . The ribosomal P proteins were obtained and purified by means as His6-tag as described [44] . The purity and specificity of the recombinant proteins were analyzed by SDS-PAGE gels and Western-blot with a pool of chagasic and non-infected sera . Protein concentration was determined by Bradford ( BioRad , Hercules , CA , USA ) , using BSA ( Sigma , St Louis , MO , USA ) as standard protein . Peptides were prepared by solid-phase method of Merrifield as described by Müller et al . with a semi-automatic multi-synthesizer NPS 4000 ( Neosystem , Strasbourg , France ) [45] . Their purity was assessed by High Performance Liquid Chromatography ( HPLC ) and identified by mass spectrometry . Peptide R13 ( EEEDDDMGFGLFD ) was derived from the 13 C-terminal amino acids of P2β , P015 ( EEEDDDDDFGMGALF ) from 15 C-terminal region of P0 protein , and peptide H13 ( EESDDDMGFGLFD ) was derived from the corresponding region of mammalian ribosomal P proteins [46] . For ELISA , these peptides were coupled at a molar ratio of 1∶30 to BSA ( Sigma , St Louis , MO , USA ) with 0 . 05% glutaraldehyde as previously described [45] . The products were assessed by analytical HPLC and amino acid analysis was used to calculate the peptide–BSA molar ratio . Microwell plates ( Nunc Maxisorp ) were coated overnight at 4°C with 50 ng protein/well of T . cruzi lysate , 2 µg/well of recombinant proteins P2β-His and CP0-His or 2 µM of synthetic peptide in 50 µL of 0 . 05 M carbonate buffer pH = 9 . 6 . Plates were washed with PBS containing 0 . 1% Tween-20 ( PBST ) and then blocked with PBST containing 2 . 5% non-fat dry milk ( PMT ) for 1 h at 37°C . After washing , 50 µL of each diluted human serum ( dilution 1/200 in PMT ) was loaded onto plates and incubated for 1 h at 37°C . Following washing , plates were incubated with 50 µl of peroxidase-conjugated goat anti-human IgG ( dilution 1/3 , 000 in PMT ) ( Sigma , St Louis , MO , USA ) . Enzyme activity was revealed with TMB and , OD was read at 415 nm with an Automated Plate Reader ( Molecular Devices , CA , USA ) . All samples were tested in duplicate . Sera from 8 non-infected individuals were also included on the plate to determine the baseline level , as the OD mean value +3 SD . Antibody level is expressed as Reactivity index which was determined as the OD mean value of each serum sample/baseline value . Peripheral blood mononuclear cells ( PBMC ) were isolated from heparinized blood by Ficoll-Hypaque density gradient centrifugation ( GE HealthCare , Uppsala , Sweden ) , washed once and resuspended in RPMI-1640 medium containing 100 U/ml penicillin , 100 mg/ml streptomycin , 2 mM L-glutamine and 5% of AB Rh-positive heat-inactivated normal human serum ( Sigma , St Louis , MO , USA ) . Cell suspensions ( 200 µl ) were cultured as triplicates in the presence or absence of different stimuli for 4 or 6 days at a density of 2 . 5×105 cells/well in 96-well sterile plates ( round bottom ) . Stimuli used in the cultures included T . cruzi lysate , P2β-His , CP0-His ( at a final concentration of 10 µg/ml for 6 days ) , peptides R13 , P015 and H13 ( at a final concentration of 5 µg/ml for 6 days ) while PHA ( Phitohemaglutinin , Sigma , at a final concentration of 5 µg/ml for 4 days ) was used as positive control . All concentrations were determined by performing titration experiments . After the incubation period , cultures were exposed to 1 μCi/well of 3H-thymidine ( 3H-TdR , specific activity , 2 Ci/mmol , Amersham , Arlington Heights , IL ) for 6 h and then harvested on glass fiber filters . The incorporated radioactivity was determined by liquid scintillation counting . All cultures were performed in triplicate . Results are expressed as Stimulation Index , calculated as the mean cpm of stimulated cultures/mean cpm of non-stimulated ( culture medium only ) cultures . 2 . 5×106 cells were cultured in 24-well plates in 1 ml cultures for 6 days with either medium alone , or T . cruzi lysate , P2β-His , CP0-His ( at a final concentration of 10 µg/ml ) . After centrifugation , cells were washed , resuspended in ice-cold PBS , stained for 30 min at 4°C with the following fluorescent-labeled monoclonal antibodies: allophycocyanin ( APC ) conjugated anti-CD3 + phycoerythrin-cyano dye Cy5 ( PE-Cy5 ) conjugated anti-CD4 + phycoerythrin ( PE ) conjugated anti-HLA-DR + fluoresceinisothiocyanate ( FITC ) conjugated anti-CD25 , or APC anti-CD3 + PE-Cy5 anti-CD8 + PE anti-HLA-DR + FITC anti-CD25 . Cells were then fixed with 4% formaldehyde in PBS and kept at 4°C until analyzed by flow cytometry . In all cases , 10 , 000 to 15 , 000 events in the lymphocyte gate were acquired using a FACSAria flow cytometer ( Becton Dickinson ) . Phenotypic analyses were carried out with FlowJo flow cytometric analysis software ( TreeStar ) , selecting the small lymphocyte population . PBMC stained with FITC , PE- , APC- and PE-Cy5- labeled Ig control Abs were included in all experiments for background fluorescence . All Abs were purchased from BD Biosciences ( San Diego , CA , USA ) . CD8+ T cells were isolated from PBMC by positive selection using EasySep CD8 Selection Kit ( StemCell Technologies , Inc . , Vancouver , Canada ) , while CD4+ T cells were separated from CD3+CD8neg T cells by negative selection ( EasySep CD3 Selection Kit , StemCell Technologies ) . The purity of both populations was assessed by flow cytometry using specific conjugated mAb ( see “Phenotypic analysis of PBMC” ) and , it was shown to be higher than 90% for both T cells subsets . IL-2 , IL-4 , IL-10 , IL-13 , IL-17 , IFN-γ , GM-CSF and TNF-α were measured in the supernatants of whole PBMC cultures stimulated in the presence or absence of the indicated antigens and collected on days 1 , 2 and 6 after stimulation . In addition , the same cytokines were quantified in cultures of isolated CD4+ or CD8+ T cells ( 5×105 cells ) co-cultured with irradiated CD3neg T cells ( ratio 1∶1 ) in the presence or absence of antigen after 6 days of stimulation . Cytokines were measured by using MILLIPLEX MAP Human Cytokine/Chemokine Kit ( for 8 cytokines ) following the manufacturer's directions ( Millipore , St Charles , MO ) and Luminex instrument and Beadlyte software were used for analysis . All samples were tested in duplicate . Results are expressed in ng/ml or Fold increase ( FI ) which was determined as [ ( cytokine in stimulated culture ) - ( cytokine in NS culture ) ]/ ( cytokine in NS culture ) , where NS denotes non-stimulated cultured PBMC . Statistical analysis was performed with GraphPad Prism statistical software ( GraphPad Software ) . The nonparametric Mann-Whitney U test was used to generate P values comparing the median experimental values between groups each of the multiple sets of experimental data . Within each experiment , overall statistical significance of each result at both 10% and 5% significance was determined using Holm-Bonferroni Correction . Differences were considered statistically significant at P<0 . 05 .
Patients included in this study were all born in endemic areas from Argentina and Bolivia , and at the time of the enrollment they have been living in Buenos Aires ( where no vectorial transmission occurs ) for more than ten years , in average . The mean age was 54 . 2±10 . 1 , and 57% were female . All T . cruzi-infected subjects were in the chronic phase of Chagas' disease , involving only cardiac alterations . According to the New York Heart Association ( NYHA ) functional classification system , patients were classified as Class I , II , III/IV . Patients with no functional limitations but with some electrocardiographic alterations were classified as Class 0 [5] . Blood samples yielded negative results for currently used PCR protocols targeting parasite DNA [47] , which is frequently the case in chronic chagasic patients due to low parasitemia . Taking this into account , we analyzed the profile of the humoral anti-TSSA ( trypomastigote small surface antigen ) response in our study patients as an indirect means of identifying the genotype of the infecting strain ( s ) [48] , [49] . To carry out this analysis , we evaluated the reactivity of serum samples against either TSSA Sy ( the TSSA isoform from DTU TcI ) or TSSA CL ( the TSSA isoform from DTUs TcII/V/VI ) in conventional ELISA and dot-blot ( see Text S1 for details and Figure S1 ) . The main characteristics of the study population are summarized in Table 1 . To characterize the humoral response in the subject population included in this study , the antibody reactivity against T . cruzi lysate , ribosomal P proteins , P2β and CP0 , together with their C-terminal peptides R13 and P015 was determined in sera of CCC patients and non-infected individuals by ELISA . The reactivity against peptide H13 , which corresponds to the C-terminal region ( residues 102–115 ) of the human ribosomal P protein was also measured . Results showed that sera from CCC patients presented reactivity against T . cruzi lysate , with titers ranging from 1/200 to 1/20 , 000 ( Table 1 ) . Only patient P19 showed a titer against parasite proteins similar to those detected in non-infected individuals ( <1/200 at OD = 1 ) . Although antibodies in the serum sample from this patient were not detected by in-house ELISA , two of three serological tests for T . cruzi infection , together with clinical and cardiological examinations confirmed patient P19 to have CCC . In addition , sera of all patients , including P19 , reacted with a broad range of T . cruzi proteins as determined by Western-blot ( data not shown ) . The majority of CCC patients ( 24/27 ) showed reactivity ( Reactivity index >1 . 7 ) to ribosomal P2β protein and its peptide R13 . The level of anti-CP0 antibodies was also elevated in the chagasic patients ( 17/27 ) compared to non-infected individuals , but the overall reactivity was lower than that observed for P2β protein ( Figure 1 ) . On the other hand , only marginal differences were determined in the median of the Reactivity Index for the anti-P015-antibodies in cardiac patients in comparison with non-infected subjects . No difference was observed against peptide H13 ( human P ribosomal protein derived ) between both groups of individuals ( Figure 1 ) . Together , these results showed that CCC patients mount a significant antibody response to ribosomal proteins as well as to peptides R13 and to a lower level to P015 in comparison to non-infected subjects . In order to investigate the cellular response to ribosomal P proteins , PBMC from CCC patients and non-infected individuals were tested for their proliferative capacity in response to different T . cruzi antigens . To determine the optimal protein and peptide concentration yielding the most consistent results , the proliferative response was initially assayed in PBMC cultures from 4 cardiac patients non-included in this study . The results showed that 10 µg/ml of T . cruzi lysate or ribosomal P proteins and 5 µg/ml of the peptides were optimal to trigger proliferative responses , and so these concentrations were used in the studies presented here . As shown in Figure 2 , the majority of PBMC from CCC patients proliferated upon stimulation with T . cruzi lysate ( Stimulation index median: 4 . 45 ) compared to PBMC from non-infected individuals ( Stimulation index median: 1 . 07; P<0 . 001 ) . On the contrary , the stimulation index of PBMC from cardiac patients and control subjects in response to ribosomal P proteins ( Figure 2 ) as well as to peptides R13 , P015 and H13 was not significantly different ( data not shown ) . PBMC from all subjects proliferated in response to PHA and the responses were not significantly different between the cardiac and non-infected individuals ( data not shown ) . To characterize the phenotype of the cells after the stimulation with the different stimuli , cells were stained with different T cell markers and analyzed by flow cytometry . The forward vs side scatter dot plots revealed that the frequency of lymphocyte population in non-stimulated cultures was significantly lower in cardiac patients compared with non-infected individuals ( 48±13% vs 62±10% , respectively; P<0 . 001 ) . However , the CD3+CD4+:CD3+CD8+ ratio was approximately 2∶1 in both groups . Interestingly , results showed that CCC patients present higher subsets of CD25 and HLA-DR positive cells on both CD3+CD4+ and CD3+CD8+ populations upon T . cruzi stimulation ( Figure 3 ) . However , the expression of these markers was similar in T cells from cardiac patients and non-infected individuals when cells were stimulated with ribosomal P proteins ( Figure 3 ) . Given the lack of proliferative response to ribosomal P proteins in the CCC patients , T cell activation was studied by analyzing cytokine secretion . Thus , PBMCs from 10 cardiac patients with different disease severity , and 8 non-infected donors were stimulated with P2β and CP0 proteins and T . cruzi lysate as well as PHA as positive control . Supernatants after 1 , 2 and 6 days post-stimulation were collected and multiplex analysis was performed to evaluate the levels of GM-CSF , IFN-γ , IL-10 , IL-13 , IL-17 , IL-2 , IL-4 and TNF-α . Despite the fact that cytokine responses have been studied by others after T . cruzi stimulation in patients with Chagas' disease [50] , [51] , reports have used different assays and stimulation/culture conditions making the direct comparison of all the cytokines difficult to achieve . In this study , we aimed to simultaneously evaluate the kinetic responses of multiple cytokines in the same culture well . Figure 4 shows the maximum fold increase detected for each cytokine and in each subject among day 1 , 2 and 6 determinations . The fold increase was determined by the difference between cytokine production ( in pg/ml ) in stimulated wells and the cytokine production in non-stimulated control wells divided the cytokine production in non-stimulated control wells . The actual fold increase for each of the days and the background production in pg/ml of each of the cytokines in non-stimulated wells are shown in Figures S2 to S5 and S6 , respectively . Upon stimulation with ribosomal P proteins , GM-CSF , IL-10 and TNF-α were secreted at higher levels in cardiac patients compared with non-infected individuals ( Figure 4 and Figure S2 and S3 ) . However , both proteins induced similar levels of IFN-γ production in PBMC from cardiac patients and non-infected subjects ( Figure 4 ) . Furthermore , the fold increase of IFN-γ production in response to both proteins was lower and statistically significant in the cardiac group after only the first days post-stimulation ( Figure S2 and S3 ) . The level of IL-2 , IL-4 , IL-13 and IL-17 secreted after stimulation with the ribosomal P proteins was very low or null at any of the 3 time points analyzed and , it was found to be similar between CCC patients and non-infected individuals ( Figure 4 and Figure S2 and S3 ) . A larger number of cytokines were produced in response to T . cruzi lysate or the universal stimulus PHA than in response to the individual ribosomal P proteins ( Figure 4 ) . Indeed , PBMC from cardiac patients in response to T . cruzi lysate also secreted statistically significant and higher levels of IFN-γ , IL-2 and IL-13 compared with non-infected individuals . IFN-γ and IL-13 were also increased in CCC patients vs non-infected individuals when PHA was used for stimulation . These results indicate that although the cells were capable of producing IFN-γ and IL-13 in response to whole parasite or PHA , their production was not detected when the ribosomal P proteins were used as stimulus . The kinetic cytokine profile for T . cruzi lysate and PHA is shown in Figure S4 and S5 . The results presented above revealed a cytokine signature expression upon stimulation with ribosomal P proteins and T . cruzi lysate in whole PBMC . To better understand the specific contribution of the T cells to this profile , CD3+CD4+ and CD3+CD8+ T cell subsets from three cardiac patients were enriched from PBMC and stimulated with the antigens in the presence of autologous antigen-presenting cells . Samples from patients RM11 , RM12 and RM14 were chosen since they were among those that showed clear cytokines response after ribosomal P proteins stimulation . As shown in Figure 5 , GM-CSF was overall produced by both , CD4+ and CD8+ subsets by the 3 patients in response to the proteins and T . cruzi lysate . In general , IFN-γ was produced at very low levels by CD4+ and CD8+ T cells in all patients in response to the proteins , but enough to be different from the non-stimulated wells in the case of CD4+ T cells ( Figure 5 ) . IL-10 was found to be secreted most frequently by both T cell subsets . IL-13 was not produced by CD8+ T cells in any of the 3 patients analyzed and in response to all the stimuli tested . However , IL-13 was produced by CD4+ T cells in response to T . cruzi lysate and/or the proteins in the 2 of the 3 patients ( RM11 and RM14 ) . TNF-α was produced by both , CD4+ and CD8+ T cells and its production was higher in response to the proteins than to T . cruzi in 2 of the 3 patients . IL-2 , IL-4 and IL-17 were not detected in response to any of the stimuli ( data not shown ) .
Since it has been widely demonstrated the relevance of antibodies directed to ribosomal P proteins in the pathophysiology of Chagas' disease [21] , [23] , [24] , this study aimed to further understand the cellular immune response raised against these proteins in CCC patients . Our results showed that PBMC did not proliferate upon in vitro stimulation with P2β and CP0 proteins . Additionally , the lack of proliferation in response to the proteins was associated with the absence of the expression of activation markers CD25 and HLA-DR on CD4+ and CD8+ T cell populations . These findings were also protein-specific , since T . cruzi lysate provoked an augmentation of both markers on the surface of T cells in agreement with data published by others [50] , [51] . Interestingly , the percentage of both T cell subtypes , CD3+CD4+ and CD3+CD8+ in PBMC were similar in cardiac patients and non-infected individuals independently of the stimulus . These results suggest that the lack of proliferative response was not due to an overall decrease on the size of the T cell population , nor to a shutdown of the proliferative capacity in these patients since the same cells responded to T . cruzi lysate and a T cell specific universal mitogen such as PHA . However , it was possible to speculate that T cells specific to these proteins have been deleted by negative selection due to the similarity to the host specificities . In this regards , the analysis of the T cell response by cytokine release discarded this possibility since indeed , several cytokines were expressed in response to ribosomal T . cruzi proteins . The use of multiplex technology allowed us to simultaneously analyze 8 cytokines , namely , IL-2 , IL-4 , IL-10 , IL-13 , IL-17 , IFN-γ , TNF-α and GM-CSF , corresponding to well-described CD4+ and CD8+ associated cytokines . In particular , GM-CSF was included because not only its production has been associated to antigen mediated activation of T cells by us and others but also , the threshold of antigen requirement for its production is lower than for other cytokines as TNF-α , IL-4 or IFN-γ [52]–[54] . Our results showed that PBMC from CCC patients secreted high levels of GM-CSF , IL-10 and TNF-α in response to P2β and CP0 proteins . Interestingly , the secretion of IFN-γ at day 1 and 2 post-stimulation with ribosomal P proteins was similar or lower in cardiac patients vs non-infected individuals . Moreover , our data demonstrated that patients with CCC developed a different cytokine profile in response to T . cruzi and PHA stimulation than non-infected subjects . Even though the secretion of GM-CSF , IL-10 and TNF-α in response to the proteins was significantly higher in cardiac vs non-infected individuals ( P<0 . 05 , nonparametric Mann-Whitney U Test ) , these P values nonetheless did not stay significant at the 5% level when a multiple comparison ( all 32 cytokine/stimulus pairings ) was performed by using Holm-Bonferroni correction . In contrast , the P values for these cytokines in response to T . cruzi lysate did reach statistical significance at the 5% level . This difference could be explained by the fact that the frequency of single specific parasite protein T cells within the bulk population is lower than the frequency developed in response to whole T . cruzi lysate and therefore it leads to lower cytokine secretion levels . However , it is important to remark that were the P values distributed at random amongst the proteins data , there would be only a chance of the three exact same cytokines ( GM-CSF , IL-10 and TNF-α ) being secreted in response to both proteins , demonstrating that the difference observed between cardiac patients and non-infected individuals was not a mere coincidence . Following with T . cruzi lysate response , we observed that all studied cytokines were elevated and significantly different in the supernatants of cultured PBMC from cardiac patients with exception of IL-4 and IL-17 . Upon PHA stimulation , PBMC from cardiac patients secreted higher amount of GM-CSF , IFN-γ , IL-10 , IL-13 , and TNF-α; similar production was observed for IL-2 , IL-4 and IL-17 between both groups of individuals . In addition , and independently of the stimulus , our results also showed that these cytokines were secreted by both T cells populations , except for IL-13 which was predominantly produced by CD4+ T cells . Despite this finding , it is well-known that non-T cells , such as monocytes or B cells , also participate in the secretion of these cytokines . Indeed , Gomes et al . [28] , by intracellular cytokine staining , reported that the majority of the IL-10-producing cells are monocytes ( CD14+ cells ) in asymptomatic patients , and the same group recently demonstrated that CD19+ B cells is another important source of this cytokine in cardiac patients [55] . Furthermore , the spontaneous release of cytokines in non-stimulated PBMC , which provides information about the basal level of cytokine production in vivo , showed a lower level for IFN-γ , IL-10 , IL-13 , and TNF-α in CCC patients ( Figure S6 ) . It should be mentioned that Dutra et al . demonstrated that the expression of IFN-γ , IL-10 , IL-13 mRNAs was increased in PBMC from chagasic patients [33] . However , this discrepancy could depend either on the use of ex vivo PBMC or on the methodology used to determine cytokine expression . Our data , together with those reported by Giraldo et al . [56] , may suggest that T . cruzi persistence provokes a general dysfunction in peripheral T cell response . The high levels of pro-inflammatory cytokines , like IFN-γ and TNF-α , together with undetectable IL-4 production in response to PHA and T . cruzi stimulation suggest that there is a shift towards polarized Th1-type of cytokine response in CCC patients . Although IL-10 was first described related to Th2 cells , now is known that is produced by all T cells , including Th1 and a regulatory T cell subsets , called Tr1 cells or IL-10-producing cells [57] . Recent studies with an experimental murine model revealed not only the protective role of IL-10 against fatal myocarditis , but also demonstrated that this cytokine was produced by both CD4+ and CD8+ subsets of IFN-γ+IL-10+ double-producing T cells [58] . Similar data were obtained in studies by Belkaid et al . [59] , where the main source of IL-10 in dermis and draining nodes of mice infected with Leishmania major is a subset of CD4+ T cells that produce both IL-10 and IFN-γ . Studies performed with others recombinant parasite proteins demonstrated that the majority of chagasic patients develop a strong humoral and cellular immune response with a tendency to the typical pattern of inflammatory response described for T . cruzi lysate [34]–[40] . On the contrary , the cytokines released upon ribosomal P proteins stimulation made difficult to set a specific Th cells responsible for their secretion . This mixed cytokine profile which could be involved in balancing heart tissue damage and parasite persistence during chronic disease , strengthens in part the fact that B cells , through antibodies directed against P2β and CP0 and not T cells , would have the major role in the development of cardiac symptoms by their interaction with β1-adrenergic and M2 muscarinic receptors . Interestingly , GM-CSF was secreted at high levels by PBMC from CCC patients when T . cruzi lysate , and both ribosomal P proteins were used as stimulus . To our knowledge , this is the first time that GM-CSF is used to evaluate the T . cruzi specific response of stimulated PBMC from cardiac patients . Instead , GM-CSF has been associated to a decrease in the rate of infection of both non-activated and IFN-γ activated macrophages infected with T . cruzi [60] . Moreover , Olivares Fontt et al . reported that the administration of exogenous recombinant GM-CSF improved the deficient immune response of chronically infected mice or , if neutralized by Ab anti-GM-CSF , it aggravated infection increasing parasitemia and host mortality in T . cruzi infected BALB/c mice [61] . In the aforementioned report , the role of GM-CSF was studied by correlating the outcome of infection with the titer of GM-CSF in plasma levels [61] . Even though it was not defined which cells were involved in GM-CSF secretion , it was speculated that lymphocytes could be in part contributing to the low but sustained amount of GM-CSF levels in infected mice . In our experiments , CD4+ and CD8+ T cells contributed almost equally with the secretion of this cytokine , independently of the stimulus , but it is not possible to discard that other cells as part of the PBMC pool also produced this cytokine . While many questions remain regarding the pathogenesis of Chagas' disease , this study represents one of the most comprehensive about the cytokine profile in response to T . cruzi and two recombinant proteins , like P2β and CP0 . The results show that a pool of PBMC in CCC patients has specificity for T cruzi proteins and that this specificity is revealed by a Th1-cytokine dominant milieu , combined with regulatory cytokines like IL-10 and IL-13 . This observation reinforces the idea that a delicate cytokine equilibrium prevails during the chronic phase of the disease . Interestingly , another cytokines , namely GM-CSF , were found significantly increased in cardiac compared to non-infected individuals , tempting us to suggest that this cytokine may be further applied for studying antigen responses at different stages of the disease . Finally , due to the limited number of patients infected with TcI ( 3/27 ) compared with TcVI ( 20/27 ) , it was not possible to determine a correlation between the intensity of humoral and cellular immune response and the T . cruzi lineage detected by TSSA reactivity . Further studies in that sense would provide valuable information on the role and contribution of genetic variability of T . cruzi to the immune response developed in humans . As a whole , our findings also demonstrate that not all parasite proteins provoke a strong T cell activation combined with a pattern of cytokines similar to those described to T . cruzi lysate or by infection with trypomastigote in CCC patients . In addition , as it was recently reported in the context of B cell-T cell recognition for other molecule-specificities [62] , [63] , it is possible to hypothesize that B cells specific for ribosomal P proteins could obtain help from T cells exhibiting different antigen reactivity . However , the interacting elements for T cell help recognition and activation may be the same for P2β and CP0 , since a positive correlation was observed between the cytokines secreted by each of them ( Figure S7 ) . Currently , we are in the process of analyzing the immunoprevalence of recognition of these ribosomal P proteins and novel specificities involved in the immune response to T . cruzi infection in a large number of infected subjects at different stages of the disease . | Chronic Chagas' disease Cardiomyopathy ( CCC ) is the most frequent and severe consequence of the chronic infection by protozoan parasite T . cruzi . Patients with CCC develop high levels of antibodies against ribosomal P proteins of T . cruzi , called P2β and P0 . These antibodies can cross-react with , and stimulate , the β1-adrenergic and M2 muscarinic cardiac receptors , inducing a functional and pathological response in cardiomyocytes . In this study , we focused on the cellular immune response developed by CCC patients in response to T . cruzi ribosomal P proteins . Peripheral blood mononuclear cells ( PBMC ) from CCC patients stimulated with both proteins neither proliferated nor induced the expression of activation markers on CD4+ and CD8+ T cells . However , these cells responded by the secretion of IL-10 , TNF-α and GM-CSF , giving evidence that there is indeed a pool of specific T cells in the periphery responsive to these proteins . Interestingly , the cytokines profile was not related with those described to whole parasite lysate or other recombinant proteins , suggesting that each parasite protein may contribute differently to the complex immune response developed in patients with Chagas' disease . | [
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"biology... | 2014 | Cytokine Production but Lack of Proliferation in Peripheral Blood Mononuclear Cells from Chronic Chagas' Disease Cardiomyopathy Patients in Response to T. cruzi Ribosomal P Proteins |
How epigenetic information is propagated during somatic cell divisions is still unclear but is absolutely critical for preserving gene expression patterns and cellular identity . Here we show an unanticipated mechanism for inheritance of DNA methylation patterns where the epigenetic mark not only recruits the catalyzing enzyme but also regulates the protein level , i . e . the enzymatic product ( 5-methylcytosine ) determines the level of the methylase , thus forming a novel homeostatic inheritance system . Nucleosomes containing methylated DNA stabilize de novo DNA methyltransferases , DNMT3A/3B , allowing little free DNMT3A/3B enzymes to exist in the nucleus . Stabilization of DNMT3A/3B on nucleosomes in methylated regions further promotes propagation of DNA methylation . However , reduction of cellular DNA methylation levels creating more potential CpG substrates counter-intuitively results in a dramatic decrease of DNMT3A/3B proteins due to diminished nucleosome binding and subsequent degradation of the unstable free proteins . These data show an unexpected self-regulatory inheritance mechanism that not only ensures somatic propagation of methylated states by DNMT1 and DNMT3A/3B enzymes but also prevents aberrant de novo methylation by causing degradation of free DNMT3A/3B enzymes .
DNA methylation is a stable gene silencing mechanism required for key biological processes including embryogenesis , genomic imprinting , X-chromosome inactivation , repression of transposons and maintenance of tissue specific gene expression patterns [1] , [2] . Aberrant methylation contributes to tumorigenesis and other diseases [3] , [4] . Thus , proper maintenance of DNA methylation patterns is essential for preserving cellular identity and preventing malignant cellular transformation . In mammals , DNA methylation patterns are generally thought to be established during embryonic development by de novo DNA methyltransferases 3A and 3B [5] and then stably maintained through multiple somatic divisions by the ‘maintenance activity’ of DNMT1 both during and after replication [6] . However , recent studies suggest that DNMT1 alone cannot ensure proper maintenance of methylation patterns [7] and requires co-operative activity of the de novo DNMT3A/3B enzymes [8] , [9] , [10] , which are ubiquitously expressed in somatic cells . A revised model of inheritance was recently proposed assigning DNMT3A/3B to a maintenance role in somatic cells [11]; however , questions still remain regarding the molecular mechanisms guiding the maintenance activity of these de novo enzymes . In embryonic stem ( ES ) cells , DNMT3A/3B establish methylation patterns in association with DNMT3L , a regulatory factor which stimulates DNMT3A/3B de novo activity [12] and targets them to nucleosomes containing unmethylated H3K4 residues [13] . Methylated H3K4 containing chromatin regions remain refractory to such DNA methylation [14] , [15] . Further , heterochromatin protein 1 ( HP1 ) recruits DNMT3A/3B to H3K9me3 residues , established by histone methyltransferase ( HMTase ) Suv39h1/2 , enabling de novo DNA methylation in pericentric heterochromatin [16] . In euchromatic regions , G9a , another H3K9 HMTase , recruits DNMT3A/3B for de novo methylation of early embryonic gene promoters [17] . UHRF1 , which assists DNMT1 in locating to hemimethylated sites [18] , also targets DNMT3A/3B for de novo methylation in ES cells [19] . However , DNMT3L is expressed only during gametogenesis and embryonic stages and not in somatic tissues [20] , [21] . Further , we and others have recently shown that HP1 and UHRF1 are not required for DNMT3A/3B's association with nucleosomes [22] and G9a does not affect maintenance of DNA methylation in somatic cells [23] , [24] . Thus , other mechanisms must exist to ensure proper localization of these enzymes to silent chromatin regions in somatic cells [25] , enabling faithful maintenance of methylated states . We and others have previously shown that the majority of DNMT3A/3B within a somatic cell are strongly anchored to nucleosomes containing methylated DNA with little free DNMT3A/3B proteins existing [22] , [26] . Here we show that the presence of such methylated regions is essential for DNMT3A/3B's association with chromatin and quite unexpectedly , also for maintaining the cellular levels of these enzymes . Reduction in DNA methylation levels results in reduced DNMT3A/3B binding to nucleosomes accompanied by selective degradation of the free enzymes by the cellular machinery . Restoration of DNA methylation increases DNMT3A/3B protein levels through their stabilization on nucleosomes . Further , pre-existing methylation stimulates propagation of DNA methylation in vivo by stably anchoring DNMT3A/3B to nucleosomes . DNMT3A/3B work synergistically to propagate methylation patterns with DNMT3B stimulating DNMT3A activity by promoting its association with nucleosomes , similar to DNMT3L . Taken together , these data suggest an inheritance model where DNMT3A/3B remain localized to silent methylated domains by binding to nucleosomes containing methylated DNA , enabling faithful maintenance of methylated states in cooperation with DNMT1; while non-anchored DNMT3A/3B enzymes get selectively degraded preventing spurious de novo methylation .
In somatic cells , DNMT3A/3B remain bound to nucleosomes containing methylated DNA [22] . To investigate the role of DNA methylation in this binding , we used a series of HCT116 colon cancer cells with homozygous deletions for DNMT1 ( DNMT1ΔE2-5; 1KO ) [27] , [28] , DNMT3B ( DNMT3B−/−; 3BKO ) or both DNMT1 and DNMT3B ( DNMT1ΔE2-5/DNMT3B−/−; double knockout , DKO ) and consequently different levels of genomic DNA methylation [7] . For the DKO cells , which still contain residual DNMT1 activity [28] , we used two clones for our analysis , DKO1 and DKO8 , having lost ∼95% and ∼50% DNA methylation respectively [7] . RT-PCR analysis of DNMT3A , DNMT3B and DNMT1 transcript levels in the various HCT116 derivative cell lines showed similar or higher levels of DNMT3A1 transcripts in HCT116 knockout cell lines compared to WT HCT116; reduced levels of DNMT1ΔE2-5 hypomorph transcripts in 1KO and the two DKO clones , with relatively higher expression in the DKO8 clone and no detectable levels of DNMT3B transcripts in 3BKO and both DKO cell lines , consistent with previous data [7] , [28] ( Figure 1A ) . Next we examined DNMT protein levels in these cell lines through immunoblotting of nuclear extracts . Similar to mRNA analysis , DNMT3B and DNMT1 protein levels were severely reduced in the respective knockout cell lines ( Figure 1B ) . Surprisingly , while DNMT3A mRNA levels were higher in both DKO clones , we found dramatically reduced DNMT3A protein in them compared to WT HCT116 cells . Similar reductions in DNMT3A protein levels were observed in whole cell lysates of both DKO cells , suggesting that the reduced nuclear levels are not the result of protein mislocalization ( Figure S1 ) . These findings were further confirmed by immunofluorescence analyses of HCT116 and DKO cells which displayed similar reduction in DNMT3A protein levels in DKO cells as observed in western blots of their nuclear extracts . Moreover , the residual DNMT3A protein displayed similar nuclear distribution in DKO cells as in WT HCT116 cells , confirming that its reduced nuclear levels in the DKO cells are not due to protein mislocalization ( Figure S2 ) . G9a , another chromatin-modifying protein , did not display such large changes in protein levels in HCT116 knockout cell lines ( Figure 1B ) . Assessment of global DNA methylation levels using methylation-sensitive restriction enzymes revealed a direct correlation between the amount of DNMT3A protein and level of methylation retained in the knockout cells , suggesting a possible role of DNA methylation in maintaining cellular DNMT3A levels ( Figure 1B , 1C ) . DKO8 cells , which had retained higher DNA methylation levels , showed higher DNMT3A protein compared to the minimal amount present in the severely hypomethylated DKO1 cells . Since no such decrease in DNMT3A protein was observed in the single DNMT1 and DNMT3B knockout cells ( 1KO and 3BKO respectively ) , which retained substantial levels of DNA methylation , maintenance of DNMT3A levels through possible protein-protein interactions with DNMT1 and/or DNMT3B seems unlikely . We have previously shown that DNMT3A/3B strongly associate with methylated chromatin regions [22] . To determine whether the residual DNMT3A protein in hypomethylated DKO cells retains similar affinity for chromatin as in WT HCT116 cells , we performed a salt extraction experiment as described previously [22] . Purified nuclei from HCT116 and DKO1 cells were incubated in buffers with increasing concentrations ( 50 mM to 400 mM ) of NaCl . Nuclear pellet and supernatant fractions were independently analyzed through western blot analysis . As expected , similar amounts of core histones remained inside the extracted nuclei under all salt concentrations . In HCT116 cells , the DNMT3A protein level remained almost constant within the nuclei up to 400 mM NaCl indicating a strong binding affinity for chromatin ( Figure 2A ) , whereas other chromatin associated proteins such as EZH2 and G9a showed relatively weaker binding affinities with substantial amounts detected in the supernatant at more than 200 mM NaCl concentrations . Interestingly , the majority of DNMT3A protein present in DKO1 cells , though greatly reduced in comparison to WT HCT116 , also remained tightly associated with the chromatin at all salt concentrations ( Figure 2A ) , possibly binding to the few methylated regions remaining in the DKO1 cells . Minimal DNMT3A protein could be detected in the supernatant fractions ( 50 to 300 mM NaCl ) of the DKO1 cells . These data suggest that binding to methylated chromatin regions might be essential for maintaining the stability of DNMT3A protein and that any free protein unable to bind to chromatin in the absence of DNA methylation possibly gets rapidly degraded by the cellular machinery . We did observe some DNMT3A protein dissociating from the chromatin at 400 mM NaCl in DKO1 cells but not in HCT116 cells suggesting a reduction in chromatin binding affinity of DNMT3A in hypomethylated DKO1 cells compared to heavily methylated WT HCT116 cells ( Figure 2A ) . Meanwhile , EZH2 and G9a showed weaker binding to chromatin in DKO1 cells , similar to that observed in WT HCT116 . Taken together , these data suggest that binding to methylated chromatin regions may be critical for stabilization of DNMT3A protein . To assess whether the dramatic transcription-independent decrease in steady-state levels of DNMT3A protein observed in hypomethylated DKO cells was due to altered protein stability , we treated WT HCT116 and DKO8 cells with the protein synthesis inhibitor cycloheximide ( CHX ) [29] and measured the DNMT3A protein remaining at different time points after treatment . DNMT3A was stable in WT HCT116 cells with 93% still remaining after 6 hrs of CHX treatment ( Figure 2B ) . However , in DKO8 cells , DNMT3A was very unstable with its level rapidly decreasing to 49% 2 hrs after treatment . The half-life of DNMT3A protein decreased dramatically from 16 hrs in WT HCT116 to 7 hrs in DKO8 cells ( Figure S3 ) . Interestingly , after a rapid initial decrease in DNMT3A protein level in DKO8 cells within the first 2 hrs of CHX treatment , a fraction of DNMT3A protein remained stable thereafter till the 8 hr time point ( Figure 2B ) . This fraction may possibly represent the stable DNMT3A protein bound to the methylated chromatin regions in DKO8 cells , similar to that observed in DKO1 cells ( Figure 2A ) . Taken together , these data indicate that a decrease in DNA methylation results in destabilization of DNMT3A protein , possibly due to reduced chromatin binding in the absence of methylated DNA regions , the main sites of DNMT3A/3B binding [22] . To ascertain if depletion of DNA methylation is primarily responsible for the decrease in DNMT3A protein , we sought to restore DNA methylation in the DKO cells . We expressed Myc-tagged DNMT3B1 , ΔDNMT3B2 [30] or DNMT3L in DKO1 and DKO8 cells using a lentiviral system and confirmed expression of the relevant proteins by immunoblotting ( Figure 3A ) . We did not use DNMT1 for the restoration of DNA methylation in DKO cells since expression of exogenous DNMT1 in DKO cells has previously been shown to result only in partial increase in DNA methylation [31] . More importantly , DNMT1 expression failed to restore methylation in these cells at the repetitive elements , the key sites of DNMT3A/3B binding [31] . Global DNA methylation levels in infected DKO cells were measured 8 weeks post-infection using methylation-sensitive restriction enzymes . Since DKO cells possess very low levels of a hypomorph of DNMT1 [28] , the primary maintenance methyltransferase in the cell , very low levels of DNMT3A protein and no DNMT3B protein , it required a long time ( ∼8 weeks ) to achieve restoration of DNA methylation in these cells . After 8 weeks of infection , we observed increased DNA methylation in both DKO cell lines infected with DNMT constructs compared to empty vector ( E/V ) controls ( Figure 3B ) . Even though there was equivalent mRNA expression of exogenous DNMT enzymes in the two DKO clones ( Figure S4 ) , DKO8 cells , with higher baseline methylation levels , showed a greater increase in methylation compared to hypomethylated DKO1 cells for each individual construct . Moreover , the increase in methylation in the infected DKO cells was preferentially localized to loci having low-levels of pre-existing methylation and minimal de novo methylation of previously unmodified sites could be observed ( De Carvalho D . and Sharma S . et . al . , unpublished observations ) , indicating that DKO cells possess similar patterns of chromatin states as present in the parental WT HCT116 cells , including histone modifications ( such as H3K4me3 and H2A . Z etc . ) which are involved in guiding DNA methylation to specific genomic loci [6] . These results also indicate a stimulatory effect of pre-existing methylation [32] on DNA methylation by DNMTs in vivo , possibly through stabilization of de novo DNMT3A/3B enzymes on methylated nucleosomes as suggested by their higher protein levels in DKO8 cells ( Figure 1B , Figure 3A ) . This process may further be enhanced by the higher levels of DNMT1 hypomorph present in DKO8 cells [33] ( Figure 1B ) . Within each DKO clone , exogenous DNMT3L expressing cells showed the most robust increase in methylation followed by DNMT3B1 and ΔDNMT3B2 expressing cells respectively , re-emphasizing the strong stimulatory effect of DNMT3L on DNMT3A/3B activity observed in ES cells [12] . These methylation data were further confirmed through Illumina Infinium analysis [34] for each infected cell line ( data not shown ) . Interestingly , immunoblotting of nuclear extracts revealed a substantial transcription-independent increase in DNMT3A protein level in all DNMT infected DKO cell lines ( Figure 3C , Figure S5 ) . Moreover , the increase in DNMT3A correlated with the increase in global DNA methylation levels ( Figure 3C , 3B ) . Considering that DNMT3A primarily associates with methylated chromatin regions [22] , these data suggest that presence of such methylated regions is required for maintaining its protein level in somatic cells . To examine whether the increase in DNMT3A protein observed upon restoration of DNA methylation is mediated by binding to nucleosomes , we used sucrose density gradient analysis which allows for the study of in vivo interactions between the chromatin modification enzymes and their actual nucleosomal substrates in the native state [22] . Mononucleosomal digests prepared by extensive micrococcal nuclease ( MNase ) digestion of nuclei from infected DKO8 cells , expressing either E/V , Myc-tagged DNMT3B1 , ΔDNMT3B2 or DNMT3L , were subjected to fractionation on sucrose gradients containing 300 mM NaCl . Western blot analysis showed similar nucleosomal profile in all gradients with mononucleosomes forming a peak at fraction 6 ( Figure 4 ) . The DNMT fusion proteins displayed distinct sedimentation profiles indicating different nucleosome binding affinities . DNMT3B1 associated strongly with nucleosomes while the truncated ΔDNMT3B2 variant showed weak association with nucleosomes with a substantial amount of ΔDNMT3B2 sedimenting in nucleosome-free fractions indicating an essential role of the N-terminal region in strong nucleosomal binding , consistent with previous data [22] . However , analysis of various other truncated DNMT3B1 proteins , which contained the N-terminal region but lacked other protein regions ( such as the catalytic , PHD and/or PWWP domains ) , revealed weak nucleosome binding for all truncated proteins ( data not shown ) . These data suggest that DNMT3B requires a full-length protein structure and synergistic activity of its various domains for achieving strong nucleosome binding . DNMT3L showed a bimodal distribution having both nucleosome-free and nucleosome-bound protein ( fractions 1–4 and 5–16 respectively ) . Strikingly , the increased DNMT3A protein in all of the infected cell lines remained strongly associated with nucleosomes similar to that in E/V control , independent of the nucleosome binding affinities of the exogenous proteins , suggesting a nucleosome anchorage dependent stabilization of the protein . DNMT3A formed a peak at fraction 7 in DNMT3B1 and ΔDNMT3B2 expressing cells . In DNMT3L expressing cells , the peak was shifted to fraction 9 , indicating the formation of heavier DNMT3A-DNMT3L tetramer encasing the nucleosome [35] . It might also be possible that DNMT3A-DNMT3L tetramer bound nucleosomal regions may be more resistant to MNase digestion and be responsible for this shift . We did not observe any DNMT3A in the nucleosome-free fractions ( 1–4 ) co-sedimenting with the unbound pool of ΔDNMT3B2 or DNMT3L fusion proteins ( Figure 4 ) , suggesting that the increase in DNMT3A is not due to stabilization through protein-protein interactions with the exogenous proteins but is actually mediated by its binding to nucleosomes upon increase in methylation . Taken together , these data suggest that DNMT3A protein is stabilized by binding to nucleosomes containing its own product ( i . e . methylated DNA ) , which is essential for maintaining its cellular levels . DNMT3B , like DNMT3A , also compartmentalizes to methylated regions in somatic cells via strong anchoring to nucleosomes containing methylated DNA [22] , [26] . To examine whether DNMT3B also binds to nucleosomes in a DNA methylation dependent manner , we expressed Myc-tagged DNMT3B1 in three DNMT3B-knockout HCT116 cell lines , 3BKO , DKO8 and DKO1 , which possess 86% , 27% and 6% of total genomic DNA methylation respectively ( Figure 1C ) . We first tested mRNA and protein expression of the exogenous DNMT3B1 in these cell lines . Interestingly , while DNMT3B1 mRNA levels were similar in all infected cell lines , we found dramatically reduced DNMT3B1 protein , similar to DNMT3A , in severely hypomethylated DKO1 cells in comparison to 3BKO and DKO8 cells ( Figure 5A , 5B ) . To assess whether the decrease in DNMT3B1 resulted from a reduction in binding affinity for nucleosomes in hypomethylated cells , we tested its distribution in mononucleosomal digests fractionated on 300 mM NaCl containing sucrose gradients . In 3BKO and DKO8 cells , the exogeneous DNMT3B1 showed strong association with nucleosomes similar to endogeneous DNMT3A ( Figure 5C , Figure 4 ) . However , DNMT3B1 weakly associated with nucleosomes in severely hypomethylated DKO1 cells with the bulk of the overexpressed protein sedimenting in nucleosome-free fractions ( 2–4 ) , suggesting a dramatic reduction in nucleosome binding affinity upon depletion of DNA methylation . Since the increase in methylation in the infected DKO cells was preferentially localized to the same loci which were originally methylated in the parental HCT116 cells , it suggests that DKO cells possess similar patterns of histone modifications involved in guiding DNA methylation as present in WT HCT116 cells ( De Carvalho D . and Sharma S . et . al . , unpublished observations ) . Therefore , taken together , these data strongly suggest that the reduction in nucleosome binding affinity of DNMT3A/3B observed in DKO cells results from depletion of DNA methylation and is not due to clonal variation of global chromatin states . To further confirm this phenomenon , we subjected Myc-tagged DNMT3B1 expressing DKO1 cells to CHX treatment and analyzed protein stability of the nucleosome-bound and -free fractions of DNMT3B1 protein . Consistent with our previous data on endogenous DNMT3A enzyme , the overexpressed free DNMT3B1 protein underwent rapid degradation compared to the stable nucleosome-bound DNMT3B1 protein , clearly displaying the instability of the unbound protein ( Figure 5D ) . Such degradation was inhibited by treatment with the proteosome inhibitor MG132 , indicating the role of proteosomal pathway in this process . However , we could not rescue degradation of DNMT3A protein in DKO cells using MG132 treatment suggesting possible involvement of other mechanisms in its degradation ( data not shown ) . The phenomenon of destabilization and selective degradation of unbound DNMT3A/3B proteins could also be observed in the case of Myc-ΔDNMT3B2 which showed substantially lower protein levels compared to Myc-DNMT3B1 in DKO8 cells even when both genes were expressed at similar mRNA levels ( Figure 3A , Figure S4 ) . Since Myc-ΔDNMT3B2 associated weakly with nucleosomes while Myc-DNMT3B1 bound strongly to nucleosomes in DKO8 cells ( Figure 4 ) , the reduction in Myc-ΔDNMT3B2 levels possibly results from a decrease in protein stability of the unbound Myc-ΔDNMT3B2 protein , similar to that previously observed for DNMT3A and 3B in DKO1 cells . Taken together , these data show that both DNMT3A/3B require the presence of DNA methylation for tight binding to nucleosomes and subsequent protein stabilization . Such a mechanism would enable faithful inheritance of methylated states through proper compartmentalization of DNMT3A/3B while preventing spurious de novo methylation through selective degradation of the free enzymes . In ES cells , DNMT3A/3B strongly interact and mutually stimulate each other's activity , thus working synergistically to establish genomic DNA methylation patterns during development [36] . To ascertain whether a similar mechanism is involved in propagation of DNA methylation in somatic cells , we expressed a Myc-tagged catalytically-inactive DNMT3B1 mutant , having a cysteine to serine alteration ( position 657 ) which destroys catalytic activity without compromising other functions [37] , in DKO8 cells and confirmed its protein expression by immunoblotting ( Figure 6A ) . To determine whether the DNMT3B1 mutant could stimulate DNMT3A activity , we measured the global DNA methylation level in the mutant expressing cells 8 weeks post-infection . We observed a substantial increase in methylation , demonstrating a stimulatory effect of DNMT3B on DNMT3A activity , independent of catalytic activity ( Figure 6B ) . Immunoprecipitation experiments showed that the mutant DNMT3B1 strongly interacted with DNMT3A , similar to WT DNMT3B1 , suggesting a DNMT3L-like stimulation mechanism which occurs through physical interaction of the two proteins [12] ( Figure S6 ) . Along with an increase in DNA methylation , we observed a substantial increase in endogenous DNMT3A protein levels in mutant DNMT3B1 expressing cells ( Figure 6A ) , similar to WT DNMT3B1 expressing cells , suggesting DNA methylation induced stabilization of DNMT3A protein . Similar results were obtained upon expression of a catalytically-inactive ΔDNMT3B2 mutant in DKO8 cells indicating a stimulatory effect of ΔDNMT3B2 on DNMT3A activity , occurring through its physical interaction with the DNMT3A protein as shown by immunoprecipitation experiments ( Figures S7 , S6 ) . In ES cells , stimulation of DNMT3A/3B activity by DNMT3L partially occurs through increased association of the enzymes with the substrate DNA , allowing these slow acting enzymes to efficiently methylate the substrate [38] . To examine whether stimulation of DNMT3A by DNMT3B in somatic cells occurs through a similar mechanism in a nucleosomal context , we analyzed mononucleosomal digests from DNMT3B1 mutant expressing cells on 300 mM sucrose density gradients . All cellular DNMT3A in infected 3BKO and DKO8 cell lines was found tightly anchored to nucleosomes suggesting that its stimulation by DNMT3B1 occurs through an increased binding to nucleosomes ( Figure 6C ) . We could not detect DNMT3A in DKO1 cells in this assay due to its extremely low levels . In DKO8 cells expressing the mutant ΔDNMT3B2 , all cellular DNMT3A protein was found strongly anchored to nucleosomes indicating that the interaction and stimulation of DNMT3A by ΔDNMT3B2 is mediated by their binding to nucleosomes ( Figure S7 ) . The DNMT3B1 and ΔDNMT3B2 mutants displayed similar binding affinity for nucleosomes as their WT counterparts suggesting that their catalytic activity has little role in nucleosome binding . Taken together , these data show that in vivo stimulation of DNMT3A by DNMT3B occurs through an increased binding to nucleosomes , similar to that observed with DNMT3L , enabling efficient methylation from these slow acting de novo enzymes and their consequent stabilization through continued association with such methylated regions .
Proper maintenance of epigenetic modifications within specific chromatin domains is critical for preserving cellular identity . Recently , a common theme for inheritance of histone marks has emerged where the mark recruits and retains its own modifying enzyme and triggers renewal by stimulating that enzyme through possible allosteric activation mechanisms [39] , [40] , [41] . Our work suggests involvement of a similar mechanism in maintenance of DNA methylation patterns through DNMT3A/3B in somatic cells . We and others have previously shown that DNMT3A/3B , but not DNMT1 , are strongly anchored to nucleosomes containing methylated DNA in somatic cells [22] , [26] . Our current data shows that the presence of DNA methylation is essential for association of DNMT3A/3B with chromatin and also for maintaining the cellular levels of the DNMT3A/3B enzymes , thereby creating a homeostatic inheritance system . Such methylation directed binding stimulates DNA methylation at target loci in vivo ensuring faithful maintenance of methylation patterns , a phenomenon previously observed in inheritance of the polycomb mark [42] . Since DNMT3A/3B are slow acting enzymes compared to DNMT1 [38] , stable association with their target methylated regions would be key for their ability to properly maintain methylated states . We further show that DNMT3A/3B work synergistically in this maintenance process and DNMT3B stimulates DNMT3A activity through increased association with nucleosomes , similar to DNMT3L . Thus , promotion of DNA methylation by selective binding of DNMT3A/3B to nucleosomes containing pre-existing methylation may serve as a critical positive feed-back loop mechanism essential for faithful propagation of epigenetic states through somatic cell divisions [25] , [43] , [44] . Another key finding of our work is the selective degradation of free DNMT3A/3B proteins which could not bind to chromatin in the absence of pre-existing DNA methylation in somatic cells . In ES cells and PGCs ( primordial germ cells ) , DNMT3A/3B are required for establishment of global DNA methylation patterns . Therefore , in these cells , DNMT3A/3B are highly expressed at the transcriptional level and their methylation activity is strongly stimulated by DNMT3L [12] , [45] . However , in somatic cells , the main role of the de novo DNMT3A/3B enzymes is to assist DNMT1 in proper maintenance of pre-established DNA methylation patterns and prevention of de novo methylation of previously unmethylated regions is required [11] , [45] . Therefore , DNMT3A/3B mRNA expression is substantially downregulated and DNMT3L is not expressed in differentiated somatic tissues in order to prevent any aberrant de novo methylation [20] , [45] . Our data suggest that to further regulate this maintenance process , DNMT3A/3B protein levels are post-translationally regulated by the levels of pre-existing DNA methylation in somatic cells . Selective degradation of free DNMT3A/3B enzymes may help explain how somatic cells , which still express low levels of de novo DNMT3A/3B enzymes , prevent aberrant de novo methylation of CpG islands . Our data suggests that once DNMT3A/3B are recruited to methylated chromatin domains , pre-existing methylation stabilizes their binding to such regions and enables faithful propagation of methylated states . However , in absence of DNA methylation , as would be the case with unmethylated CpG islands , these slow acting enzymes are unable to stably bind to the chromatin . The resulting free de novo enzymes , which could potentially cause spurious methylation , are then selectively degraded by the cellular machinery possibly through recognition of an altered conformation in the unbound state ( Figure 7 ) . As shown in the model , in hypomethylated DKO ( DNMT1ΔE2-5/DNMT3B−/− ) cells , DNMT3A loses its ability to bind to nucleosomes resulting in destabilization and selective degradation of free DNMT3A protein while the residual DNMT3A remains bound to the remaining few methylated chromatin regions ( Figure 7 ) . Since exogenous Myc-DNMT3B1 also displayed a similar DNA methylation-dependent stabilization upon nucleosomes , it suggests that a similar model might apply to the regulation of DNMT3B enzyme in somatic cells . Our data indicates that unbound DNMT3B1 is degraded through the proteosomal pathway but how DNMT3A is selectively targeted for degradation in somatic cells is still unclear . Future studies are required to further understand the exact mechanisms involved in the selective degradation of unbound DNMT3A/3B enzymes . Histone methyltransferases , however , are not regulated in such a manner and have been found to exist in both free and chromatin-bound forms within nuclei . This difference can be partially explained by the fact that histone marks are far more dynamic in nature , actively regulated by the combined action of histone methyltransferases and demethylases [46] , compared to DNA methylation which is still believed to be a relatively stable mark in differentiated tissues [6] . While initial recruitment of DNMT3A/3B to methylated regions may involve other proteins , our data strongly suggests that their anchoring to chromatin primarily depends upon pre-existing DNA methylation . However , in addition to DNA methylation , certain histone modifications and accessory proteins may also help in selective compartmentalization of these enzymes . For instance , unmethylated H3K4 , recently shown to bind DNMT3A [47] , may assist in stable binding to silent domains . Recruitment of DNMT3A/3B to such domains may involve UHRF1 [19] . On the other hand , proteins like H2A . Z , CTCF and H3K4me3 etc . which are antagonistic to DNA methylation [14] , [48] , [49] , may occlude binding of DNMT3A/3B to active/poised regions , thus constraining their activities to silent methylated domains only . Recently , Witcher and Emerson [50] have shown that loss of such boundary elements indeed results in aberrant spreading of DNA methylation beyond methylated domains . Our data suggests that these aberrations may involve DNMT3A/3B enzymes which remain bound to methylated regions [22] . During tumorigenesis , these de novo enzymes may progressively override the chromatin boundaries , gradually spreading methylation beyond their specific domains to the entire region [51] resulting in aberrant methylation of genes in clusters – a common feature of cancer-specific hypermethylation [52] , [53] . Such a mechanism may also help explain why CpG island loci having pre-existing methylation in a normal tissue are more susceptible to undergo de novo methylation in cancer [54] . Moreover , ectopic de novo methylation , correlated with overexpression of DNMT3A/3B in several types of cancer [3] , may also be maintained and propagated through continued association of DNMT3A/3B with such regions . DNA methylation inhibitors like 5-aza-CdR , widely used to inhibit aberrant methylation in cancer , target DNMTs by trapping them on DNA [55] . Since these hypomethylating drugs trap DNMTs onto the DNA , it is not feasible to use them for studying the dissociation of DNMT3A/3B from nucleosomes and destabilization upon loss in DNA methylation observed in our experiments . Nevertheless , our data suggests that destabilization of DNMT3A/3B upon removal of DNA methylation may provide another mechanism for depletion of these enzymes upon treatment with such hypomethylating drugs . However , future studies are required to further understand these mechanisms , focusing on factors determining proper compartmentalization of DNMT3A/3B to methylated regions and mechanisms responsible for selective degradation of the unbound protein . In conclusion , our data suggests a model for epigenetic inheritance of DNA methylation in somatic tissues where pre-existing methylation triggers its renewal by recruiting and stabilizing DNMT3A/3B on methylated chromatin domains , which then work synergistically to propagate DNA methylation in co-operation with DNMT1 . Such a mechanism not only ensures faithful maintenance of methylated states but also guards against aberrant methylation from the de novo DNMT3A/3B enzymes .
HCT116 derivative cell lines were maintained in McCoy's 5A medium containing 10% inactivated fetal bovine serum , 100 units/ml penicillin and 100 µg/ml streptomycin . Puromycin was included in the culture medium at 3 µg/ml to maintain infected HCT116 derivative cell lines . When indicated , cycloheximide ( Sigma ) was added to a final concentration of 50 µg/ml . The proteosome inhibitor MG132 ( Calbiochem ) was used at 10 µM for 2 h prior to CHX treatment . Detailed methods are described in Text S1 . Human 3B1 , ΔDNMT3B2 and DNMT3L cDNA sequences having the Myc tag DNA sequence ligated to their 5′ ends were amplified from the pIRESpuro/Myc constructs [22] ( a modified version of the pIRESpuro3 vector , Clontech ) , a generous gift from Allen Yang ( USC ) , using polymerase chain reaction ( PCR ) . Myc-tagged catalytically-inactive mutants of DNMT3B1 and ΔDNMT3B2 , having a cysteine to serine alteration in the catalytic domain corresponding to position 657 of DNMT3B1 protein , were prepared using a site-directed mutagenesis kit ( Stratagene ) . The mutation was confirmed by sequencing both strands of the constructs . For preparation of the constructs , the lentivirus vector pLJM1 was linearized using AgeI and EcoRI restriction enzymes and the Myc tagged DNMT cDNAs were cloned in it using In-fusion advantage PCR cloning kit ( Clontech ) following manufacturer's protocol . For lentivirus production , the vesicular stomatitis virus envelope protein G expression construct pMD . G1 , the packaging vector pCMV ΔR8 . 91 and the transfer vector pLJM1 were used as described previously [56] . Infected HCT116 derivative cells , stably expressing various DNMTs , were selected in the presence of 3 µg/ml puromycin for three weeks . Detailed methods are described in Text S1 . Nuclei from 5×106 cells were incubated in 500 µl of ice-cold RSB containing 0 . 25 M sucrose , protease inhibitors and various concentrations of NaCl for 5 min at 4°C . Nuclei were then harvested by microcentrifugation , separating the supernatant and the pellet fractions . Nuclear pellets were resuspended in RIPA buffer and subjected to sonication . Proteins in the supernatant were concentrated using TCA precipitation and later resuspended in RIPA buffer . Equivalent volumes of supernatant and pellet fractions were added to SDS loading buffer and subjected to Western blotting . MNase digestion and sucrose gradients experiments were performed as described previously [22] . For details , see Text S1 . Genomic DNA ( 10 µg ) isolated from various HCT116 derivative cell lines was digested with methylation-sensitive restriction enzymes , HpaII or MspI ( New England Biolabs ) , at 37°C over night . The digested DNA was run on an agarose gel at low voltage for 8 hrs in order to achieve good separation . The undigested DNA band in each lane was then quantified using the ImageQuant software . Percentage of genomic methylation present was calculated using the formula: where H = undigested with HpaII; M = undigested with MspI and G = genomic DNA . | Proper inheritance of DNA methylation patterns is essential for preserving cellular identity and preventing malignant cellular transformation . In mammals , DNMT3A/3B , the de novo methyltransferases , establish the DNA methylation patterns during development and then maintain them in co-operation with the maintenance methyltransferase , DNMT1 , through cell divisions . However , the mechanisms by which DNMT3A/3B assist DNMT1 in faithful inheritance of methylation patterns in somatic cells while guarding against aberrant de novo DNA methylation are still unclear . In this study , we present a novel principle of enzyme regulation where the levels of the catalyzing enzymes , DNMT3A/3B , are determined by the level of their own enzymatic product , i . e . 5-methylcytosine itself . Through biochemical analyses , we have shown that binding of DNMT3A/3B to nucleosomes with methylated DNA stabilizes these proteins , enabling faithful propagation of methylation patterns through cell divisions . However , reduction in DNA methylation results in diminished nucleosome binding of DNMT3A/3B and subsequent degradation of the free DNMT3A/3B proteins . This novel self-regulatory inheritance mechanism not only ensures faithful somatic propagation of methylated states but also prevents aberrant de novo methylation by causing degradation of free DNMT3A/3B enzymes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/epigenetics",
"molecular",
"biology/dna",
"methylation"
] | 2011 | Nucleosomes Containing Methylated DNA Stabilize DNA Methyltransferases 3A/3B and Ensure Faithful Epigenetic Inheritance |
Locomotion provides superb examples of cooperation among neuromuscular systems , environmental reaction forces , and sensory feedback . As part of a program to understand the neuromechanics of locomotion , here we construct a model of anguilliform ( eel-like ) swimming in slender fishes . Building on a continuum mechanical representation of the body as an viscoelastic rod , actuated by a traveling wave of preferred curvature and subject to hydrodynamic reaction forces , we incorporate a new version of a calcium release and muscle force model , fitted to data from the lamprey Ichthyomyzon unicuspis , that interactively generates the curvature wave . We use the model to investigate the source of the difference in speeds observed between electromyographic waves of muscle activation and mechanical waves of body curvature , concluding that it is due to a combination of passive viscoelastic and geometric properties of the body and active muscle properties . Moreover , we find that nonlinear force dependence on muscle length and shortening velocity may reduce the work done by the swimming muscles in steady swimming .
Most fish swim by rhythmically passing neural waves of muscle activation from head to tail , alternating left and right . This yields travelling waves of local muscle shortening , which in turn produce travelling waves of body curvature . These mechanical waves interact with the water , developing reactive thrust that pushes the animal forward . Breder [1] divided this type of swimming into two classes , depending on the proportion of the body undergoing undulations . In the anguilliform mode , as exhibited by , e . g . lampreys and eels , most or all of the body is flexible and participates in the propulsive movement . In carangiform swimming , as exhibited by , e . g . mackerel , the amplitude of lateral motion is concentrated near the tail . See [2] for an overview of animal locomotion , and [3]–[5] for vertebrate swimming in particular . At any point on the body , rhythmic cycles of muscle activation alternate with silence , causing cycles of muscle shortening and lengthening ( see Figure 1A ) . However , in all species which have been studied [8] except the leopard shark [9] , delays between the onsets of activation and of shortening increase along the body from head to tail ( see Figure 1C ) , i . e . , the wave of shortening travels more slowly than the wave of activation . In consequence , near the tail the greater portion of the activation phase occurs during muscle lengthening , giving rise to negative work during part of the cycle . There are a number of possible functions assigned to this change in timing ( e . g . , providing stiffness as the tail moves laterally through the water , thereby contributing to power transmission , or tuning the resonant body frequency to match tailbeats [10] ) , but the mechanism or mechanisms responsible for it are not known [11] . In this paper , we throw light on this phenomenon . Previous computational models of anguilliform swimming have incorporated the known timing of muscle activation within a mechanical representation of the body and water [12] , [13] , resulting in a travelling mechanical wave . In [13] no phase delay was seen between the waves of activation and curvature , and in [12] , none was reported . However , both models assumed specific scalings of muscle density with body location , and that muscle force was simply proportional to activation . In reality , the force developed by activated muscle takes time to develop . Furthermore , because of the changing relative timing of activation and curvature , the patterns of muscle length and velocity vary significantly along the body length . This results in changing patterns in the developed muscle force , and such variation is further complicated by the body taper . In the present study we investigate this phenomenon by incorporating a revised version of a kinetic muscle force model , originally due to Williams et al . [14] , in the continuum mechanical model for anguilliform swimming of [13] . The resulting integrated neuromechanical system models the swimmer as an elastic rod with time-dependent preferred curvature arising from interactions of muscles with the body configuration . The model's modular structure—coupled sets of differential equations—allows us to selectively “lesion” it to probe the sources of its collective behavior . We find that the wave speed difference results primarily from the body's tapered geometry and passive viscoelastic damping , and that it does not require prioprioceptive sensory feedback . Depending on force density , the nonlinear dependence of force on muscle length and shortening velocity can also contribute to the wave speed difference , although it is not necessary for it . In a preliminary study , however , we find that length and velocity dependence can reduce the mechanical work output during swimming . When further coupled with a central pattern generator and motoneurons , this integrated muscle-body-enviroment model will also allow us to examine proprioceptive feedback , cf . [15] . This paper is organized as follows . In the methods section we review the equations of motion of the actuated rod and the fluid loading model . We show that the discretized rod equations are equivalent to equations describing a chain of interconnected links . This allows us to relate torques at the joints , and the forces responsible for them , to the preferred curvature and elastic properties of the rod . The model for muscle forces is developed in the penultimate subsection and in the final subsection we combine the muscle and body models to produce an integrated computational model . Simulations of the model are presented in Results and a discussion ensues in the concluding section , in which some larger implications of the work are noted .
We model the swimmer's body as an isotropic , inextensible , unshearable , viscoelastic rod that obeys a linear constitutive relation and is subject to hydrodynamic body forces . We assume that passive material properties such as density and bending stiffness remain constant in time , but allow them to vary along the rod . We endow the rod with a time-dependent preferred curvature in the form of a traveling wave , representing muscular activations . We adopt the conventions of [16] , [17] , and use an elliptical cross section to compute hydrodynamic reaction forces , although we restrict to planar motions , since lampreys and eels in “normal” steady swimming flex their bodies primarily in the horizontal plane [18] , [19] . The calcium kinetics and muscle force model , which produces the preferred curvature , is described in the penultimate subsection and the integrated model is summarized in the final subsection of this section . The material of the first three subsections below is drawn from [13] , to which the reader should refer for further detail , and where the numerical method and validation tests are also described . The independent variable s∈[0 , l] denotes arc-length along the rod , and a configuration of the rod is given at each time t by the space curve s ↦ r ( s , t ) = ( x ( s , t ) , y ( s , t ) ) describing its centerline in the inertial ( x , y ) -plane . Derivatives with respect to s and t will be denoted by subscripts . The inextensibility condition |∂r/∂s| = 1 , can be written in terms of the angle φ between the tangent to the curve t = ∂r/∂s and the inertial x-axis: ( 1 ) see Figure 2 . The normal to r is then given by n = ( −sin φ , cos φ ) . Each element of the rod is subject to contact forces f = ( f , g ) , a contact moment M , and body forces W = ( Wx , Wy ) per unit length , vector components again being referred to the inertial frame . The contact forces and moment are those exerted on the region ( s , s+ds ) by [0 , s ) , which maintain the inextensibility constraint , and the body forces arise from interactions with the fluid environment . Balance of linear and angular momenta yields the equations of motion ( cf . [17] , [20] ) : ( 2 ) ( 3 ) ( 4 ) where ρ is the volumetric material density and A and I the cross-sectional area and moment of inertia of the rod . For an elliptical cross-section with semi-axes a and b , as in Figure 2 , A = πab and the moment of inertia for motions in the ( x , y ) -plane is . We assume that ρ is constant , but allow A = A ( s ) , I = I ( s ) to vary ( both remaining strictly positive ) ; specifically , we will study a tapered elliptical cross section based on lamprey body geometry . In [13] the activation of the rod was determined by an externally-specified function κ ( s , t ) , representing its intrinsic or preferred curvature . The muscle model developed later in this section effectively replaces κ with a function that depends on neural activation and the local curvature and its rate of change , but we retain the usual linear constitutive relation [20] so that moments are proportional to departures from preferred curvature: ( 5 ) Here E>0 and δ≥0 are the Young's modulus and viscoelastic damping coefficient and the flexural rigidity EI , with SI units N m2 , determines the overall stiffness . The equations of motion ( Equations 2–4 ) , the constraints ( Equation 1 ) , and the constitutive relation ( Equation 5 ) , along with specified body forces and suitable boundary and initial conditions , form a closed system of evolution equations . Natural boundary conditions for free swimming are that contact forces and moments vanish at the head and tail: M = f = g = 0 at s = 0 , l . In swimming the local body forces are due to hydrodynamic reactions that depend on the global velocity field of the fluid relative to the body . To avoid the complexity and computational expense of solving coupled rod and Navier-Stokes equations , we adopt the model of G . I . Taylor [21] in which W ( s , t ) depends only on the local relative velocity . This approximation accurately predicts forces on a straight rod in steady flow , but fails to capture unsteady effects including vortex shedding , which are undoubtedly important in swimming propulsion [22] , [23] . We believe that it suffices as a first approximation for the present purpose , since we are mainly concerned with the interaction of muscle forces and configuration dynamics . Unlike the Kirchhoff and Lighthill theories [24] , [25] , we neglect added mass effects . See [13] for further discussion . Taylor models the force on a rod of radius a due to perpendicular flow of fluid of density ρf and dynamic viscosity μ with speed v as ( 6 ) where the drag coefficient CN varies between 0 . 9 and 1 . 1 for Reynolds numbers 20<R<105 , and CT is closely approximated by in the range 10<R<105 , cf . Figure 1 of [21] . Drag forces for smooth oblique cylinders can be decomposed into normal and tangential components in terms of the normal and tangential velocities v⊥ and v∥ at ( s , t ) as: ( 7 ) and the body forces are given by ( 8 ) where n and t denote the normal and tangential unit vectors to the rod's centerline at s . In calculating W , we consider only the height 2a of the rod , assuming that fluid reaction forces are equal to those on a cylinder of radius a , although the constant CN does change slightly for elliptical rods . Further , we set CN = 1 , since Reynolds numbers for lampreys and eels lie well within the range 20<Re<105; for example , in their work on the eel Anguilla rostrata , Tytell and Lauder cite Re = 60 , 000 based on body length l = 20 cm for a specimen swimming at 1 . 4l/s . [22] , and speeds reported in [23] range from 0 . 5 to 2 body lengths per second . In terms of Taylor's body-diameter-based Reynolds number , this corresponds to R≈2000–8000 . We discretize the rod equations with spatial step size h = l/N in the arclength variable s , letting xi ( t ) = x ( ih , t ) , i = 0 , … , N , and similarly for the other field variables yi , φi and parameters Ai , Ii: see Figure 3 . The inextensibility constraints in Equation 1 are approximated by ( 9 ) and Equations 2–4 are approximated by the ordinary differential equations ( ODEs ) : ( 10 ) ( 11 ) ( 12 ) where mi = ρAih and Ji = ρIih . The constitutive relation in Equation 5 becomes: ( 13 ) The force and moment free boundary conditions M = f = g = 0 at s = 0 , l become: ( 14 ) The finite-difference discretization of Equations 10–13 is closely related to representions of the body as a planar chain of rigid links subject to forces and moments . In modeling lamprey Bowtell and Williams [26] , [27] take a chain of N massless rigid rods each of length h , with mass mi at each pivot and at both free ends . The pivots are actuated by passive springs , dashpots , and active force generators . Ekeberg [12] , [28] adopts a similar configuration but in place of time-dependent force generators , the spring constants vary with time , and instead of point masses at the pivots , the center of mass of each link is placed at its midpoint . Here we adopt the mass distribution of [12] , and include active muscle elements , to be described in succeeding subsections , in the force-generating components . The configuration of the ith link is described by its midpoint ( xi , yi ) and the angle φi between its centerline and the inertial basis vector êx ( Figure 3 ) . Equaions 9 then express the constraint that links remain connected at the joints . Letting ( fi , gi ) and Mi denote the components of contact force and the torque at the joint connecting link i to link i+1 and ( hWxi , hWyi ) be the body force acting on the midpoint of link i ( Figure 4a ) , balances of linear and angular momenta yield Equations 10–12 above with mass mi = ρAih and moment of inertia of the ith link . The discrepancy between the discretized rod equations and the equations for the chain of N pivoted rods thus consists only in the terms in the moments of inertia , and the two models coincide in the limit h → 0 . We employ the exact formula above for the moments of inertia Ji in all the calculations below , although the approximation Ji = ρhIi yields results ( not shown ) that are nearly identical , even for quite large values of h≈1 . As shown in section 4 . 3 . 4 of [13] , for the large segment numbers typical of eels and lampreys , the behaviors of the discrete and continuum models are very close . Additionally , the discretization reveals how activation determines preferred curvature κ ( s , t ) and affects bending stiffness EI of the continuum model . As in [26] , the joint connecting each pair of links of length h is actuated by a pair of spring-dashpot-actuators in parallel , with spring constant ν and damping coefficient γ , anchored to arms of length w that project normally from the links' midpoints ( Figure 4b ) . These arms represent myosepta , the connective tissue layers to which the muscle fibres connect . The linear springs and dashpots represent passive tissue viscoelasticity , and the actuators generate prescribed contractile muscle forces fLi and fRi on the right and left sides of the body respectively . Suppressing the dependence on i and denoting the relative extensions and of the spring-dashpot-actuators as ΔR and ΔL ( Figure 4c ) , the total forces on the right and left sides may be written ( 15 ) Since the relative extensions are dimensionless , stiffness ν and damping γ have the units N and N s . respectively . The springs are in tension ( and hence generating contractile forces ) when ΔR , ΔL>0 . The forces are applied at a distance w from the centerline of the rod , so elementary trignometry gives: ( 16 ) where ψi = φi+1−φi is the angle between neighboring links and . Finally , computing the moment arms LR , LL to the joint along normals from the lines AB and CD on which the forces act ( Figure 4c ) : ( 17 ) we find that , for small angles ψi , the resulting torque at joint i is given by ( 18 ) Comparing the linearized moment in Equation 18 in the limit h → 0 with the discretized constitutive relation in Equation 13 we see that the link and discretized rod models coincide if the stiffness EIi , intrinsic curvature κi and viscoelastic damping δ are interpreted as follows: ( 19 ) We propose that the stiffness ν and damping γ are proportional to cross-sectional area A ( s ) . Thus we set ( 20 ) so that the stiffness and damping have units N/m2 and N s . /m2 respectively . To approximate a uniform distribution of the muscle , we set w = b/2 , where b is the half-width of the body . Equations 19 now become ( 21 ) In particular , using I = πab3/4 we can write Young's modulus in terms of the spring stiffness as . One of the questions we address is the influence of force density as a function of arclength . We take up this question after a discussion of force generation in muscle fibers . Recordings such as those of [29] show that waves of motoneuronal activity consisting of bursts of closely-spaced action potentials ( APs ) , separated by near-silent interburst periods , travel the length of the lamprey spinal cord ( see Figure 1A and 1B ) . The waves are generated spontaneously by a distributed central pattern generator ( CPG ) within the spinal cord [30] , which has been modelled as a chain of coupled oscillators [31]–[33] . The waves are in antiphase contralaterally and maintain approximately constant duty cycles ( burst/cycle period ratios ) and segment-to-segment ipsilateral phase lags , regardless of overall frequency . This activity pattern is transmitted via nerves that enter the myotomes through the ventral roots [34] , producing muscle activation with similar phasing , evident in electromyograms ( EMGs ) [7] . Each myotome corresponds to a segment of the spinal cord . Bundles of myofibrils make up the muscle fibres within the myotomes . The AP bursts cause calcium release from the sarcoplasmatic reticulum ( SR ) that surrounds the myofibrils and is encircled by T-tubuli at repeated intervals . The resulting muscle contraction occurs in three phases . ( i ) A motoneuronal AP arrives at the neuromuscular junction , producing an AP at the motor end plate which spreads along the surface and T-tubular membranes of the muscle fiber . ( ii ) This depolarization opens gates in the SR and releases Ca2+ ions into the muscle protein filaments . ( iii ) Ca2+ causes conformational changes in the thick filaments which form cross-bridges to the thin filaments; a subsequent conformational change then develops a force tending to slide the thin filaments over the thick ones [35] , shortening the muscle ( unless overcome by opposing force via the muscle attachments ) . This is followed by resequestering of Ca2+ by the SR , resulting in relaxation of the muscle . The force developed during muscle activation is dependent upon both the length of the muscle and the velocity of its shortening [36] . Traditionally , shortening is taken as positive , but here we use the opposite convention , referring to the time derivative of muscle length as velocity , which is negative for shortening . To describe the forces fR ( t ) and fL ( t ) in Equations 15 , 18 , and 19 , we adapt the model developed by Williams et al . , who carried out experiments on portions of single myotomes of lamprey muscle [14] . Intermittent tetanic stimulation was applied during isometric and constant-velocity movements , and analysis and modelling of the resulting force trajectories were used to predict the trajectories recorded during applied sinusoidal movement . Experimental data are reproduced in Figure 5 below ( for details of experimental protocol , see [14] ) . We follow a modified form of the simple kinetic model used in that study , including calcium ions , SR sites and contractile filaments ( CF ) . The rates at which calcium ions are bound and released approximately follows the principle of mass action ( see Figure 6 ) . For example , the rate of binding of calcium ions to the CF is proportional to the product of concentrations of free calcium ions and unbound filaments , with rate constant k3 . The resulting equations for the kinetics of the calcium , sarcoplasmic reticulum sites and bound filaments are as follows: ( 22 ) ( 23 ) ( 24 ) ( 25 ) ( 26 ) where brackets denote concentrations of the relevant quantity . When the muscle is activated , k1>0 and k2 = 0; in the absence of activation k1 = 0 and k2>0 . We assume that the total number of calcium ions , SR binding sites and filament binding sites per liter remain constant so that [cs]+[c]+[cf] = CT , [cs]+[s] = ST , and [cf]+[f] = FT . This allows us to reduce the five Equations 22–26 to a system of two in [c] and [cf] . We further scale by the number of filament sites FT , writing Caf = [cf/FT] , Ca = [c]/FT and introducing the new constants C = CT/FT and S = ST/FT . Since the number of bound filament sites cannot exceed FT , Caf≤1 , Ca≤C , and Caf = 1 when all of the filaments are bound . Although appropriate values for C and S are not known , general knowledge of skeletal muscle indicates that C is large enough for the filament binding sites to be saturated during tetanic stimulation and that S is large enough to reduce free calcium to a negligible amount during rest . We obtain similar data fits over a range of values for these constants , so we arbitrarily set C = 2 and S = 6 . Thus twice as much calcium is available than is necessary to bind all of the filaments and thrice as many binding sites are available in the SR than are required to bind all the calcium . Following Hill [37] , each myotome is modeled as a contractile element ( CE ) in series with an elastic element ( SE ) . ( The Hill model includes a second elastic element in parallel [38] , but for our purposes this can be included in the linear spring of Figure 4b . ) Because they are in series , the CE and SE experience equal forces at steady-state . We begin by describing them separately , as a force P exerted by the SE , and a force Pc developed by the active element CE . The SE is modelled as a linear spring and hence P is proportional to the length ls of this element minus its resting length ls0: P = μs ( ls−ls0 ) . This force is never negative . The total length L of the segment is the sum of ls and the length lc of the contractile element . The length and velocity vc = l ˙c of the contractile element are therefore given in terms of the length and velocity V = L ˙ of the segment and the force P as follows: ( 27 ) ( 28 ) We assume that the the force Pc exerted by the contractile element can be described by independent multiplicative factors of its length lc and velocity vc , ( 29 ) where the constant P0 is the force exerted in isometric tetanic contraction ( Caf = 1 ) at the optimum length lc0 . The functions λ ( lc ) and α ( vc ) are estimated from force measurements ( described below ) , from which we obtain a piecewise linear function for α and a quadratic for λ: ( 30 ) ( 31 ) We additionally restrict these functions such that 0≤α ( vc ) ≤αmax and 0≤λ ( lc ) ≤1 . The fact that αp>αm>0 ( see Table 1 ) reflects the ability of muscle fibers to exert progressively greater forces during lengthening than in shortening . If we set Pc = P , the calculation suffers from instability , and in reality the stretch of the SE due to activation of the CE is not instantaneous . We therefore model the transfer of force from the CE to the SE by simple linear kinetics: ( 32 ) Combining Equations 22–32 and using the three conserved quantities CT , ST , and FT , we obtain three ODEs for the concentrations of free calcium , bound calcium and the force exerted by the preparation: ( 33 ) ( 34 ) ( 35 ) The parameters of the model are determined from analysis of the data of [14] , as follows . μs and ls0 are determined from quick-release experiments [37] . The maximum values of force P0 in the three isometric experiments ( Figure 5 ) are used in Equations 27 , 29 , and 31 to determine the values of λ2 and lc0 . The results of constant-velocity ramp experiments are then used with Equations 27–31 and the parameters λ2 and lc0 to determine αm and αp . The limiting value of αmax was not determined in [14] , so αmax is taken from results in dogfish [39] . In practice , results vary little over a range of values for αmax . We set the time constant k5 = 100 s−1 , so that Pc closely tracks P . The remaining time constants k1 , k2 , k3 , and k4 are found by fitting force trajectories from the experimental data , using the least-squares curve-fitting facilities in the software XPPAUT devised by G . Bard Ermentrout and available at http://www . pitt . edu/phase/ . The parameters k1–k4 are fit in two different ways . The isometric fit follows the approach in [14] by using only data from the isometric experiments at the three lengths L = 2 . 7 and 2 . 7±0 . 125 . The main aim of [14] was to show that a model based on isometric and constant-velocity experiments could be used to approximately predict forces that occur during swimming , even though it excludes known properties such as the observation that the length-tension and force-velocity relationships change during muscle activation and relaxation [36] . Such secondary features cause discrepancies between the predictions and the data seen in the sinusoidal traces of Figure 5 , but the model nonetheless produces forces during sinusoidal movement that capture the overall behavior well . The present study demands our best estimate of force development during swimming , and for this reason we have made a second , dynamic fit of the time constants k1–k4 based not on isometric data but on muscle force data during sinusoidal movement at 1 Hz . To best match swimming behavior , we chose the experiment with a delay of 0 . 1 from onset of stimulation to onset of shortening ( cf . Figure 1 ) , and as the upper panels of Figure 7 show , the resulting force trace is much closer to the data than the fit to isometric data . The discrepancy between the isometric data and the prediction using these parameters is primarily in the repolarisation phase ( Figure 7 , lower panel ) , reflecting the model's inadequacy during this phase of the force trajectory . Values for both fits , along with the other muscle parameters , are given in Table 1 . The most striking difference is in the rate constant k2 ( uptake of free Ca2+ by the SR ) , which doubles . Using this , the dynamic fit captures the rapid force decay seen in the sinusoidal data at low phase delays . Sinusoidal forcing data were only available at 1 Hz [14] and in most of the simulations described below we retain this frequency , but we also briefly investigate swimming behavior at 2 Hz . The muscle parameters are listed in Table 1 . It is worth noting that neither set of time constants is unique: in both cases it was possible to find more than one set of time constants that gave a good fit , by starting from different initial guesses . The primary goal of this study is not to discover accurate parameters , but to find a good prediction of muscle behaviour for use in our neuromechanical model . Muscle dynamics is incorporated into the discretized rod model as follows . The forces PRi and PLi generated by the right and left myotomes associated with the ith link are modeled by two sets of the three Equations 33–35 , with maximal force P0 scaled by cross-sectional body area at that location . Thus , if the entire body length is actuated , 6 ( N−1 ) first order ODEs describe the muscle forces in the N-link chain , and with the 3N second order ODEs in Equations 10–12 they jointly determine the body dynamics . Unlike in the simplified model of [13] , the time course of force development now depends on the proportion of activated filaments ( Caf ) and on the lengths and velocities of the muscle fibers , via appropriately scaled versions of Equations 27–32 . At joint i the lengths and velocities are ( 36 ) ( see Figure 4 and the discussion in the preceding subsections ) . Equations 36 provide the explicit coupling between the muscle and body equations . As in [13] the preferred curvature at joint i is given by , and the force at each segment is given as a scaled multiple of the force PR , L of the fibers on either side of the joint . Since the number of fibers typically depends on cross-sectional area , our first approach was to take fR , L∝abPR , L , giving a preferred curvature κi∝ ( PR−PL ) /b , but simulations with such a relation exhibited much greater motions toward the tail than those seen in the swimming animal . After extensive simulations with various scalings ( not shown ) , we found that scaling the preferred curvature as κi∝b2 ( PR−PL ) and the stiffness as EI∝ab2 provides the best qualitative match to behavior . This suggests that the Young's modulus , and hence , increases along the length of the body , while not only the magnitude , but also the density of muscle forces decreases . The former is consistent with the fact that the notochord takes up a proportionally greater portion of the cross-section of the animal toward the tail . This scaling thus corresponds to , and fR , L∝ab3PR , L , cf . the middle equation of Equation 21 . In the experiments described above the stimulus applied was tetanic , which does not occur normally . We assume that during swimming the muscle is stimulated in such a manner that it can be scaled linearly with respect to the tetanic stimulus . We thus scale the forces with a constant ζ that is chosen ad hoc , so that ( 37 ) Equation 37 completes the loop , so that upon imposing a traveling wave of activation which releases calcium by setting k1 and k2 of Equation 33 on and off in a piecewise constant square wave ( approximating the EMG recordings [29] ) , we obtain a closed system of ODEs .
Simulations readily yield results that are qualitatively similar to real anguilliform swimmers . For example , Figure 8 compares tracings from a film of a lamprey in a swimmill that approximate its body centerline at various times with centerline snapshots from a model simulation . The characteristic swimming behavior is clearly captured , in particular the larger amplitude at the tail end . Figure 9 shows snapshots of the body over one activation cycle . When the force density magnitude fR , L is the same on both sides , the center of mass travels in a nearly straight line , with small lateral oscillations that arise due to slight asymmetries in body shape ( see section 4 . 3 . 2 of [13] ) . The mechanical wave travels down the body at a speed of 0 . 78 body lengths/s , producing a forward swimming speed that rises asymptotically to a value of 0 . 40 body lengths per second , giving a speed ratio or slip of 0 . 51 . Slip values are not available for swimming lamprey , but the expected value for eels swimming at the same speed is 0 . 66 [23] . It is likely that eels are more efficient swimmers than lampreys , since they do not exhibit the side to side movements of the head seen in lamprey ( Figure 8 ) . Turns can be evoked by reducing the magnitude of force density on one side , so that the average of the rod's intrinsic curvature is nonzero: see Figure 10 . We now attempt to determine the mechanism ( s ) causing the difference in wave speeds of activation ( EMG ) and curvature . In the simulations of [13] , the preferred curvature κ = κ ( s−ct ) was externally prescribed , specifically , as that of a traveling sine wave . The curvature φs that emerged depended on the passive elastic properties of the rod and the hydrodynamic body forces , but in [13] κ itself was independent of the body dynamics and of φs . In the present model the ODEs in Equations 33–35 couple the preferred curvature to the state of the rod , via the length and contraction speed of muscle fibers ( cf . Equation 36 ) that appear in the functions α ( vc ) and λ ( lc ) of Equations 29–31 . Hence κ now depends on φs , and we are able to investigate what role this dependence plays in wave propagation . Figure 11 shows the relative timing of activation , muscle force development , and muscle shortening in a typical simulation . Activation waves travel the length of the active region with a frequency of 1 Hz , as in Figure 9 . The left panel shows time courses of muscle length and force in two segments on the same side of the body; the right panel shows the relative timing of activation and curvature in the same format as Figure 1 . We calculate the average wave speed of the maximal concave and convex curvatures by linear regression , first approximating the angle φ ( s ) along the rod by a cubic spline interpolant of the joint angles φi . This yields a continuous function of arclength s , from which we estimate the maximal and minimal curvatures . In all cases the mean speeds of convex and concave curvatures agree to 3 decimal places , so we report a single ratio of curvature speed to activation wave speed . As in the lamprey ( Figure 1 ) , the mechanical wave is slower than the activation wave , the wave speed ratio being 0 . 78 , within the range of values 0 . 72±0 . 07 ( SD ) observed in lamprey [6] . The wave speed difference could be due to several separate effects , or to some combination of them . Ostensibly , any or all of the following could play roles: We now examine these items individually and in combination . First we consider the effect of fluid loading . By setting W≡0 , we remove fluid forces , a situation approximated in the laboratory by stimulating a lamprey to “swim” on a slippery bench [26] . Figure 12 ( left panel ) shows the results of one such simulation . A difference in wave speeds persists , although in this case the speed of the mechanical wave tends to decrease slightly midbody and then increase toward the tail . Eliminating hydrodynamic reaction forces has the effect of further reducing what is already a very small body stiffness . Under the same muscle activations the rod flops around violently . We varied several parameters , including stiffness , viscoelastic damping , the length of the activated region and wavelength of the activation , and body geometry . As noted above , our value of Young's modulus , E = 10−3 MPa , is extremely small , but simulations with higher values did not yield realistic results . For example , with E≈0 . 1 MPa and an increase in muscle force density by a factor of 3 , the ratio of curvature to activation speeds is 0 . 9 , mean swimming speed increases to 0 . 5 body lengths per second , but the phase delay between activation and shortening is approximately zero throughout the rod . We found that two further properties are necessary to create the observed difference in activation and response wave speeds: taper in the body and the presence of viscoelastic damping . Figure 12 ( middle panel ) shows results of simulations performed on an untapered rod ( b ( s ) ≡1 ) , for which the wave speeds become almost identical . The strongest effect of taper is probably via the reduced muscle cross section , and hence smaller force generation , toward the tail ( recall that the “hydrodynamic cross section” used in Equation 7 remains fixed at a = 1 cm , and that a wave speed difference persists in the absence of hydrodynamic forces . ) The right panel of Figure 12 shows results of simulations performed without viscoelastic damping ( ) . In this case the speed ratio also increases significantly , to 0 . 96 . Next we consider the effects of eliminating the dependence of muscle force on length and/or velocity , by setting the functions λ ( lc ) and/or α ( vc ) of Equations 27 and 28 identically equal to constants . For the former we choose λ ( lc ) ≡0 . 86 , because this is the value of λ ( lc ) at the middle length ( 2 . 7 mm ) used in the isometric experiments of Figure 5 , and it corresponds to the average length during typical swimming motions [14] . For the latter we take α ( vc ) ≡1 , corresponding to zero velocity . Removing both effects and maintaining all other parameter values , including force density ζ = 0 . 05 N/m3 , we find that the wave speeds are approximately equal , but that mechanical wave amplitudes become unrealistically large ( Figure 13 , left panels ) . Upon reducing ζ to 0 . 025 N/m3 to achieve reasonable amplitudes , we obtain the result shown in the right panels of Figure 13: i . e . , a speed ratio nearly equal to the case in which length and velocity dependence are present , but body motions are now more pronounced near the head , unlike the shapes of Figure 8 . The swimming speed also drops slightly from 0 . 40 to 0 . 39 body lengths per second , and , as reported in the following subsection , the swimming efficiency is sharply reduced when length and velocity dependence are removed . The multiplicative dependence of muscle force on the factors λ ( lc ) and α ( vc ) also allows us to separate these effects . In the simulation illustrated in the left panel of Figure 14 , we set λ ( lc ) ≡0 . 86 but retain the function α ( vc ) , thus eliminating length dependence alone . The resulting speed ratio of 0 . 79 is almost unchanged from the control value for the full model ( cf . Figure 11 ) . The right panel of Figure 14 shows the result when only velocity dependence is abolished , by setting α ( vc ) ≡1 and retaining λ ( lc ) . The speed ratio 0f 0 . 77 is again nearly equal to the control value , although phase lags are reduced over the first half of the body length . Thus , removing either length or velocity dependence alone does not significantly affect the difference in wave speeds . In both these cases , and all those to follow , we retained the standard force density ζ = 0 . 05 N/m3 . The difference in wave speeds changes the relative timing between muscle activation and shortening as waves travel down the cord , as shown in Figure 1C . The changes in this relationship under all the conditions that we have investigated are illustrated in Figure 15 , in which the delay from the beginning of muscle activation to the time of maximal convex curvature ( approximately the beginning of shortening ) is plotted against body position . The broken line at the top reproduces values from Figure 1C , experiments of [6] showing that the delay increases from 0 . 10 of a cycle at 24% of the body length to 0 . 23 at 76% body length . Data from the full control simulation of Figure 11 are shown by the thick blue line . Although the resulting phase lags are smaller than those observed in the animal , the phase gradient is qualitatively correct . Data from the simulations of Figure 12 are also shown , illustrating that with these changes in mechanical properties , the phase lag values are very different from normal . Abolition of length and velocity dependence , as in Figure 13 , has little effect , when accompanied by halving the force density . Removing only the velocity dependence , as in Figure 14 ( right panel ) , however , abolishes the phase lag in the most rostral segment . The preceding simulations were all done for swimming at 1 Hz , the frequency for which muscle force data is available . Lampreys can of course swim over a range of speeds , by varying both activation levels and frequencies . Ichthyomyzon unicuspis has been recorded as swimming at frequencies up to ≈7 Hz . , although this probobaly does not represent steady swimming . To verify that our model can accomodate frequency variations , we performed simulations at 2 Hz , keeping all other parameters at their standard values . Figure 16 shows that body shapes and amplitudes remain similar to those at 1 Hz , although the wave speed difference is somewhat magnified , the ratio decreasing to 0 . 71 . As noted above , removing the length and velocity dependence in muscle forces , while simultaneously halving the force density ζ , leads to a nearly identical ratio of curvature to activation wave speeds with only a slight reduction in swimming speed . Since nonlinear muscle properties are not required to produce the observed speed difference , we were prompted to ask what other differences they make . Here we investigate their effect on swimming efficiency , by comparing the work done by the muscles over a full activation cycle with length and velocity dependence present and absent . We calculate the work done by the muscles on either side of joint i by computing the integralswhere fRi , Li and VRi , Li are the right- and left-hand muscle forces and velocities defined in the last two subsections of Methods ( the negative sign is due to our convention that VRi , Li are lengthening velocities ) . The left panel of Figure 17 shows the work done at each joint , illustrating that , in spite of the reduced force density used for the case without length and velocity dependence , 67% more work is done than when length and velocity dependence are included , although there is a slight reduction in swimming speed . The difference is largest near the head; the work done near the tail being slightly larger for the latter case . The center and right panels show time courses of work done over one cycle at specific locations in these two regions ( joints 3 and 18 ) , with activation beginning at the time on the left axis in both cases . In addition to substantial differences in magnitudes due to reduced muscle cross section near the tail , these panels reveal that negative work is done at the tail in the beginning of the activation phase , while muscles are still lengthening . As we have noted , this may play a role in stiffening the tail as it moves laterally through the water . Overall , these results suggest that the length and speed dependencies of the muscle fibers may provide a mechanical advantage to the animal in swimming .
This paper is primarily concerned with the role of muscle activation in the production of anguilliform swimming motions: a process that involves multipath coupling among active filaments , passive body tissues , hydrodynamic reaction forces , and proprioceptive and exteroceptive sensory feedback . To better parse this complex coupled system , here we address the influence of “feedforward” neuromechanical coupling alone by means of a mathematical model . Our model substantially extends previous ones [12] , [13] , [42] by its inclusion of nonlinear muscle dynamics , which is characterised by known physiological properties with parameters fitted to experimental data . Coupled with appropriate passive viscoelasticity and geometry of the body , this gives rise to a difference in the wave speeds of neural activation and mechanical response , as seen in swimming animals , and the model enables us to investigate the sources of this difference . We find that three factors are primarily responsible for it and for the associated lags between activation and curvature onsets , namely: viscoelastic damping , taper , and the nonlinear dependence of muscle force on length and shortening velocity . The first two factors , which are properties of passive tissues and body geometry , are necessary for the appearance of the wave speed difference . The third factor , nonlinear muscle dynamics , contributes to the values of the changing phase lags , and may also contribute to the efficiency of swimming . Figure 15 shows that the phase relationship between muscle activation and shortening produced by the model is similar to that seen in the lamprey . Significantly better data fits can be obtained by varying parameters outside the normal ranges , but rather than explore this systematically , we have instead used parameter values that best describe the lamprey . The present study illustrates the power of integrative mathematical models in revealing biological function , by allowing “experiments” which cannot be done on animals . It partially answers questions posed by Altringham and Ellerby , who conjectured that the progressive phase lag is associated with “change in muscle function along the body [11] . ” Our study shows that , at least for anguilliform swimmers , muscle and mechanical properties need not vary along the body for wave speed differences to emerge . It also shows that , during steady swimming , proprioceptive feedback is not necessary to produce this basic phenomenon . This supports the suggestion of Brown and Loeb that , in stereotypical movements , neural feedback ( reflexes ) can be partially or wholly replaced by mechanical feedback ( called “preflexes” by Brown and Loeb ( section 3 of [43] ) , who define a preflex as “the zero-delay , intrinsic response of a neuromusculo-skeletal system to a perturbation . ” ) , and therefore might not be required for stability [43]–[45] . Further model-based and experimental support for this hypothesis has recently emerged in legged locomotion studies [15] . However , mechanosensitive “edge cells” exist within the lamprey's spinal cord , which can influence the timing of muscle force generation and phase relationships via feedback to the CPG and motoneurons [46] . This mechanism may account for the deficit in phase lags produced by the model ( Figure 15 ) , and it is are presumably important during changing conditions and maneuvers . The muscle model we described in Methods cannot perfectly fit both the isometric and the sinusiodal forcing data . We chose to fit it to sinusoidal data with an activation-to-curvature phase difference of 0 . 1 , close to values seen in the data and the control simulations . This is not ideal , and may influence the results described in the results section . We plan to extend the model to include secondary muscle properties responsible for the discrepancies in its predictions . Moreover , we have used a linear model for flexural stiffness ( M = EI ( φs−κ ) , Equation 5 ) , although the lamprey's body stiffness is nonlinear . More accurate estimates of body stiffness may also influence the results . In our discretization the arms to which muscles are attached project perpendicularly from the center of each link toward the periphery ( see Figure 4 ) . In the lamprey , however , the myosepta to which the swimming muscles attach project obliquely backwards from the notochord toward the body wall so that the muscle layers interleave in a somewhat complicated fashion ( albeit considerably less complicated than in bony fish; see [11] ) . We have not examined the consequences of this attachment geometry , but it can be expected to affect torques at the joints , and we intend to include it in a future study . It is of interest to note , however , that Katz et al . [47] have shown that in spite of more more complicated interleaving of muscle layers in teleost fish , the swimming muscles undergo length changes similar to those expected for a homogeneous , continuous beam , and that curvature of the midline gives a reliable measure of muscle length at any point along the body . A further shortcoming of the present study , also noted in the methods section , is our use of an oversimplified model for fluid reaction forces . While Taylor's approximation in Equation 7 suffices for straight rods in uniform steady flow , it does not capture unsteady effects such as vortex shedding that are characteristic of swimming . These effects are likely important not only in creating propulsive thrust [22] , [23] , but the resulting reaction forces on the animal may also influence the speed at which the mechanical wave of curvature travels along its body . This would in turn affect the mechanical waves shown in Figure 11 , perhaps changing the relative speed of activation and response . A more realistic model of vortex generation will also be needed to determine if negative work and tail stiffening are important in thrust generation , and to enable more definitive studies of swimming efficiency . We also propose to use the present model , with the further addition of distributed CPG and motoneuron models [33] , [48] , to study proprioceptive feedback mechanisms in lamprey . In particular , it will allow us to investigate the influence of the aforementioned edge cells on the timing of muscle force generation . In recent experiments the isolated notochord/spinal cord preparation is rhythmically bent from side to side and the resulting edge cell feedback to motoneurons and CPG interneurons studied [49] ( cf . [46] ) . This work complements our model in that it removes muscle activation , body elasticity and hydrodynamic forces , to reveal how an isolated sensory pathway can influence CPG phase and frequency relationships . | In this article we develop a computationally tractable model for swimming in animals such as eels , lampreys , and aquatic snakes . The model combines motoneuronal activation , muscle dynamics , passive elasticity and damping in the spinal cord and body tissues , and simplified hydrodynamic reaction forces , thus allowing us to probe how neuromechanical interactions give rise to body shapes and , ultimately , motion through the water . We use it to investigate the sources of an interesting experimental observation in freely swimming fish: that waves of curvature propagating along the body lag behind and travel more slowly than the muscular activation waves measured by electromyography . By selectively “lesioning” components of the model , we deduce that the speed difference , at least in this type of fish , is largely due to passive viscoelasticity and body geometry . We also find that nonlinear muscle properties are responsible for a significant reduction in energy expenditure and that they can also contribute to the wave speed difference . This work is a key step in a general program to build integrated “whole animal” models of locomotion and other behaviors that will also allow us to incorporate proprioceptive and exteroceptive neural feedback . Such integrated models can contribute both to our understanding of how living systems work and to the further development of robot systems . | [
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] | 2008 | Nonlinear Muscles, Passive Viscoelasticity and Body Taper Conspire To Create Neuromechanical Phase Lags in Anguilliform Swimmers |
Environmental or genetic perturbations lead to gene expression changes . While most analyses of these changes emphasize the presence of qualitative differences on just a few genes , we now know that changes are widespread . This large-scale variation has been linked to the exclusive influence of a global transcriptional program determined by the new physiological state of the cell . However , given the sophistication of eukaryotic regulation , we expect to have a complex architecture of specific control affecting this program . Here , we examine this architecture . Using data of Saccharomyces cerevisiae expression in different nutrient conditions , we first propose a five-sector genome partition , which integrates earlier models of resource allocation , as a framework to examine the deviations from the global control . In this scheme , we recognize invariant genes , whose regulation is dominated by physiology , specific genes , which substantially depart from it , and two additional classes that contain the frequently assumed growth-dependent genes . Whereas the invariant class shows a considerable absence of specific regulation , the rest is enriched by regulation at the level of transcription factors ( TFs ) and epigenetic modulators . We nevertheless find markedly different strategies in how these classes deviate . On the one hand , there are TFs that act in a unique way between partition constituents , and on the other , the action of chromatin modifiers is significantly diverse . The balance between regulatory strategies ultimately modulates the action of the general transcription machinery and therefore limits the possibility of establishing a unifying program of expression change at a genomic scale .
The limited availability of the components of the cell expression machinery , for example , free RNA polymerases , cofactors , ribosomes , etc . , creates a resource allocation problem that affects their activities . This differential allocation eventually represents a global program of regulation [1][2][3][4] , with some genes expressed at the cost of others . As the dosage of these components can be modulated by the growth rate at exponential phase , the influence of the global program has typically been studied by quantifying how the expression of genes varies with growth conditions . This led to the identification of laws that predict the expression of different cellular elements [3][4][5] . Two broad models have been considered . In a first model ( model 1 , Fig 1A , top ) , the impact of the global program is recognized by partitioning the genome in three minimal sectors [4][5] . One of them contains ribosomal genes whose expression increases with growth rate verifying its role in driving cell growth [6] . Two other sectors comprise genes whose expression decreases or remains invariant with growth . The three-sector model describes therefore fundamental aspects of cellular economics [7] already advanced in the early work of bacterial physiologists [8] , and acts as a basic constituent to investigate many subjects . For instance , modifications to this framework were advanced to explain the cost of unnecessary gene expression [4] , the dependence of cellular composition with antibiotics [9] , or the rationale behind some seemingly wasteful carbon utilization strategies [10] . In a second model , the emphasis is not so much in the trade-offs between ribosomal genes and the rest , but between genes that follow a common pattern of expression and a minimal subset that diverges ( model 2 containing two sectors: nonspecific and specific , Fig 1A , bottom ) [11] . Here , the common pattern of expression is explained by a single scaling factor , which incorporates the resources of the global program that are not involved in the activation of the specific genes . This implies that most changes in expression result from a passive rather than active regulation what produces a unifying pattern that “simplifies” the expected complexities of genome-wide expression changes . In this manuscript , we integrate both models ( model 3 , Fig 1B ) . The new framework discriminates a fine-structure within the nonspecific sector of model 2 that corresponds to the three-sector partition representative of model 1 . While the integration of both views is significant per se–models 1 and 2 might appear unrelated–we also show that this scheme is fundamental to help us expose how specific regulatory strategies modulate the impact of the global transcriptional program at the genome-wide level . To this aim , we first reexamined the original data that led to model 2 , i . e . , promoter activity ( PA ) measurements of Saccharomyces cerevisiae’s genes obtained in different growing conditions [11] , to present our new scheme . However , this data only includes a subgroup of ~900 yeast genes , and thus the quantification of the resource allocation can only be approximated . To obtain a more precise classification we considered genome-wide expression data of yeasts growing in chemostats [12] and developed a method to define the new model at a large scale . We validate our partition through functional analysis of the corresponding components . Armed with this new framework , we focused on understanding how the global program becomes modulated by specific regulatory mechanisms , a question which is only beginning to be addressed . Indeed , within the framework of model 1 results are mixed . For instance , several recent reports in bacterial systems demonstrated the prevalence of the global expression program , while they have lowered the importance of transcription factors ( TFs ) controlling the assumed deviations from it . TFs seem only to fine-tune the action of the global regulation [13][14] in combination with a few metabolites [15] . More recent work in yeast argued , in contrast , about the relevance of epigenetic factors ( promoter nucleosomal stability ) as modulators of the global program [16] . The function of the specific regulation might appear more straightforward a priori in the scheme of model 2 . Specific genes are indeed enriched by specific regulation , generally coupled to the particular growth condition , whereas the behavior of those genes within the nonspecific sector does not seem connected to a particular transcription regulation strategy [11] . We thus examine within our new framework to what extent we could observe differential genetic and epigenetic regulation acting on the genes constituting each sector . We discovered well-defined regulatory patterns . More broadly , our results put forward an integrated view of the resource allocation connected to genome-wide expression and emphasize how the global program is eventually modified by the specific regulation . Active strategies of control are certainly at work in genes within the fine-structure of the “nonspecific” response and can eventually associate the metabolic status of the cell with gene expression . Overall , this complex hierarchy of regulation in eukaryotes enhances the adjustment of genome-wide expression patterns beyond the one that could be achieved through a passive unifying program of global transcriptional control .
We first examined how PA changes with the growth rate and growth conditions for a subset of Saccharomyces cerevisiae genes . Keren et al . [11] presented a binary partition to describe these changes , recognizing a common response in the absolute PA values of most genes and a specific one in a much smaller subgroup . To focus on resource reallocation , we studied here fractional PA activities instead of absolute values , i . e . , the fraction of PA of each gene in a given growth condition out of the summed activity of all genes in the set [3] , and quantified their change for each pair of conditions ( from low to high growth rate ) . Fig 1C explicitly shows one of these transitions ( glycerol to glucose conditions ) to illustrate our general framework . The most extreme deviations of the global program correspond to the specific genes [11] . Note also that the stronger allocation of “expression resources” to these genes the fewer resources to biosynthesis ( reduction of the nonspecific sector ) affecting growth rate ( Fig 1A , bottom ) . We revised the two-sector partition by further separating specific genes as specifically activated genes ( fractional PA becomes much larger between conditions ) , and specifically repressed genes ( fractional PA becomes much smaller ) , and also delimiting three components within the nonspecific sector: genes whose fractional PA remains approximately invariant ( diagonal in Fig 1C ) , positive genes , whose fractional PA moderately increases between conditions , and negative genes ( fractional PA decreases to only a limited extent , see Methods for details ) . These three sectors relate model 2 to model 1 . We validated this new partition in two ways . First , that a set of genes follows a precise proportional response between conditions was suggested by [11] as an evidence that they share a common functionality ( e . g . , being part of the same pathway , etc . ) . Here , we find that invariant , positive and negative genes also follow a precise proportional response ( S1 Fig ) suggesting that our analysis identifies a biologically relevant fine-grained structure . Second , we similarly expect that this fine-grained structure would parallel that proposed in model 1 [4] in terms of functional categories . This is the case . The invariant class is enriched by transcription regulation and ribosomal proteins; the latter being more extensively observed in the positive class . Indeed , positive genes are enriched by ribosomal genes ( ~65% of genes code for small or large subunits of the ribosome ) , while negative genes are enriched in ATP metabolic processes , e . g . , oxidative phosphorylation or the TCA cycle ( S1 Table , Methods ) . To substantiate the previous five-sector model based on ~900 genes , we examined a genome-wide DNA microarray dataset of yeast cells exhibiting the same range of growth rates for six different growth conditions defined by the limiting nutrient [12] . We studied again changes of relative expression ( Methods ) and applied singular value decomposition ( SVD ) to each nutrient separately . The first and second SVD components ( Fig 2A ) explain >90% of the variance in each condition ( the components exhibited an analogous trend in all nutrients , S2 Fig ) . As a result , the fractional expression response to growth rate of each gene can be approximated by the linear combination of these two components ( Fig 2B ) . Furthermore , we can interpret the first element of the linear combination ( v→1 ) as the baseline fractional expression of the gene , which does not change with growth rate , and the second element ( v→2 ) as its monotonic response to growth ( Fig 2B ) . This reading enables us to generalize the previous partition framework obtained with PA data . More specifically , a gene that maintains the same baseline expression ( similar loading of v→1 that we denoted ai ) between two nutrients involves a nonspecific response . When this type of response in observed in at least half of all possible pairwise changes ( >8 ) then we reason that the gene is nonspecific ( recall that there are 6 different nutrients and consequently a total of 15 pairwise nutrient changes ) . Genes are considered specific otherwise ( Fig 2C ) . Beyond the classification above , the second component ( v→2 ) provides a quantitative score ( the second loading , bi ) to classify genes as invariant , positive or negative ( bi~0 , bi>0 , bi<0 , respectively; Fig 2C . Methods ) . We labelled as invariant those nonspecific genes which exhibit this behavior in at least half of the nutrient conditions ( >3 of a total of 6 ) . Nonspecific and not invariant genes appearing more times as positive than as negative are categorized as positive , and likewise for negative . Finally , specific genes which appear more times as positive than as negative ( again in all 6 conditions ) are categorized as activated , and analogously for repressed ( Fig 2C ) . Finally , the functional analysis of genes within each sector agrees with that obtained with the PA data and previous reports , what substantiates the biological significance of the partition ( S2 Table; see also S1 Table showing how this classification maps onto the subset of genes with PA data ) . In this way , we have generalized at a large scale the new resource allocation model , a genome-wide classification that we use throughout the next sections . We inspect next the influence of the most direct elements related to specific regulation , i . e . , TFs . But before examining their impact on the activity of the global transcription program , we asked how TFs themselves are framed in the previous partition . We observed that most TFs constituting the transcriptional regulatory network ( 122 of a total of 133 comprising the network , Methods ) are nonspecific genes , i . e . , they normally present similar basal fractional expression ( ai loadings ) across all pairwise condition changes . Within this set , 31% exhibits an invariant response in more than half of the conditions ( bi~0 in >3 nutrient conditions , of a total of 6 ) , with five genes acting as invariant in all six conditions ( rsc1 , mbp1 , pho2 , rgr1 , and swi6 ) . Two of these ( mbp1 , swi6 ) are at the top of the network hierarchy ( being involved in the mitotic cell cycle ) , and two are elements of relevant complexes that interact with RNA Polymerase II ( rsc1 of the RSC chromatin complex , and rgr1/med14 of the mediator complex ) ; they can be considered as elements of a general transcriptional machinery , for which maintaining the concentration invariant across conditions could be essential . Moreover , 32% of nonspecific TFs are mostly negative in all nutrient conditions , and only 4% mostly positive . Of note , some of the negative TFs–whose expression decreases with growth ( bi<0 ) –are positive regulators of transcription in response to stress ( e . g . , bur6 , gcn4 , rpn4 ) , which justifies their overexpression at low growth rates . Is the extent of regulation of target genes dependent on which sector they belong to ? We computed the mean number of regulators acting on genes within each sector . Nonspecific genes are less regulated , on average , by TFs than specific ones as expected [by 3 . 09 TFs vs . 5 . 06 TFs , p = 1 . 20 10−4 , two-sample Kolmogorov-Smirnov ( KS ) test] . Within nonspecific genes , invariant genes are less regulated than nonspecific and not invariant ones ( by 2 . 56 TFs vs . 3 . 3 TFs , p = 8 . 16 10−13 , two-sample KS test ) . Finally , nonspecific and positive genes are slightly more regulated than nonspecific and negative genes ( by 3 . 33 TFs vs . 3 . 27 TFs , p = 0 . 0018 , two-sample KS test ) . Overall , specific genes are subjected to more regulation ( larger number of TFs ) , while nonspecific and invariant ones show the least ( Fig 3A ) . Although Fig 3A shows how the number of transcriptional interactions is reflected differentially in the sectors of the partition it does not assure us whether these interactions are functioning , e . g . , regulation by TFs have been shown to play a minor role during physiological transitions in bacteria [13] . To evaluate this , we examined several features . We initially inspected if target genes presenting a particular growth response are enriched by TFs showing the very same response , as the similarity of the responses could indicate that these TFs do influence the expression of the target . We quantified this similarity for each nutrient condition ( glucose , ammonium , etc . ) and considered again the loading of the second component to define TFs or target genes as presenting negative , invariant or positive responses to growth rate ( bi~0 , bi>0 , bi<0 , respectively , Methods ) . We thus computed–for each target gene–the fraction of its regulators that behave as negative , invariant , or positive with the growth rate in a given nutrient ( TFneg , TFinv , TFpos , respectively ) . For the glucose condition , Fig 3B shows the mean of these fractions for target genes which themselves display a negative , invariant , or positive response , respectively . TFs exhibiting a negative response are more likely to be found acting on target genes that are also negative ( higher mean TFneg on negative genes ) , while invariant ( TFinv ) and positive ( TFpos ) TFs regulate more often invariant and positive target genes , respectively ( the latter signal is weaker and depends on the particular condition , S3 Fig ) . In sum , TFs that exhibit the same behavior as their cognate target gene with respect to growth tend to predominate , on average , on its regulation; this suggests that part of the regulatory structure is functional . To further assess the active regulatory impact of TFs , we measured the correlation of the response to growth rate between any particular gene and all its cognate TFs ( “regulatory coherence” , S4 Fig ) and then calculated if this correlation is statistically significant ( Methods ) . The fraction of specific genes whose regulatory coherence is larger than expected by chance is superior to that of nonspecific ones ( S5A Fig ) , and the former also show significant coherence in a greater number of nutrient conditions ( S5B Fig ) . Both results imply an existing contribution of TFs to deviate gene expression from the global program . Can we identify those TFs which take part in the statistically significant coherent regulation ? Fig 3C shows that within this set some TFs acts principally on genes belonging to a precise sector of the partition more than anticipated by chance ( Methods ) . One can identify here that TFs coherently controlling specific genes are normally not acting on nonspecific ones [this is supported by earlier reports[17]] . Moreover , some of the TFs that work coherently on positive genes ( gray color , Fig 3C ) are particularly absent in negative ones ( pink color , Fig 3C ) and vice versa . In addition , those that coherently control nonspecific genes are higher up in the network hierarchy ( Methods ) . We also noted that some of these TFs are involved in chromatin remodeling ( Cyc8 , Ume6 , Spt6 , Msn4 , Abf1 , Msn2 , Nhp6A , acting on nonspecific ones; Hypergeometric distribution’s p-value = 1 . 5 10−9 , and Holm-Bonferroni multiple testing correction ) , or chromatin organization ( Spt3 , Spt2 , Pho4 , FKh2 , Sin3 , Spt20 , Wtm2 , Wtm1 , Hif1 , acting on specific genes; p-value = 5 . 8 10−8 , distribution and multiple testing as before ) . We examine epigenetic aspects next . To inspect the influence of the epigenetic control mechanisms , we first quantified the proportion of general transcription factors ( GTFs ) found within the set of TFs acting on a given gene [18] . GTFs ( Rap1 , Abf1 , Reb1 , Cbf1 , and Mcm1 ) usually have little intrinsic regulatory activity and comprise–together with the presence of chromatin remodelers ( in particular , RSC–Remodeling the Structure of Chromatin ) –an alleged general machinery of expression . We observed that GTFs constitute a significant fraction of the TFs acting on of positive genes , while the opposite is observed for negative ones ( Fig 4A ) . GTFs are also associated with particularly fragile nucleosome promoter architectures [19] , a connection recently investigated [16] . Using this data , we computed the nucleosome landscape for the different gene classes ( Methods ) . Promoters of positive genes are certainly enriched in fragile nucleosomes ( Fig 4B ) while both negative and invariant genes lack these architectures . Positive genes are therefore more sensitive to adjust the global program of expression by means of chromatin modulation . Enrichment of other promoter features contribute to this observation ( Fig 4C , Methods ) , like the absence of TATA boxes [20] , the action of TFIID over SAGA [21] [but this precise grouping has been recently reexamined [22]] , the presence of nucleosomal free regions closer to the transcriptional starting site ( shNFRs ) [23] ( partially associated to the previous score of fragile nucleosomes ) , and the dominant effect of trans variability ( S6 Fig ) [24] . Finally , we examined the effects of mutating different types of trans-acting chromatin regulators on the genes constituting the sectors using a previously assembled compendium [25] ( Methods ) . We considered first the magnitude of the change of expression ( i . e . , absolute values of expression ) before and after the mutation of several types of modifiers . With the exception of histone acetyltransferases ( HATs ) and TATA-binding protein related factors ( TAFs ) , the influence of most chromatin modifiers is stronger in specific genes as compared to nonspecific ones ( S7 Fig , Methods ) , which implies that some TFs require the recruitment of chromatin modifiers to act [25] . Within nonspecific genes , we also quantified the type of expression change experienced after mutation of the modifiers and found three broad relationships ( Fig 5 ) : 1 ) Epigenetic regulators acting as part of a general machinery ( HATs–including SAGA– , TAFs and methyltransferases ) whose mutation causes a general decrease in expression , very particularly in invariant and positive classes . Indeed , work by [22] and [26] demonstrated that SAGA and TFIID are recruited to RNA Polymerase II promoters genome-wide and that each complex is generally required for Polymerase II transcription , i . e . , its mutation would lead to a genome-wide decrease of gene expression . 2 ) Regulators ( e . g . , histones , etc . ) acting in a dual manner: increasing the expression of negative genes after mutation ( remodeler as a repressor ) or reducing their expression in positives ( remodeler as an activator ) . This underlines the enrichment of negative and positive classes by stress and ribosomal genes , respectively , which are largely regulated in an opposite manner [27]; a dual role of remodelers as activators and repressors have been previously reported [28][29] . And 3 ) regulators as broad repressors ( mutation increases expression ) , e . g . , those that represent regulation by gene silencing .
How can we then explain the monotonic variation of fractional expression of the genes in the negative and positive partition components if , as suggested by Hansen and O’Shea [33] , some TFs can mostly transmit qualitative ( presence/absence of a particular nutrient ) rather than quantitative ( amount of nutrient ) information . One explanation is that this monotonic variation is the result of cell population shifts with growth rate , instead of changes in single-cell resource allocations . Indeed , Brauer et al . ( 12 ) observed a decreasing fraction of unbudded cells ( proportion of cells in G0/G1 division cycle ) as populations grew faster . However , variations with growth rate cannot solely be attributed to the fact that slow-growing cells enlarged their G1 cell cycle phase as neither [12] nor we observed a bias in positive/negative genes with any particular phase of cell cycled genes . Another explanation is that the variation could also be part of an intrinsic and mechanistically complex environmental stress response ( ESR ) [34] , so it is interesting to examine how the genes linked to this response fits into our partition . These genes included two complementary groups representing rather small subsets within the partition sectors: induced ESR genes enriched in nonspecific negative and repressed genes and repressed ESR genes enriched in nonspecific positive genes ( Methods ) . Thus , ESR genes appear to represent a particularly extreme case of deviations to the global transcriptional program . While previous studies indicated that part of these genes might still be associated to cell cycle cell populations shifts [35] , Ho et al . [36] recently discussed the preservation of a core ESR signal after controlling for these effects . But maybe the dominant explanation is metabolism , which is highly sensitive to the limiting nutrient [37] and can act as a regulator of many of the epigenetic factors discussed above . Indeed , several metabolites ( e . g . , GlcNAc , NAD+ , acetyl-CoA , alpha KG , ATP ) are known to regulate transcription through interactions with enzymes involved in epigenetic modifications [38] . For example , acetyl-CoA induces cell growth and proliferation by promoting the acetylation of histones at growth genes [39] ( histone acetylation affects rather similarly specific and nonspecific genes , S7 Fig , which supports its potential role as a widespread mechanism ) . All these previous readings contribute to a general picture in which it can be conceived two ways to coordinate gene expression to the available nutrients: a regulation by signaling pathways , i . e . , specific responses , that dictates growth rate by sensing a certain environmental condition ( feed-forward ) , or a mechanism that senses growth rate , or another related internal cell variable , and then modifies expression ( feedback ) [40] . In this context , Model 2 ( Fig 1A , bottom ) could explain that the global program is responding always to the environment ( feed-forward ) , although indirectly since it can only use those resources that were not consumed in the mounting of the specific response . This agrees with the observation that ribosomal genes ( representatives of the nonspecific program in Model 2 ) follow the feed-forward path [41] . The fine-grained structure of the nonspecific class ( invariant/positive/negative ) , Model1 ( Fig 1A , top ) could nevertheless monitor growth rate , at least partially , with the feedback being mediated by epigenetic mechanisms driven by the metabolic state of the cell ( see paragraph above ) . In this work we have studied changes in fractional expression but not in mRNA abundances . It is known that the global program dictates that the faster a population of cells growths , the higher the promoter activity ( rate of RNA synthesis ) [11] or total mRNA abundance ( rate of RNA synthesis and degradation ) [42] . We expect most ( if not all ) gene products to follow this ( absolute ) global program , with potential additional layers of regulation ( which are nutrient and gene dependent ) that increases or decreases mRNA levels . Expression of the genes within the invariant group best follows the absolute global program , with positive genes being slightly above -and negative genes slightly below- this response ( but all of them incrementing mRNA levels or promoter activities ) ( e . g . , S1B Fig ) . On the other hand , it would be interesting to quantify the degree to which single cells can present a distribution of resources that is separated from the model here discussed [43] , as well as to understand the mechanisms that lead to such divergence . In summary , although one could argue that cellular physiology can indeed determine a global transcriptional program of gene expression control , our work highlights that this program is adjusted by the integration of effective genetic and epigenetic modes of regulation . This modulation limits the prospect of “simplifying” our understanding of genome-wide expression change and calls for a combination of mechanistic and phenomenological approaches–like the work presented here–to finally unravel such complexity .
Keren et al . [11] measured the activities of ~900 S . cerevisiae promoters in 10 different growing conditions using a library of fluorescent reporters . For each strain in every growth condition , PA was obtained as the YFP production rate per OD per second in the window of maximal growth . We computed fractional PA ( fPA ) for each growth condition as the ratio of the PA of each gene to the summed PA of all promoters ( for a gene i , fPA , i = PAi / Σi PAi ) . Ratios of fPAs for each pair of conditions ( with increasing growth rate ) were also calculated . We then computed the absolute distance of these ratios to ratio 1 ( i . e . , same fPA in both conditions ) , and defined as invariant genes the top 350 genes ( distance closest to 0 ) and as activated ( repressed ) the bottom 50 with ratio >0 ( < 0 ) . The rest of genes with ratio >0 ( <0 ) , and both fPAs > 10−4 , were designated as nonspecific positive ( negative ) . We used the “typical” class of a gene ( the most frequently occurring category that a gene presents in all pairwise growth rate changes ) to functionally characterize the sectors ( S1 Table ) what confirmed their biological significance . Minor modifications on the threshold values defining these sectors did not alter the conclusions . Brauer et al . [12] grew yeast cultures in chemostats under different continuous culture conditions ( six different limiting nutrients each at six dilution rates ) and measured mRNA abundance with two-color microarrays . Since the original reference channel for all samples corresponded to a particular glucose condition , which mixes the response of different nutrients , we reanalyzed the data without this reference by considering the red processed signal as independent channel [44] , and normalizing by the corresponding sum for each case to obtain a fractional score; more specifically , the fractional expression value of gene i is given by fi = log10[106 ( gi/Σi gi ) ] , with gi being the corresponding red-processed signal . SVDs were computed on this processed data . We defined as nonspecific genes those whose difference on the loadings of the 1st component ( ai’s ) between two conditions is less , or equal , than three standard deviations of all gene differences ( in absolute values ) . Genes are otherwise considered specific ( activated or repressed if the difference of ai’s is positive or negative , respectively ) . Moreover , absolute values of the loadings of the 2nd component ( bi’s ) were sorted to define those with smallest values ( top 2500 ) as invariant genes , with the rest being positive or negative ( determined by the sign of bi ) . To define the partition , we classify as nonspecific those genes that act as nonspecific in >8 pairwise conditions ( out of 15 ) . Nonspecific genes acting as invariant in > 3 conditions ( recall that the total number is 6 ) are labeled as invariant . Nonspecific and not invariant genes appearing more times as positive than as negative ( in all 6 conditions ) are categorized as positive , and likewise for negative . Specific genes which appear more times as positive than as negative ( again in all 6 conditions ) are categorized as activated , and analogously for repressed . The number of genes for each category is then ( repressed , negative , invariant , positive , activated ) = ( 70 , 2503 , 1749 , 1914 , 20 ) . This partition is the one used for all the regulatory analysis ( S2 Table shows the connection between these sectors and those obtained with PA data ) with the exception of Figs 3B and S3 in which only the second loading was considered to classify all genes as positive , negative and invariant . Finally , note that minor modifications on the threshold values defining these sectors did not alter the conclusions and that the biological significance of the sectors is eventually validated by the regulatory and functional signals observed throughout the results . Regulatory data was obtained from http://yeastmine . yeastgenome . org . We used data from different manuscripts using chromatin immunoprecipitation , chromatin immunoprecipitation-chip , chromatin immunoprecipitation-seq , combinatorial evidence , and computational combinatorial evidence , but discarded microarray data to avoid any possible circularity . The network consists of a total of 20 , 673 interactions with 133 TFs involved whose expression was also quantified in Brauer et al . [12] . We also computed the hierarchical organization of the network using a vertex-sort algorithm , which first finds the strongly connected components of the network to then apply an iterative leaf removal algorithm [45] . The network has three hierarchical levels . Bas1 , Mbp1 , Med6 , Spt7 , and Swi6 appear at the top of the hierarchy . We identified the set of TFs regulating each gene and quantified the Pearson’s correlation coefficient between the expression vectors ( as a function of growth rate ) of each TF within the set and the target gene , to then take the mean ( S4 Fig ) . This quantity is what we termed regulatory coherence , which is computed for each particular nutrient condition , i . e . , six scores corresponding to the six different nutrients . The resulting scores are compared to a null distribution to assess their statistical significance ( we computed a z score ) . This distribution is obtained by randomizing the expression vectors for each gene ( again for a given nutrient ) , 1000 times , and then calculate the equivalent regulatory coherence . We define those genes with the z score > 2 as the ones displaying significant regulatory coherence . Finally , to identify those TFs that are acting more significantly on each partition component we first measure the proportion in which each TF acts on ( significantly ) coherent genes , within the five-sector partition , and then estimate the extent that this proportion departs from a chance expectation by randomization of the partition classes . This enables us to compute the z score shown in Fig 3C . Nucleosome occupancy and position have been measured by analysis of MNase-digested chromatin . Recent work noted that certain nucleosomes were extremely sensitive to this digestion , and thus obtained a quantitative score of nucleosome fragility that we used for our analysis , S6 Table in [16] . We showed in the main text that positive genes are enriched in promoters displaying fragile nucleosome architectures , which have been discussed recently as an epigenetic mechanism of regulation . Other features contributed to the sensitivity to regulation of this class , like the absence of TATA boxes , the action of TFIID over SAGA , etc . Our results build on previous studies that showed how different genes exhibit different control strategies to regulate expression [20 , 21 , 23 , 27] . For instance , it was observed that housekeeping ( constitutive ) genes where enriched among those with TATA-less promoters and related to TFIID transcription , in contrast to stress-related genes enriched in among TATA-box promoters , and regulated by SAGA complex . This enrichment of promoter features further validates the biological significance of the partition . This set includes 170 gene expression profiles for chromatin-regulation related mutations ( expressed in log2 ratios ) taken from 26 different publications [25] . It covers more than 60 potential interacting chromatin modifiers such as histone acetyltransferases ( HATs; the NuA4 , HAT1 and SAGA complexes ) , histone deacetylases ( HDACs; the RPD3 , HDA1 and SET3 complexes ) , histone methyltransferases ( the COMPASS complex ) , ATP-dependent chromatin remodelers ( the SWI/SNF , SWR1 , INO80 , ISWI and RSC complexes ) , and other chromatin-affecting genes and cofactors such as Spt10 , Sir proteins and the TATA-binding protein ( TBP ) . We normalized each dataset to unit variance [24] . For S7 Fig , we took absolute values to estimate the strength of the chromatin regulator effect . Note here that growth rate reduction can be connected to the impact associated to many of these deletions , so we controlled for the possible contribution of cell cycle population shifts as described ( next section ) . This enables us to better identify expression changes due to regulation [35] . We took the full data in [46] to obtain the slow growth profile and remove the slow growth signature in the epigenetic data following [35] . In brief , the slow growth profile is obtained as the first-mode approximation of the data after SVD decomposition . To compare with the epigenetic compendium data , we chose the column of this approximation with the largest norm as the slow growth signature . The correlation with the slow growth signature is removed by transforming the epigenetic data in Gram-Schmidt fashion by subtracting from their projection onto the basis vector , given by the normalized slow growth profile . Null models associated to most results are obtained by randomly assigning each gene to one of the sectors , to then compute the precise statistic ( e . g . , mean number or regulators in Fig 3A ) . We typically considered 10000 randomizations unless it is stated otherwise and show the mean and +/- 2 std deviations of the corresponding statistic ( e . g . , gray shading in Fig 3A ) . Moreover , all p values shown in enrichment analyses ( S1–S2 Tables ) are calculated using the Hypergeometric distribution with Holm-Bonferroni correction for multiple testing . There are 281 stress-induced and 585 stress-repressed genes–as defined in [34]–within the set of genes delineating the five-sector partition . A subset of nonspecific negative genes and specific repressed genes corresponds to stress-induced ( 232 out of 2053 , and 10 out of 70 , respectively ) , while a subset of nonspecific positive genes corresponds to stress-repressed ( 485 out of 1914 ) . Note that most of the features discussed in the main text associated with the five-sector partition remain when controlling for ESR genes . | How can we understand expression changes observed as a result of environmental or genetic perturbations ? This issue has been conventionally answered by examining small groups of genes whose expression becomes qualitatively altered after these perturbations . But this approach is too simplistic , as we now know that extensive variation is typically observed . To explain this variation , recent works proposed a model in which genome-wide changes are explained by the action of a general program of transcription . Our manuscript reasons that given the complexity of eukaryotic transcriptional control , a unifying program of regulation cannot be achievable . Instead , we propose within an integrated framework of resource allocation that a rich structure of deviations from it exists and that by characterizing these deviations we can fully appreciate large-scale expression change . | [
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"prot... | 2019 | Complex genetic and epigenetic regulation deviates gene expression from a unifying global transcriptional program |
The vertebrate ovary and testis develop from a sexually indifferent gonad . During early development of the organism , primordial germ cells ( the gamete lineage ) and somatic gonad cells coalesce and begin to undergo growth and morphogenesis to form this bipotential gonad . Although this aspect of development is requisite for a fertile adult , little is known about the genetic regulation of early gonadogenesis in any vertebrate . Here , we provide evidence that fibroblast growth factor ( Fgf ) signaling is required for the early growth phase of a vertebrate bipotential gonad . Based on mutational analysis in zebrafish , we show that the Fgf ligand 24 ( Fgf24 ) is required for proliferation , differentiation , and morphogenesis of the early somatic gonad , and as a result , most fgf24 mutants are sterile as adults . Additionally , we describe the ultrastructural elements of the early zebrafish gonad and show that distinct somatic cell populations can be identified soon after the gonad forms . Specifically , we show that fgf24 is expressed in an epithelial population of early somatic gonad cells that surrounds an inner population of mesenchymal somatic gonad cells that are in direct contact with the germ cells , and that fgf24 is required for stratification of the somatic tissue . Furthermore , based on gene expression analysis , we find that differentiation of the inner mesenchymal somatic gonad cells into functional cell types in the larval and early juvenile-stage gonad is dependent on Fgf24 signaling . Finally , we argue that the role of Fgf24 in zebrafish is functionally analogous to the role of tetrapod FGF9 in early gonad development .
The vertebrate gonad consists of germ cells , the lineage directly responsible for creating the next generation , and somatic gonad cells ( SGCs ) . The somatic gonad serves two important functions . First , it creates an environment that protects germ cells and nurtures their development . For example , with the exception of mammalian females , most animals examined retain the ability to produce gametes throughout their adult life , an activity permitted by the presence of germline stem cells [1] . SGCs form the niche that is required to maintain these germline stem cells . In the adult mouse testis , several SGC types ( Sertoli , Leydig , and peritubular myoid cells ) secrete growth factors that promote proliferation and suppress differentiation of the germline stem cell population , thereby maintaining fertility through adulthood [2 , 3] . Second , a subset of SGCs secretes hormones required for the development of secondary sexual characteristics , so defects in gonad development can result in disorders of sexual development ( reviewed in [4] ) . Although the importance of the somatic gonad to fertility is clear , much remains to be learned about the genes that regulate its early development . In mammals , the somatic gonad is derived from intermediate and lateral plate mesoderm , and its early development has been divided into three major steps: initiation , growth , and sexual differentiation . During the initiation phase , a portion of coelomic epithelium on the ventral surface of the mesonephros begins to thicken to form the bilateral genital ridges [5–8] . Soon after their formation , primordial germ cells ( PGCs ) migrate into the ridges [9] . The growth phase is characterized as a period of somatic and germ cell proliferation that results in a larger , multilayered primordium . Finally , sexual differentiation transforms the bipotential gonads into either an ovary or testis . Although the molecular mechanisms that regulate sexual differentiation of the gonad are relatively well understood ( reviewed in [10] ) , less is known about the genetic regulation of the initiation and growth phases . In mice , several transcription factors have been identified that are required for initiation and growth of the primordium: mutations in Gata4 , Wilms tumor 1 ( Wt1 ) , Steroidogenic factor 1 ( Sf1/Nr5a1 ) , Lim homeobox protein 9 ( Lhx9 ) or the paired-like homeobox gene Emx2 cause failure to initiate gonad development or undergo early gonad regression [11–17] . However , it is not known if cell signaling is important for these early events . The Fgf signaling pathway regulates many developmental events in metazoans . The pathway generally consists of secreted ligands that complex with heparan sulfate proteoglycans in the extracellular matrix to bind and activate transmembrane Fgf receptors [18 , 19] . One such ligand , Fgf24 , is known to play roles in the development of zebrafish posterior mesoderm , forelimb , and pancreas [20–22] . Fgf24 is a member of the FgfD subfamily of Fgf ligands , which , in zebrafish , consists of six members: fgf8a , fgf8b , fgf17 , fgf18a , fgf18b , and fgf24 [20 , 23 , 24] . Although this subfamily is conserved in mammals , the fgf8 and fgf18 duplications are a result of the teleost-specific whole-genome duplication [24] . Furthermore , while Fgf24 is present in basal tetrapods and teleosts ( coelacanths and spotted gar , respectively ) , it was lost early in the tetrapod lineage , so is not present in the mammalian genome [23–27] . In this study , we show that the majority of fgf24 null mutants ( ikahx118 , hereafter referred to as fgf24hx118; [21] ) are sterile as adults , suggesting that it plays a role in either gonad development or maintenance . We show that fgf24 is first expressed in a subset of SGCs by 8 days post fertilization ( dpf ) —a period that we argue is analogous to the early growth stage of mammalian gonad development—and that this expression is required for early somatic gonad proliferation and morphogenesis into a bi-layered tissue that in wild-type normally occurs by 10 dpf . Coincident with bi-layer formation , we show that cells expressing fgf24 are restricted to the epithelial layer on the surface of the gonad . Furthermore , we show evidence that the cells responding to Fgf24 signaling are a mesenchymal population of SGCs that localizes to the interior of the early gonad , and that loss of Fgf24 function leads to a failure of these cells to differentiate into functional cell types . Finally , we argue that the loss of germ cells in fgf24 mutants is an indirect consequence of defective somatic gonad development .
Gonads are composed of both germ cells and somatic cells that enclose the germ cells and regulate their development . In vertebrates , the somatic gonad is also the main source of sex hormones that regulate secondary sexual characteristics , such as sexually dimorphic appearances and behavior . Zebrafish primordial germ cells ( PGCs ) are specified during the early cleavage stage by maternal factors , and shortly thereafter migrate to where the somatic gonad will eventually form [28] ( Fig 1 ) . Though it is not known with certainty when formation of the somatic gonad initiates , histological analysis has detected SGCs surrounding germ cells as early as 5 dpf [29] . Domesticated zebrafish lack sex chromosomes and it is still unclear how sex determination is regulated or precisely when it occurs [30] . Zebrafish embryos hatch from the chorion around 3 dpf and are free-swimming larvae by 5 dpf . The transition from the larval to juvenile stage occurs around 25 dpf , which coincides closely to when overt sex-specific differentiation becomes apparent ( Fig 1 ) . During the early larval stage ( i . e . 3–20 dpf ) , there are no overt differences between animals that will become male or females , and all animals initially produce varying numbers of early stage oocytes ( beginning ~13 dpf; [31] ) . Mutations that reduce or eliminate germ cells , or more specifically , the ability to produce early stage oocytes during the larval period , result in all male development [32–34] . During this period of development , the somatic gonad is bipotential as evident by the simultaneous expression of genes that will eventually be sex-specific . For example , a subset of somatic cells in the larval gonad expresses the female-specific aromatase-encoding gene , cyp19a1a , while neighboring cells express the male-specific amh gene ( [35]; this report ) . These studies have led to the hypothesis that oocytes produce a signal that stabilizes female-specific gene expression in the somatic gonad , thereby promoting female development . It has previously been shown that zebrafish fgf24 homozygous mutants , called fgf24hx118 , are viable but lack pectoral fins [21] . In addition to this defect , we discovered that all fgf24hx118 mutants were males as adults ( Fig 2A–2C’ ) . Previous studies have established that zebrafish lacking germ cells , or the ability to produce early-stage oocytes , invariably develop as phenotypic males [32–34] . We therefore examined 27 fgf24 mutants at 3 . 5 months post fertilization ( mpf ) and found that 21 animals contained no detectable gonads and were thus sterile ( ~78% ) . Each of the remaining 6 animals had one small testis measuring approximately 1/3 the size of a wild-type testis ( ~22%; S1A and S1B Fig ) . High-resolution confocal analysis of these latter testes revealed an overall wild-type organization where germ and somatic cells were properly arranged into tubule structures that contained germ cells in all stages of spermatogenesis , including mature sperm ( S1C and S1D Fig ) . Furthermore , in situ hybridization with the Sertoli cell gene markers anti-müllerian hormone ( amh ) and gonadal soma derived factor ( gsdf ) showed a similar pattern of expression in both fgf24 mutants and wild-type controls ( S1E–S1H Fig ) . Finally , while mutant fish that have gonads were unable to induce wild-type females to spawn due to their lack of pectoral fins [37] , sperm extracted from these gonads and used for in vitro fertilization of wild-type eggs produced viable embryos at frequencies indistinguishable from wild-type sperm ( S1 Table ) . Although the fgf24hx118 allele is an N-ethyl-N-nitrosourea ( ENU ) -induced point mutation that introduces a premature stop codon within exon 4 that should truncate the protein within the core Fgf homology domain [21] , the incomplete penetrance of its adult sterility phenotype prompted us to investigate whether this allele was hypomorphic ( S1I Fig ) . To test this , we used the CRISPR/Cas9 genome editing technology to induce a new mutation within exon 3 and identified a 5 bp insertion allele , called fgf24uc47 , that results in a translational frameshift , and is therefore expected to be a null mutation ( S1I Fig ) . We found that both fgf24uc47 homozygous mutants and fgf24hx118/uc47 transheterozygotes have a phenotype that is indistinguishable from that caused by the fgf24hx118 mutation alone: the resulting animals are homozygous viable , lack pectoral fins , and develop as male with a partially penetrant sterility defect . In subsequent experiments , we use these two alleles interchangeably and conclude that null mutations in fgf24 lead to an incompletely penetrant adult sterility defect . fgf24 mutants are all male as adults , a phenotype that suggest that mutants may have defects in early gonad development ( see Background , above ) . We therefore first determined if they had defects in early germ cell development by comparing the number of germ cells present in the gonads of wild-type and mutant fish at several stages throughout larval development . For these experiments , we collected confocal images at 5 μm intervals through the whole gonad and quantified distinct germ cells identified by anti-Vasa antibody and DAPI DNA staining . We found that fgf24 mutants had similar numbers of germ cells to wild-type siblings at 8 and 10 dpf . However , at 12 and 14 dpf , mutant animals had significantly fewer germ cells than their wild-type siblings ( Fig 2D–2F ) . In addition to our finding that fgf24 mutants have fewer germ cells than wild-type , they also appear to have fewer SGCs ( arrowheads in Fig 2E and 2F ) . This feature was most apparent when we compared testes isolated from wild-type and fgf24 mutant animals . A 40 dpf wild-type testis is organized into tubules that contain many premeiotic and spermatogenic germ cells surrounded by SGCs ( Fig 2G and 2G’ ) . One such SGC is the Sertoli cell , which expresses the teleost gonad-specific Tgf-β ligand , Gsdf [38] . At 40 dpf , Gsdf-expressing Sertoli cells are abundant in wild-type testes and can be visualized using the Tg ( gsdf:mCherry ) uc46 transgene as previously reported [38] ( n = 7; Fig 2G and 2G’ ) . In stark contrast , the gonads of all fgf24 mutants contain few germ cells that are not organized into tubule-like structures and lack detectable Tg ( gsdf:mCherry ) uc46 expressing SGCs ( n = 7; Fig 2H and 2H’ ) . Notably , gsdf is also expressed in ovarian granulosa cells ( S2 Fig ) . Thus the inability of fgf24 mutant gonads to express Tg ( gsdf:mCherry ) uc46 argues that fgf24 is required for the development of both male and female SGCs . Finally , the testes and ovaries of wild-type juvenile animals ( 25-90dpf ) contain both premeiotic and postmeiotic germ cells at different stages of gametogenesis ( Fig 2G’; S2 Fig ) in contrast to juvenile fgf24 mutant gonad , which contain only premeiotic germ cells as evident by their large size and prominent nucleoli ( Fig 2H’ , inset ) . In conclusion , loss of fgf24 function affects the development of both testes and ovaries , which , together with the expression of fgf24 during the early bipotential phase , suggests that it is required for development of the early bipotential gonad ( Fig 1 ) . To further test the role of Fgf24 in early bipotential gonad development , we used high resolution fluorescent RNA in situ hybridization to determine which gonadal cells express fgf24 . The larval gonad is a rod-like structure that is oriented along the anterior/posterior axis . Germ cells are restricted to the interior core of the gonad and are surrounded by SGCs ( Fig 2E ) . Because the germ cell phenotype of fgf24 mutants is first evident by 12 dpf , we examined the expression of fgf24 mRNA in gonads at various stages between 5 and 20 dpf in whole-mount gonads . In addition , we co-stained gonads for the germ cell-specific Vasa protein to aid in gonad identification during dissection . At 5 dpf , when germ cells and SGCs have just begun to coalesce [29] , fgf24 was not detected ( n = 4; Fig 3A ) . However , by 8 dpf , we could detect fgf24 expression in some , but not all , SGCs in 23/27 wild-type animals ( Fig 3B ) . In 10 and 16 dpf animals , fgf24 could be detected in all gonads examined ( n = 19 and 12 , respectively ) . As in 8 dpf gonads , fgf24 was detected exclusively in SGCs , but only in a subset of SGCs that appeared to be restricted to the surface of the gonad ( Fig 3C and 3D ) . Finally , in 20 dpf animals , we continued to detect fgf24 only in SGCs , though expression appeared highest in a population of cells that localize to the dorsal edge of the gonad ( n = 9; Fig 3E ) . We conclude that fgf24 is expressed in gonads during the time when the development of fgf24 mutant gonads begins to deviate from those of wild-type animals ( Fig 3A’ , 3B’ , 3C’ , 3D’ and 3E’ ) . Furthermore , these results reveal that there are at least two distinct SGC populations in the larval gonad soon after its formation: fgf24 ( + ) and fgf24 ( - ) . The apparent defect in both the somatic and germ cell components of the gonad led us to investigate which cell type ( s ) responds to Fgf24 signaling . Fgf signaling can activate downstream signaling cascades that can result in gene expression changes . Transcription of the Ets variant ( Etv ) family of transcription factors is known to be upregulated by Fgf signaling in many developmental contexts ( e . g . [39–41] ) . We therefore asked whether one of these family members , etv4/pea3 , is expressed in the wild-type larval gonad . At 8 dpf , when we first detect fgf24 expression in ~85% of wild-type animals ( Fig 3B ) , we can detect expression of etv4 in SGCs in 56% of wild-type gonads ( 13/23; Fig 4A and 4B ) . Notably , it is only detected in gonads that also express fgf24 ( Fig 4A ) and is not detected in 8 dpf fgf24 mutant gonads ( n = 14; Fig 4C ) . By 10 dpf , however , we found that etv4 is strongly expressed in SGCs in all wild-type gonads examined ( n = 25 ) . Interestingly , double in situ hybridization of both etv4 and fgf24 reveals that etv4 is expressed in a population of SGCs distinct from that of fgf24 and one that subtends the fgf24-expressing layer ( n = 10; Fig 4D and 4D’ ) . In contrast , the gonads of 10 dpf fgf24 mutant siblings express no , or greatly reduced levels of , etv4 ( n = 8; Fig 4E ) . These results suggest that Fgf24 acts as a paracrine signal to regulate the development of an inner population of SGCs . In various contexts , the Fgf activation of etv4 transcription is mediated by Erk , a terminal kinase of the Map kinase signaling pathway [42 , 43] . Erk phosphorylation by the upstream kinase , Mek , allows it to translocate to the nucleus and activate numerous transcription factors ( reviewed in [44] ) . We therefore asked whether Erk is phosphorylated in larval SGCs . Indeed , we detected substantial phosphorylated Erk ( pErk ) in wild-type SGCs , but not in SGCs of fgf24 mutants ( n = 13 and 11 , respectively; Fig 4F–4G’ ) . In contrast to etv4 expression , pErk does not appear to be restricted to the inner layer of SGCs , indicating that fgf24-dependent Map kinase activity is present in apparently all SGC populations . These data therefore suggest that Fgf24 may activate etv4 expression via the MAPK pathway , but that other factors must act to limit etv4 expression to only the inner SGC . Our analysis thus far supports a model where the primary role of Fgf24 is to promote the development of the somatic gonad and that the loss of germ cells in fgf24 mutants is a secondary consequence of defective somatic gonad development . To further this analysis , we analyzed the expression of two main classes of genes: 1 ) genes reportedly required for early gonad development in the mouse , and 2 ) genes known to be important for later gonad development and function in both mammals and fish . The transcription factors Gata4 , Nuclear receptor subfamily 5 group a1 ( Nr5a1 , also called Steroidogenic factor 1 , Sf1 ) , and Wilms tumor protein 1 ( Wt1 ) regulate early mouse gonad development . Gata4 is required for epithelial proliferation during the initiation phase , while the latter two promote cell survival during the growth phase [11–14] . Using fluorescent RNA in situ hybridization we found that zebrafish gata4 and nr5a1a orthologs are readily detectable in SGCs of wild-type 10 dpf gonads ( n = 14 and 15 , respectively ) , but absent or reduced in fgf24 mutant gonads ( n = 13 and 12 , respectively; Fig 5A–5D ) . In contrast , we found that wt1a , one of two zebrafish Wt1 orthologs , is expressed in the SGCs of both wild-type and fgf24 mutant gonads at 11 dpf ( n = 19 and 12 , respectively; Fig 5E and 5F ) . Notably , wt1a appears to be expressed at lower levels in inner SGCs ( arrows , Fig 5E ) and most robustly in the outer layer of SGCs on the dorsal edge of the gonad ( arrowheads , Fig 5E ) . We next assessed the expression of genes associated with differentiated cell types of the larval gonad: cyp19a1a , which encodes an aromatase normally expressed in granulosa and theca cells of the adult ovary , and the Anti Müllarian Hormone-encoding gene amh , which is normally expressed in Sertoli cells of the adult testis [35 , 36] . Because the larval gonad is initially bipotential , some genes that are later expressed sex-specifically , including cyp19a1a and amh , can be detected in the gonads of all animals during the early larval stages [35] . Indeed , we found that the expression of both genes can be detected in most wild-type gonads starting at 11 dpf ( 15/16 , 16/16 respectively; Fig 5G and 5I ) . By contrast , the expression of these genes was not detected , or was detected at very low levels in gonads of 11 dpf fgf24 mutants ( 2/12 and 1/11 showed low expression , respectively; Fig 5H and 5J ) . Because we found that two populations of somatic cells can be distinguished in the early larval gonad based on fgf24 and etv4 expression ( Fig 4D ) , we performed high resolution fluorescent RNA in situ hybridization to determine in what cell layer cyp19a1a and amh are expressed . Similar to etv4 , we found that both genes are expressed in only interiorly-localized cells . However , we did not detect co-expression of cyp19a1a , amh , or etv4 in the same cells , indicating that the inner SGCs of 12 dpf gonads are comprised of at least three distinct cell populations ( Fig 5K–5M ) . Together , these results further support the model that the primary function of Fgf24 is to promote development of SGCs . In addition to the defects in somatic gonad gene expression , mutants older than 12 dpf have gonads that are smaller than their wild-type siblings because they have fewer germ cells and apparently fewer SGCs ( Fig 2D–2F ) . Decreased cell numbers could be due to decreased cell proliferation , increased cell apoptosis , or a combination of the two . We first asked if mutant gonads have an increase in cell apoptosis relative to wild-type . We assessed the extent of apoptosis by staining for Cleaved caspase 3 ( Cc3 ) and by performing a TUNEL assay . At 10 and 14 dpf , neither wild-type nor fgf24 mutant gonads displayed appreciable apoptosis in either SGCs or germ cells ( Fig 6A–6C’; S3A–S3B’ Fig ) . In addition , we asked if we could rescue the fgf24 phenotype by blocking Tp53-mediated apoptosis . Using the tp53 M214K allele [45] we produced tp53;fgf24 double mutants , which phenocopied fgf24 single mutants: 100% of double mutants were phenotypically male as adults and 69 . 2% lacked gonads completely ( n = 13 ) . In comparison , only 28 . 6% of tp53 single mutants were male and 100% had two fully developed gonads ( n = 14 , S2 Table ) . Together , these data suggest that the decreased number of cells in fgf24 mutant gonads is not a result of increased apoptosis . Finally , we asked if decreased cell numbers in fgf24 mutants was the result of reduced proliferation rates . We therefore exposed wild-type and fgf24 mutant animals to the thymidine analog 5-ethynyl-2’-deoxyuridine ( EdU ) from 8 to 9 dpf to label cells in S-phase of the cell cycle . Although we detected EdU in SGCs of both genotypes , the percentage of SGCs that were EdU-positive was significantly higher in wild-type compared to mutant gonads ( 70% and 41% , respectively; P < . 001; Fig 6D–6F ) . In contrast to SGCs , we never observed EdU incorporation in germ cells during this or later time frames ( Fig 6D–6E’ and S4A–S4D’ Fig ) . Because there is a vast increase in germ cells during larval development ( Fig 2D ) , we concluded that germ cells may incorporate this thymidine analog less efficiently , and therefore utilized an antibody against the mitosis-specific phospho-Histone H3 ( pHH3 ) to identify germ cells in prophase , when pHH3 is detected throughout the nucleus [46] . While the percentages of pHH3-positive germ cells are similar between genotypes at 8 dpf , by 10 dpf wild-type gonads have a significantly higher proportion of pHH3-positive germ cells than mutant gonads ( 58% and 34% , respectively; P < . 01; Fig 6G–6I ) . These results argue that decreased SGC and germ cell numbers in fgf24 mutant gonads are due primarily to a decrease in cell proliferation . The data above indicate that the early zebrafish somatic gonad is composed of two somatic layers . It is therefore possible that the outer fgf24-expressing layer is a developing epithelium . To test this hypothesis , as well as to compare the overall structure of wild-type and fgf24 mutant gonads at high resolution , we analyzed transverse sections by transmission electron microscopy ( TEM ) . At 10 dpf , we found that both wild-type and mutant gonads were arranged with germ cells in the center , surrounded by SGCs ( Fig 7A and 7B ) . However , the thickness of the SGC portion surrounding germ cells appeared to be greater in wild-type compared to mutant gonads . Furthermore , the SGCs in wild-type gonads were divided into two layers , likely corresponding to the fgf24-expressing cells and the etv4-expressing cells ( Fig 7A ) . We noted that these layers were separated by an electron-lucent space , perhaps due to the presence of a basement membrane ( n = 6; Fig 7A’ ) . We therefore asked whether Laminin , a central component of the basal lamina , could be detected in this region . While Laminin is not detected in wild-type 8 dpf gonads ( n = 8; S5A and S5A’ Fig ) , there is abundant Laminin deposited between the two layers of SGCs of wild-type 10 dpf gonads , indicating the presence of a basement membrane ( n = 14; Fig 7C and 7C’ ) . In contrast to wild-type , fgf24 mutant gonads have only one layer of SGCs ( n = 5; Fig 7B’ ) and lack Laminin staining altogether ( n = 10; Fig 7D and 7D’ ) . These results suggest that Fgf24 is required for normal morphogenesis of the early larval gonad . Frequently , Fgf ligands are secreted by one cell layer ( e . g . epithelial ) and signal across a basement membrane to Fgf receptor-expressing cells of a second layer ( e . g . mesenchymal; [47] , reviewed in [48] ) . Analysis of our TEM data revealed that the outer layer of SGCs in wild-type gonads makes many cell-cell contacts , seen as electron dense patches , characteristic of epithelial cells ( Fig 7A” ) . We therefore asked whether the fgf24- and etv4-expressing cells were adopting epithelial and mesenchymal fates , respectively . To address this question , we determined the cell junction landscape of each cell layer by staining for components of adherens and tight junctions . Adherens junctions are mediated by transmembrane cadherins , of which there are several types . Cadherin homodimerization helps similar cells associate with each other , and can promote cell sorting within a tissue . Inside the cell , catenins link the intracellular tail of the cadherin to actin , providing mechanical linkage between adjacent cells . In wild-type gonads , we found that β-catenin is expressed in virtually all cells , suggesting that all SGCs and germ cells have some type of adherens junctions ( n = 7; Fig 8A and 8A’ ) . Interestingly , we found that Cdh2/N-cadherin is highly localized to the membranes of the outer SGCs and weakly to the membranes of inner SGCs and germ cells ( n = 17; arrowheads and arrows , respectively , Fig 8C and 8C’ ) . In contrast , we see very low levels of Cdh1 ( E-cadherin ) in both germ cells and SGCs ( n = 15; S6A–S6C’ Fig ) . Tight junctions are a hallmark of epithelial cells , where they function to block the passage of fluids and molecules between cells . Tight junctions are formed by interactions between the transmembrane occludins and claudins and the intracellular Tjp1 ( Tight junction protein 1/Zo-1 ) , the latter of which interacts with actin . We found that Tjp1 was expressed in SGCs only and localized most intensely to the outer SGC membranes , similar to Cdh2 ( n = 15; arrowheads , Fig 8E and 8E’ ) . These data argue that the outer layer of fgf24-expressing SGCs forms an epithelium . Our TEM data reveal that both wild-type and mutant SGCs make many electron-dense cell-cell contacts ( arrows , Fig 7A” and 7B” ) . We therefore hypothesized that mutant gonads would also maintain the expression and localization of cell adhesion molecules . Indeed , we found that Cdh2 and Tjp1 remain strongly localized to some SGC membranes ( n = 15 and 14 , respectively; Fig 8D , 8D’ and 8F , 8F’ ) , while β-catenin and Cdh1 appear slightly reduced ( n = 7 and 10 , respectively; Fig 8B and 8B’ and S6D and S6D’ Fig ) . Finally , we sought to determine the identity of the SGCs that are present in the early fgf24 mutant gonads . In wild-type animals , the outer , fgf24-expressing layer of SGCs showed strong localization of Cdh2 and Tjp1 , the two cell adhesion molecules that were maintained in fgf24 mutant somatic gonad cells . We therefore hypothesized that the SGCs that remain in the mutants are most similar to the fgf24-expressing epithelial cells of the wild-type gonad . To test this , we asked whether we could detect fgf24 transcript in fgf24 mutants , as it is known that nonsense mediated decay of transcripts with premature stop codons varies in efficiency [49 , 50] . Using fluorescent in situ hybridization , we found that gonads of 11 dpf fgf24 mutants have both SGCs with and without detectable fgf24 transcripts ( arrowheads and asterisks in S7A–S7B’ Fig , respectively ) . Thus , it appears that the fgf24 mutant gonad , like the wild-type gonad , contains two distinct populations of SGCs , but that they fail to form the bi-layered organization observed in wild-type gonads .
We initiated these studies because of the discovery that all fgf24 mutants are male as adults . This phenotype could suggest that the primary role of Fgf24 is to promote female sex determination or differentiation . However , our results strongly argue that the primary role of Fgf24 is instead to promote the development of the early bipotential gonad , the precursor to both ovaries and testes , and that the effect on sex determination is a secondary consequence of this primary defect . It is well established that germ cells , and in particular oocytes , are required for female sex determination and/or differentiation , as mutations that reduce or eliminate early germ cell development , or specifically early-stage oocytes during the bipotential phase , result in an all-male phenotype [32–34 , 51 , 52] . We have established here that all fgf24 mutants have significantly reduced germ cells numbers relative to wild-type as early as 12 dpf ( early bipotential stage ) , which can thus explain why all fgf24 mutants develop as males . Importantly , we have presented evidence that gene expression and cell proliferation defects in the somatic gonad can be detected in fgf24 mutants as early as 8 dpf , several days prior to when we can detect significant differences in germ cell numbers between wild-type and mutant larvae . Finally , the expression of both male- and female-specific genes is equally affected by loss of fgf24 function ( e . g . amh and cyp19a1a; Fig 9 ) , a result that is inconsistent with Fgf24 having a sex-specific role . Thus , we strongly favor a model where the primary role of Fgf24 is in promoting somatic gonad development during the bipotential phase , and that defects in female development are a secondary consequence to earlier defects in the development of the bipotential gonad . Our current data suggest that the gonads of fgf24 mutants have fewer SGCs in comparison to their wild-type siblings . This phenotype could result from a failure in the specification and/or migration of somatic gonad precursors cells , a failure of these cells to proliferate , or a combination of these factors . In mammals , SGCs are derived from cells of the coelomic epithelium and , in males , there is also contribution from the mesonephric mesenchyme [54–56] . However , in fish , the origin of SGCs has so far only been investigated in Medaka . Using cell lineage-labeling , SGCs in Medaka have been shown to originate from the lateral mesoderm , which also likely includes precursors of the coelomic epithelium [57] . While similar studies have not been completed in zebrafish , a clear association of SGCs with germ cells can be detected in 5 dpf larvae , at a time when the primitive gonad is in direct contact with the adjacent coelomic epithelium lining the swim bladder [29] . This suggests that the coelomic epithelium is the likely source of SGCs in zebrafish . Although at present we cannot rule out the possibility that fgf24 is involved in early specification of somatic gonad precursor cells , our analysis shows that SGCs are present in fgf24 mutants , and that mutant cells , based on EdU incorporation , have significantly lower rates of proliferation than do wild-type SGCs . These data strongly argue that fgf24 is necessary for the expansion of the SGC population , but not for their initial specification . An interesting finding from this study is that even though the early larval somatic gonad in zebrafish is composed of relatively few cells , by 8 dpf at least two distinct cell populations can be identified . In wild-type animals , fgf24 is expressed in most gonads by 8 dpf , but at this time point its expression does not appear to be spatially restricted . By 10 dpf , however , all fgf24-expressing cells are localized to an outer layer of SGCs surrounding an inner population of fgf24 ( - ) cells . Coincident with this observation , formation of a basal lamina between these two layers is evident based on TEM and Laminin localization , and cells of the outer layer begin to express cell-junction components that are characteristic of epithelial cells ( e . g . Cdh2 and Tjp1 ) . Thus , it appears that by 10 dpf the zebrafish somatic gonad has organized into an inner mesenchymal-like layer of cells that are in direct contact with the germ cells , surrounded by an outer fgf24-expressing epithelial layer . Our data also suggest that development of the inner mesenchymal cells is dependent on Fgf24-mediated cell interactions with the epithelial layer reminiscent of the role of Fgf signaling in other developmental contexts ( e . g . limb bud development ) . It is possible that the single cell layer present in 10 dpf fgf24 mutant gonads represents only one of these two cell populations , but this has been difficult to determine as the expression of nearly all SGC marker genes so far examined is either greatly reduced or absent in mutant gonads . An exception to this is Wilms tumor 1a ( wt1a ) , which appears to be expressed in both outer and inner SGCs in wild-type and in mutant gonads . This indicates that the expression of wt1a is independent of fgf24 and therefore places Wt1a either upstream of , or in parallel with , Fgf24 . In addition to wt1a , fgf24 expression can be detected in a subset of the mutant SGCs suggesting that at least some of the remaining cells have an outer SGC-like characteristic , and consistently , analysis of cell-junction components suggests that a population of epithelial-like SGCs is still present in mutants . Thus , it appears that fgf24 mutant gonads may contain at least two populations of somatic gonad precursor cells , but that these cells fail to mature into functional cell types in the absence of Fgf24 signaling . In wild-type gonads , etv4 appears to be expressed in most if not all of the inner somatic cells present in a wild-type gonad at 10 dpf . As development proceeds , the number of cells expressing etv4 appears to decline , while at the same time , cells expressing differentiation marks such as cyp19a1a and amh increase in number . Interestingly , in 12 dpf gonads , we find little to no overlap between cells that express etv4 and those that also express cyp19a1a or amh . Furthermore , cyp19a1a-expressing cells are also distinct from amh-expressing cells . These results together indicate that by 12 dpf , the inner population of SGCs is composed of at least three distinct cell populations: etv4 ( + ) , cyp19a1a ( + ) , and amh ( + ) . Given the dynamics of etv4 expression relative to cyp19a1a and amh , we speculate that the etv4-expressing cells are a somatic gonad progenitor cell population that in turn gives rise to the differentiated functional cells of the gonad . If this is the case , the role of Fgf24 may therefore be to promote the development and proliferation of this progenitor population . Cell lineage analysis will be necessary to test this hypothesis . Thus far , no Fgf ligand has been shown to be necessary for mammalian bipotential gonad development analogous to the role we have described here for Fgf24 in zebrafish . Although it is possible that early gonad development in zebrafish is fundamentally different from that of tetrapods , there is reason to believe that this is not the case . Even though our understanding of gonad development in any teleost lags behind what is known in mammals , there are likely to be more similarities than differences in the genetic mechanisms that regulate gonad development and function in these two vertebrate lineages; many genes that are known to play essential roles during gonad development and sex determination in mammals are expressed at comparable time points during the development of the teleost gonad , and in some instances mutational analysis has confirmed their conserved roles . Examples include , but are not limited to: Wt1/wt1a ( [58]; this report ) , Nr5a1/nr5a1a ( Steroidogenic factor 1/Sf1; [59]; this report ) , gata4 ( this report ) , Sox9/sox9a [35 , 60] , and dmrt1 [61 , 62] . In mice , Gata4 , Nr5a1 , and Wt1 are all required for early gonad development [11–14] . Molecular epistasis analysis has shown that Gata4 is required for the expression of Nr5a1 , but not Wt1 [11] . Interestingly , we have shown that while all three genes are expressed in SGCs in wild-type zebrafish , gata4 and nr5a1a transcripts are not detected in fgf24 mutants , while wt1a expression appears to be normal . Thus , as in mice , the regulation of wt1a expression appears to be independent of gata4 and nr5a1a . An apparent exception to this conservation appears to be the role that Fgf signaling plays in gonad development , although in mammals , FGF9 and FGFR2 are known to play an important role during sex determination and differentiation [63–65] . Specifically , in mice , Fgf9 is initially expressed in the sexually indifferent gonads of both sexes ( starting as early as E9 . 5 ) after which its expression is stabilized only in the male gonad in response to expression of the male sex determinant Sry [64] . FGF9 both antagonizes the expression of WNT4 , a female-promoting signal , and promotes the stable expression of the male-promoting SOX9 transcription factor ( [66 , 67]; reviewed in [68] ) . Disrupting the FGF9 signaling pathway leads to partial male-to-female sex reversal ( [63 , 64]; reviewed in [68] ) . However , similar to the fgf24 mutant phenotype we present here , XY mice mutant for Fgf9 experience a defect in SGC proliferation prior to the expression of Sry , signifying an earlier role for FGF9 in gonad development [62] . In addition to mice , the role of FGF9 has also been investigated in chick . In both sexes of chick , FGF9 is expressed first in the mesonephroi immediately adjacent to the early bipotential gonads and later in the epithelium that surrounds these bipotential gonads [69] , a pattern that is strikingly similar to what we have reported here for fgf24 . Furthermore , and regardless of sex , ectopic expression of FGF9 in the early chick gonad is sufficient to expand the apparent number of SGCs , while inhibition of FGF signaling using the Fgf receptor inhibitor SU5402 leads to an apparent reduction of SGCs . Collectively , these data argue that in tetrapods , FGF9 may function first during the formation of the early bipotential gonad in both sexes ( similar to the role of Fgf24 reported here ) and then again later to promote testis differentiation in males . Finally , although there are striking similarities between the expression patterns and functions of FGF9/Fgf9 in chick and mouse early gonads and that of fgf24 in the early zebrafish gonad , Fgf24 and FGF9 belong to different FGF superfamilies: FGF9 is a member of the FGF9 superfamily , which includes FGF16 and FGF20 , whereas Fgf24 is a member of the FGF8 superfamily , which includes FGF17 and FGF18 ( [20 , 23]; reviewed in [70] ) . It should be noted however that the FGF8 and FGF9 subfamilies are thought to have similar , though not identical , receptor binding specificity as measured in cell culture assays ( reviewed in [70] ) . Interestingly , while genes encoding FGF9 and Fgf24 are both present in the genomes of representative basal ray-finned and lobed-finned fish ( i . e . Spotted Gar and Coelacanth , respectively; [27 , 71 , 72] ) , FGF9 orthologs have not been found in any teleost genome to date , including zebrafish [73] , and Fgf24 appears to have been lost in the lobed-fin lineage prior to the evolution of land dwelling tetrapods [23 , 24] . An attractive hypothesis is that in animals with both genes , Fgf9 and Fgf24 function redundantly during early gonad development . If so , this could provide a means by which they could be lost after the divergence of the two main vertebrate lineages . Although a limited role for Fgf signaling in early mammalian gonadogenesis has been established , it is noteworthy that upregulation of the FGF-FGFR signaling pathway has been implicated as a causal factor for promoting certain types of aggressive ovarian cancers . For example , increased expression of each of the four mammalian Fgf receptors have been found in various epithelial ovarian cancers ( EOC ) , and drugs that block or attenuate Fgf signaling have been shown to sensitize some EOCs to certain chemotherapeutic drugs [74–79] . In addition , the Fgf-responsive gene Etv4 and its close family member Etv5 are overexpressed in certain ovarian cancers [80–82] . It is therefore possible that , like in many cancers , ovarian cancers are caused , in part , by the unregulated activity of genetic pathways that are required for normal ovarian development during embryogenesis . We have so far focused our attention on the role of Fgf24 during the development of the larval gonad . While all fgf24 mutants have severe defects in larval gonad development , as adults 22% of fgf24 mutants have partial testes that can produce functional sperm . Although it is not known how the mutant gonads resume apparently normal development , it is clear that this development can occur in the absence of Fgf24 function . One explanation for this phenomenon is that Fgf signaling is not involved in gonad formation at later stages; however it is also plausible that a second Fgf ligand expressed during juvenile development can rescue somatic gonad development in the absence of Fgf24 . If this is the case , then latent testis development could occur in mutants that retain germ cells until expression of this ligand initiates . Future experiments will be necessary to test these models . Despite the important role the vertebrate somatic gonad plays in protecting germ cells and in regulating their maintenance and differentiation into gametes , little is known about the genetic regulation of somatic gonad development , and , in particular , the cell-cell interactions that are necessary for its development . Here , we have identified the Fibroblast growth factor ligand Fgf24 as a key player in this process . These results help to establish the zebrafish as a model for understanding the genetic regulation of early somatic gonad development in vertebrates .
The University of California Davis IACUC approved all animals used in this study ( protocol #18483 ) , and all animals used were euthanized using the American Veterinary Medical Association-approved method of hypothermal shock . The wild-type strain *AB was used for the generation of fgf24uc47 . Zebrafish husbandry was performed as previously described [83] , with the following modifications to the larval fish ( 5-30dpf ) feeding schedule: 5-12dpf: 40 fish/250mL in static fish water ( 4parts/thousand ( ppt ) ocean salts ) were fed rotifers ( Brachionus plicatilis , L-type ) twice daily ad libitum . 12–15 dpf: 40 fish/ 1 liter gently flowing fish water ( <1ppt ocean salts ) were fed both rotifers and freshly hatched Artemia nauplii ad libitum twice daily . 15-30dpf: 40 fish/1 liter gently flowing fish water ( <1ppt ocean salts ) were fed freshly hatched Artemia nauplii ad libitum twice daily . The following alleles were used in this study: fgf24hx118 , fgf24uc47 , tp53zdf1 . The following transgenic lines were used: Tg ( ziwi:EGFP ) uc02 , Tg ( gsdf:mCherry ) uc46 . The sgRNA and cas9 mRNA components were produced as previously described [84] . Briefly , the sgRNA was designed to target exon 3 of fgf24 ( zifit . partners . org/ZiFiT/ ) . Two oligonucleotides ( 5’-TAGGCAAGAAGATTAACGCCAA-3’ and 5’-AAACTTGGCGTTAATCTTCTTG-3’ ) were annealed and cloned into plasmid pDR274 ( Addgene Plasmid #42250 ) . The plasmid was linearized with DraI , and in vitro transcription was performed with the T7 polymerase ( Roche , Cat . No . 10881775001 ) . The cas9-expressing pMLM3613 plasmid was also obtained from Addgene ( Plasmid #42251 ) and mRNA synthesis was performed as described [84] . The sgRNA and cas9 mRNA were coinjected into one-cell embryos with phenol red ( 5% in 2M KCl ) at a concentration of 12 . 5 ng/μL and 300 ng/μL , respectively . CRISPR efficiency was determined by comparing the DNA isolated from eight injected embryos with eight uninjected control embryos ( 24 hpf ) using High Resolution Melt Analysis ( HRMA ) as described [85] ( the primers used are listed in S1 Methods ) . At three months post-injection , germline mutations were identified from extracted sperm of injected males by PCR analysis and gel electrophoresis . PCR products with evident indels were cloned into the pGEM-T Easy vector ( Promega , Cat . No . A137A ) and sequenced . The individual containing the fgf24uc47 allele was outcrossed to *AB to obtain a heterozygous line . Fish were genotyped by extracting gDNA from caudal fin tissue . fgf24uc47 , Tg ( ziwi:EGFP ) uc02 , and Tg ( gsdf:mCherry ) uc46 were assayed using standard PCR conditions and gel electrophoresis . fgf24hx118 and tp53zdf1 were analyzed using HRMA . See S1 Methods for primer sequences . Male fish were euthanized in an ice water bath . Testes were removed and macerated with scissors in Hank’s solution [83] . Eggs were squeezed from wild-type females according to standard protocols [81] and were fertilized with 30 μL sperm from either wild-type or fgf24-/- males . After three hours , the numbers of fertilized and unfertilized eggs were recorded . RNA probes that detect the following genes were used: cyp19a1a and amh [33]; gata4 [86]; wt1a [58]; etv4 [87] . For all other plasmids for probe synthesis , mRNA was isolated from 24 hpf embryos , adult testis , or ovary using TRI reagent ( Sigma-Aldrich , Cat . No . T9424 ) and synthesized into cDNA using the RETROScript Reverse Transcription Kit ( ThermoFisher , Cat . No . AM1710 ) . Targets were PCR amplified with Takara Ex Taq ( Clontech , Cat . No . RR001A ) and primers found in S1 Methods , Table 2 . PCR products were cloned into an appropriate vector , and plasmids were linearized using endonucleases from New England Biolabs ( see S1 Methods for details ) . in vitro transcription yielded antisense probes ( Roche T7 or SP6 RNA polymerases ( Cat . Nos . 10881775001 or 10810274001 , respectively ) ) . Probes were ethanol precipitated and G-50 sephadex column purified to remove excess nucleotides ( GE Healthcare , Cat . No . 45-001-398 ) . Probes were used at a concentration of 0 . 5-2ng/μL in hybridization solution . Fish were fixed in 4% paraformaldehyde ( PFA ) overnight at 4°C or 4 hours at room temperature . Samples were transferred to 100% methanol and stored at -20°C for at least 16 hours . Samples were bleached for 10–15 minutes , as needed , prior to proteinase K digestion in 3% H2O2 , 0 . 5% KOH . Color in situ hybridizations were performed similar to Thisse and Thisse , 2008 [88] , with the exception that 5% dextran sulfate was included in the hybridization solution . Fluorescent in situ hybridizations were performed as in Lauter et al . , 2011 [89] . Tyramide reactions were performed with commercially available tyramides ( Life Technologies , Cat . Nos . T20948 , T20950 , and T20951 ) . Gonads were dissected , and DNA was stained with DAPI . Samples were dehydrated by an increasing glycerol gradient . Gonads were mounted whole and imaged with an Olympus FV1000 laser scanning confocal microscope . Tissue was prepared and treated as for ISH . After initial washing , nonspecific antibody was blocked with 2% BSA and 2% goat serum in PBS-DT ( 1% PBS + 0 . 1% Triton-X + 1% DMSO ) for one hour at room temperature . Antibodies were diluted in blocking solution according to S1 Methods and applied to tissue overnight at 4°C . Following washing , blocking was repeated . Alexa Fluor secondary antibodies ( Thermo Fisher Scientific , Cat . nos . A-11008 , A-11012 , A-11005 , A-11001 , A-11039 , A-11042 ) were diluted at 1:500 in blocking solution and incubated with tissue overnight at 4°C to detect primary antibodies . DNA was stained with DAPI , and samples were dehydrated by an increasing glycerol gradient . Gonads were dissected , mounted whole , and imaged with an Olympus FV1000 laser scanning confocal microscope . For the experiment shown in Fig 6G–6H’ , an anti-Mouse-HRP conjugated secondary antibody ( ThermoFisher , Cat . no . G-21040 ) was used to detect anti-pHH3 , and tyramide reactions were performed as described above . For the experiment shown in Fig 7C and 7D , both Laminin and Vasa antibodies were raised in rabbit . Staining was therefore performed as above , but sequentially; briefly , samples were incubated overnight at 4°C with Rabbit anti-Laminin , washed , and incubated with a Goat anti-Rabbit IgG , Alexa Fluor 488 overnight at 4°C . After extensive washing , samples were incubated overnight at 4°C with Rabbit anti-Vasa , washed , and incubated with a Goat anti-Rabbit IgG , Alexa Fluor 594 overnight at 4°C . Samples were then treated as above . Apoptosis was detected with the ApopTag Apoptosis Detection Kit ( Millipore , Cat . No . S7110 ) . Samples were treated according to the manufacturer’s manual , with additional post-fixation steps after proteinase K digestion: Samples were treated with 4% PFA for 20 minutes at room temperature , washed 5 X 5 minutes in PBSTw ( PBS + 0 . 1% Tween-20 ) , incubated in pre-chilled 2:1 EtOH:acetic acid for 10 minutes at -20°C , and washed 3 X 5 minutes in PBSTw . Larvae were allowed to swim freely in 200 μM EdU + 0 . 1% DMSO . To diminish any systemic affects of treatment , fish were kept at normal densities and on normal feeding schedules . Fish were euthanized and fixed in 4% PFA immediately following exposure . After extensive washing in PBSTw , EdU was detected by “click” chemistry ( 10 μM Alexa Fluor 594 Azide ( ThermoFisher , Cat . no . A10270 ) , 1 mM CuSO4 , 100 mM Tris pH8 . 5 , 100 mM Ascorbic acid; incubate for 30 minutes at room temperature ) and visualized on an Olympus FV1000 laser scanning confocal microscope . Sagittal optical sections were collected at 5 μm intervals throughout the entirety of whole-mount gonads with an Olympus FV1000 laser scanning confocal microscope . Intervals of 5 μm were used to allow for identification of virtually every cell in a gonad . Individual cells were manually documented with the Cell Counter plugin for FIJI . Germ cells were identified by Vasa expression . Because we lack a pan-SGC marker and because gonads of the stages described here do not readily dissect from the body wall , we were not confident in our ability to quantify the total number of SGCs in any given gonad . However , for the EdU experiment , we were able to roughly identify SGCs based on their proximity to germ cells and overall shape of the tissue . Once somatic cells of a gonad were identified and recorded , overlap of EdU signal was scored . Tg ( ziwi:gfp ) uc02 fish were euthanized in an ice water bath . Tails were removed immediately posterior to the gonads as visualized by the germ cell-specific GFP . They were then fixed 24 hours in Karnovsky's fixative ( 2 . 5% glutaraldehyde + 2% paraformaldehyde in 0 . 1 M sodium cacodylate ) , following 2 X 15 min rinses in 0 . 1 M sodium cacodylate buffer . Samples were then treated with 2% osmium tetroxide for 1 hour , followed by 2 X 15 minutes rinses in 0 . 1 M sodium cacodylate buffer . Tissue was dehydrated in an ethanol gradient ( 30 minutes each: 50% , 75% , 95% EtOH; 2 X 20 minutes 100% EtOH ) and treated with propylene oxide 2 X 10 minutes . Tissue was pre-infiltrated with 1:1 propylene oxide:Poly/Bed 812 resin overnight and infiltrated with 100% Poly/Bed 812 resin for three hours ( Polysciences , Inc ) . Finally , samples were embedded in fresh resin , polymerized in a 60°C oven for 24 hours , and sectioned to 100 nm . Sections were imaged using a Philips BioTwin CM120 TEM . Images were analyzed using FIJI , and only linear manipulations of brightness and contrast were applied . Statistical analysis and graphing were completed in R using standard packages , ggplot2 , and ggbeeswarm . | The genes involved in the early stages of vertebrate gonad development remain largely undefined . The gonad begins to form when primordial germ cells and somatic gonad precursor cells coalesce during early development . However , we know little about the signaling events that lead to the subsequent morphogenesis of the early developing gonadal primordium . Using the zebrafish , a model vertebrate , we show that the early somatic gonad organizes into a bi-layered structure , with an outer epithelial layer surrounding an inner mesenchymal core . We demonstrate that the gene encoding the Fibroblast growth factor 24 ligand , fgf24 , is expressed by the epithelial population . Utilizing a null mutation in fgf24 , we show that Fgf24 signaling is required for the expression of several genes by the inner mesenchymal cells , including those encoding transcription factors , a hormone biosynthesis enzyme , and a TGF-β ligand . Furthermore , the somatic cells in fgf24 mutant gonads have reduced proliferation rates , do not organize into the bi-layered structure seen in wild-type gonads , and most fgf24 mutants are infertile as adults due to the inability of the somatic gonad to support germ cell development . Finally , we argue that the function of Fgf24 during development of the early teleost gonad is analogous to the proposed role of FGF9 during development of the early tetrapod gonad . | [
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"systems"... | 2017 | Fibroblast growth factor signaling is required for early somatic gonad development in zebrafish |
The human filarial parasite Brugia malayi harbors an endosymbiotic bacterium of the genus Wolbachia . The Wolbachia represent an attractive target for the control of filarial induced disease as elimination of the bacteria affects molting , reproduction and survival of the worms . The molecular basis for the symbiotic relationship between Wolbachia and their filarial hosts has yet to be elucidated . To identify proteins involved in this process , we focused on the Wolbachia surface proteins ( WSPs ) , which are known to be involved in bacteria-host interactions in other bacterial systems . Two WSP-like proteins ( wBm0152 and wBm0432 ) were localized to various host tissues of the B . malayi female adult worms and are present in the excretory/secretory products of the worms . We provide evidence that both of these proteins bind specifically to B . malayi crude protein extracts and to individual filarial proteins to create functional complexes . The wBm0432 interacts with several key enzymes involved in the host glycolytic pathway , including aldolase and enolase . The wBm0152 interacts with the host cytoskeletal proteins actin and tubulin . We also show these interactions in vitro and have verified that wBm0432 and B . malayi aldolase , as well as wBm0152 and B . malayi actin , co-localize to the vacuole surrounding Wolbachia . We propose that both WSP protein complexes interact with each other via the aldolase-actin link and/or via the possible interaction between the host's enolase and the cytoskeleton , and play a role in Wolbachia distribution during worm growth and embryogenesis .
Nematodes are responsible for the most common parasitic infections of humans . In particular , the tissue-dwelling filarial nematodes—including Onchocerca volvulus , Loa Loa , Wuchereria bancrofti , Brugia timori and B . malayi ( Bm ) —cause the most severe pathologies associated with these infections , including blindness , extensive skin lesions ( in long-standing disease ) and elephantiasis [1]–[3] . O . volvulus , the causative agent of onchocerciasis , affects nearly 37 million people in 34 countries and is most abundant in Africa , with small foci in Southern and Central America [3] . Approximately 120 million individuals are infected with the causative agents of lymphatic filaria W . bancrofti and B . malayi , and 40 million exhibit clinical manifestations of disease [4] , [5] . The present control programs are based on the mass administration of a small arsenal of microfilaricidal drugs , and thus are vulnerable to possible failure due to the potential development of drug resistance [5]–[9] . Additional research is critically needed to support the discovery of novel drug targets , and thus expand the arsenal of agents targeting the adult worm for the ultimate elimination of these infections [8] . Most filarial parasite species carry a Wolbachia endosymbiont , a member of a genus of intracellular bacteria commonly found in arthropods [10] , [11] . In insects , Wolbachia are primarily reproductive parasites [12]–[14] . Therefore , much of the research on Wolbachia endosymbiosis in arthropods has focused on the phenotypic changes caused by infection with the endobacterium , as well as the potential practical applications of the phenotypic alterations , which include cytoplasmic incompatibility , feminization , reduction in host longevity [10] and resistance to viruses and parasites [15] . In filarial nematodes , Wolbachia appear to have evolved toward a mutualistic interaction . Spurred by the availability of the genome data from both B . malayi [16] and its Wolbachia endosymbiont ( wBm ) [17] , research initially focused on pathways that appeared to be defective in one organism and compensated for by genes expressed in the symbiotic partner . Such comparative research suggested that the intact biosynthetic pathways for haem , nucleotides , riboflavin , and FAD comprise the contributions potentially made by the bacteria to the development and survival of the filarial nematodes [1] , [18] , [19] . Conversely , the wBm genome lacks the complete biochemical pathways for de novo synthesis of biotin , coenzyme A , NAD , ubiquinone and folate . Therefore , the filarial worms might provide these and other molecules required for bacterial growth [16] , [17] . The co-dependency between Wolbachia and the filarial worms was demonstrated by examining the worms after elimination of Wolbachia by treatment with antibiotics such as tetracycline , doxycycline or rifamycin [3] , [20] , [21] . Antibiotic treatments in multiple in vitro and in vivo studies , including several clinical trials in humans , were shown to induce an apoptotic response in treated parasites [22] leading to strong anti-filarial effects , confirming the essential role of Wolbachia in worm survival and reproduction [23]–[30] . For instance , in the Onchocercidae , antibiotic treatment induced retarded larval growth [31] , embryostasis in female worms [32] , and even death of the adult filarial worms [3] , [33] . As the survival and reproduction of the filarial host is dependent on the presence of Wolbachia and its interactions with the endosymbiont , this essential interaction has been the subject of intensive studies to identify the Achilles' heel of the symbiotic relationship and thus novel putative chemotherapeutic targets for the treatment of filarial infections [3] , [18] , [34] , [35] . To date , however , little is known about the underlying molecular basis for the B . malayi - Wolbachia co-dependency . In arthropods , a Wolbachia surface protein ( WSP ) was thought to be a key player for the establishment and persistence of symbiosis , but little is known about the role of this protein or its possible interacting partners in arthropods [36] . The Wolbachia surface proteins in filaria were hypothesized to interact with host proteins in the formation of functional complexes necessary for worm survival [20] , [36] . The B . malayi endosymbiont Wolbachia has seven outer membrane proteins ( OMPs ) and WSPs [17] . These proteins are highly conserved in Wolbachia from filarial nematodes and have a heterogeneous pattern of amino acid diversity characteristic of other OMPs known to be involved in bacteria-host interactions in other systems [36]–[39] . Moreover , analysis of the B . malayi secretome established that a number of Wolbachia OMPs were secreted or released by the worm [40] . In a recent study , an interacting pair of proteins comprised of a WSP-like protein ( wBm0284 ) and a B . malayi protein expressed in the cytoplasm of the worms ( Bm1_46455 , accession# EDP30508 . 1 ) was identified [41] . The co-localization of both proteins in similar locations within Wolbachia as well as in the worm's tissues , cuticle and nuclei within embryos provided indirect evidence that this specific interaction might have functional importance for the filarial nematode and Wolbachia symbiosis [41] . In this study , we focused on two other members of the OMP/WSP protein family of Wolbachia , wBm0432 and wBm0152 . First , we demonstrated that both wBm surface proteins bind specifically to B . malayi crude protein extracts . Second , using in situ cross-linking methodology of metabolically labeled worms , we established that wBm0432 interacts in vivo with several key glycolytic enzymes ( GEs ) : fructose-bisphosphate aldolase , triosephosphate isomerase , L-lactate dehydrogenase , enolase , glyceraldehyde-3-phosphate dehydrogenase ( G3PD ) , and phosphoglycerate kinase . Notably , Wolbachia lacks two glycolytic enzymes ( 6-phosphofructokinase and pyruvate kinase ) , and consequently its glycolytic pathway is thought to be defective and replaced by gluconeogenic enzymes [17] , [18] . Accordingly , the energy source utilized by Wolbachia will depend on products produced by the B . malayi glycolytic pathway , such as pyruvate . Moreover , we show that wBm0152 interacts in vivo with the host cytoskeletal proteins . Finally , we confirmed these interactions in vitro and verified that wBm0432 and B . malayi aldolase , as well as the proteins from the second functional complex wBm0152 and B . malayi actin , co-localize to the vacuole surrounding Wolbachia within the hypodermal cord in female B . malayi worms . We further provide evidence to support the theory that these two complexes—wBm0152/Bm-actin and wBm0432/GEs—might be connected to each other via the B . malayi aldolase-actin linkage , and/or the possible interaction between the host's enolase and cytoskeletal proteins . The results of this study provide a novel molecular perspective on some of the molecular complexes that support the endosymbiotic relationship between B . malayi and Wolbachia .
All animal studies were carried out in compliance with the guidelines from the Institutional Animal Care and Use Committee ( IACUC ) of the New York Blood Center and in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals from the National Institutes of Health . The animal protocol ( #224 ) was approved by the IACUC of the New York Blood Center , New York , NY . The cDNA corresponding to the Wolbachia surface protein genes wBm0152 and wBm0432 , as well as B . malayi aldolase , were amplified by PCR from female B . malayi random-primed cDNA using gene-specific primer sets ( Table 1 ) . Cloning , expression and purification of corresponding recombinant His tagged proteins in E . coli was performed according to a previously reported procedure [41] . The ∼28 kDa His-wBm0432 and 48 kDa His-Bm-aldolase fusion protein was purified under denaturing conditions in 6 M urea using His Bind Columns ( Novagen ) , according to the manufacturer's instructions and then dialyzed using 50 mM Tris-HCl , 18 mM NaCl , 1 mM EDTA , pH 7 . 6 . The soluble 18 kDa His-wBm0152 fusion protein was purified using His•Bind Columns ( Novagen ) , according to the manufacturer's instructions and then dialyzed with PBS . The purified recombinant proteins were analyzed by SDS-PAGE . The protein concentration was determined using NanoDrop 2000 ( Thermo Scientific ) . B . malayi adult female worms ( from 120 days post infection of Mongolian jirds ) were obtained from the NIAID/NIH Filariasis Research Reagent Repository Center ( FR3; Athens , GA; www . filariasiscenter . org ) . Soluble phosphate buffered saline ( PBS ) crude protein extracts of B . malayi female worms were prepared as described previously [42] using Protease Inhibitor Cocktail ( Roche , Mannheim , Germany ) . Soluble crude protein extract from adult A . viteae female worms was prepared in PBS ( pH 7 . 4 ) containing N-alpha -p-tosyl-L-lysine chloromethyl ketone ( 50 µg/ml ) , N-tosyl-L-phenylalanine chloromethyl ketone ( 50 µg/ml ) , and phenylmethylsulfonyl fluoride ( 1 mM ) using a glass hand held homogenizer . The A . viteae extract was a gift from Drs . William Harnett and Katrina Houston from the University of Strathclyde , Glasgow , Scotland . A 96-well polystyrene plate ( Corning Inc . , Corning , NY , USA ) was coated with parasite crude protein extract ( 10 µg/ml ) in 0 . 1 ml of PBS ( pH 7 . 2 ) overnight at 4°C . The wells were then washed 5 times with PBS-T ( PBS plus 0 . 05% Tween 20 ) and blocked with 3% BSA in PBS-T for 1 h at room temperature to prevent nonspecific binding . After an additional washing step ( 5 times with PBS-T ) His-wBm0152 or His-wBm0432 recombinant fusion proteins were added to duplicate wells at different concentrations ( 1–10 µg/ml ) in binding buffer , and incubated for 2 h at room temperature . 3% BSA in PBS-T was used as a control for non-specific binding of the detecting antibodies to the parasite extracts . Wells were washed three times with PBS-T and the bound His-tagged protein was detected by probing with HRP conjugated mouse anti-His monoclonal antibody ( Genscript ) followed by development with a tetramethyl benzidine substrate ( Thermo Scientific ) , and reading the absorbance at 450 nm using SpectraMAX190 ( Molecular Devices ) . The ELISA-based assay was repeated 3 times using crude protein extracts prepared from different batches of B . malayi worms and one batch of A . viteae extract . The BSA background values were subtracted from the WSPs test wells absorbance . The absorbance in the control wells was consistently below 0 . 08 . A group of five female BALB/c mice was immunized subcutaneously with 30 µg of recombinant His-wBm0152 or His-wBm0432 formulated in Sigma Adjuvant System as recommended by the manufacturer ( Sigma-Aldrich , St . Louis , MO , USA ) using an approved IACUC protocol ( #224 ) . Boost immunizations were given on days 14 and 28 post-immunization . Blood was collected pre-immunization and on day 14 after the second boost . Pooled serum was analyzed by Western blot . The corresponding bands of the recombinant His-wBm0152 and His-wBm0432 proteins as well as their corresponding native proteins in the B . malayi crude protein extract were detected using the antigen-specific antibodies , whereas no bands were recognized when pre-immunization serum was used ( data not shown ) . We adapted a method used routinely for protein-protein interaction studies in mammalian cells –in vitro metabolic labeling with L-Photo-Leucine and L-Photo-Methionine amino acids , followed by photo-activated in vivo cross linking and purification of protein complexes for analysis [42] . Thermo Scientific L-Photo-Leucine and L-Photo-Methionine are analogs of L-Leucine and L-Methionine amino acids that have activatable diazirine side chains capable of chemical crosslinking to adjacent molecules when exposed to ultraviolet light . When used in combination with specially formulated limiting cell media that is devoid of leucine and methionine , the photo-activatable derivatives are treated like naturally occurring amino acids by the protein synthesis machinery . As a result , they can be substituted for leucine or methionine in the primary sequence of proteins . When exposed to UV light the diazirine rings become reactive intermediates that form covalent bonds with nearby protein side chains and backbones . Naturally associating binding partners are then instantly trapped . Briefly , 300 adult B . malayi female worms were incubated overnight in Dulbecco's Modified Eagle's Limiting Medium ( DMEM-LM ) ( Thermo Scientific ) containing 2 mM L-Photo-Methionine and 4 mM L-Photo-Leucine ( Thermo Scientific ) without serum . The next morning the media containing the photo-amino acids was removed from the worms and after washing twice with PBS , the worms were covered with a minimal layer of cold PBS and exposed to UV light ( 2×15 watt bulbs , emission at 350 nm; F15T8/350BLS/ECO ) from a 4 cm distance using the XX-15S Shortwave UV Bench Lamp ( UVP , Upland , CA ) . Under these conditions , the photo-reactive amino acid half-life was determined to be 4 min . We irradiated the worms for 20 min; 5 times the half-life , as recommended by the manufacture . A soluble crude protein extract of the labeled worms , including various naturally associating cross-linked binding partners , was prepared in PBS as described above . The protein concentration was determined using NanoDrop 2000 ( Thermo Scientific ) . To affinity purify putative Bm–wBm protein complexes associated specifically with wBm0152 or wBm0432 , affinity columns containing IgG raised against wBm0152 or wBm0432 and immobilized to Protein A/G Plus Agarose were prepared using the Pierce Crosslink Immunoprecipitation Kit ( Thermo Scientific ) according to the instruction of the manufacturer . The PBS soluble crude protein extract prepared from the metabolically-labeled B . malayi worms containing the UV induced cross-linking of interacting proteins was first precleared with normal mouse IgG immobilized to Protein A/G . Precleared aliquots of the extract were then loaded onto the wBm0152 or wBm0432 immunoaffinity columns and allowed to incubate overnight at 4°C . After extensive washes of the columns , the bound material was eluted using elution buffer , neutralized with TRIS-HCl , and then analyzed by Western blotting . The eluted material ( 6 µg per lane ) was loaded on a 12% SDS-Tris-glycine gel ( Bio-Rad ) , and the corresponding nitrocellulose membrane strips were then washed , blocked with 1× Casein , and probed with primary mouse anti-wBm0152 or mouse anti-wBm0432 antibodies . Binding was detected using goat anti-mouse secondary antibodies conjugated to horseradish peroxidase ( KPL ) , and a chemiluminescent substrate ( SuperSignal ) . MS-based protein identification of the wBm0152- or wBm0432-specific bound samples containing putative UV induced cross-linked interacting proteins was initiated by filter-aided sample preparation ( FASP ) , as previously described ( Protein Discovery , Knoxville , TN ) [43] . Tryptic peptides resulting from the preparation were analyzed by liquid chromatography mass spectrometry ( LC-MS ) . Briefly , chromatography was performed using a Nano-LC Ultra 2D+ ( Eksigent , Dublin , CA ) equipped with a Proteopep 2 IntegraFrit trapping column ( 100 µm i . d . ×2 . 5 cm; C18 , 5 µm , 300 ? ? ? ) and a Proteopep 2 IntegraFrit analytical column ( 75 µm i . d . ×10 cm; C18 , 5 µm , 300 ? ? ? , New Objective , Woburn , MA ) . Samples were loaded onto the trap column at 2 µl/min ( Solvent A ) for 12 minutes , after which a valve was switched to include the analytical column . Peptides were then eluted with a gradient ( 300 nl/min ) of 2% B to 80% B over 80 minutes ( Solvent A: 97 . 5% H2O , 2% acetonitrile , 0 . 5% formic acid , Solvent B: 1 . 5% H2O , 98% acetonitrile , 0 . 5% formic acid ) . Nano-LC effluent was analyzed on-line by positive-ion micro-electrospray with a linear ion trap ( LTQ XL ) or LTQ OrbiTrap XL ( Thermo Fisher Corp . , Bremen , Germany ) operated in ‘top-5 data-dependent’ acquisition mode . Resulting data were searched against a custom built database with MASCOT ( Matrix Science , Boston , MA ) . Identified peptides and proteins were validated and visualized with Scaffold 3 . 6 ( Proteome Software , Portland , OR ) at a 2% false positive rate . To verify the putative protein-protein interaction between the recombinant His-wBm0152 and actin we used the ELISA-based assay . The 96-well polystyrene plates ( Corning Inc . ) were coated with the purified recombinant His-wBm0152 protein at 0 . 2 µg/ml or 1 µg/ml as described above . The reactant bovine actin protein ( Sigma ) was then added in duplicates at 1 µg/ml , 5 µg/ml or 20 µg/ml . The bound actin was detected using rabbit anti-actin polyclonal antibody ( Genscript ) followed by HRP-conjugated goat anti-rabbit IgG ( KPL ) . HRP was detected as described above . B . malayi female worms were fixed in a mixture of 4% paraformaldehyde and 0 . 25% glutaraldehyde in 0 . 1 M sodium cacodylate buffer ( pH 7 . 4 ) containing 1% sucrose for 60 min at room temperature and processed for immunoelectron microscopy as described previously [45] . Thin sections of embedded worms were blocked and probed with rabbit antibodies raised against recombinant His-wBm0432 ( 1∶ 5 dilution ) and mouse anti-Bm-aldolase ( 1∶2 dilution ) antibodies followed by 15 nm or 10 nm gold labeled goat anti-rabbit IgG ( H+L ) or 18 nm gold labeled goat anti-mouse IgG ( H+L ) ( Jackson ImmunoResearch Laboratories , Inc . , USA ) , respectively . Similarly , thin sections of embedded worms were blocked and probed with mouse antibodies raised against recombinant His-wBm0152 ( 1∶2 dilution ) and rabbit anti-actin antibodies ( 1∶20 dilution ) followed by 15 nm or 18 nm gold labeled goat anti-mouse IgG ( H+L ) or 15 nm gold labeled goat anti-rabbit IgG ( H+L ) ( Jackson ImmunoResearch Laboratories , Inc . , USA ) , respectively . Pre-immunization serum was used as the control . No signals were detected in control experiments utilizing pre-immunization sera ( data not shown ) . In addition , worms were processed for transmission immunoelectron microscopy as described above with the exception of sectioned material being post stained with 1% tannic acid , 2% osmium tetroxide , saturated ethanolic uranyl acetate and Reynolds lead citrate . Regular epon embedding was also performed on the same sample in order to compare the effects of the two different fixation protocols on the morphology of the B . malayi host vacuole surrounding Wolbachia . Epon embedded processing consisted of fixing the worms in modified Karnovsky's fixative consisting of 2 . 5% glutaraldehyde and 2% paraformaldehyde in 0 . 1 M sodium cacodylate buffer , pH 7 . 4 , containing 1% sucrose for 60 minutes at room temperature . Worms were then washed 3×10 min in 0 . 1 M sodium cacodylate buffer and post fixed with 2% osmium tetroxide for 60 min . Following additional buffer washes , worms were dehydrated through an ethanol series and immersed in propylene oxide for 2×10 min before being embedded in Epon resin . Ultrathin sections were cut using an RMC MTX ultramicrotome with a Diatome diamond knife followed by post staining of the grids with saturated ethanolic uranyl acetate and Reynolds lead citrate . Samples were imaged on a FEI Tecnai 12 spirit TEM operated at 80 kV .
To evaluate the possible interaction between the Wolbachia surface proteins and B . malayi proteins , we utilized an in vitro ELISA-based assay [41] using recombinant His-tagged WSPs and B . malayi crude protein extract . The worm's components contained in the B . malayi soluble crude protein extract were immobilized on the ELISA plates and then incubated with varying concentrations of the recombinant His-tagged WSP proteins of wBm0100 , wBm0152 or wBm0432 . The crude protein extract of Acanthocheilonema viteae , a filarial nematode that is free of Wolbachia , was used as a control for possible non-specific binding [12] , [31] , [41] , [46] . As shown in Figures 1A and 1B , 2 out of the 3 WSP proteins , wBm0152 and wBm0432 , bound specifically ( P<0 . 05 ) in a dose-dependent manner to the B . malayi crude protein extract , whereas these Wolbachia proteins exhibited minimal binding capacity to the A . viteae crude protein extract using similar assay conditions . Based on the data presented later , it is possible that the minimal binding to the A . viteae crude protein extract observed is due to cross-reactivity with the glycolytic enzymes or the actin/tubulin proteins in A . viteae that are presumed to be highly similar to those of B . malayi . Notably , wBm0100 did not bind to the B . malayi crude protein extract ( Fig . 1C ) . These results indirectly established the presence of putative binding partners within the B . malayi crude protein extract that bind more specifically with the Wolbachia surface proteins wBm0152 and wBm0432 . To identify the possible B . malayi interacting partners of wBm0152 and wBm0432 in vivo , we adapted a method used routinely for protein-protein interaction studies in mammalian cells–in vitro metabolic labeling with L-Photo-Leucine and L-Photo-Methionine amino acids , followed by photo-activated in vivo cross linking , and immune-purification of protein complexes for analysis [47] . In these experiments , B . malayi adult females were metabolically labeled with L-Photo-Leucine and L-Photo-Methionine amino acids , the parasites lysed and the metabolically labeled proteins photo-cross-linked . The crude protein extract prepared from these cross-linked metabolically-labeled B . malayi worms was first precleared by passing it over an immunoaffinity column consisting of IgG from a naive mouse immobilized with Protein A/G . The native Wolbachia–B . malayi complexes were then affinity-purified using IgG from mice immunized with recombinant wBm0152 or wBm0432 , again immobilized with Protein A/G . The corresponding eluted fractions from anti-wBm0152 and anti-wBm0432 immunoaffinity columns were then analyzed by Western blot . An antiserum against wBm0432 revealed two discrete bands in the eluted fraction: ∼28 kDa and ∼110 kDa ( Figure 2A ) . The expected molecular weight ( MW ) of an unassociated native wBm0432 molecule is 26 kDa . Therefore , we concluded that the lower band represented the native unbound wBm0432 , and that the higher broad band indicated the presence of some putative wBm0432 – B . malayi protein complexes . The anti-wBm0152 antibodies reacted with eluted proteins of ∼90 kDa and ∼120 kDa ( Fig . 2B ) . As the molecular weight of the native protein wBm0152 protein is only 18 kDa , we concluded that the two recognized protein bands correspond to some possible wBm0152 – B . malayi protein complexes . Both of the affinity purified fractions containing the putative protein complexes of Wolbachia WSP and B . malayi proteins were analyzed by mass spectrometry . The identity of the proteins contained within the affinity-purified complexes were resolved using liquid chromatography mass spectrometry ( LC-MS ) [48] . The complexes purified using the anti-wBm0432 affinity column contained 9 peptides corresponding to the sequence of the native wBm0432 protein . In addition , peptides derived from six B . malayi proteins involved in the glycolytic pathway were found in the digested affinity-purified complexes: fructose-bisphosphate aldolase , triosephosphate isomerase , enolase , glyceraldehyde-3-phosphate dehydrogenase and phosphoglycerate kinase , and L-lactate dehydrogenase ( Table 3 ) . The protein with the most abundant tryptic peptides ( five ) was fructose-bisphosphate aldolase ( Bm1_15350 , accession# EDP36623 . 1 ) ( Table 3 ) . The proteins eluted from the anti-wBm0152 affinity column included actin ( Bm1_16810 , accession# EDP36330 . 1 ) and α- and β- tubulin ( Table 3 ) . However , wBm0152 was not included in the list of proteins identified in this analysis . These results suggested that wBm0432 may interact directly or indirectly with six key enzymes involved in the host glycolytic pathway , while wBm0152 may interact with the host cytoskeleton . To confirm the interaction between the two Wolbachia WSPs and their B . malayi partner proteins , we utilized an in vitro overlay assay [44] . Initially , we determined which of the GEs interact directly and distinctively with wBm0432 by performing overlay assays using commercially available rabbit glycolytic enzymes , which were immobilized onto nitrocellulose . The sequence identity of B . malayi proteins and Oryctolagus cuniculus ( European rabbit ) proteins ranged from 62% to 72% ( Table 2 ) . As shown in Figure 3B , wBm0432 binds strongly with enolase ( Lane 1 ) and aldolase ( Lanes 2 and 3 ) . The wBm0432 also interacted with triosephosphate isomerase to some extent ( Fig . 3B , Lane 5 ) , and had its weakest binding to L-lactate dehydrogenase ( Fig . 3B , Lane 4 ) . None of the GEs cross-reacted with the anti-His detecting antibodies ( Fig . 3A , Lanes 1–5 ) . To validate that wBm0432 interacts specifically with the filarial host aldolase , we repeated the overlay assay with recombinant B . malayi His-tagged aldolase ( Bm1_15350 ) immobilized onto nitrocellulose . As shown in Figure 4A , His-wBm0432 also interacts specifically with His-Bm-aldolase ( Fig . 4A , Lane 3 ) . Moreover , anti-His-wBm0432 ( Fig . 4A , Lane 2 ) or anti-His-wBm0293 , an unrelated Wolbachia protein , ( data not shown ) antibodies did not cross-react with the immobilized aldolase . To determine the experimental dissociation constant ( Kd ) for the wBm0432 and B . malayi aldolase interaction , individual strips of the immobilized recombinant B . malayi aldolase were incubated with different concentrations of wBm0432 ( Fig . 4B ) . The calculated Kd value of 0 . 51±0 . 2 µM further highlighted the specificity of the interaction between wBm0432 and B . malayi aldolase . As shown in Figure 4C , soluble bovine actin , which is >90% identical to B . malayi actin , interacted specifically with the His-wBm0152 protein ( Lane 3 ) . The specificity of the anti-actin antibodies ( Fig . 4C , Lane 1 ) was verified by establishing that they did not cross-react with His-wBm0152 ( Fig . 4C , Lane 2 ) . Notably , the interaction between actin and wBm0152 is the strongest when the Wolbachia protein is polymerized and runs in the gel as a tetramer ( ∼54 kDa ) ( Fig . 4C , Lanes 3 and 4 ) . Subsequently , using ELISA-based interaction assays , the experimental dissociation constant ( Kd ) of wBm0152 and bovine actin was determined to be 0 . 57±0 . 03 µM , indicating a high binding affinity [49] ( Fig . 4D ) . In summary , these in vitro binding assays further supported our LC-MS analyses that wBm0432 interacts specifically with Bm-aldolase , and that wBm0152 interacts specifically with actin . The interaction for each of the two protein complexes , wBm0432-aldolase and wBm0152-actin , was confirmed in situ by immunoelectron microscopy using rabbit anti-wBm0432 antiserum and mouse anti-Bm-aldolase antiserum , and mouse anti-wBm0152 antiserum and rabbit anti-actin antibodies , respectively . The wBm0432 protein localized to the surface of Wolbachia ( Fig . 5A and 5C ) , as previously shown [41] , [50] . Immunolocalization of Bm-aldolase established that aldolase was also present close to the surface of Wolbachia ( Fig . 5B and 5C ) . Subsequent double labeling of similar cross-sections of B . malayi adult female worms with both antibodies co-localized the corresponding proteins to the surface of the vacuole that surrounds Wolbachia within the cytoplasm of the B . malayi host ( Fig . 5C ) . Additional transmission electron microscopy experiments were performed to examine the structure of the B . malayi host vacuole surrounding Wolbachia in the hypodermal chord . The appearance of the vacuole was found to be considerably altered in a fixation-dependent manner . Worms processed for immunoelectron microscopy utilizing a less stringent fixation protocol were observed to have a large halo surrounding the bacteria ( Fig . S1 , Panels A and B ) . To better examine the host vacuole structure in the immunoelectron microscopy samples , sectioned material was post stained with 1% tannic acid , 2% osmium tetroxide , saturated ethanolic uranyl acetate and Renolds lead citrate in order to stain membranes and microfilament structures associated with the host vacuole . For comparison , worms were processed for structural electron microscopy studies from the same sample but utilizing a more stringent fixation protocol . These samples were found to lack the large halo seen in immunoelectron microscopy preparations ( Fig . S1 , Panels C and D ) and in fact were virtually indistinguishable from the host cytoplasm in some areas . Closer examination of the vacuole from the samples in Figure S1 , Panels A and B revealed that the perceived vacuole boundary was comprised of a dense material that appeared to lack a bilayer membrane that is found in traditional membrane bound vacuoles ( black arrow heads , Fig . S2 ) . However , it is possible that a membrane is either masked by the large amount of proteinaceous material present at the boundary , or not well preserved in these samples . In addition , microfilaments were observed to be adjacent to the vacuole boundary ( broad white arrows , Fig . S2 ) . Labeling of adult female B . malayi worms using mouse anti-wBm0152 antiserum demonstrated that the protein is present on the surface and in the areas surrounding Wolbachia ( Fig . 5D ) . Rabbit anti actin antibodies appeared to cross-react with a B . malayi actin protein within the tissue surrounding Wolbachia ( Fig . 5E ) . Notably , double labeling of similar cross-sections of B . malayi adult female worms with both antibodies co-localized the corresponding proteins to the surface of the vacuole that surrounds Wolbachia within the cytoplasm of the B . malayi host ( Fig . 5F ) . These results verify that the Wolbachia WSP proteins , wBm0432 and wBm0152 , interact with their corresponding complex partner proteins Bm-aldolase and Bm-actin within B . malayi . Previous studies have shown that aldolase not only catalyzes a key step in glycolysis but that it is also able to bind to F-actin in cells such as endothelial cells and fibroblasts , as well as in apicomplexan parasites [51]–[53] . Given this role in other organisms , the possible interaction between B . malayi aldolase and actin was explored . The overlay assay demonstrated that B . malayi aldolase binds specifically to actin ( Fig . 6 , Lane 3 ) but not to tubulin ( data not shown ) . The specificity of the goat anti-actin antibodies is shown in Figure 6 , which shows a specific interaction with actin ( Lane 1 ) but no cross-reaction with aldolase ( Lane 2 ) . This interaction between the filarial aldolase and actin might therefore provide a link between the two Wolbachia - B . malayi protein complexes we identified by LC-MS analysis and confirmed by other assays: wBm0432 with B . malayi glycolytic enzymes and wBm0152 with the B . malayi host cytoskeleton .
The filarial nematode and its endosymbiont are known to be co-dependent , but the cellular and molecular basis of this relationship has yet to be elucidated . Eliminating Wolbachia from the parasites using antibiotics affects molting , reproduction , and survival of the worms , indicating that the bacteria are crucial for the development of the parasite; thus , they represent an attractive target for control of the infections [20] , [54] , [55] . Wolbachia occupy the lateral cords of all stages of the filarial worms , and in female worms , they can be found in oocytes and embryonic stages within the uteri [56] . The Wolbachia OMPs , including the WSP-like family proteins were predicted to play an important role in communicating with the parasite to maintain homeostasis in the endosymbiotic relationship [36]–[39] . The Wolbachia surface protein wBm0432 was found to associate with six enzymes involved in glycolysis: fructose-bisphosphate aldolase , triosephosphate isomerase , L-lactate dehydrogenase , enolase , glyceraldehyde-3-phosphate dehydrogenase ( G3PD ) , and phosphoglycerate kinase . Notably , analysis of the available genome data revealed that Wolbachia lacks two glycolytic enzymes ( 6-phosphofructokinase and pyruvate kinase ) , and consequently its glycolytic pathway is thought to be defective and replaced by gluconeogenic enzymes [17] , [18] . Accordingly , the energy source utilized by Wolbachia will depend on products produced by the B . malayi glycolytic pathway , such as pyruvate . The ability of Wolbachia to sequester several GEs onto their surface by creating a complex with wBm0432 can increase the speed of glucose breakdown and thus synthesis of pyruvate . The pyruvate , once transported into the bacterial cell , can enter the TCA cycle , resulting in energy production [57] . The Wolbachia wBm0152 protein was found to form a complex with the B . malayi cytoskeleton proteins actin and tubulin . This finding is concordant with many previous studies that have demonstrated a close association of Wolbachia and other intracellular bacteria with the host cell cytoskeleton . Both actin and tubulin are known to play an important role in distribution of intracellular organisms [58]–[63] . Rickettsia , obligate intracellular gram-negative bacteria and close relatives of Wolbachia , exhibit actin-based motility in the cytosol of host cells involving the RickA surface protein [60] , [62] . Listeria monocytogenes and Shigella flexneri bacteria are internalized first into the host cells and then rapidly escape from the internalization vacuole into the cytosol , where they polymerize actin on their surface and initiate actin-based motility [64] . This property is not only restricted to Rickettsia , Listeria and Shigella but it also applies to other pathogens including apicomplexa and mycobacterial species such as Mycobacterium marinum and Burkholderia pseudomallei [59] , [62] . The functional interactions between Wolbachia and the host microtubules have been well documented in arthropods where Wolbachia utilize microtubules for normal anterior localization in the Drosophila oocyte to ensure its transmission to the next generation [58] . Treatment with colchicine resulted in complete depolymerization of microtubules within the germ cells resulting in the failure of Wolbachia to localize to the anterior of the Drosophila oocyte . It was proposed that these interactions might also play a role in bacterial motility and replication , ultimately leading to their efficient maternal transmission [58] . However , the exact cellular and molecular mechanism underlying this association is still unknown . In the present study of B . malayi-wBm endosymbiotic relationship , we show that wBm0152 forms a complex with both actin and tubulin but that it interacts directly only with actin based on the overlay assays . Accordingly , we hypothesize that Wolbachia interact with the host microtubules indirectly through the wBm0152-actin link . In B . malayi , Wolbachia was previously shown to be present near the host's actin bundles and actin-rich rachis as determined by immunofluorescence [65] . It was shown that Wolbachia localize to the posterior of the egg upon fertilization and segregate asymmetrically during early embryogenesis in a lineage-specific manner . Therefore , it was speculated that these segregation patterns are responsible for determining the ultimate colonization of adult female tissues [65] . In this study we show that the WSP wBm0152 protein co-localized with actin to the surface of the vacuole that surrounds Wolbachia by immunoelectron microscopy . Hence , the interaction between wBm0152 and the host cytoskeleton might support wBm migration and segregation in host tissue during development , a process needed for its fitness and survival . WSP wBm0152 has been previously identified as a peptidoglycan associated lipoprotein ( PAL ) , which is instrumental in the induction of innate toll receptor-mediated immune responses to Wolbachia that are associated with the pathogenesis of the human filarial parasites [66] . The diacyl lipid moieties present on native wBm0152 have been shown to be important mediators in this response [66] . Thus , wBm0152 is likely to form an important part of the peptidoglycan layer of the Wolbachia cell wall , and as such it is expected to be tightly embedded into the peptidoglycan matrix . If this is the case , wBm0152 would become highly crosslinked in the experiments described above; perhaps explaining why no native peptides corresponding to wBm0152 were detected in the analysis of the crosslinked products immunoaffinity purified using columns containing antibodies raised against recombinant wBm0152 . Although the presence of wBm0152 on the outer surface of Wolbachia is expected based upon its functional classification as a PAL , the present immunolocalization studies also suggest that it is present as well in the vacuole surrounding the endosymbiont . This finding is in keeping with previous studies that have shown the production of secretory vacuoles from Wolbachia [67] and the fact that wBm0152 has previously been identified as a member of the secretome of the Wolbachia endosymbiont of B . malayi [40] . Together , these studies support the hypothesis that wBm0152 might play an important role in the association of the endosymbiont to the cytoskeleton of the host cell . Several reports have shown the dependency of nematode fitness on tubulin and actin functions . The targeting of β-tubulin in B . malayi adult worms and Haemonchus contortus larvae using the RNA interference ( RNAi ) technology led not only to a reduction in the levels of their transcripts but also to detrimental phenotypes [68] , [69] . RNAi targeting of B . malayi β-tubulin resulted in parasite death [68] while in H . contortus it resulted in decreased L3 worm motility that slowed their development to L4 , in comparison to control larvae [69] . RNAi targeting γ-tubulin in B . malayi resulted in cellular disorganization in embryos [70] . Similar effects were observed after knocking down transcript levels of actin ( Ls-act ) by RNAi in the rodent filaria Litomosoides sigmodontis [71] . Two phenotypes were seen with Ls-act targeted RNAi: paralysis , as demonstrated by the worm being stretched out and having slower movements and significant reduction in the release of microfilaria . It would be interesting to expand on these filarial RNAi studies and establish whether there is also a synergistic impact of the RNAi upon the biology of Wolbachia and its distribution within the filarial host . In mammalian tissues , the enzymes of the glycolytic pathway utilize cytoskeleton as a matrix to keep phosphofructokinase , aldolase and G3PD in an optimal alignment for rapid substrate conversion [72] , [73] . For instance , in red blood cells , several GEs ( GAPDH , aldolase , and phosphofructokinase ) assemble in complexes with the cell's cytoskeleton [72] , [73] , and their proximity with each other increases the speed of glucose breakdown . In previous studies of bovine brain tissue , aldolase , lactate dehydrogenase type-M , pyruvate kinase , and G3PD were shown to co-pellet with microtubules , with Kd values between 1 and 4 µM [74] . More recent studies have shown that enolase isoforms purified from mouse brain and mouse striated muscles interact with microtubules during muscle satellite cell differentiation [75] . Aldolase in other systems is known to play a dual role , participating in glycolysis as soluble enzyme and forming a complex with the actin cytoskeleton filaments ( F-actin ) in vitro and in vivo , when it is enzymatically inactive [76] , [77] . Aldolase is tetrameric and each monomer has the capacity to bind to F-actin [77] . In intracellular apicomplexan parasites , the translocation of parasites is facilitated by a link between cell surface adhesins , aldolase and actin where aldolase is a bridge between the adhesins and the cytoskeleton [51] . The data we present suggest that B . malayi aldolase might provide a link between the two protein complexes we identified in this study . Bm-aldolase binds to actin , while wBm0432 binds strongly to aldolase and possibly also to enolase and to some extent to G3PD and triosephosphate isomerase , but not to actin or tubulin . We therefore hypothesize that as in the apicomplexa , malaria and Toxoplasma [78] , [79] , aldolase might play a dual role also in the B . malayi-Wolbachia endosymbiotic relationship . In addition to its central role in glycolysis , aldolase might also mediate the interaction between Wolbachia and the host's cytoskeleton ( Fig . 7 ) . First , it may complex with the WSP wBm0432 protein and other GEs , providing Wolbachia with sequestered production of pyruvate and thus ATP . This would be in keeping with the “supplemental mitochondrion” hypothesis , which has been proposed as one role that the Wolbachia endosymbiont might play in the host-endosymbiont relationship [80] . It may also function as an anchor between Wolbachia and the B . malayi cytoskeleton using the ATP produced at the surface as an energy source to engage the actin cytoskeletal network to support its motility and distribution within the host . Future studies will verify the functional involvement of the wBm0432/glycolytic enzymes and wBm0152/cytoskeletal proteins in Wolbachia's transmission patterns within the B . malayi host . Additional studies will also be needed to validate the essential role of these two Bm-wBm interactomes for the survival of B . malayi and its co-dependency on Wolbachia . | The human filarial parasite Brugia malayi harbors a Wolbachia endosymbiotic bacterium that is required for normal reproduction and development . However , the molecular basis of how this essential endosymbiotic relationship is maintained is not understood . As a first step in trying to understand the molecular interactions that might be essential in this process , we focused on the Wolbachia surface proteins ( WSPs ) , which are known to be involved in bacteria-host interactions in other systems . Our aim was to determine whether there are any functional interactions between some of these WSPs and the proteins produced by the host parasite cells . We found that two of the WSP family members specifically interact with proteins produced by the host . Wolbachia wBm0432 interacted with several key enzymes involved in the host glycolytic pathway , the primary energy-producing pathway in the cell . Wolbachia wBm0152 interacted with the host cytoskeleton . These findings suggest that WSP family proteins might play important roles in both optimization of the energy production pathway in B . malayi as well as in anchoring the endosymbiont to the host's cytoskeleton . | [
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] | 2013 | A Potential Role for the Interaction of Wolbachia Surface Proteins with the Brugia malayi Glycolytic Enzymes and Cytoskeleton in Maintenance of Endosymbiosis |
Chagas disease , caused by the parasite Trypanosoma cruzi , can lead to long term cardiac morbidity . Treatment of children with benznidazole is effective , but no pediatric pharmacokinetics data are available and clinical pharmacology information on the drug is scarce . Prospective population pharmacokinetic ( PK ) cohort study in children 2–12 years old with Chagas disease treated with oral benznidazole 5–8 mg/kg/day BID for 60 days . ( clinicaltrials . gov #NCT00699387 ) . Forty children were enrolled in the study . Mean age was 7 . 3 years . A total of 117 samples were obtained from 38 patients for PK analysis . A one compartment model best fit the data . Weight-corrected clearance rate ( CL/F ) showed a good correlation with age , with younger patients having a significantly higher CL/F than older children and adults . Simulated median steady-state benznidazole concentrations , based on model parameters , were lower for children in our study than for adults and lowest for children under 7 years of age . Treatment was efficacious in the 37 patients who completed the treatment course , and well tolerated , with few , and mild , adverse drug reactions ( ADRs ) . Observed benznidazole plasma concentrations in children were markedly lower than those previously reported in adults ( treated with comparable mg/kg doses ) , possibly due to a higher CL/F in smaller children . These lower blood concentrations were nevertheless associated to a high therapeutic response in our cohort . Unlike adults , children have few adverse reactions to the drug , suggesting that there may be a direct correlation between drug concentrations and incidence of ADRs . Our results suggest that studies with lower doses in adults may be warranted . ClinicalTrails . gov NCT00699387
Chagas disease ( ChD ) is a parasitic infection caused by Trypanosoma cruzi . [1] Approximately 15 million people are affected with ChD in Latin America , with 10 , 000 annual deaths due to complications . [1] , [2] Infection occurs most commonly in children , by the vectorial or congenital route . Left untreated , ChD leads to cardiac and/or gastrointestinal morbidity and mortality years to decades later . [1] , [3] ChD is endemic in the Americas , including the US , but infected patients can also be found in Europe , Australia , Japan and other non-endemic countries due to migration . [4] , [5] , [3] , [6] Only two medications are currently available for the treatment of ChD , benznidazole and nifurtimox . [7] , [8] Even though both drugs were developed over 4 decades ago , there is little information available on their clinical pharmacology , particularly for special populations such as children . [8] , [9] Based on a small number of studies , treatment of pediatric ChD with benznidazole is considered to be effective and well tolerated , with observed response rates nearing 90% in some series . [10] , [8] , [9] , [11] , [12] , [13] However , treatment schedules are based on limited data , mostly coming from the only two pharmacokinetics ( PK ) studies conducted , with a limited number of adult patients . [14] , [15] No information on benznidazole PK is available for the pediatric population . [9] , [16] This lack of important data may lead to significant risks for children , a particularly vulnerable population . Given this knowledge void , we have conducted the first pediatric population PK study of benznidazole in a cohort of children with ChD .
The study was approved by the Ethics and Research Review Boards , Buenos Aires Children's Hospital “R Gutierrez” , and the Argentine National Drug and Food Administration ( ANMAT ) , Ministry of Health , Argentina . Written informed consent was required from patients' legal representatives , as well as assent from the patient when appropriate . The study was registered in clinicaltrials . gov ( #NCT00699387 ) . Adult data was obtained from the original benznidazole studies by Raaflaub et al . [14] , [15] , which contain tables with individual blood concentrations of benznidazole after single dose[14] and multiple dose ( 30 days treatment ) treatments [15] , in healthy volunteers ( N = 6 , all female ) and adult ChD patients ( N = 8 , 50% female ) , respectively . Mean weight of the individuals in these studies was 55 . 4 kg ( sd = 7 . 8 ) , and mean age was 33 . 3 years ( sd = 12 ) .
Forty children diagnosed with ChD were enrolled in the study ( Figure 1 ) . Thirty eight patients contributed 117 samples for PK analysis and 37 ( 93% ) completed 60 days of treatment ( one patient contributed PK samples before withdrawing due to an adverse drug reaction ) . Most patients ( 90% ) resided in the city of Buenos Aires or the greater Buenos Aires area , a non-endemic area for vector transmission; the remaining patients ( 10% ) were referred from rural areas . Route of infection was congenital in 55% of the patients , vectorial in 5% and undefined in 40% . Mean age was 7 . 3 years ( SD: 3 . 5 ) . Eighteen patients were girls ( 45% ) . Mean weight was 27 . 2 kg ( SD 12 . 8; range 11 . 5–64 . 0 kg ) . No subjects received chronic concomitant medications . All patients were asymptomatic with no cardiac involvement or other ChD–associated pathology , and no comorbidities or laboratory abnormalities , at enrollment . At diagnosis , qPCR was positive in 31/37 ( 84% ) patients for which samples were available . No initial samples were available for qPCR in the remaining 3 enrolled patients due to difficult blood extraction . Mean benznidazole dose was 6 . 4 mg/kg/day ( SD 1; range 5 . 0–8 . 7 ) in two divided daily doses . Good adherence was observed based on tablet count and treatment diary review . Treatment response was high , with all 37 patients who completed 60 days of treatment having negative qPCR at the end of treatment ( day 60 ) . All patients were followed for at least 1 . 5 years after treatment completion and a steady decrease of specific T . cruzi antibodies and persistently negative qPCR was observed . Also , qPCR was negative in all 21 patients for which samples were available after 2 years of treatment . No cardiac involvement and no long-term adverse consequences of treatment have been observed in any of the patients . Most of the patients still had positive antibodies at 2 years follow up ( albeit at lower titers than before treatment ) , a finding consistent with previous observations in this age group and in adults . This confirms that antibody tests require much longer periods of follow up for negative results to be observed , unlike disappearance of parasitemia which commonly occurs early after treatment . Four ( 10% ) patients had adverse drug reactions ( ADRs ) related to benznidazole , all mild ( 1 mild rash , 1 moderate prurigo , 1 generalized rash without systemic involvement , and 1 moderate eosinophilia ) . Mean age of children with ADRs was 8 . 6 years . Three out of four children with ADRs were over 7 years old . All ADRs subsided with symptomatic treatment ( antihistamines ) and temporary drug discontinuation , and all patients recovered uneventfully . In 2 cases rash reappeared with drug reintroduction , requiring patient withdrawal from the study ( Figure 1 ) . These 2 patients were successfully treated with nifurtimox later on , with good response . One further patient withdrew from the study due to maternal decision . Samples for population PK analysis were obtained from 38 patients ( 95% ) in our cohort , and 14 adult patients from the literature . [14] , [15] For 2 patients in our cohort , samples could not be obtained due to early withdrawal from the study . The total number of samples for PK analysis from our cohort was 117 , and 168 benznidazole plasma concentrations were obtained from published adult data . [14] , [15]
Observed benznidazole concentrations in children were markedly lower than those reported in adults ( treated with comparable mg/kg doses ) . In spite of these lower concentrations , treatment was effective and well tolerated , with few ADRs , a marked difference from adults . If confirmed , our results would suggest that further studies to evaluate dosing modifications in adults may be beneficial . | Chagas disease is a parasitic disease endemic to the Americas . Long term complications include severe cardiac involvement , which can lead to severe disability and death of affected individuals . Treatment options for Chagas disease are limited to 2 drugs ( benznidazole and nifurtimox ) , but knowledge of the pharmacology of these drugs is lacking , particularly in childhood . To date , no pharmacokinetics data are available in children . We conducted the first population pharmacokinetic study of benznidazole in children . Interestingly , we found that elimination of the drug is significantly faster in children than in adults , leading to lower plasma concentrations . However , unlike adults , all children in the study responded well and had few adverse reactions to the drug . These findings suggest that adjustment of current adult doses may be beneficial . | [
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"ph... | 2014 | Population Pharmacokinetic Study of Benznidazole in Pediatric Chagas Disease Suggests Efficacy despite Lower Plasma Concentrations than in Adults |
Severe fever with thrombocytopenia syndrome ( SFTS ) is an emerging hemorrhagic fever caused by a tick-borne bunyavirus ( SFTSV ) in East Asian countries . The role of human leukocyte antigen ( HLA ) in resistance and susceptibility to SFTSV is not known . We investigated the correlation of HLA locus A , B and DRB1 alleles with the occurrence of SFTS . A total of 84 confirmed SFTS patients ( patient group ) and 501 unrelated non-SFTS patients ( healthy individuals as control group ) from Shandong Province were genotyped by PCR-sequence specific oligonucleotide probe ( PCR-SSOP ) for HLA-A , B and DRB1 loci . Allele frequency was calculated and compared using χ2 test or the Fisher's exact test . A corrected P value was calculated with a bonferronis correction . Odds Ratio ( OR ) and 95% confidence intervals ( CI ) were calculated by Woolf’s method . A total of 11 HLA-A , 23 HLA-B and 12 HLA-DRB1 alleles were identified in the patient group , whereas 15 HLA-A , 30 HLA-B and 13 HLA-DRB1 alleles were detected in the control group . The frequencies of A*30 and B*13 in the SFTS patient group were lower than that in the control group ( P = 0 . 0341 and 0 . 0085 , Pc = 0 . 5115 and 0 . 252 ) . The ORs of A*30 and B*13 in the SFTS patient group were 0 . 54 and 0 . 49 , respectively . The frequency of two-locus haplotype A*30-B*13 was lower in the patient group than in the control group ( 5 . 59% versus 12 . 27% , P = 0 . 037 , OR = 0 . 41 , 95%CI = 0 . 18–0 . 96 ) without significance ( Pc>0 . 05 ) . A*30-B*13-DRB1*07 and A*02-B*15-DRB1*04 had strong associations with SFTS resistance and susceptibility respectively ( Pc = 0 . 0412 and 0 . 0001 , OR = 0 . 43 and 5 . 07 ) . The host HLA class I polymorphism might play an important role with the occurrence of SFTS . Negative associations were observed with HLA-A*30 , HLA-B*13 and Haplotype A*30-B*13 , although the associations were not statistically significant . A*30-B*13-DRB1*07 had negative correlation with the occurrence of SFTS; in contrast , haplotype A*02-B*15-DRB1*04 was positively correlated with SFTS .
Sever fever with thrombocytopenia syndrome ( SFTS ) is an emerging infectious disease in China , South Korea and Japan [1–3] . SFTS is caused by a novel bunyavirus , SFTS virus ( SFTSV ) , which is transmitted through tick bite [1 , 4–6] . Shandong Province is the second highest incidence area of SFTS in China . Since starting surveillance on SFTS patients in 2010 , 761 SFTS cases were reported in Shandong Province from 2011 to 2014 with annual case fatality of 12 . 5% [7] . Number of reported SFTS cases was rising and SFTS inflict areas was expanding from the initial 6 cities in 2010 to 15 cities in 2014 among 17 cities in Shandong Province . SFTS is a hemorrhagic fever disease with fever and thrombocytopenia as the main clinical manifestations . The body temperature of most SFTS cases usually exceeds 38°C . Over 70% of the patients have fever >39°C [8 , 9] . Patients often had headaches , muscle aches , gastrointestinal symptoms such as lack of appetite , nausea , vomiting , abdominal pain , diarrhea and hematochezia , leukopenia , liver and kidney dysfunction . The vast majority of patients has a good prognosis and recovered . On the contrary , some patients have a poor prognosis because of accompanied by basic diseases , older ages , the emergence of neuropsychiatric symptoms , bleeding tendency obviously and hyponatremia . Those patients who had severe bleeding tendency and in critical condition might die of multiple organ failure [1] . The overall mortality rate of SFTSV infection is about 12% , ranging from 6 . 3% to 30 . 0% in previous studies [10 , 11] . A sustained serum viral load may indicate that disease conditions will worsen and lead to death [8] . In multivariate analysis , the odds for SFTS were 2 . 4~4 . 5 fold higher with patients who reported tick bites or presence of tick in the living area [12] . Our previous results revealed that age was the critical risk factor or determinant for SFTS morbidity and mortality [13] . However , the mechanism of susceptibility to SFTSV is not clear . There is no evidence on the role of T cells in the pathogenesis of SFTS because SFTSV is a newly discovered virus . Most studies have focused on humoral immunity and innate immunity of SFTSV . We are not aware any study on T cell immunity of SFTSV . The human leukocyte antigens ( HLA ) were the human versions of the major histocompatibility complex ( MHC ) genes that were found in most vertebrates . The HLA genes encoded cell-surface antigen-presenting proteins , which regulated the immune system in humans and were essential elements for immune function . HLA was highly polymorphic and was significantly different in populations in different geography , ethnic and race [14–16] . HLA determined the individual differences in susceptibility to pathogens or diseases . Studies showed that HLA was related to ankylosing spondylitis , diabetes , psoriasis and other autoimmune diseases [17–19] . It also correlated to AIDS , hepatitis B and other viral infections [20–22] , but the correlation between HLA polymorphism and SFTS had not yet investigated . In this study , we analyzed the frequency of three important HLA alleles and haplotypes comprised by these alleles in SFTS patients and healthy individuals to determine whether HLA alleles and/or haplotypes correlated to the occurrence of SFTS .
Subjects in the study were all adults . SFTS samples were collected for disease surveillance and disease diagnosis . Serum samples from healthy individuals ( non-SFTS patients ) who were volunteers of China Marrow Donor Program ( CMDP ) were collected for HLA matching . All samples from SFTS cases and healthy individuals were pre-existing relative to the start of the study , and were examined as anonymous samples . The study was approved by the Ethic Committee of Preventive Medicine of Shandong Center for Disease Control and Prevention ( no . 2011–12 ) . All infected adults subjects had signed a written informed consent document for collecting their serum specimen . All patients and healthy persons were from Shandong Province , which located in the eastern coast of China between north latitude 34° 22 . 9 '- 38° 24 . 01' and longitude 114° 47 . 5 '- 122° 42 . 3' . The province consisted of 15 . 50% mountain area , 13 . 20% of hill area , 55% plains , 4 . 10% depression area , 4 . 40% lake plain , and 7 . 80% other area . Shandong's climate was temperate monsoon types with annual average temperature between 11°C and 14°C , annual average rainfall between 550 and 950 mm . As of December 2013 , the resident population in the province was 97 . 33 million . Han Chinese was the dominant population with 0 . 70% ethnic minorities in the total population of Shandong Province . 84 cases had whole blood specimens and were used in this study for HLA allele typing . The patient group consisted of 84 SFTS patients , who were diagnosed at local hospitals in 2013 in Shandong Province and reported to the China Information System for Diseases Control and Prevention . All SFTS patients were laboratory confirmed for bunyavirus , SFTS virus ( SFTSV ) . The control group consisted of 501 unrelated healthy individuals ( non-SFTS patients ) who were volunteers of China Marrow Donor Program ( CMDP ) and their blood was available and obtained from the Blood Center of Shandong Province . All participants including SFTS patients and healthy individuals were native Han ethnic Chinese from Shandong Province . Human blood DNA was extracted from whole blood using EZBeadTM whole blood DNA extraction kit ( Texas BioGene Inc , Richardson , Texas ) . DNA concentration was 20–100ng/μL and DNA purity was 1 . 70 to 1 . 85 at OD260 / OD280 . Patients and healthy individuals’ HLA-A , B and DRB1 alleles were genotyped using PCR-SSOP methods with low resolution LABType SSO Typing Tests ( One Lambda Inc . , Canoga Park , CA ) according to the manufacturer’s instructions . The test results were analyzed with HLA Tools ( One Lambda Inc . , Canoga Park , CA ) . HLA allele frequencies ( AF ) were calculated and haplotype frequencies ( HF ) in patients and healthy individuals were estimated using the maximum-likelihood method with the expectation-maximization ( EM ) algorithm in the Arlequin V3 . 5 software . The frequency difference between the patient group and control group was compared using χ2 test or the Fisher's exact test . The extent of correlation of HLA alleles and haplotypes between the patient group and the control group was indicated by the odds ratio ( OR ) , which was obtained by Woolf’s method . A corrected P value ( Pc ) was further to be calculated with a bonferronis correction by multiplying the P-value with the number of alleles tested for each locus .
In 2013 , 296 SFTS cases were clinically reported in Shandong Province including 85 clinical diagnosed cases and 211 laboratory confirmed cases . 84 cases had whole blood specimens and were used in this study for HLA allele typing . The 84 confirmed cases were all Han ethnic Chinese from Shandong Province including 7 . 14% ( 6/84 ) death cases . Majority of patients were from Weihai City ( 65 . 48% , 55/84 ) and Tai’an City ( 17 . 86% , 15/84 ) . The remaining 14 patients were from five other cities . Patients ranged from 28 years old to 84 years old with median age of 62 years old . Patients’ age distribution was summarized in Table 1 . Majority of patients went to clinic for treatment within 5 days ( 58 . 33% , 49/84 ) to 10 days ( 86 . 90% , 73/84 ) after onset of illness . The clinical manifestations of the patients included fever , dizziness , headache , nausea , vomiting , fatigue , muscle aches , cough , sputum and gastrointestinal symptoms of anorexia , diarrhea , and abdominal pain . The clinical manifestations of 52 SFTS cases with complete information were listed in Table 2 . A total of 11 HLA-A alleles were detected in the patient group and 15 HLA-A alleles were detected in the control group . Six HLA-A alleles ( A*01 , A*02 , A*11 , A*24 , A*30 and A*33 ) were all found with a frequency greater than 5% in both the SFTS patient group and the control group , with a cumulative frequency of 86 . 30% and 84 . 43% , respectively . HLA-A locus was dominated by the A*02 allele with a frequency of 28 . 57% and 27 . 25% in the SFTS patient group and the control group , respectively . The next five most common alleles were A*24 ( 17 . 26% ) , A*11 ( 16 . 07% ) , A*33 ( 10 . 71% ) , A*30 ( 8 . 33% ) and A*01 ( 5 . 36% ) in the SFTS patient group , and A*30 ( 14 . 37% ) , A*11 ( 13 . 87% ) , A*24 ( 12 . 87% ) , A*33 ( 10 . 28% ) and A*01 ( 5 . 79% ) in the control group . Statistical analysis of the frequency of different HLA-A alleles in the patient and control groups indicated that the frequency of A*30 in the patient group ( 8 . 33% ) was lower than that in the control group ( 14 . 37% ) and the difference between two groups was noted ( P = 0 . 0341 , OR = 0 . 54 , 95%CI , 0 . 30–0 . 96 ) . However , A*30 did not reach statistically significant after Bonferroni correction ( Pc = 0 . 5115 ) . Other HLA-A alleles were not significantly different between the patient group and the control group ( Table 3 ) . For HLA-B , a total of 23 HLA-B alleles were identified in the SFTS patients and 30 HLA-B alleles were identified in the control group . The highest frequencies of HLA-B antigen specificities in the patient group were as follow: B*15 ( 14 . 88% ) , B*40 ( 13 . 1% ) , B*13 ( 9 . 52% ) , B*51 ( 9 . 52% ) , B*46 ( 7 . 14% ) , B*35 ( 5 . 95% ) and B*44 ( 5 . 36% ) , consisting of 65 . 47% of the HLA-B alleles of the patient group . In the control group , the most common alleles in descending order were B*13 ( 17 . 66% ) , B*15 ( 14 . 47% ) , B*40 ( 11 . 38% ) , B*51 ( 6 . 99% ) , B*44 ( 6 . 39% ) and B*35 ( 5 . 29% ) consisting of 62 . 18% of HLA-B alleles of the control group . In the patient groups , a 1 . 87 fold decrease was observed with HLA-B*13 allele compared to healthy controls ( P = 0 . 0085 , OR = 0 . 49 , 95%CI = 0 . 29–0 . 84 ) . Although we observed the negative association , B*13 did not reach statistically significant after Bonferroni correction ( Pc = 0 . 252 ) ( Table 4 ) . For HLA- DRB1 alleles , 12 alleles were detected in SFTS patients and 13 alleles were detected in the control group . The most prevalent DRB1 genes in the patient group were DRB1*15 ( 20 . 83% ) , DRB1*07 ( 14 . 29% ) , DRB1*04 ( 11 . 9% ) , DRB1*12 ( 10 . 12% ) , DRB1*13 ( 6 . 55% ) , DRB1*14 ( 5 . 95% ) and DRB1*08 ( 5 . 36% ) , which were found in 75% of SFTS patients . The most prevalent DRB1 genes in the control group were DRB1*15 ( 19 . 76% ) , DRB1*07 ( 17 . 37% ) , DRB1*12 ( 9 . 98% ) , DRB1*04 ( 8 . 58% ) , DRB1*13 ( 7 . 78% ) , DRB1*08 ( 6 . 39% ) and DRB1*14 ( 5 . 49% ) , which were found in 75 . 35% of healthy persons in the control group . DRB1*15 was the most common HLA- DRB1 allele in the patient group ( 20 . 83% ) and in the control group ( 19 . 76% ) . There were no positive or negative associations of HLA-DRB1 alleles were observed between patient group and control group ( Table 5 ) . Table 6 shows the comparison of two-locus and three-locus haplotype frequencies in SFTS patient group and control group . For HLA-A-B , the HLA-A*30-B*13 haplotype was found with a lower frequency in SFTS patient group than in control group ( 5 . 59% versus 12 . 27% , P = 0 . 037 , OR = 0 . 41 , 95%CI = 0 . 18–0 . 96 ) . It displayed association with SFTS resistance . But HLA-A*30-B*13 did not reach statistic significant after Bonferroni correction ( Pc >0 . 05 ) . For HLA-A-DR , no association was noted between two groups . Through statistical analysis of the frequency of HLA-B*-DR* haplotypes , haplotype B*15-DRB1*04 displayed association with SFTS susceptibility ( P = 0 . 0224 , OR = 2 . 95 , 95%CI = 1 . 12–7 . 77 ) . Otherwise , the difference was not statistically significant after multiple comparisons ( Pc>0 . 05 ) . Comparing the frequency of HLA-A*-B*-DR* haplotypes in the patient group and the control group , the results showed A*30-B*13-DRB1*07 and A*02-B*15-DRB1*04 had strong associations with SFTS resistance and susceptibility respectively ( Pc = 0 . 0412 and 0 . 0001 , OR = 0 . 43 and 5 . 07 ) . Although there were differences of the other 5 three-locus haplotypes including A*02-B*46-DRB1*09 , A*02-B*50-DRB1*07 , A*02-B*40-DRB1*15 , A*33-B*44-DRB1*07 and A*24-B*15-DRB1*04 among two groups , they did not reach statistic significant ( Pc>0 . 05 ) ( Table 6 ) .
HLA are cell surface transmembrane glycoproteins and these glycoproteins can bind peptides from inside and outside the cells to form HLA-polypeptides; antigen presenting cells transfer the polypeptide complex to T cells , which stimulates T cell’s differentiation and development , triggering immune response and adjusting the intensity of the immune response . Therefore , HLA determine the outcome of the infection of pathogenic microorganisms . HLA genes are divided into three categories: HLA class I genes encoded HLA molecules are widely distributed in the surface of nucleated cells; HLA class II genes encoded molecules are mainly distributed in the antigen presenting cells and activated T cell surface; HLA class III genes encode complement components . We analyzed the association of SFTS with human leukocyte antigens ( HLA ) because HLA correlates to AIDS , hepatitis B and other viral infections , but the correlation between HLA polymorphism and SFTS has not yet investigated . SFTS is an emerging infectious disease and has been reported in 23 provinces in China with most cases came from central China including Henan , Shandong , Hubei , Anhui , Liaoning , Zhejiang and Jiangsu provinces . Most cases ( 88 . 3% ) are famers and the distribution of the disease is associated with geography [7] . Clinical symptoms of SFTS include subclinical infection , mild , severe and fatal infections . The mechanisms of pathogenesis of different clinical symptoms are not clear . In this study we analyzed the correlation of HLA alleles with the occurrence of SFTSV infection . We selected 84 cases of SFTS patients and 501 healthy individuals as control to genotype the HLA-A , B , DRB1 alleles . Song et al . reported that A*02 was the most common allele with a frequency of 28 . 86% in northern Han Chinese population [23] . Our results also confirmed it . The HLA-A*02 has the highest frequency of 28 . 57% in patient group and of 27 . 25% in control group . But there was no difference between two groups . The results showed the frequencies of HLA-A*30 and HLA-B*13 were lower in SFTS patient group than in control group , indicating that HLA-A*30 and HLA-B*13 may confer resistance to SFTS , although no significant differences were observed after Bonferroni correction . Alleles A*23 , A*32 , A*69 , A*74 , B*14 , B*18 , B*42 , B*45 , B*47 , B*49 , B*67 , B*81 and DRB1*10 were not detected in the patient group . A*32 has a high frequency in the control group , but was not detected in the patient group . Similarly , B*67 and DRB1*10 also had high frequency in the control group , but were not detected in patient group . The frequency of these alleles were not significantly different between the two groups and their difference between the two groups was most likely caused by small sample size of the patient group , which need to be further explored for their correlation with SFTS by increasing SFTS patient sample size in future study . In northern Han Chinese population , the most common HLA-A-B haplotypes ( HF>0 . 0300 ) were A* 30-B* 13 , A* 02-B* 46 , A* 33-B* 58 , A* 33-B* 44 , A* 02-B* 40[20] . We also found these HLA haplotypes were most common in the SFTS patient group and in the control group except for HLA-A* 33-B* 58 . Through statistical analysis of the frequency of HLA-A-B haplotypes , we found that A*30-B*13 was less common in the patient group than in the control group ( P = 0 . 0347 , OR = 0 . 41 , 95CI , 0 . 18–0 . 96 ) and the frequencies of these displayed HLA-A-B and HLA-A-DR haplotypes were not significantly different between the patient group and the control group by statistic analysis . Moreover , haplotype A*30-B*13-DRB1*07 was significantly less commonly distributed in the SFTS patients group ( P = 0 . 0002 , Pc = 0 . 0412 , OR = 0 . 43 ) , indicating the haplotype negatively related to the incidence of SFTS as well as Zhang et al . reported that HLA haplotype A*30-B*13-C*06 confers HIV-1 infected patients with a long-term non-progressing condition [24] . Miao et al . showed that the frequency of HLA-B*13:01:01G increased significantly in HBsAg clearance group than that in the persistent group ( 8 . 57%versus 3 . 46% , P = 0 . 0004 , OR = 2 . 62 , 95% CI: 1 . 51–4 . 54 ) [25] . Chiewsilp et al . also reported a negative relationship for HLA-B*13 with dengue shock syndrome ( DSS ) and/ or dengue hemorrhagic fever ( DHF ) [26] . These studies suggested that B*13 allele and haplotype A*30-B*13-C*06 is a protective factor against AIDS and hepatitis B , respectively . Our results and previous results indicate that HLA-A*30 , HLA-B*13* , and A*30 -B*13* haplotypes play important roles in the outcome of viral infection . However , the role of HLA-B*13 in dengue virus infection is not the same . Appanna et al . demonstrated that HLA-B*13 is probably associated in dengue hemorrhagic fever susceptibility [27] . The specific mechanisms of HLA-B*13 in different viral infections need to be further studied . On the other hand , another three-locus haplotypes A*02-B*15-DRB1*04 showed strong associations with SFTS susceptibility ( Pc = 0 . 0001 ) . It suggested that this haplotype might have caused individuals more susceptible to SFTS . Using low resolution typing method , we preliminary showed that A*30 and B*13 might have negative correlation with the occurrence of SFTS . Our results have expanded the knowledge of the association of HLA genes with SFTS . Our study may be further expanded by increased sample size and using high resolution typing method to verify the correlation between HLA -A*30 and B*13 alleles and SFTS and identify susceptible genes . Currently , there is no vaccine for SFTSV . Our work tried to disclose the association between SFTS and HLA . Identifying SFTS associated HLA alleles will potentially allow to define the SFTSV epitopes that are restricted by the specific HLA alleles . These HLA restricted epitopes of SFTSV ( especially CTL epitopes ) may be incorporated into vaccine design to prevent SFTSV infection[28 , 29] . | Severe fever with thrombocytopenia syndrome ( SFTS ) is an emerging hemorrhagic fever caused by a tick-borne bunyavirus ( SFTSV ) in East Asian countries . The role of human leukocyte antigen ( HLA ) in resistance and susceptibility to SFTSV is not known . In this study , we investigated the correlation of HLA locus A , B and DRB1 alleles with the occurrence of SFTS . Our results have expanded the knowledge of the association of HLA genes with SFTS . Our study may be helpful to state the relationship between the occurrence of SFTS with HLA alleles or haplotypes and provide scientific basis for study on pathogenesis and vaccine development . | [
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"and",... | 2016 | Correlation Between HLA-A, B and DRB1 Alleles and Severe Fever with Thrombocytopenia Syndrome |
The HIV-1 envelope glycoprotein ( Env ) trimer is located on the surface of the virus and is the target of broadly neutralizing antibodies ( bNAbs ) . Recombinant native-like soluble Env trimer mimetics , such as SOSIP trimers , have taken a central role in HIV-1 vaccine research aimed at inducing bNAbs . We therefore performed a direct and thorough comparison of a full-length unmodified Env trimer containing the transmembrane domain and the cytoplasmic tail , with the sequence matched soluble SOSIP trimer , both based on an early Env sequence ( AMC011 ) from an HIV+ individual that developed bNAbs . The structures of the full-length AMC011 trimer bound to either bNAb PGT145 or PGT151 were very similar to the structures of SOSIP trimers . Antigenically , the full-length and SOSIP trimers were comparable , but in contrast to the full-length trimer , the SOSIP trimer did not bind at all to non-neutralizing antibodies , most likely as a consequence of the intrinsic stabilization of the SOSIP trimer . Furthermore , the glycan composition of full-length and SOSIP trimers was similar overall , but the SOSIP trimer possessed slightly less complex and less extensively processed glycans , which may relate to the intrinsic stabilization as well as the absence of the membrane tether . These data provide insights into how to best use and improve membrane-associated full-length and soluble SOSIP HIV-1 Env trimers as immunogens .
The HIV-1 envelope glycoprotein ( Env ) trimer is the target of broadly neutralizing antibodies ( bNAbs ) that arise during HIV-1 infection and is , therefore , an attractive immunogen for vaccine design . Previous studies have reported that bNAbs provide passive protection from viral challenges in macaques [1–3] . One approach to induce protective bNAbs is the use of Env-based vaccines that mimic native Env on the virus . Previously , we described a soluble Env trimer , BG505 SOSIP . 664 , which contains an I559P substitution in gp41 that stabilizes the prefusion state of Env and a disulfide bond that covalently links the two subunits of the Env protein , gp120 and gp41 [4–6] . Soluble SOSIP trimers from different clades have now been described and characterized biophysically and biochemically [4 , 7–11] . The high resolution structures of several of these SOSIP trimers have also been solved by cryo-electron microscopy ( cryo-EM ) and x-ray crystallography , enabling structure-based Env trimer vaccine design [7 , 11–17] . Soluble SOSIP Env trimers have been tested as immunogens in animals and elicited neutralizing antibody ( NAb ) responses against the autologous viruses but generally not against heterologous Tier-2 viruses [10 , 18–22] . Native-like soluble Env trimers , such as SOSIP trimers , lack the membrane proximal external region ( MPER ) , a target for broadly neutralizing antibodies , the transmembrane domain ( TM ) , and the cytoplasmic tail . It is not entirely clear whether or how the absence of these domains and the presence of the SOSIP mutations influence the detailed properties of the trimers . Cryo-EM studies showed that a membrane-derived trimer that lacked the cytoplasmic tail and that was bound to bNAb PGT151 closely resembled SOSIP trimers at the structural level [23] , but the lack of sufficient amounts of protein prevented a detailed antigenic and glycan characterization of these membrane-derived trimers . There is only limited data available comparing the antigenic and biophysical properties of SOSIP trimers versus corresponding full-length trimers because full-length trimers , as well as virion-derived trimers , are difficult to purify in sufficient quantities to perform such comparative studies . In this study we compared the structural , biochemical and biophysical properties of a highly expressed full-length Env trimer from an elite neutralizer who was infected with a subtype B virus ( AMC011 ) [24 , 25] , with those of the corresponding sequence matched AMC011 SOSIP trimer . Using cryo-EM we generated structural models of the full-length AMC011 Env trimer bound to either the PGT145 Fab or the PGT151 Fab . Both of these quaternary specific bNAbs bind to and stabilize a similar conformation that resembles all previously reported membrane-derived and soluble SOSIP Env structures . Furthermore , we investigated the composition of the glycan shield of full-length and SOSIP trimers by site-specific glycosylation profiling using mass spectrometry , and the antigenic structure by bio-layer interferometry ( BLI; Octet ) , flow cytometry and neutralization assays . The results show that the antigenic profile and glycan composition of full-length and SOSIP trimers was similar , but also revealed subtle and interesting differences . In a similar manner to previous observations comparing virally derived N-glycans and corresponding SOSIP trimers [26 , 27] , there was a noticeable decrease in the number and processing of complex-type glycans on the SOSIP trimer . This most likely can be attributed to its enhanced stability and reduced conformational flexibility due to the presence of the SOSIP mutations , as well as the lack of the membrane tether . Furthermore , in contrast to the full-length AMC011 trimer , the corresponding SOSIP trimer did not bind to a number of non-neutralizing antibodies ( non-NAbs ) , which probably also relates to the enhanced stability and reduced sampling of alternative conformations . These results should guide the use and improvement of full-length and soluble Env trimers as vaccine immunogens .
To allow for a direct structural , antigenic and biophysical comparison between SOSIP . 664 and full-length trimers , we selected the AMC011 clade B env gene , which is a consensus sequence of early Env sequences from an HIV-infected individual enrolled in the Amsterdam Cohort Studies on HIV/AIDS ( ACS ) [24 , 25] . The selection of the AMC011 sequence was based on three criteria . First , the patient that was infected with the AMC011 virus developed bNAbs and qualified as an elite neutralizer [24 , 25] . As such , this particular Env is of relevance for vaccine design aimed at inducing bNAbs . The ACS202 bNAb that was isolated from this patient targets the fusion peptide and the gp120-gp41 interface [24] . Second , the neutralization sensitivity of the AMC011 virus places it in the grey area between the Tier-1B and Tier-2 categories of neutralization sensitivity . While the AMC011 virus was initially categorized as a Tier-2 virus , typical of circulating difficult to neutralize viruses [24 , 28] , follow-up experiments using a broader set of antibodies and plasma reagents revealed that the AMC011 virus could be neutralized by some V3 and CD4-targeting antibodies , placing it in the Tier-1B category ( S1 Table ) . Because neutralization sensitivity relates to conformational plasticity of the Env trimer [28 , 29] , we argued that the Tier-1B status of the AMC011 virus would allow us to observe a discernable impact of SOSIP stabilization . Third , the full-length AMC011 trimer was expressed at high yields ( see below ) , making a thorough biophysical characterization feasible from a practical perspective . Using a previously described procedure for the purification of Env trimers from membranes [23] , we isolated wild-type , unmodified , full-length AMC011 Env bound to PGT151 Fab or PGT145 Fab from the surface of HEK293F ( Thermo Fisher Scientific ) cells that were transiently transfected with expression vectors for the full-length AMC011 gp160 and furin . bNAbs PGT145 and PGT151 both require a prefusion quaternary structure conformation , recognizing the trimer apex and fusion peptide-gp120-gp41 interface , respectively . In contrast to previous experiments using different Env sequences that resulted in only modest yields of purified full-length Env ( between ~45 and ~75 μg/L ) [30] , we successfully produced full-length AMC011 trimers at yields of ~350 μg/L using PGT151 Fab and ~65 μg/L using PGT145 Fab ( Fig 1A and 1B and S1A Fig ) . For comparative experiments , we also purified soluble AMC011 SOSIP . 664 trimers with PGT151 or PGT145 affinity chromatography columns , as previously described ( Fig 1B ) [10 , 31] . Next , we modified the cryo-EM protocol that was optimized for the JR-FL Env trimer lacking the cytoplasmic tail ( ΔCT ) [23] to be applicable for the full-length AMC011 Env trimer , which was reconstituted in to a homogeneously shaped bicelle . Specifically , full-length AMC011 trimers bound to the PGT151 Fab were solubilized from the cell membrane TX-100 detergent micelles , followed by exchange into n-Dodecyl-β-D-maltoside ( DDM ) micelles and finally reconstituted into a lipid bicelle containing different concentrations of 1 , 2-dioleoyl-sn-glycero-3-phosphocoline ( DOPC ) . While the incorporation of 0 . 3 mM of DOPC led to the formation of liposomes containing full-length Env , the incorporation of 0 . 1 mM of DOPC facilitated the formation of bicelles with different sizes ( Fig 1C and 1D ) . Next , we used DOPC in a 1:1 molar combination with cholesteryl hemisuccinate ( CHS ) , which resembles the lipid composition of microdomains present at the cell membrane of infected CD4+ T cells [32] . The detergent exchange from DDM to DOPC:CHS of the full-length Env trimer sample led to the generation of homogeneously shaped bicelles , which surround the transmembrane domains of the Env protein and increased the stability of the full-length Env trimer , as shown by an elevation of the midpoint of thermal denaturation ( Tm ) from ~56 . 1°C to ~63 . 1°C , reaching similar stability levels as soluble AMC011 SOSIP . 664 ( Tm value of 61 . 8°C ) ( Fig 1C and 1D and S1B Fig ) . Reconstitution of the full-length Env trimer bound to PGT145 in DOPC:CHS led to the formation of similarly homogeneous shaped bicelles and also increased thermostability ( Tm value of ~60 . 4°C ) ( Fig 1E and 1F ) . Finally , negative stain ( NS ) -EM reconstructions of the trimer Fab complexes in DOPC:CHS showed that both bNAbs PGT151 and PGT145 bound to the full-length Env trimer in an asymmetric manner as previously described , with two and one Fab per trimer , respectively ( Fig 1G ) [33 , 34] . The 3D reconstruction of a seemingly open negative stained PGT151-bound trimer is a reconstruction artifact due to the lack of sufficient top views in the presence of the bicelle . Importantly , this artifact is not present in the cryoEM reconstruction described below . Overall , while the wild type , full-length Env trimers are inherently unstable and cannot be purified easily , the addition of the quaternary PGT151 or PGT145 Fabs together with the use of DOPC and CHS , resulted in the purification of stable complexes . The AMC011 SOSIP . 664 trimer , which was purified with the PGT151 affinity chromatography column , displayed Tm value of 61 . 8°C . The trimer was however more stable than the full-length trimer when analyzed in complex with PGT151 or PGT145 ( Tm values of 73 . 6°C and 67 . 0°C , respectively; increases of 10 . 5°C and 6 . 6°C compared to the full-length trimer in complex with PGT151 or PGT145; increases of 11 . 8°C and 5 . 2°C compared to SOSIP . 664 alone ) ( S1B Fig ) . We compared the conformation of the full-length and soluble SOSIP . 664 trimers by negative-stain ( NS ) -EM and found that all complexes yielded similar 2D class-averages , suggesting that neither antibody nor the presence or absence of the bicelle biased the trimer conformation ( Fig 1D and 1F and S1B Fig ) . BLI was used to assess binding of a panel of well-characterized bNAbs and non-NAbs ( n = 21 in total ) to the purified SOSIP . 664 and full-length trimers in solution . We defined non-NAbs as antibodies that cannot neutralize typical neutralization-resistant Tier-2 viruses . Such non-NAbs might , however , neutralize Tier-1 viruses , and in fact the Tier-1B virus AMC011 is neutralized by some of these non-NAbs ( see below and S1 Table ) . We also note that the BLI experiments were carried out with unliganded SOSIP . 664 trimers ( purified with PGT151 affinity chromatography column ) and full-length trimers bound to either PGT145 Fab or PGT151 Fab , resulting in competition effects when analyzing the binding of mAbs to overlapping epitopes [35] . Overall , we observed that full-length and SOSIP . 664 trimers were very similar antigenically , with a few interesting exceptions that are specified below . First , we determined the binding of quaternary antibodies that target the apex of the trimer ( PGT145 , PGDM1400 and PG16 ) [34 , 36] . All three bNAbs bound to the full-length trimer that was bound to PGT151 Fab , but as expected PGT145 and PGDM1400 did not bind to the trimer that was already bound to PGT145 ( Fig 2A and S2 Fig ) . While PGT145 and PGDM1400 showed increased recognition of the SOSIP . 664 compared to the full-length trimer , PG16 showed increased binding for the full-length trimer ( Fig 2A ) . We also determined binding of bNAbs that target the outer-domain and the V3 glycan region of the Env trimer [37–40] . While PGT121 , PGT122 and PGT128 , which target the N332 glycan outer domain region , bound similarly to the full-length and the SOSIP . 664 trimer , PGT135 and PGT136 , which also target the N332 glycan , but from a different angle of approach , showed an increased binding to full-length compared to SOSIP . 664 trimers ( Fig 2A ) . The interaction of these antibodies with glycans at N386 and N392 is critical for neutralization [38 , 41] . Therefore , it is likely that differences in N392 and/or N386 might explain the reduced binding to the SOSIP trimer . The presence of a glycan of increased complexity in the N392 of full-length trimer might favor the accommodation of the glycan in the PGT135 epitope ( see below ) . However , there is no coverage for the N386 glycan in the full-length construct in the mass spectrometry glycopeptide analyses . Next , we tested the binding of the broadly neutralizing antibody isolated from the AMC011 elite neutralizer , ACS202 , which targets the fusion peptide and the glycan at position 88 [24] . As expected , PGT151 , ACS202 , 35O22 and VRC34 , which target the interface of the Env trimer , showed little to no binding full-length trimer that was already bound to PGT151 Fab . On the other hand , ACS202 , PGT151 , 35O22 and VRC34 interacted strongly with the PGT145 bound full-length trimer . Furthermore , ACS202 bound to the full-length Env trimer with a higher on-rate than to the SOSIP . 664 ( Fig 2A and S2 Fig ) . Since this antibody binds to the glycan at position N88 , this difference could be explained by the presence of different glycoforms at position N88 on full-length and SOSIP . 664 trimers ( see below ) . On the other hand , VRC34 , which also targets the fusion peptide and the N88 glycan [42] , showed similar binding to both SOSIP . 664 and full-length trimer ( Fig 2A ) . The membrane proximal external region ( MPER ) is only present in the full-length Env trimer . Hence , the MPER-directed antibody 4E10 only showed binding to the full-length Env trimer and not to the SOSIP . 664 trimer ( Fig 2A ) . We also tested the binding of five antibodies directed to the CD4 binding site ( CD4bs; b6 and F105 ) , the CD4-induced site ( CD4i; 17b ) and the V3 ( 14e and 19b ) that typically only neutralize Tier-1 viruses ( easy to neutralize viruses ) and that we term non-NAbs . Except for b6 , all the non-NAbs bound to the full-length AMC011 trimer by BLI , a finding that is probably related to the Tier-1B status and intrinsic conformational flexibility . In the particular case of full-length AMC011 Env bound to PGT145 Fab the binding of non-NAbs could also be related to the presence of a very small portion of dimeric Env ( see below ) . Conversely , the non-NAbs showed a lower binding to the SOSIP . 664 trimer compared to the full-length AMC011 trimer . Finally , in order to assess potential differences in antigenicity between the SOSIP . 664 Env trimer and SOSIP . 664 Env trimer bound to the PGT151 Fab we assessed the binding of the entire panel of antibodies against both constructs . Overall , antigenicity was similar to the SOSIP . 664 , and , as expected , we observed a lower binding of interface antibodies due to the presence of PGT151 Fab bound to the SOSIP . 664 trimer ( S2A Fig ) . We compared the antigenicity of full-length trimers bound to either PGT145 Fab or PGT151 Fab . For comparison , we omitted the antibodies that competed with the Fabs bound to the full-length trimers because of overlapping epitopes ( i . e . PG16 , PGDM1400 and PGT145 for Env bound to PGT145 Fab , and VRC34 , ACS202 , 3BC315 and 35O22 for Env bound to PGT151 Fab ) . A correlation between the BLI values against the two full-length trimers was observed ( Spearman correlation coefficient r = 0 . 7345 , p = 0 . 0100 , S3 Fig ) , indicating that full-length trimers purified in complex with PGT145 or PGT151 Fab showed similar antigenic profiles . When full-length and SOSIP . 664 AMC011 trimers were compared , the correlation was weak ( r = 0 . 5317 , p = 0 . 0231; Fig 2B ) , likely the result of binding of the non-NAbs and the MPER-directed 4E10 bNAb to full-length , but not to SOSIP . 664 Env trimers . Indeed , when the non-NAbs and 4E10 were excluded , the correlation between binding to full-length and SOSIP . 664 trimers was strong ( r = 0 . 7664 , p = 0 . 0111; Fig 2C ) . To provide a frame of reference for these BLI data , we assayed the same panel of bNAbs and non-NAbs in neutralization assays with the parental AMC011 virus , and performed binding assays to full-length AMC011 trimers on the surface of transfected cells by FACS analysis ( Fig 3A ) . To correlate parameters from the different assays we used maximal response values derived from BLI assays ( plateau ) and area under the curve ( AUC ) values from FACS assays and neutralization assays . A very strong correlation was observed when comparing antibody binding to membrane-bound AMC011 trimers by FACS analysis versus AMC011 virus neutralization in TZM-bl assays ( r = 0 . 8343 , p<0 . 0001 , Fig 3B ) , indicating that prior to purification from the membrane , the full-length AMC011 Env trimer was a close mimic of the functional AMC011 trimer on the virus . We next investigated how antibody binding to the purified full-length AMC011 trimer , as assessed by BLI , compared to binding to the cell-associated trimer and the virus-associated trimer as assessed by FACS ( EC50 ) and neutralization ( IC50 ) , respectively . Strong correlations were observed between antibody reactivity assessed by BLI and FACS , and BLI and neutralization ( r = 0 . 7289 , p = 0 . 0016 and r = 0 . 7146 , p = 0 . 0009; Fig 3C and 3D ) . Notably , PGT145 , and to a far lesser extent PG16 , binding to PGT151 purified full length trimer was lower than expected and therefore did not fit the overall trend ( Fig 3C and 3D , and S2 Fig ) . The presence of the PGT151 bound to the trimer therefore appears to impact the binding of these quaternary specific ( PGT145 ) or quaternary preferring ( PG16 ) bnAbs . In a previous study we showed that subtle conformational changes at the trimer apex were required for PGT145 binding [34] . Thus , the presence of PGT151 may subtly alter the structure or flexibility of the trimer apex and weaken the affinity for apex bnAbs when pre-bound . Overall , we conclude that the full-length membrane-derived and purified AMC011 trimers , the unpurified trimers on the surface of HEK293F cells and the functional trimers on the AMC011 virus , show very similar antigenicity . In line with the BLI data , four of five non-NAbs tested ( F105 , 17b , 19b , 14e , but not b6 ) neutralized the virus and bound the full-length Env trimer in neutralization assays and FACS , respectively ( Fig 3A , S4 and S5 Figs ) . This observation is likely due to the conformational plasticity of the unmodified , non-SOSIP stabilized full-length Env trimer , in line with the Tier-1B status . The AMC011 virus , being a Tier-1B virus probably samples conformations that transiently expose the CD4bs , CD4i and V3 epitopes of these non-NAbs . The corresponding AMC011 SOSIP . 664 trimers , on the other hand , did not show appreciable binding to the non-NAbs ( Fig 2A ) . Hence , the SOSIP mutations resulted in desirable stabilization of the prefusion Env trimer that had a decreased propensity to sample alternative conformations and expose undesirable non-NAb epitopes . We generated structures of the full-length AMC011 trimer bound to PGT151 or PGT145 Fab by single particle cryo-EM at resolutions of ~4 . 2 Å and ~5 . 7 Å , respectively ( Fig 4A and S6A Fig ) . We note that we could not reconstruct the MPER , TM and CT domains on the full-length AMC011 trimers , probably because of the flexible nature of these domains in the bicelle milieu . Therefore , the following comparison was performed using the ectodomains only ( up to residue 664 ) . First , we assessed the similarities between the AMC011 trimer bound to the PGT151 Fab and to the PGT145 Fab . The two full-length AMC011 trimer structures were very similar to one another ( backbone Cα r . m . s . d . between the gp120 domains is ~1 Å and between the gp41 domains is ~1 Å; Fig 4A ) , confirming that the structure was not altered to any large extent by the presence of either quaternary bNAb . The two structures also shared a highly similar architecture with the JR-FL ΔCT trimer ( backbone Cα r . m . s . d . between the gp120 domains is ~1 Å and between the gp41 domains is <1 Å for the comparison of AMC011 trimer bound to PGT151 with JRFL ΔCT trimer bound to PGT151; backbone Cα r . m . s . d . between the gp120 domains is ~1 Å and between the gp41 domains is ~1 Å for the comparison of AMC011 trimer bound to PGT145 with JRFL ΔCT trimer bound to PGT151; S6C and S6D Fig ) [23] . Despite the low resolution of the previously solved structure of the AMC011 SOSIP . v4 . 2 trimer ( 6 . 2 Å ) [24] , both full-length structures appeared similar in conformation ( backbone Cα r . m . s . d . between the gp120 domains is ~1 . 2 Å and between the gp41 domains is ~1 Å; Fig 4B and 4C ) . The presence of PGT151 or PGT145 in the structures allowed us to investigate the binding interaction between the AMC011 trimer and the two bNAbs in detail . We compared the structure of the full-length AMC011 Env trimer in complex with PGT151 with that of JR-FL Env in complex with the same antibody , Both structures were similar to previous Env structures [14 , 23] ( Fig 5A ) . The glycans belonging to the PGT151 epitope were ordered , and branched complex glycans at positions N611 and N637 in gp41 were resolved ( Fig 5B ) , as observed in Lee et al . , 2016 . We next compared the structure and orientation of PGT145 bound to full-length AMC011 trimer and PGT145 bound to the BG505 SOSIP . 664 trimer [34] . Interestingly , when the Env trimers were aligned we found that the PGT145 Fab was tilted by 23° and rotated by 4 . 5° when bound to the AMC011 trimer compared to when bound to the BG505 SOSIP . 664 trimer ( Fig 5C ) [34] . Therefore , the trimer and PGT145 Fab were fit independently into the AMC011-PGT145 map to generate a pseudo-atomic model ( Fig 5D and S6E Fig ) . Despite this angular difference the location of the HCDR3 at the trimer 3-fold axis and the overall epitope was similar in both complexes ( Fig 5E and 5F ) . Finally , to determine whether the difference in angle of approach was due to the SOSIP mutations or whether it was strain specific , we compared a NS-EM reconstruction of the AMC011 SOSIP trimer bound to PGT145 Fab with the structure of the previously mentioned full-length AMC011 trimer bound to PGT145 Fab ( S7A Fig ) . The comparison showed that the PGT145 apex bNAb approach is similar in both the SOSIP and the full-length trimer . This observation suggests that PGT145 binds different Env trimers at subtly different angles , potentially as a means to navigate apex glycans . Interestingly , during our 3D classification of the latter cryo-EM dataset we observed a class , representing only ~2 percent of particles , of dimeric full-length AMC011 Env bound to PGT145 and obtained a ~10 . 2 Å resolution reconstruction of this class ( middle panel in S7B Fig ) . This AMC011 dimer structure fits well into the structure of the AMC011 trimer bound to PGT145 ( map to map correlation = 0 . 9635 ) , which is consistent with the previous finding that PGT145 strongly interacts with two protomers ( S7C Fig ) [34] . This class of AMC011 dimers was excluded from the trimer reconstructions . Because glycans play important roles in the antigenicity and immunogenicity of Env , we next investigated the overall N-linked glycan profile of the soluble SOSIP . 664 trimer and the full-length Env trimer bound to PGT151 Fab by using hydrophilic interaction liquid chromatography-ultra-performance liquid chromatography ( HILIC-UPLC ) [43] . While the full-length trimer contained 47% complex glycans , only 33% of the glycans were of the complex-type on SOSIP . 664 ( Fig 6A ) . This observation is in line with the earlier overall glycan profile of pseudoviral gp160 of JR-CSF and BG505 SOSIP . 664 trimers [43–45] . In addition , these observations are consistent with recent comparison of full-length virally-derived Env with corresponding soluble SOSIP [26 , 27] . In these studies , glycosylation sites displaying a mixed population of oligomannose and complex glycans in the SOSIP format were more uniformly of the complex type . To investigate the different glycan structures in more detail , we identified the N-glycans present in gp120 and gp41 by electrospray ionization time-of-flight mass spectrometry ( ESI TOF MS ) [45] . Consistent with the HILIC-UPLC data , an increase in branching and terminal elaboration of complex glycans was observed on both gp120 and gp41 in full-length compared to SOSIP . 664 Env trimers ( S8 Fig ) . Analysis of individual processed glycans showed that the full-length trimer contained a larger proportion of tri- and tetra-antennary complex glycans than the SOSIP . 664 trimers ( S8 Fig ) . The differences in mass spectrometry ( MS ) data between the full-length and SOSIP . 664 trimers probably arise from the differences in stability and in access to glycan processing enzymes in the endoplasmic reticulum ( ER ) . These differences might arise from at least three sources . First , SOSIP stabilization and lower propensity to sample more open conformation , might limit access of processing enzymes at specific sites . Second , glycan processing enzymes are all membrane tethered so the full-length trimer is more membrane proximal and hence more accessible to these enzymes than the soluble SOSIP . 664 trimers . Third , soluble and membrane tethered Env trimers might have transit through the ER and Golgi compartments with different speeds , allowing more or less time for processing enzymes to act . To probe the glycan structures at a site-specific level we used in-line liquid chromatography-ESI ( LC-ESI ) MS as previously described [45] . We were able to characterize 27 glycan sites for the SOSIP . 664 trimer and 24 for the full-length trimer ( Fig 6B ) . However , the other sites ( one for SOSIP . 664 and four for full-length trimer ) could not be determined and/or quantified ( Fig 6B and 6C ) . When we plotted the type of glycan at each glycosylation site onto a low resolution map of the AMC011 SOSIP . v4 . 2 trimer [24] , glycans at the apex and base of the Env trimer in particular , were more processed on full-length than on SOSIP . 664 trimers ( Fig 6C ) . This observation was similar to the recently described PC64 Env and the Env present on the virus [26 , 27 , 30] . However , glycans located on the core of the trimer were predominantly oligomannose-type on both trimers ( Fig 6C ) . This echoes the differences in glycosylation observed between BG505 SOSIP . 664 and virally derived BG505 gp120 [27] . We could corroborate , as previously shown by Behrens et al . , the high number of oligomannose glycans across the gp120 trimer surface and the high amount of complex glycans across the gp41 surface [45 , 46] . However , at the individual glycan level , a number of glycans have different compositions on full-length and SOSIP . 664 trimers as discussed below . The N88 site on gp120 at the interface with gp41 , contains an increased population of oligomannose glycans on the full-length compared to the SOSIP . 664 trimer ( Fig 6B ) . This may be the result of reduced accessibility to mannosidases to this membrane proximal glycan on full-length Env . At the trimer apex , the N130 site is populated by predominantly tetra-antennary complex-type glycans on the full-length trimer , whereas a large proportion of Man9GlcNAc2 moieties are present at the corresponding site on the SOSIP . 664 trimer , implying that this region is somewhat protected from mannosidase digestion on SOSIP . 664 but not on full-length ( Fig 6B ) . This observation might relate the more plastic nature of the trimer apex in the full-length Env . This again recapitulates comparative glycan analyses from both BG505 and JRFL where the equivalent sites N133 for BG505 and N135 for JRFL present elevated oligomannose-type glycans in the SOSIP format [26 , 27] . Furthermore , near the trimer apex , the N142 site is occupied by mostly tetra-antennary complex glycans on the full-length trimer while this site contains tri-antennary glycans in the SOSIP . 664 trimers . On the other hand , the N156 site is occupied by predominantly oligomannose glycans on both SOSIP . 664 and full-length trimers , while the N160 site , oligomannose on the SOSIP . 664 trimer , could not be assigned on the full-length trimer ( Fig 6B ) . The same observation as for the N130 site was also made for the N289 site , where the full-length trimer is populated by tetra-antennary glycans and the SOSIP . 664 trimer with oligomannose glycans ( Fig 6B ) . A similar , but less pronounced case can be observed for N197 , a glycan situated near the triad of glycans at N392 , N362 , and N386 . This site is mixed for SOSIP . 664 and processed on full-length Env ( Fig 6B ) . The N197 site displays the same elevation in processing when BG505 is expressed in a viral context or recombinantly with a membrane tether [26 , 27] . The glycan sites near the CD4bs , N234 , N276 and N241 , are well-conserved oligomannose-type glycans in both constructs ( Fig 6B ) . The oligomannose patch , formed by residues N295 , N262 , N448 and N332 , is a dense region with oligomannose glycans that interact with each other and is also conserved between full-length and SOSIP . 664 trimers , as might be expected ( Fig 6B ) [46] . Previous comparison of site-specific glycosylation of recombinant BG505 gp120 and BG505 SOSIP . 664 revealed a trimer-associated mannose patch ( TAMP ) with up to seven sites showing elevated oligomannose levels in the SOSIP format [46] . Here , we find that some of these sites are more processed in the full-length format ( e . g . N197 ) whereas the limited mannosidase trimming at other TAMP sites are conserved ( e . g . N156 and N276 ) . The glycan sites in gp41 showed an increased processing at all sites for the full-length trimer compared to the SOSIP . 664 trimers . N637 contains some oligomannose glycans on the SOSIP . 664 Env but not on full-length Env ( Fig 6B ) . This observation is in line with the ESI MS data ( S8 Fig ) , where oligomannose-type glycans were observed in the spectra for SOSIP . 664 gp41 but not for the gp41 present on the virus [44] . Overall , full-length Env trimers contain fewer oligomannose-type glycans more complex-type glycans that are predominantly highly processed , tri- and tetra-antennary sialylated structures , compared to SOSIP . 664 trimers ( Fig 6 and S8 Fig ) .
We assessed the structural , biophysical and antigenic differences between the full-length AMC011 trimer and its SOSIP . 664 counterpart . We purified full-length Env protein in complex with PGT145 and PGT151 Fab and we concluded that both structures showed a highly similar structure to that of published membrane-derived and SOSIP . 664 trimers from various isolates , irrespective of the presence of a quaternary-specific conformational antibody . These data further support that the SOSIP trimer show remarkable structural similarity to unmodified , full-length Env trimer bound to two different bnAbs that target different regions on the surface of Env . However , our studies do reveal several subtle but interesting differences related to glycan processing and antigenicity . The Env protein is highly glycosylated and steric restriction of glycan processing leads to high amounts of oligomannose glycans ( predominantly Man8GlcNAc2 and Man9GlcNAc2 ) on the outer domain of gp120 [47 , 48] . The data presented here compares site-specific glycan composition of full-length HIV-1 Env trimers and HIV-1 soluble immunogens and this knowledge can therefore be used for immunogen preparation since the glycans have a key role and are a component of bNAb epitopes [48] . Our glycan site-specific analysis showed that the full-length Env trimers contain higher amounts of complex glycans and a larger proportion of tri- and tetra- antennary complex types compared to SOSIP . 664 trimers . The more extensively processed glycans on full-length trimers might be caused by a prolonged transit time in the Golgi apparatus compared to its SOSIP . 664 counterpart , which is not membrane bound and contains smaller complex glycans , suggesting that the soluble version transits faster through the Golgi . Moreover , the differences in glycan processing may arise from the differences in accessibility to glycan processing enzymes in the ER and the Golgi , where most of the glycan processing enzymes are membrane tethered so the full-length and the soluble trimer will have different proximity and accessibility to these enzymes . Finally , the differences might arise from conformational rigidity , as the higher stability and lower propensity to sample alternative conformations of SOSIP . 664 trimers might reduce accessibility of glycans to processing enzymes . A previous report showed a comparison of soluble and full-length and ΔCT Env structures [23 , 30] , but differences in antigenicity were not assessed due to limited protein yields . Here , we identified a high expressing full-length Env clone ( AMC011 ) that enabled such comparisons . Based on our analyses , several differences in antigenicity could be explained by site-specific glycosylation alterations . The apparent increased conformational flexibility of the full-length AMC011 trimer , resulting in the exposure of epitopes targeted by non-NAbs , and the presence of the MPER , targeted by 4E10 and other MPER-bNAbs also demarcate key differences . Keeping the MPER domain would maximize the presentation of potent bNAb epitopes in a potential HIV-1 soluble vaccine , but whether that should come at the expense of exposing multiple non-NAb epitope remains a question . Since AMC011 is a Tier-1B virus , which are known to be flexible , additional studies that assess stability and antigenicity of Tier 2 derived full-length Env trimer should be performed , such as full-length BG505 Env trimer . A stable full-length Env that presents all bNAb epitopes and none of the non-NAbs epitopes could be a suitable vaccine candidate that mimics the native spike on the virus . Furthermore , such a construct may be suitable for nucleic acid delivery vectors such as integrase-deficient lentiviral vector or modified vaccinia Ankara ( MVA ) vectors as the stable full-length construct could be expressed as an immunogen on the patient’s cell surface . SOSIP . 664 trimers embody many of the properties of native Env and represent the best in class trimeric Env vaccine platform currently under investigation . Here we show that subtle differences in the glycosylation may have important antigenic consequences . Moreover , the addition of the MPER epitope , as present in the full-length clone described here , may be desirable because MPER directed antibodies can be very broad and potent [49–51] . While we have now produced full-length Env in sufficient quantities for animal immunizations it may be prudent to further engineer the trimer to have a more biased antigenic profile similar to corresponding SOSIP immunogens , for example by building the SOSIP design into full-length Env trimers .
AMC011 is a consensus sequence from three different clonal variants of env genes found 8 months post-seroconversion in an elite neutralizer patient from the Amsterdam Cohort Studies on HIV-AIDS [24] . The consensus sequence was used to generate full-length AMC011 gp160 and SOSIP . 664 constructs . The full-length AMC011 gp160 expression vector was created by cloning the complete sequence of env into the pcDNA3 . 1 mammalian protein expression vector . AMC011 SOSIP . 664 was generated following previously published methods [4] . In short , the sequence was codon optimized for better production in mammalian cells; the A501C and T605C substitutions were introduced to form a disulfide bond between gp120 and gp41 subunits of the trimer; the I559P mutation was included to stabilize gp41; the RRRRRR cleavage site was replaced the original furin cleavage site ( REKR ) to enhance cleavage; the tissue plasminogen activator ( tPA ) signal peptide replaced the natural one to increase secretion and a stop codon at position 664 was introduced for production of the soluble protein . In this study we used AMC011 SOSIP . 664 and full-length protein . AMC011 SOSIP . 664 was produced as previously described for other recombinant SOSIP . 664 proteins [18] . In brief , HEK293F ( human embryonic kidney; Thermo Fisher Scientific ) cells were transfected with a 1:3 ratio furin:Env DNA using PEImax ( 1 mg/mL ) , at a cell density of ~1x106cells/mL . Culture supernatants were harvested 7 d after transfection . SOSIP . 664 trimers were purified using PGT145 affinity chromatography column as previously described [8 , 10] . The eluted Env proteins were further purified by size exclusion chromatography ( SEC ) using TBS buffer . The fractions corresponding to the trimer peak were pooled and concentrated using a MWCO concentrator ( Millipore ) with a 100 kDa cutoff . PGT151 TEV IgG and PGT145 TEV IgG , in which a TEV protease cleavage site is inserted between the Fc and the Fab regions was generated and expressed in HEK293F cells as described [34 , 52] . In short , IgGs were expressed in HEK293F cells at 1x106cells/mL using PEImax with a ratio of 2:1 heavy:light chain . 5 d after transfection , the supernatant was harvested and passed through a 5 mL MAb select column ( GE Healthcare ) . IgGs were eluted with 0 . 1 M Glycine and buffer exchanged to TBS pH 7 . 4 . Full-length AMC011 Env was expressed and purified as a previously published [23 , 30] . In brief , HEK293F cells were transfected with plasmids encoding furin and Env ( furin:Env ratio of 1:3 ) using PEImax , at a cell density of 1 . 6x106 cells/mL . Cells were harvested 60–65 h post-transfection . Cells were incubated with PGT151 TEV IgG or PGT145 TEV IgG and solubilized . Cell debris was centrifuged and the supernatant incubated with Protein A resin ( Genscript ) . The protein was incubated with 0 . 25 mg of TEV protease/L of initial HEK293F culture and eluted with buffer containing 0 . 1% w/v DDM , 50 mM Tris pH 7 . 4 , 150 mM NaCl and 0 . 03 mg/mL sodium deoxycholate . The same buffer was used to further purify the trimers using a Superose 6 ( GE Healthcare ) column . The size exclusion was run at 0 . 4 mL/min and fractions corresponding to the full-length AMC011 Env—PGT151 Fab complex or full-length AMC011 Env—PGT145 Fab complex were concentrated , with a MWCO concentrator to 8–9 mg/mL and 3–4 mg/mL , respectively . Protein was measured by absorbance at 280 nm using theoretical extinction coefficients calculated with Expasy ( ProtParam Tool ) . A lipid mix containing 1:1 ratio of 1 , 2-dioleoyl-sn-glycero-3-phosphocoline ( DOPC ) and cholesteryl hemisuccinate ( CHS ) was added to the concentrated Env-PGT151 Fab and Env-PGT145 Fab complex such that the final concentration was 0 . 14 mM . The resulting lipid-Env-Fab mixture ( 14 μL in total ) was incubated on ice for 3 h with 15 Biobeads ( Biorad ) to partially remove the deoxycholate and the DDM . C-flat holey carbon and gold grids ( Electron Microscopy Sciences ) were plasma cleaned for 5 s using solarus advanced plasma cleaning system ( Gatan ) and used for the Env-PGT151 Fab and Env-PGT145 Fab sample . Blotting was performed at 4°C and 100% relative humidity using vitrobot ( FEI ) as follows . First , 1 μL of amphipol A8-35 ( Anatrace ) was applied to the grid followed by 3 μL of the Env sample . The grid was blotted for 5 s and plunged into liquid ethane . Grids were stored in liquid nitrogen until use . The AMC011 full-length trimers were imaged using the Titan Krios ( Thermo Fisher Scientific ) , operating at 300 keV and K2 Summit direct electron detector ( Gatan ) . The data were collected using Leginon image acquisition software using a dose of 10e-/Å2/sec . At the magnification of 34 , 247 x , the resulting pixel size was 1 . 03Å/pixel . Particles from individual frames were automatically picked using DogPicker [53] . CTF estimation was carried out using CTFFIND3 or GCTF [54 , 55] . Following CTF estimation , the particles were sorted by 2D classification using RELION 1 . 3 Relion 3 . 0 or cryoSPARC [56 , 57] . The particles were further sorted by 3D classification . The initial reference for the 3D classification was an unliganded SOSIP trimer ( EMD-5782 ) low pass filtered to 60 Å . The particles of two 3D reconstructions were used to generate a final reconstruction , refined in RELION 1 . 3 or Relion 3 . 0 using the automated refinement procedure [58 , 59] . To improve the resolution , two more rounds of refinement were run by applying an Env ectodomain mask that excludes the disorded micelle portion from the refinement . This yielded a Fourier shell correlation of 4 . 2 Å and 5 . 7 Å for the AMC011 full-length trimer bound to PGT151 Fab and bound to PGT145 Fab , respectively . Model building was done using successive rounds of manual steps in COOT and real space refinement with Phenix ) [60 , 61] . UCSF Chimera was used for generating figures [62] . To measure the differences in angles of approach of different Fabs to the gp120 , we used one method that has been previously described [12] . We calculated the angle between the pseudo-2-fold axes ( these axes point toward the epicenter of the epitope ) and also between the axes bisecting the canonical disulfides . These two angles define the difference in the overall angular approach ( rotation and tilt ) of the Fab to the gp120 , and then how much the Fabs rotate to interact with antigen . The sum of these two angles is roughly equivalent to the total angular difference in binding orientation calculated by the first method . Purified Env trimers were analyzed by negative-stain EM . A 3 μL aliquot containing ~0 . 03 mg/mL of the trimer was applied for 10 s onto a carbon-coated 400 Cu mesh grid that had been glow discharged at 20 mA for 30 s , then negatively stained with uranyl formate for 30 s . Data were collected using a FEI Tecnai F20 or T12 electron microscope operating at 120 keV , with an electron dose of ~55 e-/Å2 and a magnification of 52 , 000x that resulted in a pixel size of 2 . 05 Å at the specimen plane . Images were acquired with a Gatan US4000 CCD or Tietz TemCam-F416 CMOS camera using a nominal defocus range of 900 to 1300 nm . N-linked glycans were enzymatically released from envelope glycoproteins via in-gel digestion with Peptide-N-Glycosidase F ( PNGase F ) , subsequently fluorescently labeled with 2-aminobenzoic acid ( 2-AA ) and analyzed by HILIC-UPLC , as previously described [43 , 45] . Digestion of released glycans with Endo H enabled the quantitation of oligomannose-type glycans . The compositions of the glycans were determined by analyzing released glycans from trimers by PNGase F digestion using ion mobility MS [63] . Negative ion mass , collision-induced dissociation ( CID ) and ion mobility spectra were determined with a Waters Synapt G2Si mass spectrometer ( Waters Corp . ) fitted with a nano-electrospray ion source . Waters Driftscope ( version 2 . 8 ) software and MassLynx™ ( version 4 . 1 ) was used for data acquisition and processing . Spectra were interpreted as described previously [43 , 45] . The results obtained served as the basis for the creation of sample-specific glycan libraries , which were used for subsequent site-specific N-glycosylation analyses . Before proteolytic digestion , trimers were denatured and alkylated by incubation for 1h at room temperature ( RT ) in a 50 mM Tris/HCl , pH 8 . 0 buffer containing 6 M urea and 5 mM dithiothreitol ( DTT ) , followed by the addition of 20 mM iodacetamide ( IAA ) for a further 1h at RT in the dark , and then additional DTT ( 20 mM ) for another 1 h , to eliminate any residual IAA . The alkylated trimers were buffer-exchanged into 50 mM Tris/HCl , pH 8 . 0 using Vivaspin columns and digested separately with trypsin , chymotrypsin and elastase ( Mass Spectrometry Grade , Promega ) at a ratio of 1:30 ( w/w ) . Glycopeptides were selected from the protease-digested samples using the ProteoExtract Glycopeptide Enrichment Kit ( Merck Millipore ) . Enriched glycopeptides were analysed by LC-ESI MS on an Orbitrap fusion mass spectrometer ( Thermo Fisher Scientific ) , using higher energy collisional dissociation ( HCD ) fragmentation . Data analysis and glycopeptide identification were performed using Byonic™ ( Version 2 . 7 ) and Byologic™ software ( Version 2 . 3; Protein Metrics Inc . ) . Cell surface expression of AMC011 Env protein was assessed using flow cytometry as previously described , with some modifications [57] . HEK29F cells were transfected with plasmids encoding furin and Env ( full-length AMC011 gp160 ) in a 1:3 ratio using PEImax at 1 . 75x106 cells/mL . Cells were spun down 60–65 h post transfection and diluted with PBS to a final density of 105 cells/mL . 10 μL of cells were incubated for 2 h with three fold serial dilutions of several monoclonal antibodies . Cells were washed twice with PBS before staining the cells with 1:50 dilution of Alexa 647-conjugated mouse anti-human IgG . Binding of MAbs was analyzed with flow cytometry as previously described [57] . Nonlinear regression curves were determined using Graphpad prism and IC50 values calculated . Neutralization assays were carried in TZM-bl cells , which express high levels of CD4 , CCR5 and CXCR4 and contain luciferase genes under the control of the HIV-1 long terminal repeat promoter . We used the AMC011 chimeric molecular clone virus to perform neutralization assays . Production of the virus and neutralization assays was performed as previously described [4] . The virus was incubated with three fold serial dilutions of MAbs for 1 h . The mixture was added to the TZM-bl cells together with diethylaminoethyl . Cells were lysed ~48 h later and luciferase activity was subsequently measured with a Glomax luminometer . IC50 values were calculated with the determined nonlinear regression curves using Graphpad prism . Binding studies were performed using Octet Red96 instrument ( ForteBio ) . All the assays were performed at 500 rpm in BLI buffer ( PBS supplemented with 0 . 01% ( w/v ) BSA , 0 . 002% ( v/v ) Tween 20 and 0 . 1% ( w/v ) DDM at 25°C . Anti-human IgG ( AHI ) probes were equilibrated in BLI buffer for 60 s . mAbs , which were diluted with kinetics buffer at a final concentration of 0 . 025 μg/mL , were loaded to the AHI probes for 300 s . To remove partially bound antibody to the probe , the sensors were equilibrated for 60 s with kinetics buffer . Binding to Env proteins , at final concentration of 250 nM , was determined for 300 s and dissociation for 300 s . Data analysis was carried out using Octet software and curve fitting using Graphpad prism . Thermostability of AMC011 full-length and SOSIP . 664 trimers was determined with a Nano-DSF ( Prometheus ) . Proteins were diluted to a final concentration of 1 mg/mL . After loading 10 μL of the sample to the grade capillaries , intrinsic fluorescence signal and therefore thermal denaturation was assessed at a linear scan rate of 1°C/min with an excitation power of 15% . Unfolding transition points were detected from changes in the tryptophan fluorescence wavelength emission at 350 and 330 nm . The thermal onset ( Tonset ) and thermal denaturation ( Tm ) of the proteins were calculated with Prometheus NT software .
Coordinates and structure factors for full-length AMC011 Env trimer complexed with PGT151 Fab and full-length AMC011 Env trimer complexed with PGT145 Fab have been deposited in the PDB under accession codes 6OLP and 6NIJ , respectively . The EM reconstruction data have been deposited in the Electron Microscopy Data Bank under codes EMD-20118 for full-length AMC011 in complex with PGT151 Fab , EMD-9378 for full-length AMC011 in complex with PGT145 Fab , EMD-20106 for full-length AMC011 DOPC bicelle in complex with PGT151 Fab , EMD-20107 for full-length AMC011 DOPC-CHS bicelle in complex with PGT145 Fab and EMD-20108 for AMC011 SOSIP in complex with PGT145 Fab . | HIV-1 envelope glycoprotein ( Env ) trimer is the primary antigenic target for neutralizing antibodies . As such , it is the focus of subunit vaccine design efforts that aim to recapitulate the structure and native antigenic profile in a soluble , stable form capable of eliciting neutralizing antibody responses . Here , we compare the antigenicity , glycosylation and structure of a full-length , wild-type Env trimer with a corresponding soluble , SOSIP trimer that is representative of many ongoing subunit vaccine design efforts . Overall , both exhibit similar properties , and the SOSIP trimer is an accurate mimic of the wild-type Env . | [
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... | 2019 | Similarities and differences between native HIV-1 envelope glycoprotein trimers and stabilized soluble trimer mimetics |
Herpesvirus gH/gL envelope glycoprotein complexes are key players in virus entry as ligands for host cell receptors and by promoting fusion of viral envelopes with cellular membranes . Human cytomegalovirus ( HCMV ) has two alternative gH/gL complexes , gH/gL/gO and gH/gL/UL128 , 130 , 131A which both shape the HCMV tropism . By studying binding of HCMV particles to fibroblasts , we could for the first time show that virion gH/gL/gO binds to platelet-derived growth factor-α ( PDGFR-α ) on the surface of fibroblasts and that gH/gL/gO either directly or indirectly recruits gB to this complex . PDGFR-α functions as an entry receptor for HCMV expressing gH/gL/gO , but not for HCMV mutants lacking the gH/gL/gO complex . PDGFR-α-dependent entry is not dependent on activation of PDGFR-α . We could also show that the gH/gL/gO—PDGFR-α interaction starts the predominant entry pathway for infection of fibroblasts with free virus . Cell-associated virus spread is either driven by gH/gL/gO interacting with PDGFR-α or by the gH/gL/UL128 , 130 , 131A complex . PDGFR-α-positive cells may thus be preferred first target cells for infections with free virus which might have implications for the design of future HCMV vaccines or anti-HCMV drugs .
Human cytomegalovirus ( HCMV ) is a human herpesvirus which is spread worldwide and can cause life-threatening infections in immunocompromised patients . Additionally , it is one of the major causes of virus-induced birth defects after congenital infection . Like all herpesviruses , it persists lifelong in its host . HCMV disease in immunocompromised patients and intrauterine infection with HCMV can be observed both after primary infection and after reactivation [1] . One hallmark of HCMV infection is the broad cell tropism observed in vivo [2] , which is shaped by a number of different envelope glycoprotein complexes . Initial attachment of HCMV particles to cells is promoted by heparan sulfate proteoglycans on the surface of cells [3] . Both , the HCMV glycoprotein gB and the HCMV gM/gN glycoprotein complex are involved in this initial attachment [4 , 5] . This step is believed to be followed by a more stable and specific interaction of cellular entry receptors with either the trimeric gH/gL/gO or the pentameric gH/gL/UL128 , 130 , 131A envelope glycoprotein complex [6] . Once the receptor—gH/gL complex interaction is stabilized , the core gH/gL complex in concert with gB is believed to promote fusion of the viral envelope with cellular membranes [6 , 7] . The in vitro phenotypes associated with a loss of the trimeric or the pentameric complexes are completely different . Mutants unable to form gH/gL/UL128 , 130 , 131A lose their broad cell tropism and classical host cells like endothelial , epithelial , monocytic or dendritic cells can no longer be infected in vitro [8–13] . Yet , the capacity to infect fibroblasts and virus production in fibroblasts is not affected [10 , 12] . Mutants unable to form gH/gL/gO or mutants with low amounts of gH/gL/gO in their envelopes primarily spread cell-associated , because gO-negative virus particles released from infected cells are hardly infectious [14–17] . Yet , their host cell range is not restricted [14 , 15] . Mutants unable to form either of the gH/gL complexes do not release infectious virus nor can they spread in a cell-associated manner [15] . The roles of the HCMV gH/gL complexes in vivo are not clear . A study on the role of the gH/gL/gO complex of murine cytomegalovirus ( MCMV ) showed that in primary infection , gH/gL/gO is crucial for infection of first target cells including epithelial cells , endothelial cells and macrophages [18] . Comparable to observations in cell culture [14 , 15 , 19 , 20] , spread of infection from these first targets within organs is not dependent on gO as long as an alternative gH/gL complex can be formed [18] . Several studies indicated that gH/gL/gO and gH/gL/UL128 , 130 , 131A use distinct receptors for entry [20–22] . Until today , a number of different host cell surface molecules have been shown to enhance HCMV infection of cells in culture [23–28] . Additionally , it has been shown that binding of HCMV to some of them can result in activation of signaling pathways [23 , 27 , 29 , 30] . Among those signaling cell surface receptors are growth factor receptors like platelet-derived growth factor receptor-α ( PDGFR-α ) , epidermal growth factor receptor ( EGFR ) or integrins [23 , 26–28] . PDGFR-α , EGFR , and integrins have been shown to bind gB or gH [23 , 27 , 28 , 31] . Recently , it has been shown that PDGFR-α binds recombinant gH/gL/gO [32] . Here , we could for the first time show that the gH/gL/gO complex in concert with gB binds PDGFR-α when HCMV virus particles attach to host cell surfaces . This confirms the interaction of recombinant gH/gL/gO with PDGFR-α reported recently [32] . We could also show that the PDGFR-α—gH/gL/gO interaction starts the predominant entry pathway for infection of fibroblasts with free virus . Cellular PDGFR-α expression levels determined whether infection was dependent on the gH/gL/gO or the alternative gH/gL/UL128 , 130 , 131A complex . Interestingly , infection of fibroblasts was not dependent on activation of PDGFR-α . By silencing PDGFR-α , we could show that the PDGFR-α –gH/gL/gO interaction not only promoted infection with free virus , but also cell-associated virus spread . The dominance of gH/gL/gO-driven entry in infections with supernatant virus suggests that the PDGFR-α—gH/gL/gO interaction may play a crucial role in horizontal infection with free virus from body fluids like urine or breast milk and thus be an interesting target for vaccines or antiviral drugs designed to prevent HCMV primary infection .
It has been shown that recombinant gB [23] and recombinant gH/gL/gO [32] bind to cell surface PDGFR-α . This was interpreted as PDGFR-α being a cofactor for HCMV infection or PDGFR-α being an entry receptor , respectively . To find out whether PDGFR-α also interacts with these glycoprotein complexes in envelopes of HCMV particles , we co-incubated virus particles of the HCMV mutant TB40-UL131Astop [19] , which is unable to form the gH/gL/UL128 , 130 , 131A complex , with surface-biotinylated human foreskin fibroblasts ( HFF ) , lysed cells and surface-bound virions , and precipitated the HCMV glycoprotein gH with an anti-gH antibody . A streptavidin-stained blot of precipitated proteins showed a biotinylated cell surface protein of around 180 kDa which was co-precipitated from cell-virion lysates ( S1 Fig , lane 3 ) . This protein could also co-precipitated from mixtures of separately lysed HFF and virions ( S1 Fig , lane 2 ) , but not from HFF control lysates ( S1 Fig , lane 1 ) . The corresponding gel slices from a silver-stained gel ( S1 Fig ) were analyzed by mass spectrometry which showed one prominent hit for the cell-virion lysate , peptide KLVYTLTVPEATVKD , which matches the sequence of human PDGFR-α . Co-precipitation of PDGFR-α and gH could be confirmed by Western blot analysis ( Fig 1 ) . As expected , anti-gH antibodies co-precipitated the gH/gL/gO component gO [33 , 34] and also , as reported before , gB [7 , 35] . Using recombinant PDGFR-α-Fc fusion protein , we could co-precipitate gH , gO and gB from lysates of TB40-UL131Astop virions , whereas a control PDGFR-β-Fc protein did not bind any of these HCMV glycoproteins ( Fig 2a ) . Co-precipitation of gH , gO and PDGFR-α and additionally gB could either reflect an interaction of the gH/gL/gO complex with gB bound to PDGFR-α or interactions of gH/gL/gO with PDGFR-α and independently also with gB . To clarify the role of gO in interactions of gB and gH with PDGFR-α , we compared precipitations of gH , gO , gB and PDGFR-α from lysates of wildtype ( wt ) TB40 virus , which contains both , the trimeric and the pentameric gH/gL complex , with precipitations from lysates of a TB40 mutant lacking gO ( TB40-ΔgO ) [19] . As observed for TB40-UL131Astop virus , anti-gH antibodies co-precipitated gO and gB from cell-free wt TB40 virion lysates and additionally PDGFR-α from lysates of wt TB40 virions mixed with HFF ( Fig 2b ) . PDGFR-α-Fc also co-precipitated gO , gH and gB from cell-free wt TB40 virion lysates ( Fig 2b ) . Precipitation of gB with an anti-gB antibody did not result in co-precipitation of gH or gO or PDGFR-α ( Fig 2b ) . Comparable failures of different anti-gB antibodies to precipitate complexes formed between recombinant gH/gL and gB have been described before and have been attributed to masking of gB epitopes by bound gH/gL complexes [7 , 35] . For a direct comparison of wt TB40 and TB40-ΔgO virions , the virion lysates used for co-precipitation assays were adjusted to contain equal amounts of HCMV major capsid protein ( MCP ) as a measure for equal numbers of particles ( Fig 2c , right panel ) . TB40-ΔgO virions contained more UL128 protein than wt TB40 virions . In wt TB40 virions , UL128 could only be detected after enrichment by anti-UL131A co-precipitation ( Fig 2c , right panel ) . As described before for the HCMV strain AD169 [12] , gH proteins in lysates of cells infected with wt TB40 or TB40-ΔgO virus ran as double bands in the Western blot , whereas in lysates of purified virions only the respective upper bands could be detected ( S2 Fig ) . gH from TB40-ΔgO virions , which contain only gH/gL/UL128 , 130 , 131 complexes , had a slower electrophoretic mobility than gH from wt TB40 virions , which predominantly contain gH/gL/gO complexes [36] ( Fig 2d and S2 Fig ) . A similar size difference for gH has been described for extracts of cells expressing either recombinant gH/gL/gO or recombinant gH/gL/UL128 , 130 , 131A and thus resembling extracts of wt TB40 or TB40-ΔgO virions , respectively [21] . Although wt TB40 and TB40-ΔgO lysates contained comparable amounts of gH and gB , PDGFR-α-Fc could only co-precipitate gH , gO and gB from lysates of wt TB40 virions , but not from lysates of TB40-ΔgO virions ( Fig 2c , left panel ) . This indicates that gH and gB can only interact with PDGFR-α when a gH/gL/gO complex is formed . In the same line , anti-gH antibodies failed to co-precipitate PDGFR-α from lysates of TB40-ΔgO virions mixed with lysates of HFF ( Fig 2d ) . Fig 2d additionally shows that independently of whether cell-free wt TB40 , TB40-UL131Astop , or TB40-ΔgO virions were analyzed or whether virions mixed with cells were analyzed , anti-gH antibodies co-precipitated gB . This probably reflects the recently described interaction of gH/gL complexes with gB in virions [35] . In summary , our co-precipitation data imply that the observed PDGFR-α –gH interaction is strictly dependent on gO which is indicative of a direct interaction of gH/gL/gO with PDGFR-α . An interaction of gB and PDGFR-α can only be observed when gH/gL/gO is formed which suggests that gB either binds to PDGFR-α after gH/gL/gO has formed a complex with PDGFR-α or that a preformed gB—gH/gL/gO complex binds to PDGFR-α . A recent publication by Vanarsdall et al . , which showed that gB does not interact with gH/gL/gO in virions [35] , rather supports the first scenario . Our data confirm the interaction of recombinant gH/gL/gO with PDGFR-α [32] , but not a gH/gL/gO-independent interaction of gB with PDGFR-α [23] . Different host cells of HCMV show different levels of PDGFR-α expression [37] . We could easily detect PDGFR-α in extracts of fibroblasts ( HFF and MRC-5 ) , but not in extracts of 293 cells , ARPE-19 cells , or HUVEC ( S3a Fig ) . Using a recombinant HCMV gO—mouse IgG2b-Fc fusion protein ( gO-Fc ) expressed in 293 cells and purified from cell culture supernatants ( S3b Fig ) , we tested binding of gO to the cell surface of HCMV host cells . When gO-Fc was co-incubated with HFF or HUVEC , a strong and specific binding to HFF but not to HUVEC was observed ( S3c Fig ) which correlates with the respective PDGFR-α expression levels in these cells . This supports the recently described direct interaction of gO with PDGFR-α [32] . The co-precipitation data indicate that PDGFR-α only interacts with gH when virions contain gO . To study whether PDGFR-α and gO are indeed co-players in the same entry pathway , we compared infections with wt TB40 and TB40-ΔgO viruses in several independent experiments . Infections with wt TB40 and TB40-ΔgO viruses are both dependent on gB and gH which could be deduced from neutralization assays using anti-gB and anti-gH antibodies ( S4a Fig ) . Since TB40-ΔgO virus is highly attenuated when compared to wt TB40 virus [15] , infections with wt TB40 and TB40-ΔgO viruses had to be adjusted such that they resulted in equal numbers of IE1-positive cells after 24 hours ( S4b and S4c Fig ) . We observed an approximately 1000-fold reduced infectivity for fibroblasts for TB40-ΔgO virus when infection efficiencies and MCP contents of virus preparations of wt TB40 and TB40-ΔgO viruses were compared . In a first set of experiments , we pre-incubated virus preparations with soluble PDGFR-α-Fc fusion protein and as a control with PDGFR-β-Fc . PDGFR-α-Fc specifically and nearly completely inhibited infection of HFF with wt TB40 virus but not infection with TB40-ΔgO virus ( Fig 3a ) . Interestingly , a residual infection of about 0 . 5% was observed when wt TB40 virus was co-incubated with PDGFR-α-Fc which matches the 1000-fold difference in infectivity between wt and gO-negative TB40 virions . Thus , blocking PDGFR-α –gH/gL/gO interactions results in a phenotype comparable to deletion of gO . The residual infection observed after pre-incubation of wt TB40 virus with PDGFR-α-Fc very likely reflects infection through a gH/gL/UL128 , 130 , 131A-dependent entry pathway . To show this , we compared inhibition of wt TB40 and TB40-UL131Astop virus infections by PDGFR-α-Fc . We started at a higher multiplicity of infection ( m . o . i ) . to facilitate discrimination of the inhibition levels . This way , we could show that infection with TB40-UL131Astop virus , which can only form the gH/gL/gO complex , could be completely inhibited , whereas even increasing amounts of PDGFR-α-Fc could not abolish the residual infection of wtTB40 virus ( Fig 3b ) . To exclude , that abundant TB40-ΔgO virus particles prevented inhibition of TB40-ΔgO virus just by unspecifically binding PDGFR-α-Fc protein , TB40-UL131Astop virus was mixed with TB40-ΔgO virus and inhibition by PDGFR-α-Fc analyzed . As before , infection with TB40-UL131Astop virus could be completely inhibited and infection with TB40-ΔgO virus could not be inhibited . When both virus preparations were mixed , infection was reduced to the level of the TB40-ΔgO infection , indicating that also in the presence of abundant TB40-ΔgO virus particles , infection with gH/gL/gO-positive wt TB40 virions could specifically be inhibited ( S5 Fig ) . It has been shown before that co-incubation of fibroblasts with the PDGFR-α ligand PDGF-AA blocks infection of fibroblasts with HCMV [23] . We co-incubated HFF with PDGF-AA and then infected the cells with wt TB40 , TB40-UL131Astop , or TB40-ΔgO virus . PDGF-AA , which dose-dependently blocked infection of fibroblasts with wt TB40 virus ( Fig 3c ( left panel ) ) , only inhibited infections with wt TB40 and TB40-UL131Astop viruses , but not infection with TB40-ΔgO virus ( Fig 3c ( right panel ) ) . In a recent publication , it has been shown that infection of ARPE-19 epithelial cells with the HCMV strain TR , which has both gH/gL complexes , can be strongly enhanced by over-expression of PDGFR-α in ARPE-19 cells [37] . We over-expressed PDGFR-α in 293 cells ( S6a and S6b Fig ) and studied the effect on infections with wt TB40 , TB40-UL131Astop , and TB40-ΔgO viruses . wt TB40 or TB40-UL131Astop viruses were used at an equal m . o . i . ( titrated on HFF ) and TB40-ΔgO was adjusted such that an infection comparable to the wt TB40 infection of empty vector-transfected 293 cells was achieved ( Fig 3d ) . Over-expression of PDGFR-α strongly enhanced infections with wt TB40 and TB40-UL131Astop viruses ( Fig 3d and S6c and S6d Fig ) but not infection with TB40-ΔgO virus ( Fig 3d ) . This indicates that an increased PDGFR-α level can only enhance infections with gO-positive HCMV . As expected , TB40-UL131Astop virus , which lacks the pentameric complex , could less efficiently infect non-transfected 293 cells than wt TB40 virus ( Fig 3d ) . Taken together , all three sets of experiments ( Fig 3 ) clearly show that PDGFR-α-dependent and gO-dependent entry are congruent . It has been reported that co-incubation of human embryonic lung fibroblasts ( HELF ) with HCMV virions results in phosphorylation of PDGFR-α and downstream Akt [23] . Similarly , co-incubations of MRC-9 cells with AD169 and VR1814 virions have been shown to result in a weak tyrosine phosphorylation of PDGFR-α [32] . Both studies concluded that PDGFR-α-dependent entry is indispensably linked to activation of PDGFR-α . We studied phosphorylation of PDGFR-α and downstream Akt in HFF and in a lung fibroblast cell line ( MRC-5 ) . Cells were co-incubated with HCMV and as controls with the PDGFR-α ligands PDGF-AA and PDGF-BB . To exclude an FCS-driven phosphorylation of PDGFR-α , we used a virus preparation which contained only very low levels of FCS . Both , PDGF-AA and PDGF-BB induced phosphorylation of Akt , but only PDGF-BB induced detectable phosphorylation of PDGFR-α ( Fig 4a ) . Yet , we could not observe phosphorylation of PDGFR-α or Akt when HFF or MRC-5 fibroblasts were co-incubated with HCMV at an m . o . i . of 10 ( Fig 4a ) . The failure of PDGF-AA to efficiently induce phosphorylation of PDGFR-α in dermal fibroblasts has been described before [38] and turned out here also to be a property of MRC-5 cells . Independently of these cell type-specific phosphorylation patterns , our data show that , although HCMV binds to PDGFR-α on the surface of fibroblasts , it does not necessarily activate it . In line with this , we did also not observe an inhibition of infection when HFF were pretreated with the protein kinase inhibitor imatinib mesylate , although we could show that it interfered with phosphorylation of PDGFR-α ( Fig 4b ) . If PDGFR-α-driven entry was independent of activation of PDGFR-α , signaling-incompetent PDGFR-α and intact PDGFR-α should equally be able to enhance the susceptibility of host cells to HCMV infection . Using site-directed mutagenesis , we introduced a stop codon at amino acid position 559 in pCMV-PDGFR-α which resulted in a truncated protein consisting of the extracellular domain and the transmembrane anchor of PDGFR-α but lacking the cytoplasmic kinase domains ( S7a Fig and Fig 4c ) . After transfection of 293 cells , both proteins were equally expressed on the surface of transfected 293 cells ( S7b Fig ) . Over-expression of full-length PDGFR-α and truncated PDGFR-α equally enhanced wt TB40 virus infections of 293 cells which strongly suggests that PDGFR-α rather acts as a fusion-triggering receptor than as a signaling ligand ( Fig 4d ) . For HCMV and other herpesviruses , there is an ongoing discussion whether cell-free and cell-associated modes of virus spread are dependent on interactions of the same virion glycoproteins and cellular receptors or whether different protein-protein interactions or direct fusions between cells drive cell-associated virus spread [39–41] . In cell culture , cell-associated virus spread is defined as spread in the presence of neutralizing antibodies . However , it is not clear whether antibody-resistant spread reflects spread promoted by cell—virion interactions not recognized by the neutralizing antibodies or whether it reflects a failure of these antibodies to access viral envelope glycoproteins or neutralize locally concentrated virions when virions are transmitted from infected cells to neighboring cells . One approach to clarify this would be to knockdown the cellular receptor instead of blocking the receptor binding proteins . Pooled PDGFR-α siRNAs efficiently silenced PDGFR-α expression in HFF ( Fig 5a and 5b ) . Silencing of PDGFR-α strongly reduced infections of HFF with cell-free wt TB40 or TB40-UL131Astop viruses , but not infection with TB40-ΔgO virus ( Fig 5c and 5d ) which confirmed our inhibition experiments using recombinant PDGFR-α or PDGFR-α ligand to block infection . To evaluate the role of PDGFR-α in cell-associated spread , we first performed experiments with non-transfected HFF . Confluent monolayers of HFF were infected with wt TB40 at a very low m . o . i and infected cells in discrete foci were counted . Addition of a methylcellulose overlay , which impedes spread via supernatant virus , and neutralizing anti-gB and anti-gH antibodies significantly reduced the sizes of foci but did not abolish their formation ( Fig 6a ) . Similarly , addition of PDGFR-α-Fc reduced focal spread ( Fig 6a ) . PDGFR-α-Fc equally inhibited infections of HFF with wt TB40 and TB40-UL131Astop viruses which excludes that PDGFR-α-Fc-resistant spread just reflects spread driven by the pentameric complex ( Fig 6b ) . It is of note that spread of TB40-ΔgO virus , which in HFF cultures is less efficient than spread of wt TB40 virus ( S8a Fig , [15] ) , can also be decreased by methylcellulose and also is resistant to neutralizing antibodies ( S8a Fig ) . However , an anti-UL131A antiserum could completely block cell spread ( focus size = 1 ) ( S8a Fig ) . This extraordinary neutralization indicates that spread of TB40-ΔgO virus is completely dependent on the pentameric complex . Interestingly , in HUVEC cultures , in which spread of TB40-ΔgO virus is more efficient , the anti-UL131A antiserum could not completely block focal spread ( S8b Fig ) . To find out whether the PDGFR-α-Fc-resistant spread of wt TB40 and TB40-UL131Astop viruses reflects a PDGFR-α-independent spread mode or not , we repeated the spread experiments using HFF transfected with pooled PDGFR-α siRNAs or as a control non-targeting ( NT ) siRNAs . The experiments were all performed in the presence of methylcellulose to simultaneously block spread via supernatant virus . Virus spread of wt TB40 and TB40-UL131Astop viruses was clearly reduced , albeit to significantly different levels ( Fig 6c and S9a Fig ) . While spread of TB40-UL131Astop virus was virtually abolished , spread of wt TB40 virus ( Fig 6c ) was strongly reduced , but not completely abolished . For comparison , spread of the HCMV strain VR1814 , which , like wt TB40 , also expresses both gH/gL complexes , was similarly inhibited in PDGFR-α-silenced HFF ( S9b Fig ) . Spread of TB40-ΔgO virus was not affected by cellular PDGFR-α levels , but could be completely inhibited ( focus size = 1 ) by anti-UL131A antibodies ( Fig 6c ) . The residual spread of wt TB40 virus in PDGFR-α-knockdown HFF was in the same range as spread of TB40-ΔgO virus in PDGFR-α-knockdown HFF and very likely represents gH/gL/UL128 , 130 , 131A-dependent focal spread . In summary , using PDGFR-α-knockdown cells and comparing gO-positive and gO-negative viruses , we could show that cell-associated HCMV spread in HFF cultures is either driven by gH/gL/gO binding to PDGFR-α or by the gH/gL/UL128 , 130 , 131A complex but not by other independent pathways .
Expression of PDGFR-α is important for infection of cells with HCMV . Knockdown of PDGFR-α , soluble PDGFR-α , anti-PDGFR-α antibodies or PDGF-AA all strongly inhibit HCMV infection and re-introduction of PDGFR-α in knockout cells reconstitutes infection [23 , 32] . Using recombinant HCMV gB , binding of gB to PDGFR-α has been shown [23 , 42] . Similarly , recombinant gH/gL/gO complex has been used to precipitate PDGFR-α from cell lysates [32] . In the latter study , a PDGFR-α—gH/gL/gO complex with PDGFR-α directly interacting with gO could be visualized by electron microscopy . Additionally , all three studies proposed an enhancement of infection by activation of PDGFR-α [23 , 32 , 42] . Thus , PDGFR-α as an entry receptor for HCMV recognized by gB and/or gH/gL/gO and PDGFR-α as a signaling ligand for HCMV are two concepts which require confirmation or disproof . We analyzed the interaction of the HCMV envelope gH/gL/gO complex with cell surface proteins using virus particles bound to the surface of fibroblasts and could confirm the interaction of gH/gL/gO with PDGFR-α . We found that PDGFR-α interacted with gH , gO and gB . By using an HCMV mutant lacking gO , we could sort out that the gH/gL/gO complex is the binding partner for PDGFR-α . An interaction of gB and PDGFR-α could only be observed when gH/gL/gO is formed . This suggests that gB either binds to PDGFR-α after PDGFR-α has formed a complex with gH/gL/gO or that a preformed gB—gH/gL/gO complex binds to PDGFR-α . gH/gL—gB interactions have been described before in cells co-expressing recombinant gB and gH/gL [7] and very recently also in HCMV virions [35] . The latter publication showed that gB does not interact with gH/gL/gO complexes in virions , [35] which would suggest a scenario in which a PDGFR-α—gH/gL/gO complex recruits gB to form a functional fusion complex [7] . Our co-precipitation experiments confirm the interaction of recombinant gH/gL/gO with PDGFR-α [32] , but not a gH/gL/gO-independent interaction of gB and PDGFR-α [23] . The direct interaction of recombinant gB and PDGFR-α in cells over-expressing gB might reflect an over-expression phenomenon [23 , 42] . In summary , the binding studies by Kabanova et al . [32] and our co-precipitation experiments support the concept that PDGFR-α is an entry receptor for HCMV recognized by gH/gL/gO . We also addressed the aspect of activation of PDGFR-α by HCMV . The first report proposing PDGFR-α as a co-receptor for HCMV entry strongly focused on PDGFR-α activation by HCMV [23] . It described HCMV-dependent phosphorylation of PDGFR-α and downstream Akt and inhibition of this activation by the protein kinase inhibitor imatinib mesylate or anti-PDGFR-α antibodies . In the same line , Kabanova et al . [32] claimed that binding of gH/gL/gO to fibroblasts results in tyrosine phosphorylation of PDGFR-α . We could not confirm these findings . As PDGFR-α and Akt phosphorylations can also be induced by FCS , we strictly controlled that both mock treatment and HCMV preparations contained equal amounts of FCS . We looked for activation of PDGFR-α after binding of HCMV to HFF and MRC-5 fibroblasts and neither observed phosphorylation of PDGFR-α nor of downstream Akt , although we used an m . o . i . of 10 for infection . Additionally , we could not inhibit infection of HFF with the protein kinase inhibitor imatinib mesylate . As Soroceanu et al . [23] showed phosphorylation of PDGFR-α after infection of HELF , the observed discrepancy might be due to differences in PDGFR-α-activation patterns between HFF and HELF [38] . By comparing infection of 293 cells overexpressing either full-length PDGFR-α or a signaling-incompetent truncated PDGFR-α protein , we could show that PDGFR-α-dependent enhancement of infection is independent of activation of PDGFR-α . Thus , gH/gL/gO-driven entry , although it may be accompanied by activation of PDGFR-α in certain cell types , is not dependent on activation of PDGFR-α . This identifies PDGFR-α rather as a fusion-triggering entry receptor than as a signaling ligand enhancing HCMV infection . It has repeatedly been shown that effective infection of fibroblasts is dependent on the gH/gL/gO complex [14–16 , 21] . By using gO-negative HCMV , we could unequivocally show that gH/gL/gO-dependent and PDGFR-α-dependent infection are congruent . We could efficiently block infection of fibroblasts by pre-incubating virions with PDGFR-α-Fc fusion protein and by pre-incubating cells with the PDGFR-α ligand PDGF-AA when virions were gO-positive , but not when gO was knocked out and infection was dependent on the pentameric gH/gL/UL128 , 130 , 131A complex [14 , 15] . Importantly , inhibition of wt TB40 virus by co-incubation of virions with recombinant PDGFR-α-Fc protein reduced the infectivity for fibroblasts about 500 fold . When we normalized infectivity of wt TB40 and TB40-ΔgO virus preparations for their MCP content , we observed a difference in infectivity in the same range . This indicates that inhibition of wt HCMV virions with PDGFR-α-Fc mirrors the phenotype of a genetically gO-negative mutant . This finding and our experiments showing ( i ) neutralization of an HCMV ΔgO mutant by anti-gB , anti-gH , and anti-UL131A antibodies and ( ii ) that , in contrast to spread in HFF cultures , ΔgO virus spread in HUVEC cultures is more efficient than spread of wildtype virus strongly support that virus particles lacking gO are not just defect particles with a residual undefined infectivity , but rather virus particles whose infectivity is restricted to gH/gL/UL128 , 130 , 131A-driven entry exerted in concert with gB . This makes them a valuable tool to study for example gH/gL/UL128 , 130 , 131A-driven entry . The PDGFR-α—gH/gL/gO interaction seems to start the predominant entry pathway for infection of cells with free virus , provided the cells abundantly express PDGFR-α on their surface . Different HCMV strains incorporate different relative amounts of the gH/gL complexes into their virions [16 , 17 , 43 , 44] which makes it difficult to evaluate the contribution of each complex to entry by comparing different strains [32] . Yet , independently of the HCMV strain used , deletion of gO massively reduces infectivity of cell-free virus [14–17 , 45] . Thus , independent of the HCMV strain , gH/gL/gO very likely is the predominant driver of infection with cell-free virus . In line with findings from Vanarsdall et al . [37] , we could show that over-expression of PDGFR-α strongly increases the susceptibility of cells to infection , both in the absence and in the presence of the pentameric complex . As infection with TB40-ΔgO virus was not affected by overexpression of PDGFR-α , we can conclude that the PDGFR-α-dependent enhancement is gH/gL/gO-dependent . Cell-associated virus spread is based on coordinated changes in cell architecture and cell surfaces to efficiently transfer virus to neighboring cells [39 , 46] . There are reports suggesting that cell-associated HCMV spread may be totally independent of virion glycoproteins or dependent on glycoproteins different from those promoting cell-free HCMV spread [40 , 41] . This was supported by findings that cell-associated spread in cell culture cannot be inhibited by neutralizing antibodies directed against virion proteins know to be involved in cell-free virus spread [47] . Similarly , by using recombinant PDGFR-α-Fc , we could not reduce spread of wt TB40 or TB40-UL131Astop viruses to a level below the level achieved by methylcellulose overlays known to block spread of supernatant virus . Yet , when we silenced PDGFR-α in HFF , cell-associated spread of TB40-UL131Astop virus was abolished and cell-associated spread of wt TB40 virus was reduced to the level observed for TB40 virus lacking gO . This indicates that virus spread in cell culture , independently of whether it can be inhibited by antibodies or not , is either driven by the gH/gL/gO—PDGFR-α interaction or by gH/gL/UL128 . 130 , 131A . The relative contributions of gH/gL/gO and gH/gL/UL128 , 130 , 131A very likely depend on the HCMV strain or isolate . They very likely also vary with the relative amounts of these complexes in virions and the relative amounts of PDGFR-α and the receptor ( s ) recognized by the pentameric complex on host cell surfaces . Our finding that TB40-ΔgO virus spread can be completely inhibited by anti-UL131A antibodies in HFF cultures , but not in HUVEC cultures additionally suggests that the failure of antibodies to block spread is rather due to local high concentrations of receptors or their ligands than to a failure of antibodies to access their target proteins . According to our data , infection of epithelial and endothelial cells which express no or undetectable levels of PDGFR-α should be equally susceptible to infection with virions expressing no , low or high amounts of gH/gL/gO . Yet , it has been shown that particles containing both gH/gL complexes show a higher capacity to infect endothelial or epithelial cells than particles containing only the pentameric complex [14 , 17] . Our findings described here cannot explain this . We can only speculate that the gH/gL/gO complex , by promoting PDGFR-α-independent attachment to cell surfaces , may enhance binding of virus particles and thus increase the chance for a subsequent interaction between gH/gL/UL128 , 130 , 131A and its cellular receptor . Our hypothesis would follow a proposal of Zhou et al . [17] who suggested an entry enhancement by gH/gL/gO for epithelial cells which is independent of the interaction between the gH/gL/UL128 , 130 , 131A complex and its receptor . In the same line , Kabanova et al . [48] observed that epithelial cell infection could be blocked with antibodies directed against gO although they do not express detectable levels of PDGFR-α receptor . To summarize our findings , we believe that we could confirm that PDGFR-α is the cellular receptor for the HCMV gH/gL/gO complex . Our findings very well fit prominent phenotypes found in cell culture for gH/gL-associated mutations , for example the strongly reduced infection of fibroblasts by mutants expressing less gO or lacking gO [14–16 , 19] , the inability of laboratory strains , which have lost the ability to form the pentameric complex , to infect cell types with no or low levels of PDGFR-α [24 , 37] , and the susceptibility of HCMV infection to cellular PDGFR-α levels [24 , 37 , 42 , 49 , 50] . Consequently , for HCMV expressing high levels of gH/gL/gO , the PDGFR-α level of target cells is the major factor determining cell tropism . Only if cells express no or low levels of PDGFR-α , entry pathways dependent on the gH/gL/UL128 , 130 , 131A complex will become important . This finding is of importance when the capacity of HCMV to infect cells in culture is used to characterize the HCMV cell tropism . Culture conditions [51 , 52] and also transforming antigens [24] may down- or up-regulate PDGFR-α and thus affect the susceptibility of cells to HCMV infection . HCMV tropism defined in cell culture may thus be misleading . The role of PDGFR-α-driven susceptibilities to infection should also be kept in mind when discussing the role of HCMV in glioblastoma [53] and arteriosclerosis [54] , two disease conditions associated with tissues which express high levels of PDGFR-α . In MCMV infections of the mouse , gH/gL/gO has been shown to play a central role in efficient infection of first target cells [18] . Transferred to HCMV infections , the gH/gL/gO—PDGFR-α interaction may thus determine which cells and how efficiently they are initially infected . We suggest therefore that PDGFR-α—gH/gL/gO interactions might be crucial for horizontal transmission via free virus from body fluids like urine or breast milk and perhaps also for vertical transmission of HCMV . This makes the PDGFR-α—gH/gL/gO interaction an interesting target for HCMV vaccines or antiviral therapies against HCMV .
Primary human foreskin fibroblasts ( HFF ) ( PromoCell , Germany ) , passage 7–23 , human fetal lung fibroblasts ( MRC-5 cells; ATCC-CCL-171 ) and human embryonic kidney cells ( 293 cells; ATCC-CRL-1573; [55] ) were maintained in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% fetal calf serum ( FCS ) . Primary human umbilical vein endothelial cells ( HUVEC ) ( LONZA , USA ) , passage 1–6 , were maintained in an EGM-2 MV BulletKit medium system ( LONZA , USA ) . Human retinal pigment epithelial cells ( ARPE-19; ATCC-CRL-2302 ) were maintained in DMEM F12 supplemented with 10% FCS . For co-precipitation and competition experiments , human PDGFR-α-Fc and human PDGFR-β-Fc fusion proteins ( R&D Systems ) and recombinant human PDGF-AA and human PDGF-BB ( R&D Systems ) were used . For inhibition of PDGFR-α phosphorylation , imatinib mesylate ( Sigma ) was used . Antibodies specific for HCMV proteins were mouse anti-gH ( 14-4b and AP86-SA4 ) , human anti-gB ( SM5-1 [56] ) , mouse anti-MCP ( all kindly provided by M . Mach , University Erlangen-Nürnberg , Germany ) , mouse anti-gO . 02 [57] , mouse anti-UL128 ( 4B10 , kindly provided by T . Shenk , University of Princeton , USA ) , mouse anti-gB ( 2F12; Abcam ) , mouse anti-HCMV immediate early protein 1 ( IE1 ) ( Perkin Elmer ) , rabbit anti-UL131A antiserum [12] . Antibodies specific for cellular proteins were rabbit anti-Akt ( Cell Signaling ) , rabbit anti-Phospho-Akt ( Ser 473 ) ( Cell Signaling ) , mouse anti-PDGFR-α ( C9; Santa Cruz Biotechnology ) , mouse anti-PDGFR-α ( 35248; R&D Systems ) , mouse anti-PDGFR-α ( 2D2-1A11 , Sigma ) , rabbit anti-Phospho-PDGFR-α ( Y742; R&D Systems ) , rabbit anti-Phospho-PDGFR-α ( Y762; Cell Signaling ) , mouse anti-β actin ( AC-74; Sigma ) , mouse anti-GAPDH ( GA1R , Thermo Scientific ) . Detecting antibodies/reagents used were peroxidase-coupled goat anti-mouse ( Sigma ) , peroxidase-coupled kappa chain-specific goat anti-mouse ( Dianova ) , peroxidase-coupled goat anti-rabbit ( Sigma ) , Fluor488-coupled goat anti-mouse antibody ( Invitrogen ) and streptavidin-peroxidase polymer ( Sigma ) . All viruses used were bacterial artificial chromosome ( BAC ) -derived HCMV: wt TB40 virus [58] , TB40-UL131Astop virus ( TB40 mutant carrying a stop codon after the first 6 aa ) [20] , TB40-ΔgO virus ( TB40 mutant lacking the 533 N-terminal nucleotides ) [15 , 20] , wt TB40-luc virus [19] , TB40-UL131Astop-luc virus [19] , and TB40-ΔgO-luc virus [19] . For preparation of cell-free virus , supernatants of infected HFF showing a complete cytopathic effect were cleared of cellular debris at 3 . 000 g for 15 min and then virus pelleted at 26 . 000 g for 3 hours . Virus pellets were resuspended in DMEM containing 5% FCS and stored at -80°C . Titers of virus stocks were determined by a TCID50 assay performed on 96 well plates on HFF . To infect cells , medium was removed from 90% confluent cell monolayers and replaced by virus diluted in DMEM containing 5% FCS . In infection experiments using TB40-ΔgO , all virus infections were enhanced by a centrifugation step ( 30 min , 2 . 000 g at RT ) , followed by incubation at 37°C for 90 min . HFF were biotinylated in 0 . 5 mg/ml EZ-Link Sulfo-NHS-LC-LC-Biotin ( Thermo Scientific ) in PBS for 30 min at RT . The reaction was stopped by washing the cells 3 times with 100 mM glycine in PBS . Cells or virus stocks were lysed in lysis buffer ( 20 mM TrisHCl ( pH 8 . 0 ) , 150 mM NaCl and 1% Triton; phosphatase inhibitor cocktail ( cOmplete mini; Roche ) ) . Lysates were precleared with protein A sepharose beads ( GE Healthcare ) and co-incubated with antibodies or Fc fusion proteins at 4°C overnight . Then protein-antibody complexes were precipitated with protein A sepharose beads . The precipitates were dissociated in sample buffer ( 0 . 13 M Tris-HCl ( pH 6 . 8 ) , 6% SDS , 10% a-thioglycerol ) and subjected to SDS-polyacrylamide gel electrophoresis ( SDS-PAGE ) , followed by either silver stain ( FireSilver Staining Kit , Proteome factory , Berlin ) or Western blot analysis using nitrocellulose membranes ( GE Healthcare ) for protein transfer and Super Signal West Pico chemiluminescence substrate ( ThermoScientific ) for detection . Proteins in gel slices from silver stained gels were identified by high resolution nanoHPLC-ESI-MSMS chromatography ( Proteome Factory , Berlin , Germany ) and searched against human cellular proteins . The open reading frame of gO derived from HCMV strain TB40-BAC4 [59] lacking the N-terminal signal peptide ( aa 1–28 ) was cloned into the pFUSE-mIgG2BFc2 vector ( Invivogen ) . 293 cells were transfected with pFUSE-gO-mIgG2BFc2 using polyethylenimine . Three hours after transfection medium was exchanged for OptiPRO serum-free medium ( LifeTechnologies ) and supernatants were harvested 96 hours after transfection . Total supernatant proteins were precipitated with ethanol/125 mM NaCl to control expression by Western blot analysis . Fc proteins were purified from supernatants by precipitation with protein A sepharose ( GE Healthcare ) followed by elution using ImmunoPure IgG elution buffer , pH 2 . 8 ( Thermo Scientific ) . Fc protein amounts were determined by a capture ELISA on NeutrAvidin coated plates ( Thermo Scientific ) using a biotinylated goat anti-mouse IgG ( Fc-specific ) antibody ( Sigma ) to capture Fc proteins and a peroxidase-conjugated goat anti-mouse IgG ( Fc-specific ) antibody ( Sigma ) and TMB substrate ( BD Biosciences ) to detect Fc proteins . For indirect immunofluorescence , adherent cells were fixed in 50% acetone/50% methanol and stained using anti-IE1 antibody and Fluor488-coupled anti-mouse antibody . For counterstaining of cell nuclei , cells were incubated in PBS containing 5 mg ml-1 Hoechst 333258 ( Invitrogen ) . To detect surface expression of PDGFR-α or to detect cell surface binding of gO-Fc fusion proteins , HFF , HUVEC or 293 cells were detached from plates using 0 . 5 mM Na-EDTA and co-incubated with anti-PDGFR-α antibodies or Fc proteins , respectively . PDGFR-α or binding of Fc proteins was detected with Fluor 488-coupled anti-mouse antibodies and analyzed on a BD FACS Canto II cytometer using BD FACS Diva software ( BD Biosciences ) . HFF or 293 cells were grown in 96 well plates ( 20 , 000 cells/well ) and infected in triplicates for 90 min . Inocula were then replaced by medium supplemented with 300 μg ml-1 phosphono acetic acid ( PAA ) to block viral replication ( Sigma ) . 48 hours after infection cells were lysed in 50 μl lysis buffer ( 25 mM Tris/H3PO4 , 2 mM EDTA , 2 mM DTT , 10% glycerol , 5% Triton-X 100 ) and luciferase activity was determined for 20 μl of lysate with a luciferase assay system ( PromoCell ) according to the manufacturer’s instructions . 293 cells were transfected with pCMV-human PDGFR-α ( Sino Biological Inc . ) or pCMV-PDGFR-α ( 1–558 ) using Fugene ( Promega ) . 24 hours after transfection cells were seeded in 96 well plates and 24 hours later infected with luciferase-expressing HCMV . pCMV-PDGFR-α ( 1–558 ) was cloned by introducing a stop codon at position 559 into pCMV-PDGFR-α using the QuikChange II XL Site-Directed Mutagenesis Kit ( Agilent Technologies ) according to the manufacturer’s instructions ( mutagenesis primers: forward 5’-TGATTGATTCAATGACCCTTCAGCGAATTTCATACCTCG-3’ and reverse 5’-CGAGGTATGAAATTCGCTGAAGGGTCATTGAATCAATCA-3’ ) . HFF were transfected in 12 well plates ( 80 . 000 cells per well ) with 50 nM siGENOME human PDGFR-α ( 5156 ) siRNA SMARTpool ( Dharmacon ) or siGENOME Non-Targeting siRNA Pool #2 ( Dharmacon ) using RNAiMAX ( Fischer Scientific ) as transfection reagent . | The identification of cellular receptors recognized by viral glycoproteins promoting entry is central for understanding virus pathogenesis and transmission for any virus . Although the roles of alternative gH/gL complexes of HCMV in cell tropism and virus spread have been extensively studied in cell culture , transfer to HCMV tropism in vivo is a controversial issue . Our characterization of the PDGFR-α –gH/gL/gO interaction offers an explanation for the tropism of HCMV for cells and tissues with high levels of surface PDGFR-α in vivo . Discrepant findings , when similar cell types were analyzed in culture , may retrospectively be attributed to a culture-dependent loss or up-regulation of PDGFR-α protein levels . Our finding that the PDGFR-α—gH/gL/gO interaction starts the predominant entry pathway for infection with free virus moves the gH/gL/gO complex in the center of interest for vaccines designed to prevent horizontal or vertical transmission and also for the development of CMV vaccine or gene therapy vectors . | [
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"fibrob... | 2017 | Human cytomegalovirus glycoprotein complex gH/gL/gO uses PDGFR-α as a key for entry |
We previously mapped a type 2 diabetes ( T2D ) locus on chromosome 16 ( Chr 16 ) in an F2 intercross from the BTBR T ( + ) tf ( BTBR ) Lepob/ob and C57BL/6 ( B6 ) Lepob/ob mouse strains . Introgression of BTBR Chr 16 into B6 mice resulted in a consomic mouse with reduced fasting plasma insulin and elevated glucose levels . We derived a panel of sub-congenic mice and narrowed the diabetes susceptibility locus to a 1 . 6 Mb region . Introgression of this 1 . 6 Mb fragment of the BTBR Chr 16 into lean B6 mice ( B6 . 16BT36–38 ) replicated the phenotypes of the consomic mice . Pancreatic islets from the B6 . 16BT36–38 mice were defective in the second phase of the insulin secretion , suggesting that the 1 . 6 Mb region encodes a regulator of insulin secretion . Within this region , syntaxin-binding protein 5-like ( Stxbp5l ) or tomosyn-2 was the only gene with an expression difference and a non-synonymous coding single nucleotide polymorphism ( SNP ) between the B6 and BTBR alleles . Overexpression of the b-tomosyn-2 isoform in the pancreatic β-cell line , INS1 ( 832/13 ) , resulted in an inhibition of insulin secretion in response to 3 mM 8-bromo cAMP at 7 mM glucose . In vitro binding experiments showed that tomosyn-2 binds recombinant syntaxin-1A and syntaxin-4 , key proteins that are involved in insulin secretion via formation of the SNARE complex . The B6 form of tomosyn-2 is more susceptible to proteasomal degradation than the BTBR form , establishing a functional role for the coding SNP in tomosyn-2 . We conclude that tomosyn-2 is the major gene responsible for the T2D Chr 16 quantitative trait locus ( QTL ) we mapped in our mouse cross . Our findings suggest that tomosyn-2 is a key negative regulator of insulin secretion .
Genetic factors are estimated to contribute approximately 50% towards the risk of developing type 2 diabetes ( T2D ) [1] . Recent genome-wide association studies have identified a number of “diabetes genes” , gene loci that act in an additive manner and conspire with obesity to augment the risk of T2D . Nearly all of these genes affect β-cell function and/or the maintenance of β-cell mass . Thus , it appears that diet and obesity place demands on β-cells for insulin by causing insulin resistance , but the genetic bottleneck that can lead to T2D involves genes that affect the capacity of pancreatic β-cells to meet the increased insulin demand . Mouse intercrosses provide high power to detect linkage of gene loci to physiological traits related to obesity and diabetes . The mapping resolution of these intercrosses is typically not fine enough to identify individual genes . However , by generating panels of congenic strains carrying meiotic recombinations within disease loci , it is possible to map the genes underlying those loci with high resolution . We recently reported the positional cloning of a T2D gene , Sorcs1 , to sub-genetic resolution using this approach [2] . Several other genes have been identified using a similar approach; Zfp69 , which encodes a transcription factor involved in regulating glucose levels , was identified as the gene underlying a diabetes susceptibility locus on mouse chromosome 4 [3] . Similarly , Lisch-like was identified as the gene underlying a T2D locus on chromosome 1 and was shown to be involved in regulating β-cell mass and plasma glucose levels [4] . Borrowing from microbial genetics , mouse genetic studies employ a powerful tool for increasing the sensitivity to detect heritable phenotypes . This involves sensitized screens wherein a severe stressor provokes phenotypes that would otherwise be silent . The stressor need not be a normal feature in human disease pathogenesis to evoke phenotypes of great relevance to disease . For example , the apoE-deficient mouse is the most widely used animal model of atherosclerosis even though apoE deficiency is extremely rare in humans [5] . Similarly , a mutation in the Leptin gene ( Lepob ) promotes morbid obesity in mice ( Lepob/ob ) and evokes dysregulation of many pathways , enabling a greater understanding of their regulatory mechanisms [6] , [7] . Using the Lepob mutation as a stressor , we found that the BTBR T ( + ) ( BTBR ) mouse strain develops severe T2D , whereas the C57BL/6 ( B6 ) strain has moderate hyperglycemia and expands its β-cell mass [8] , [9] . In an F2 intercross derived from these two strains , we identified a T2D susceptibility locus on chromosome 16 ( Chr 16 ) [9] . In the present study , we developed a panel of congenic strains that enabled us to narrow this locus to just 0 . 94 Mb . Lean congenic mice that contain this genomic region derived from the BTBR strain have elevated glucose and reduced insulin levels . Islets from these mice show deficiencies in insulin secretion . Within this small interval , we identified a novel diabetes susceptibility gene , Syntaxin binding protein 5 like ( Stxbp5l ) , also known as Tomosyn-2 . We showed that the tomosyn-2 protein is an inhibitor of insulin secretion .
We previously identified a fasting plasma glucose locus on Chr 16 from a Lepob/ob F2 intercross derived from the B6 and BTBR mouse strains [9] . This locus acts in a fully dominant fashion on plasma glucose and a semi-dominant fashion on fasting plasma insulin [9] . The LOD peak on Chr 16 of the fasting glucose locus from the F2 intercross is located at approximately 36–38 Mb ( Figure 1A ) . To determine if the Chr 16 locus could act autonomously to affect T2D susceptibility , we derived a chromosome substitution ( i . e . consomic ) mouse strain by introgression of Chr 16 from BTBR into B6 ob/ob mice ( B6 . 16BT Lepob/ob ) . The fasting plasma glucose levels of the resulting consomic mice were significantly elevated at 4 , 6 , 8 , and 10 weeks compared to control ( B6 . 16B6 Lepob/ob ) mice and accounted for a major proportion of the plasma glucose phenotype of the parental BTBR strain . Fasting plasma insulin levels were reduced at 8 and 10-weeks in B6 . 16BT Lepob/ob mice compared to B6 . 16B6 Lepob/ob mice ( Figure 1B and 1C ) . These data suggested that the hyperglycemia caused by BTBR Chr 16 substitution is due to reduction in insulin levels . The data also indicate that the locus on Chr 16 acts autonomously ( i . e . in the absence of BTBR alleles on other chromosomes ) to affect glucose and insulin levels . To assess whether the B6 . 16BT Lepob/ob mice have a defect in insulin secretion , we isolated pancreatic islets from 10-week old B6 . 16BT Lepob/ob mice and measured fractional insulin secretion in response to high glucose ( 16 . 7 mM ) . We observed a ∼50% reduction in fractional insulin secretion in the B6 . 16BT Lepob/ob islets relative to control mice ( B6 . 16B6 Lepob/ob ) ( Figure 2 , left graph ) . To avoid the metabolic complexities that are attributed to the leptin mutation in the Lepob/ob mice [10] , we performed experiments in lean mice . Islets isolated from the congenic B6 . 16BT and B6 . 16B6 lean mice were treated with 8-bromo cAMP ( 3 mM ) at sub-maximal glucose ( 11 . 1 mM ) ; this combination of secretagogues was used for phenotyping lean congenic mice because it evoked more insulin secretion from lean islets than glucose alone . We observed ∼40% reduction in fractional insulin secretion in islets isolated from the lean B6 . 16BT mice relative to the lean control B6 . 16B6 mice ( Figure 2 , right graph ) . The data show that the insulin secretion defect , although initially mapped in a screen of F2 mice sensitized by the Lepob mutation , manifests itself independent of leptin deficiency . To investigate the region of the BTBR Chr 16 that confers the insulin secretion defect , a panel of lean congenic mouse strains was generated from the B6 . 16BT mice , each containing a small introgressed region from the BTBR Chr 16 in the B6 background ( Figure 3 , left panel ) . The B6/BTBR boundaries for each congenic strain were determined via microsatellite marker , single nucleotide polymorphism ( SNP ) sequencing or deletion/insertion polymorphism ( DIP ) sequencing ( Dataset S1 ) . By phenotyping each strain , we were able to fine-map the location of the gene responsible for the insulin secretion defect . Islets were isolated from each lean congenic mouse strain and fractional insulin secretion was determined in the presence of 3 mM 8-bromo cAMP at sub-maximal 11 mM glucose . The islets isolated from the lean congenic mice that carry the 2 Mb Chr 16 derived from the BTBR strain ( B6 . 16BT36–38 ) were defective in insulin secretion when compared to islets isolated from congenic mice B6 . 16B6 , B6 . 16BT37–55 , or B6 . 16BT24–37 ( Figure 3 , right panel ) . The overlapping region of BTBR Chr 16 represented by congenic strains , B6 . 16 BT36–38 and B6 . 16 BT37–55 enabled us to further narrow the region to 1 . 6 Mb . Islets isolated from congenic mice , B6 . 16BT24–55 , B6 . 16BT24–47 , or B6 . 16BT24–38 , which contained the 1 . 6 Mb region derived from the BTBR strain , also displayed a reduced level of insulin secretion , indicating that this 1 . 6 Mb region contains a gene responsible for regulating insulin secretion . We next determined the effect of introgression of the BT36–38 region of Chr 16 into B6 mice on susceptibility to obesity-induced diabetes . Plasma insulin and glucose levels were determined in random-fed 10-week B6 . 16BT36–38 and control B6 . 16B6 Lepob/ob congenic mice . We observed a ∼40% reduction in plasma insulin levels in the B6 . 16BT36–38 Lepob/ob compared to B6 . 16B6 Lepob/ob mice ( Figure 4A ) . The reduction in plasma insulin was accompanied by an increase in plasma glucose by ∼100 mg/dL ( Figure 4C ) . Although not nearly as dramatic as in the Lepob/ob mice , lean congenic mice with the 36–38 Mb BTBR insert had a significant reduction in plasma insulin and increase in plasma glucose ( Figure 4B and 4D , respectively ) . Detection of this very small rise in plasma glucose required a very large sample size ( n = 80 ) to achieve statistical significance . Clearly , we would not have found this modest phenotype in a screen of lean mice , showing that severe stressors like the Lepob mutation are required to identify subtle allelic variation in QTLs that contribute to T2D risk . Insulin secretion from pancreatic β-cells is biphasic . The first phase represents a brief but rapid secretion from pre-docked insulin granules in response to an initial depolarization of the plasma membrane . Non-nutrient secretagogues like KCl predominantly invoke the first phase of insulin secretion . The second phase of insulin secretion is associated with metabolic signals derived from the metabolism of fuel-based insulin secretagogues like glucose . Glucose affects both the first and second phase of insulin secretion . Briefly , glucose oxidation increases the ATP/ADP ratio , resulting in the closure of ATP-sensitive KATP channels . This causes depolarization of the plasma membrane and influx of Ca2+ via L-type voltage-dependent calcium channels . Glucose promotes the second phase of insulin secretion without causing a further increase in intracellular Ca2+ levels . Diazoxide inhibits closure of the K+ channels , therefore adding this drug along with 16 . 7 mM glucose will result only in the glucose-induced second phase of insulin secretion and eliminates any glucose induction of the first phase of insulin secretion [11]–[13] . To determine which phase of insulin secretion is defective in our lean congenic B6 . 16BT36–38 mice , we carried out perifusion studies of isolated islets . Islets were perifused in Krebs-Henseleit Ringer bicarbonate ( KRB ) buffer at the rate of 1 ml/min . The perfusate was sampled every 30 sec , and the secreted insulin was measured by ELISA . After an initial 60 min equilibration period in KRB containing 1 . 7 mM glucose , islets were perifused for 10 min in KRB containing 40 mM KCl and 250 µM diazoxide to elicit first phase of insulin secretion . After 10 min , the islets were perifused for an additional 30 min in KRB containing 16 . 7 mM glucose with 40 mM KCl and 250 µM diazoxide to evoke the second phase of insulin secretion . The peak of the first phase of insulin secretion from B6 . 16B6 islets was observed within 1–2 min of KCl treatment . Following the first peak , the more sustained second phase of insulin secretion was observed for an additional 30 min , mimicking the well-studied biphasic kinetics of insulin secretion [14] . Islets from B6 . 16BT36–38 lean mice secreted ∼40% less insulin during the second phase than islets from the B6 . 16 B6 mice , as determined by calculating the area under the curve ( AUC ) ( Figure 5A , 5B ) . There was also a small , but statistically significant reduction of first-phase insulin secretion ( p = 0 . 044 ) ( Figure 5A , 5B ) . To complement the perifusion experiments , static insulin secretion experiments were performed in islets isolated from the B6 . 16BT36–38 and control B6 . 16B6 lean mice . Isolated islets were incubated for 45 min in KRB containing 1 . 7 mM glucose . Following a 45-min incubation , the islets were treated with 40 mM KCl in KRB containing 1 . 7 mM glucose . No difference in fractional insulin secretion was observed in response to KCl ( Figure 6C , left panel ) . However , we observed a significant decrease in fractional insulin secretion between islets from the B6 . 16BT36–38 lean mice and those from the control B6 . 16B6 lean mice in response to 15 mM arginine , 3 mM 8-bromo cAMP in KRB containing 11 mM glucose , and 16 . 7 mM glucose alone ( Figure 5C , middle and right panels ) . To further narrow the 1 . 6 Mb region of BTBR Chr 16 responsible for the phenotype , we used Agilent's SureSelect Target Enrichment to capture DNA from 35 . 35 Mb to 38 . 65 Mb on mouse Chr 16 . 55 , 336 RNA baits were designed using the Agilent eArray and were used to enrich for our region from tail DNA of the B6 , BTBR , B6 . 16BT36–38 , B6 . 16BT24–37and B6 . 16BT24–38 mice . DNA was sequenced by Next Generation Sequencing using an Illumina GA IIx sequencer at the UW-Madison Biotechnology Center . Using CLC Genomics 4 . 0 . 3 Software , we were able to identify 470 SNPs; 3 non-synonymous coding SNPs and 46 DIPs between the B6 and BTBR DNA ( Figure 6A ) . 83 SNPs and 8 DIPs were further confirmed by manual base reading to confirm the accuracy of the software ( listed in Dataset S2 ) . Using this sequence and known overlapping regions derived from the BTBR strain in the sub-congenic strains exhibiting normal insulin secretion ( B6 . 16BT24–37 , B6 . 16BT37–55 ) , we were able to narrow the region responsible for the insulin secretion defect to 0 . 94 Mb containing 13 genes ( Figure 6B ) . To identify the gene ( s ) responsible for the insulin secretion defect , each candidate gene in the 0 . 94 Mb region was scored for the difference in mRNA abundance between the islets B6 . 16B6 and B6 . 16BT36–38 islets , the presence of non-synonymous coding SNPs , and similarity to a protein that have a functional role in exocytosis . Tomosyn-2 or Stxbp5l ( syntaxin binding protein 5-like ) quickly emerged as the top candidate gene . The mRNA abundance of tomosyn-2 was elevated 2 . 6 fold in the B6 . 16BT36–38 lean mice compared to control B6 . 16B6 mice ( Figure 7A ) . Tomosyn-2 has a coding SNP ( Ser-912→Leu ) . Eight other SNPs were also identified in the introns and additional SNPs were identified in the intergenic regions 5′ and 3′ of the gene ( Dataset S1 , Table S1 ) . The tomosyn-2 protein shares 95% identity in the C-terminal soluble NSF ( N-ethylmaleimide-sensitive factor ) attachment protein receptor ( SNARE ) domain with several syntaxin-binding proteins . Finally , a related protein , tomosyn-1 , has been shown to inhibit insulin secretion [15] . To understand the role of tomosyn-2 in the regulation of insulin secretion , the expression of tomosyn-2 was determined in key metabolic tissues; islet , liver , brain , cerebellum , kidney , adrenal , adipose ( perigonadal ) , heart , skeletal muscle ( gastrocnemius , soleus , and quadriceps ) of the lean B6 . 16BT36–38 and B6 . 16B6 mice . The mRNA expression of tomosyn-2 in islets of the lean B6 . 16BT36–38 mice was ∼2 . 6-fold higher than in islets from the B6 . 16B6 mice ( Figure 7A ) . No allele-dependent difference in the tomosyn-2 expression was observed in liver , brain , cerebellum , kidney , adrenal , gastrocnemius , adipose , heart , soleus , and quadriceps between the lean B6 . 16BT36–38 and B6 . 16B6 mice . Four tomosyn-2 isoforms have been identified in mice: xb- , b , s , and m-tomosyn-2 . We determined the relative expression of the tomosyn-2 isoforms in islets of the lean B6 . 16BT36–38 and B6 . 16B6 mice . We found that the b-tomosyn-2 isoform is the most abundant isoform in mouse islets . This was confirmed by RT-PCR with a primer pair that simultaneously amplified all of the tomosyn-2 isoforms ( data not shown ) . The relative expression of b-tomosyn-2 and s-tomosyn-2 mRNA was ∼6-fold higher in islets of the lean B6 . 16BT36–38 mice than in lean B6 . 16B6 mice ( Figure 7B ) . We observed no significant difference between the two-congenic mouse strains in the expression of xb- and m-tomosyn-2 isoforms . Together , the data indicate that increased expression of tomosyn-2 may be responsible for the insulin secretion defect observed in the lean B6 . 16BT36–38 mice . To investigate the role of tomosyn-2 in insulin secretion , we investigated the effect of overexpressing b-tomosyn-2 in the pancreatic β-cell line , INS1 ( 832/13 ) . The cells were transfected with GFP or b-tomosyn-2 expression plasmids . After 36 h , the cells were incubated in KRB containing 1 . 5 mM glucose for 2 h . Following the low glucose incubation , the cells were incubated for additional 10 min or 2 h in 3 mM 8-bromo cAMP at 7 mM glucose . Overexpressing b-tomosyn-2 decreased insulin secretion by ∼40% vs . GFP expressing cells at both 10 min and 2 h ( Figure 8A ) . No inhibition in fractional insulin secretion was observed at low glucose ( 1 . 5 mM ) ( data not shown ) . To determine if b-tomosyn-2 binds to syntaxin-1A and syntaxin-4 , key t-SNARE proteins involved in the fusion of insulin granules to the plasma membrane , in vitro binding experiments were performed using GST fused syntaxin-1A and syntaxin-4 recombinant proteins ( soluble , lacking transmembrane domains ) by pull-down assays using glutathione beads . All isoforms of tomosyn-2 bound to GST-syntaxin-1A and GST-syntaxin-4 ( Figure 8B ) . The quantitation for the amount of bound tomosyn-2 isoforms as a fraction of total is shown in Figure 8C . Tomosyn-1 was used as a positive control for binding . The GST tag did not pull down tomosyn-1 or tomosyn-2 , confirming that the interaction between tomosyn-2 and syntaixn-1A and -4 is specific . Together , these data suggests that the mechanism by which b-tomosyn-2 inhibits insulin secretion involves binding to the syntaxin proteins . This suggests the possibility that tomosyn-2 , like tomosyn-1 , inhibits insulin secretion by preventing the binding of VAMP2 to syntaxin-1A and syntaxin-4 . We have shown that tomosyn-2 is a negative regulator of insulin secretion and also binds to syntaxin-1A and syntaxin-4 . To investigate the possibility that the serine-912leucine SNP in tomosyn-2 affects its stability , HEK293T cells were transfected with empty vector ( mock ) , b-tomosyn-2 ( Serine-912 ) , or b-tomosyn-2 ( Leucine-912 ) . After 16 h , the cells were treated with or without the proteasomal inhibitor , MG132 ( 100 µM ) for 6 h . The MG132 treatment rescued the B6 allelic form of the protein , b-tomosyn-2 ( serine-912 ) , from proteasomal degradation by ∼50% ( Figure 9A and 9B ) . However , the BTBR allelic form of the protein , b-tomosyn-2 ( leucine-912 ) was not resistant to MG132 treatment , suggesting that an increased stability of the tomosyn-2 protein might be responsible for the attenuation in insulin secretion from islets of the BTBR mice .
Our pursuit of genes conferring susceptibility to obesity-induced T2D focuses on two mouse strains that differ in diabetes susceptibility . BTBR mice , when made obese with the Leptinob mutation , are susceptible to T2D , whereas B6 mice with the same mutation are relatively diabetes resistant [16] , [17] . The diabetes susceptibility of the obese BTBR mice has multiple causes , including an insulin secretion defect and a failure to increase β-cell mass . As early as 4 weeks of age , islets from the obese B6 , but not BTBR mice have an increase in expression of a module of cell cycle genes whereas the obese BTBR mice fail to induce the expression of this module [16] . Through genetic mapping in an F2 intercross , we identified a strong QTL on Chr 16 wherein the BTBR allele is linked to increased glucose levels . In complex trait genetics , it is often the case that gene loci do not act autonomously , but must act along with specific alleles at other loci to exert their phenotypic effects [18]–[20] . To determine if the Chr 16 locus acts autonomously , we derived a chromosome substitution strain . Substitution of Chr 16 in the B6 strain with Chr 16 from the BTBR strain led to a ∼100 mg/dl increase in glucose and a ∼50% decrease in plasma insulin . This established that a locus on Chr 16 acts autonomously and is sufficient to account for a major part of the diabetes phenotype of the BTBR mouse strain . QTL mapping in an F2 intercross does not provide the resolution required to identify individual genes . To narrow the interval , we derived a panel of interval-specific congenic strains . The strains were phenotyped on the basis of insulin secretion from isolated islets , a far more robust phenotype than fasting glucose or insulin levels . This enabled us to narrow the position of the QTL to <1 Mb , containing just thirteen genes . Of these thirteen genes , tomosyn-2 was the only gene that had both altered mRNA abundance and a coding SNP ( S912L ) . Recent studies by Williams et al . show that tomosyn-2 inhibits exocytosis in PC12 cells [21] . Our experiments establish a role for tomosyn-2 in insulin secretion . When we overexpressed tomosyn-2 in the pancreatic β-cell line INS1 ( 832/13 ) , insulin secretion in response to 8-bromo cAMP at sub-maximal glucose was attenuated . In vitro GST-pull-down experiments showed that tomosyn-2 has the ability to bind t-SNARE proteins , syntaxin-1A and syntaxin-4 . Syntaxin-1A is involved in the first phase and syntaxin-4 is involved in regulating both the first and second phase of insulin secretion [22] . These results establish that tomosyn-2 , like its homologue , tomosyn-1 , inhibits insulin secretion [23] . The fact that allelic variation in tomosyn-2 is sufficient to produce this phenotype suggests that tomosyn-1 cannot compensate for this deficiency , implying that their functions may not completely overlap . The mRNA abundance of tomosyn-2 was increased in the congenic mouse strains expressing the BTBR allele . It is difficult to determine if this difference in expression level is sufficient to produce the difference in insulin secretion in islets of the two mouse strains because it would require accurately titrating the gene dosage ( and the amount of protein product ) in a null background . Sequence analysis revealed a SNP in tomosyn-2 ( S912L ) . We utilized a recent study showing that the proteasome inhibitor MG132 increases the abundance of tomosyn-2 [21] . We found that the S912L SNP abolishes the ability of MG132 to rescue tomosyn-2 from proteasomal degradation , thus establishing a functional role for the S912L SNP . Therefore , the decreased insulin secretion associated with the BTBR allele might be the result of increased stability of the tomosyn-2 protein as a consequence of the SNP at amino acid 912 . We also indentified SNPs in the introns and the intergenic regions 5′ and 3′ of the tomosyn-2 gene ( Figure 7 ) . The intronic SNPs may regulate the stability of the tomosyn-2 mRNA and the intergenic SNPs might affect the level of transcription of the gene . To investigate the role of the S912L SNP in pancreatic islets , we conducted perifusion experiments . Our perifusion experiments demonstrated that islets from the B6 . 16BT36–38 congenic mice were defective in the 2nd phase of insulin secretion . Our results suggest that tomosyn-2 is likely to be responsive to metabolic signals . With static incubation experiments , we tested various insulin secretagogues . Islets with the BTBR allele of tomosyn-2 were clearly less responsive to cAMP or arginine at sub-maximal glucose . These secretagogues are involved in both phases of insulin secretion , but the exact mechanisms by which they stimulate the 2nd phase of insulin secretion are not fully understood . The involvement of tomosyn-2 provides a plausible new target for the actions of these secretagogues . We show that tomosyn-2 , similar to tomosyn-1 , binds to syntaxin-1A and syntaxin-4 [24]–[27] . Recent studies suggest that the binding to syntaxin is necessary but not sufficient for tomosyn-2′s inhibition of insulin secretion [21] . Our studies suggest that tomosyn-2 imposes a critical brake on insulin secretion . This is particularly important during fasting when inappropriate insulin secretion could cause life-threatening hypoglycemia . We hypothesize that under fasting conditions when glucose levels are low , tomosyn-2 blocks exocytosis and prevents hypoglycemia . In mice , the two tomosyn genes , tomosyn-1 and tomosyn-2 , encode seven alternatively spliced variants [23] . Tomosyn-1 contains three distinct isoforms ( s , m , and b ) , whereas tomosyn-2 has four different spliced variants ( s , m , b , and xb ) . The spliced exons encode the hypervariable region ( HVR ) , which in tomosyn-1 has been shown to be subject to SUMOylation and PKA-mediated phosphorylation [21] , [28] . The amino acid sequences of tomosyn-1 and tomosyn-2 are quite similar in the N-terminal WD40 repeats and C-terminal VAMP-like domain ( VLD ) [23] . Tomosyn-1 was identified in neurons as a syntaxin-1-binding protein that sequesters t-SNAREs on the plasma membrane by forming a “dead end” , nonfusogenic SNARE complex , resulting in inhibition of the formation of the SNARE complex [24] , [26] . Deletion of the tomosyn-1 gene in C . elegans or in mice resulted in enhanced asynchronous neurotransmitter release [29] , [30] . Gain of function studies demonstrated that tomosyn-1 is responsible for inhibiting exocytosis of dense core granules in primary adrenal chromaffin cells [27] , PC12 cells [25] , and pancreatic β-cells [15] . Moreover , in vitro biochemical evidence further supports the conclusion that tomosyn-1 inhibits the formation of the SNARE complex [31] , [32] . The Ca2-independent inhibitory effects of the tomosyn-1 have been attributed to the VLD . More recently , Yamamoto et al demonstrated that in the presence of Ca2+ , tomosyn-1 , via the N-terminal WD40 domain , binds to synaptotagmin and inhibits SNARE complex-mediated neurotransmitter release [26] , [30] , [33] , [34] . Together , the evidence is accumulating for tomosyn-1 as a negative regulator of exocytosis in both the stimulated and unstimulated states . Insulin resistant animals compensate for their insulin resistance and maintain normal glucose levels by increasing insulin secretion . Our studies show that mutations in tomosyn-2 that increase its inhibitory activity can create a bottleneck and in the presence of obesity-induced insulin resistance , tip the balance towards T2D . However , it is also possible that tomosyn-2 plays an important role in regulating insulin secretion during daily starve/feed cycles by preventing inappropriate insulin secretion during fasting . Tomosyn-2 may regulate exocytosis by modulating the formation of the SNARE complex in tissues other than islets . We observed significant tomosyn-2 expression in brain , cerebellum , islets , kidney , liver , and gastrocnemius ( Figure 7 ) . Therefore it is possible that tomosyn-2 , like tomosyn-1 , may have an important regulatory role in tissues where regulation of the SNARE complex can be limiting for an important transport process; e . g . insulin-mediated GLUT4 translocation in adipocytes [35] and transport of LDL-derived cholesterol from the trans-Golgi network to the endoplasmic reticulum in hepatocytes [36] . Thus , this tomosyn-2 could be playing a critical role in regulating vesicle trafficking in other tissues . In summary , we have identified tomosyn-2 as a gene underlying a T2D susceptibility QTL on Chr 16 . We show that tomosyn-2 is a negative regulator of insulin secretion . We identified a SNP in tomosyn-2 that affects the stability of the protein and thus suggest a molecular mechanism by which allelic variation in this gene increases diabetes susceptibility . Future studies will focus on the pathways that link nutrient sensing with the role of tomosyn-2 in the regulation of insulin secretion .
The enzymatic glucose reagent was purchased from Thermo Scientific . Insulin in lean mice was measured using a radioimmunoassy kit from Linco Research ( St . Charles , MO ) . In Lepob/ob mice , insulin was measured with an in-house ELISA using an anti-insulin antibody from Fitzgerald Industries ( Acton , MA ) . The mouse anti-myc antibody and Z-Leu-Leu-Leu-al ( MG132 ) were purchased from Sigma-Aldrich , USA . The mouse secondary antibodies were purchase from Cell Signaling Technology ( Boston , MA ) . Glutathione 4B Sepharose beads were purchased from GE Healthcare , USA . The C57BL/6 ( B6 ) and BTBR T+ tf ( BTBR ) mice were intercrossed and were crossed to generate F1 mice . The B6 . 16B6 and B6 . 16BT mice were created by backcrossing the F1 mice to B6 using microsatellite markers to select for BTBR ( or B6 in the case of B6 . 16B6 ) homozygosity on mouse chromosome 16 in an otherwise B6 background . The B6 . 16BT mice were further backcrossed to B6 with marker assisted selection to create congenic strains . Further identification of the B6/BTBR genetic boundaries were determined by SNP sequencing for some of the congenic strains ( listed in Dataset S2 . The Lepob mutation was introgressed into all strains using Lepob/+ mice as breeders [37] . All mice were maintained at the Department of Biochemistry , University of Wisconsin-Madison animal care facility on a 12 h dark-light cycle ( 6 PM to 6 AM ) . The mice were fed Purina Formulab Chow 5008 and water ad libitum . The mice were kept in accordance with the University of Wisconsin-Madison Research Animals Resource Center and the NIH guidelines for care and use of laboratory animals . For plasma glucose and insulin measurements , blood was taken from the retro orbital sinus from random fed mice at 8 AM or from fasting mice at 12 PM ( fasted at 8 AM ) . For both Lepob/ob and lean mice , glucose was measured via glucose oxidase method ( Thermo Scientific ) . For Lepob/ob mice , insulin was measured via ELISA using a matched rat insulin antibody pair ( Fitzgerald Industries International Inc . ) . For lean mice , insulin was measured by Linco Sensitive rat insulin radioimmunoassay . Intact pancreatic islets were isolated from mice using a collagenase digestion procedure [38] . Static insulin secretion assays were performed on preparations consisting of three islets incubated with various secretagogues [38] . For perifusion insulin secretion assays , approximately 100 medium sized islets were washed three times , placed in a sterile Petri dish , and incubated overnight in culture media ( RPMI 1640 , with 11 . 1mM glucose , antibiotics and 10% heat inactivated fetal bovine serum ) . The following day , 50 islets were washed and transferred in 100 µl of Krebs Ringer Buffer ( KRB ) to the Swinnex filter holder ( Millipore ) . The islets were sandwiched between two layers of Bio-Gel P-2 bead ( Bio-Rad ) solution ( 200 mg beads/ml in KRB; bottom layer , 150 µl and top layer , 300 µl ) . The Swinnex filter holder was attached in-line with a Minipuls 3 pump ( Gilson ) and a FC 204 Fraction Collector ( Gilson ) . Islets were perifused at the rate of 1ml/min and samples were collected at 30 sec intervals . Islet insulin content and secretion were determined by ELISA . Tail DNA was extracted from B6 , BTBR , B6 . 16BT36–38 , B6 . 16BT24–37and B6 . 16BT24–38 mice using the QIAGEN Puregene Core Kit . RNA baits were designed using Agilent eArray and used for Agilent SureSelect Target Enrichment to capture sequence from a 35 . 35 to 38 . 65 Mb region on mouse chromosome 16 . Target enrichment was followed by DNA amplification and confirmation of enrichment using SNP sequencing inside and outside of the target region . DNA was sequenced by Next Generation Sequencing using an Illumina GA IIx sequencer at the University of Wisconsin-Madison Biotechnology Center . CLC Genomics 4 . 0 . 3 Software was used to identify SNPs and DIPs between B6 and BTBR sequence . For some SNPs and DIPs an additional visual base calling confirmation step was used to test the accuracy of the software ( listed in Dataset S1 ) . RNA from islets , kidney , and liver was extracted using the QIAGEN RNeasy Plus Kit . RNA from epididymal fat pads , brain , cerebellum , and adrenal glands was extracted using QIAGEN RNeasy Lipid Kit . RNA from heart , soleus , gastrocnemius and quadriceps was extracted using QIAGEN RNeasy Fibrous Tissue Kit . Following extraction , RNA was used for cDNA synthesis ( Applied Biosystems ) . The mRNA abundance was determined by quantitative PCR using FastStart SYBR Green ( Roche ) and gene expression was represented by comparative ΔCT method . MMLV-based retroviral vector ( RVV , 3051 ) ( gift from Dr . Bill Sugden , University of Wisconsin , Madison ) containing a MCS-IRES GFP was used to generate b-tomosyn-2-RVV construct for expression studies . The pcDNA3-m-tomosyn-1 , pCR-Script-xb , -b , -m , and s-tomosyn-2 constructs were generously provided by Dr . Alexander Groffen , Virije Universiteit , Netherlands . We corrected a mutation ( AG ) at nucleotide 3245 of the b-Tomosyn-2 cDNA . The tomosyn-1 or tomosyn-2 cDNA from these vectors were used for subsequent subcloning . The b-tomosyn-2-RVV construct was generated by subcloning the b-tomosyn-2 cDNA with 5′-BspDI and 3′-NotI overhangs into the compatible 5′-BstBI and 3′-NotI ends of the RVV vector . For binding studies , the tomosyn-2-pcDNA/TO/myc-His was generated by subcloning a PCR-amplified tomosyn-2 cDNA in to 5′-BamHI and 3′-XhoI sites of the pcDNA4/TO/myc-His C vector ( Invitrogen ) . The following primers that were used to amplify tomosyn-2 cDNA with the restriction sites for cloning , a partial KOZAK , and a 3′-precision protease cleavage site are: forward ( 5′-TTAAAGGATCCGCCACCATGAAGAAGTTTAATTTCCG ) and reverse ( 5′-ATATCTCGAGGGGCCCCTGGAACAGAACTTCCAGGAACTGGTACCACTTCTTATCCT ) . Similar subcloning strategy was used for generating m-tomosyn-1-pcDNA/TO/myc-His construct . The primers used are as follows: forward ( 5′-CGAGACCGGATCCGCCACCATGAGGAAATTCAACATC ) and reverse ( 5′-ATATCTCGAGCCCCTGGAACAGAACTTCCAGGAACTGGTACCACTTCTTATCTTTG ) primes . The pGEX-4T1-syntaxin-4 construct encoding soluble GST-syntaxin-4 ( 1-273 ) fusion protein was generated as previously described [39] . The pGEX-2T1-syntaxin construct ( 1-265 ) was a generous gift from Dr . Tom Martin , University of Wisconsin , Madison . All constructs were verified by sequencing . The glucose responsive rat β-cell line , INS1 ( 832/13 , a gift from Dr . Chris Newgard , Duke University ) was cultured in RPMI 1640 medium containing 11 mM glucose supplemented with 10% heat inactivated fetal bovine serum , 2 mM L-glutamine , 1 mM sodium pyruvate , 10 mM HEPES , 100 Units/ml of antibiotic-antimycotic , and 50 µM β-mercaptoethanol . Approximately 100 , 000 cells/well were plated in a 96-well plate . The following day , INS1 ( 832/13 ) cells at 80–90% confluency were transfected with 0 . 4 µg of plasmid DNA using Lipofectamine 2000 ( Invitrogen ) . After 36 h of incubation , cells were washed once with 200 µl and incubated for 2 h in 100 µl of modified Krebs-Henseleit Ringer bicarbonate buffer ( KRB: 118 . 41 mM NaCl , 4 . 69 mM KCl , 1 . 18 mM MgSO4 , 1 . 18 mM KH2PO4 , 25 mM NaHCO3 , 20 mM HEPES , 2 . 52 mM CaCl2 , pH 7 . 4 , and 0 . 2% BSA ) containing 1 . 5 mM glucose . After 2 h , cells were stimulated for 2 h in 100 µl of KRB buffer containing 7 mM glucose + 3 mM 8-bromo-cAMP . The incubation buffer was collected to determine the amount of insulin secreted under varying conditions . The cells were lysed ( lysis buffer: 1 M Tris-HCl , pH 8 . 0 , 1 M NaCl , 0 . 5 M NaF , 200 mM Na3VO4 , 2% NP-40 , and protease inhibitor cocktail tablet ( Roche ) ) to determine insulin content . The percent fractional insulin secretion was calculated as the amount of insulin secreted divided by total insulin content . Insulin was determined using ELISA . The human embryonic kidney 293T cells ( HEK293T ) were cultured in Dulbecco's modified Eagle's medium ( DMEM ) containing 25 mM glucose were supplemented with 10% fetal bovine serum , 0 . 1 mM nonessential amino acid , 6 mM L-glutamine , 1 mM sodium pyruvate , 100 units/ml of penicillin , 100 units/ml of streptomycin , and 500 µg/ml of geneticin . HEK293T cells at 70–80% in 100 mm tissue culture dishes were transfect with plasmid DNA constructs using 40 µl of 1 mg/ml polyethylenimine . Following day , cells were lysed ( lysis buffer: 20 mM Tris-HCl ( pH 7 . 5 ) . 150 mM NaCl , 1 mM Na2EDTA , 1mM EGTA , 1% Triton , 2 . 5 mM sodium pyrophosphate , 1 mM β-glycerophosphate , 1 mM Na3VO4 , 1 mM PMSF , and protease inhibitor cocktail tablet ( Roche ) ) and total protein lysates were prepared and the immunoblot was performed as described [40] . For protein stability , 16 h post transfection , cells were treated with or without 100 µM MG132 for 6 h . After 6 h , cells were lysed and whole cell lysates were prepared . Recombinant proteins encoding GST or GST-fusion proteins with the cytoplasmic domain of syntaxin-1A and syntaxin-4 were expressed in E . Coli strain BL21 ( DE3 ) and were purified using glutathione-affinity chromatography [41] . The concentration and the purity of the fusion proteins were assessed by SDS-PAGE followed by Coomassie-blue staining against BSA standards . The binding studies were preformed by incubating 10 µg of recombinant proteins with 25 µl of 100% Glutathione-Sepharose 4B beads ( Amersham Biosciences ) with 1 mg of HEK293T lysate overexpressing tomosyn-1 or isoforms of tomosyn-2 ( xb , b , m , and s ) in 1% Triton-lysis buffer for 2 h at 4°C . After 2 h , the complexes were washed three times with Triton lysis buffer and was eluted in Western loading buffer . The denatured samples were subjected to 10% SDS-PAGE gels followed by transfer to PVDF membrane for immunoblotting . The immunobloting was performed using a standard protocol [40] . Data was expressed as means ± standard error of means . The statistical comparisons were made using Student's t test at p<0 . 05 . | Humans carry many genetic variants that confer small effects on metabolic traits relevant to type 2 diabetes . These effects are amplified by environmental stressors like obesity . We used morbid obesity as a sensitizer to identify genes that contribute to the diabetes susceptibility of the BTBR mouse strain . Using mapping and breeding strategies , we were able to narrow a genetic region to one containing just 13 genes . One of these genes , tomosyn-2 , emerged as a prime candidate . Our functional studies showed that tomosyn-2 is an inhibitor of insulin secretion , and it binds to the proteins involved in the fusion of insulin containing granules with the plasma membrane . We found a coding mutation and demonstrated that this mutation affects the stability of the protein product . Our work with Tomosyn-2 provides new insights into the regulation of insulin secretion and emphasizes that negative regulation is critical for avoiding insulin-induced hypoglycemia . | [
"Abstract",
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"Results",
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"genetics",
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] | 2011 | Positional Cloning of a Type 2 Diabetes Quantitative Trait Locus; Tomosyn-2, a Negative Regulator of Insulin Secretion |
Bst-2/Tetherin inhibits the release of HIV by tethering newly formed virus particles to the plasma membrane of infected cells . Although the mechanisms of Tetherin-mediated restriction are increasingly well understood , the biological relevance of this restriction in the natural target cells of HIV is unclear . Moreover , whether Tetherin exerts any restriction on the direct cell-cell spread of HIV across intercellular contacts remains controversial . Here we analyse the restriction endogenous Tetherin imposes on HIV transmission from primary human macrophages , one of the main targets of HIV in vivo . We find that the mRNA and protein levels of Tetherin in macrophages are comparable to those in T cells from the same donors , and are highly upregulated by type I interferons . Improved immunocytochemistry protocols enable us to demonstrate that Tetherin localises to the cell surface , the trans-Golgi network , and the macrophage HIV assembly compartments . Tetherin retains budded virions in the assembly compartments , thereby impeding the release and cell-free spread of HIV , but it is not required for the maintenance of these compartments per se . Notably , using a novel assay to quantify cell-cell spread , we show that Tetherin promotes the transfer of virus clusters from macrophages to T cells and thereby restricts the direct transmission of a dual-tropic HIV-1 . Kinetic analyses provide support for the notion that this direct macrophage-T cell spread is mediated , at least in part , by so-called virological synapses . Finally , we demonstrate that the viral Vpu protein efficiently downregulates the cell surface and overall levels of Tetherin , and thereby abrogates this HIV restriction in macrophages . Together , our study shows that Tetherin , one of the most potent HIV restriction factors identified to date , can inhibit virus spread from primary macrophages , regardless of the mode of transmission .
The replication of viruses can be inhibited by a number of cellular proteins , collectively referred to as restriction factors [1] . In many cases , the expression of restriction factors is induced or enhanced by type I interferons ( IFN ) , which are upregulated following infection with intracellular pathogens such as viruses . The primate lentiviruses , including human immunodeficiency viruses ( HIV ) , are subject to restriction at multiple stages of their life cycles [1] . In a number of these cases , viruses have evolved mechanisms to abrogate the influence of specific cellular restriction factors . Recently , HM1 . 24/CD317/Bst-2/Tetherin ( Ensembl: ENSG00000130303 ) was identified as a restriction factor of particular significance , as the ability to antagonise Tetherin appears to have been a major factor in the adaptation of SIVcpz to man [2]–[4] . As implied by its name , Tetherin has the ability to tether HIV particles to the surface of infected cells , and this function is attributable to its unusual topology . Tetherin contains two membrane anchors , an N-terminal transmembrane domain and a C-terminal GPI-anchor [5] , [6] . During assembly and budding of HIV particles at the plasma membrane ( PM ) of infected cells , Tetherin can be incorporated into nascent virions via one of its membrane anchors , leaving the second anchor in the PM , and thereby preventing virus release into the extracellular milieu [7]–[9] . The failure to release free particles inhibits the cell-free spread of HIV , which relies on the diffusion of released virus toward its target cells . HIV and related viruses have evolved mechanisms to overcome Tetherin restriction and ensure their efficient propagation . In the case of HIV-1 main ( M ) group viruses , the accessory protein Vpu enhances lysosomal sorting and degradation of Tetherin , thereby reducing the levels of the restriction factor at the cell surface , and promoting HIV-1 release [10]–[12] . In addition to cell-free transmission , HIV can be transferred across intercellular contacts . This cell-cell spread appears to be significantly more efficient than cell-free propagation , and has been proposed to occur via filopodial bridges , membrane nanotubes , and , most prominently , virological synapses ( VS ) [13] . VS between T cells are characterised by the recruitment of viral proteins and HIV receptors to the cellular interface [14] , however , little is known about VS between HIV-infected macrophages and T cells [15] , [16] . Moreover , whether Tetherin also inhibits cell-cell spread of HIV , or whether a direct contact between the infected and the target cell eliminates the need for HIV to fully detach from its host cell , remains controversial [17]–[23] . CD4+ T cells and macrophages are the main cellular targets of HIV in vivo . Significantly , some aspects of viral replication vary with the target cell type . A prominent example is the site of virus assembly: Whereas in T cells HIV assembles and buds at the cell surface , in monocyte-derived macrophages ( MDM ) budding intermediates are almost exclusively detected on deeply invaginated PM domains [24] , [25] , which we have termed Intracellular Plasma Membrane-connected Compartments ( IPMC ) [26] . IPMCs have a neutral pH [27] and contain numerous molecules typically found at the PM , including phosphatidylinositol-4 , 5-bisphosphate [28] , the tetraspanins CD9 and CD81 , the hyaluronan receptor CD44 [24] , and focal adhesion proteins including the integrins CD11b , CD11c , and CD18 [26] . Though IPMCs are also present in uninfected MDMs , and thus not induced by infection , HIV triggers changes in both the composition and morphology of the compartments: For example , IPMCs in HIV-infected MDMs contain the tetraspanin CD63 [24] and may be larger than in uninfected cells [29] . Given these variations in HIV replication in different cell types , it is imperative to analyse the localisation , function and antagonism of Tetherin specifically in macrophages . This notion is re-enforced by a recent study suggesting important differences in Tetherin antagonism in macrophages and non-monocytic cells , namely that in MDMs Tetherin is only mildly induced by IFN , and that cell surface Tetherin is inefficiently antagonised by Vpu [21] . Here we show that Tetherin expression in primary MDMs is as sensitive to IFN as in primary T cells . At steady state , endogenous Tetherin localises to the cell surface , the trans-Golgi network ( TGN ) , and IPMCs . Vpu efficiently antagonises cell surface Tetherin in MDMs and , in cells devoid of Vpu , Tetherin retains mature HIV particles in IPMCs , which may cause a virus particle-induced expansion of the assembly compartments . Furthermore , our experiments indicate that cell-cell transmission allows efficient spread of HIV from MDMs to autologous CD4+ T cells . Using a novel assay that strongly favours cell-cell over cell-free propagation , we show that Tetherin can restrict cell-cell transmission of HIV from macrophages to T cells . Thus , our data indicate that Tetherin has the potential to impose a major restriction to HIV spread , regardless of the mode of transmission .
Numerous studies have shown that type I IFNs induce the expression of Tetherin in cell lines and primary T cells , but whether this is also true for primary macrophages remains controversial [21] , [30] . To determine whether type I IFNs induce endogenous Tetherin expression in macrophages , we isolated monocytes and CD4+ T cells from buffy coats of HIV-negative donors , differentiated the monocytes into MDMs in vitro , and proliferated the T cells in the presence of lectin and IL-2 . Subsequently , all cells were stimulated with 544 U/ml ( 2 ng/ml ) IFN-β for 24 h , and analysed by RT-qPCR and western blotting . Increased mRNA levels of the IFN-induced gene IFIT1 in both MDMs and T cells confirmed that the IFN-β preparation was biologically active and induced the IFN pathway ( Fig . 1A ) . Notably , IFN-β treatment of MDMs upregulated both the mRNA and protein levels of Tetherin by approximately one order of magnitude ( Fig . 1A , B ) . In autologous T cells , IFN-β treatment increased the Tetherin levels two- to five-fold ( Fig . 1A , B ) . We next examined the concentration dependence of the Tetherin upregulation by type I IFNs . Following treatment of MDMs with 20–500 U/ml IFN-β , we observed significant increases in both IFIT1 and Tetherin mRNA levels ( Fig . 1C ) , as well as Tetherin protein levels ( Fig . 1D ) , even at the lowest IFN-β concentration tested . Together our data demonstrate that primary MDMs upregulate Tetherin expression , even at low concentrations of a type I IFN , to an extent comparable to autologous CD4+ T cells . We selected two commonly used , commercially available antibodies to examine the cellular distribution of endogenously expressed Tetherin in primary MDMs . Immunolabelling was performed on live cells , as all Tetherin antibodies tested exhibited reduced binding efficiency when applied after aldehyde fixation . When we incubated unpermeabilised MDMs in antibody-containing media on ice , then fixed and stained with a fluorescent secondary antibody , endogenous Tetherin was readily detected on the surface of primary MDMs ( Fig . 2A ) . Consistent with an increase in mRNA and overall protein levels ( Fig . 1 ) , the levels of cell surface Tetherin were increased following IFN treatment ( Fig . 2A ) . We next permeabilised MDMs and incubated them with polyclonal antibodies against Tetherin and TGN46 , or monoclonal antibodies against Tetherin and CD9 , on ice . In both cases Tetherin was detected in two distinct intracellular locations: In almost all cells Tetherin was found in spots ( double arrows in Fig . 2B , C ) , often distributed around the nucleus , which co-localised with TGN46 ( double arrows in Fig . 2B ) . Some MDMs showed additional accumulations of Tetherin ( arrowheads in Fig . 2B , C ) , which co-stained for the IPMC protein CD9 ( arrowheads in Fig . 2C ) . Also in permeabilised MDMs , Tetherin levels were higher in IFN-stimulated than in untreated cells ( compare Fig . 2B , C to Fig . S1A , B ) . We conclude that in primary MDMs , endogenous Tetherin localises to the cell surface , TGN , and IPMCs , without any obvious enrichment of the protein in IPMCs . This is consistent with the notion that IPMCs are continuous with , and biochemically similar to , the PM [24] , [25] . To investigate whether HIV-1 Vpu antagonises endogenous Tetherin in primary macrophages , we disrupted the Vpu gene of the dual-tropic HIV-1 strain NL4 . 3-R3A ( from hereon referred to as R3A- ( + ) ) : R3A- ( − ) carries a start codon mutation , and R3A-Udel an internal deletion in Vpu ( Fig . 3A ) . We used both Vpu mutants , R3A- ( − ) and -Udel , since MDMs infected with a Vpu start codon-deleted HIV-1 have been suggested to express increased levels of Env , which may affect Tetherin antagonism and viral infectivity [31] . Western blot analyses confirmed that Vpu was expressed in MDMs infected seven days after monocyte isolation with R3A- ( + ) for seven days , but not in cells infected with R3A- ( − ) or -Udel ( Fig . 3B ) . Significant differences in Env expression were not detected ( Fig . 3B ) and , consistently , single-cycle infectivity assays indicated that MDM-derived R3A- ( − ) and -Udel were as infectious as R3A- ( + ) ( Fig . 3C ) . We performed western blot analyses to test whether Vpu antagonises Tetherin in primary MDMs . We found that Tetherin levels in MDMs infected with R3A- ( + ) for seven days were decreased compared to R3A- ( − ) and –Udel-infected cells ( Fig . 3B ) , showing that Vpu reduces the overall levels of endogenous Tetherin in MDMs . The higher Tetherin levels detected in infected compared to uninfected MDMs were most likely due to a cellular IFN response . Consistently , western blotting showed that Tetherin levels in R3A-infected MDMs were reduced when we infected and cultured the cells in the presence of 1 µg/ml of an IFN-α/β receptor antibody that has been shown to prevent activation of the IFN receptor ( Fig . S2 , [32] ) . To analyse the effects of Vpu on cell surface Tetherin in MDMs , we immunolabelled R3A-infected cells in media containing Tetherin antibody on ice . Following fixation , the MDMs were permeabilised and immunolabelled with a p24 Gag antiserum , which also recognises cytosolic p55 Gag and thus allows the unambiguous identification of infected cells . Compared to uninfected cells of the same populations , the levels of cell surface Tetherin were slightly reduced on R3A- ( + ) -infected , but significantly increased on R3A- ( − ) and -Udel-infected MDMs ( Fig . 4A ) . Flow cytometry analyses confirmed these observations , with Tetherin levels on R3A- ( − ) and –Udel-infected MDMs at least two-fold higher than on R3A- ( + ) -infected cells ( Fig . 4B , C ) . Interestingly , the cell surface Tetherin levels on uninfected MDMs within infected cell populations were higher than on completely untreated cells ( Fig . 4B , C ) . These observations are consistent with our hypothesis that long-term HIV infection can trigger a cellular IFN response , which would lead to the secretion of IFNs and an upregulation of Tetherin in surrounding cells . A cellular IFN response could also explain the increased cell surface Tetherin levels seen on R3A- ( − ) and –Udel-infected compared to uninfected MDMs ( Fig . 4B , C ) . Together , our data show that Vpu reduces the overall levels of endogenous Tetherin in MDMs . The high efficiency of Tetherin labelling we achieved allowed us to also detect Vpu-induced downregulation of the restriction factor from the cell surface . We next sought to examine if and where Tetherin retains HIV on primary macrophages . When we infected MDMs with R3A for seven days and performed western blot analyses of the cell lysates , we found significantly more p24 Gag associated with R3A- ( − ) and –Udel , than with R3A- ( + ) -infected cells ( Fig . 5A ) . p24 and p17 Gag are predominantly found in mature HIV particles , maturation involving the cleavage of p55 Gag during or shortly after budding . The increased p24 Gag levels therefore indicated that , in the absence of Vpu , budded HIV particles were retained on MDMs , presumably by the elevated levels of Tetherin . Consistently , when we depleted MDMs of Tetherin by RNAi , only low levels of p24 Gag were associated with R3A- ( + ) , - ( − ) and –Udel-infected cells ( Fig . 5B ) . To directly test whether Tetherin-mediated retention of virus impedes the release and thus cell-free spread of HIV from primary macrophages , we quantified virus released from R3A- ( + ) , - ( − ) , and –Udel infected MDMs . p24 ELISAs showed that HIV release into the supernatant was lower in the absence of Vpu than in its presence ( Fig . 5C ) , and this restriction was overcome by depleting MDMs of Tetherin ( Fig . 5D ) . To examine the localisation of retained HIV particles , we immunostained R3A-infected MDMs with a p17 Gag antibody that specifically labels mature HIV particles , and a p24/p55 Gag antiserum to identify infected cells . Consistent with the western blot analyses , more p17 Gag was associated with R3A- ( − ) and –Udel than with R3A- ( + ) -infected MDMs ( Fig . 6A , B ) , and quantification of this effect by flow cytometry revealed a two- to three-fold difference in the p17 Gag mean fluorescence intensities ( Fig . S3 ) . Most retained HIV was intracellular ( Fig . 6A , B ) , and co-staining experiments showed that both in the presence and absence of Vpu the intracellular virus co-localised with the IPMC-enriched tetraspanin CD9 ( Fig . 6A ) , but not with the lysosomal marker LAMP1 ( Fig . 6B ) . Thus , in the absence of Vpu , endogenous Tetherin retains mature HIV in the IPMCs of primary MDMs , and restricts cell-free viral spread . Tetherin-mediated retention of HIV in the IPMCs of MDMs likely leads to a passive expansion of the assembly compartments , but it is unclear whether the restriction factor is required for the integrity of IPMCs per se . When we co-stained R3A-infected MDMs for Tetherin , CD9 and p17 Gag , as expected , Tetherin was found to accumulate in the IPMCs of R3A- ( − ) and –Udel-infected cells ( Fig . 7A ) . Tetherin levels in the IPMCs of R3A- ( + ) -infected cells were lower than in R3A- ( − ) or -Udel-infected MDMs , even when IPMCs of similar sizes , which contained similar amounts of virus , were compared ( Fig . 7A ) . These observations indicated that Vpu reduces Tetherin levels also in IPMCs , but that the assembly compartments are maintained even at low concentrations of the restriction factor . Consistently , when we treated uninfected MDMs with Tetherin or control siRNA and quantified the proportion of cells with intracellular co-localisation of the IPMC proteins CD9 and CD81 , around 40% of MDMs contained IPMCs regardless of the Tetherin levels ( Fig . 7B–D ) . Overall , these data show that Tetherin is not required to maintain IPMCs in primary MDMs . To determine whether Tetherin restricts cell-cell spread of HIV from macrophages to autologous CD4+ T cells , we initially examined the mode , kinetics , and efficiency of the direct MDM-T cell transmission . Immunofluorescence studies showed that primary CD4+ T cells readily associated with uninfected as well as HIV-1 BaL-infected MDMs within 2 . 5 h of co-culture ( Fig . S4 ) . When T cells were associated with infected MDMs , clusters of p17 Gag were occasionally found at the intercellular junctions , and in some cases CD4 co-clustered as well ( Fig . 8A and Fig . S4 ) . Junctions between infected MDMs and uninfected T cells characterised by an accumulation of mature HIV particles are from hereon referred to as virological synapses ( VS ) . It has been suggested that MDM-T cell VS form by re-localisation of virus-filled IPMCs in MDMs to the MDM-T cell interface [16] . We observed that the IPMC proteins CD9 ( Fig . S5A ) and CD81 ( Fig . S5B ) , as well as the β2 integrin CD18 ( Fig . S5C ) , were also occasionally enriched at VS . To determine the kinetics of MDM-T cell association and VS formation , we co-cultured BaL-infected MDMs with uninfected autologous CD4+ T cells , and fixed and immunostained the cells at 30 min intervals from 0 min ( no T cells added ) to 240 min . Though T cells rapidly associated with MDMs , few VS were detected after the first 30 min of co-culture ( Fig . 8B ) . The proportion of infected MDMs with VS gradually increased between 30 and 120 min , and then remained relatively constant ( Fig . 8B ) . These observations suggested that the transfer of HIV from MDMs to T cells is mediated by VS . However , successful infection requires the fusion of viral particles with a target cell membrane , and reverse transcription of viral RNA genomes . We used qPCR to quantify the levels of HIV Gag DNA in T cells after co-culture with BaL-infected MDMs . Low levels of HIV DNA were detected in T cells after 1 and 2 . 5 h of co-culture ( Fig . 8C ) , when VS formation had already peaked ( Fig . 8B ) , but significantly higher levels were detected after 6 h ( Fig . 8C ) . The reverse transcriptase inhibitor nevirapine ( NVP ) prevented HIV DNA accumulation , confirming that the qPCR assay detected only newly synthesised viral DNA ( Fig . 8C ) . We conclude that on co-culture of CD4+ T cells with autologous MDMs , T cell association with MDMs precedes VS formation , which in turn precedes the efficient MDM to T cell transfer of HIV , and T cell infection . This suggests that VS formation is an active process that plays a major role in the transmission of HIV from macrophages to T cells . Finally , we determined the relative efficiencies of cell-cell and cell-free transmission of HIV . T cells co-cultured for 6 h with BaL- or R3A-infected MDMs contained at least ten times more HIV DNA than T cells incubated with cell-free virus released by the same MDMs during the preceding 6 h period ( Fig . 8D and Fig . S6 ) . Having shown that short-term co-cultures strongly favour cell-cell over cell-free HIV transmission ( Fig . 8D and Fig . S6 ) , we used this assay to study whether endogenous Tetherin can restrict the direct cell-cell spread of HIV from primary MDMs to autologous CD4+ T cells . Following 6 h of MDM/T cell co-culture , the T cells were washed off the MDMs with PBS , incubated further , and their overall infection assayed by western blotting ( Fig . 9A ) . This approach allowed us to accurately determine the levels of T cell-associated p55 Gag , which is important when comparing the infection levels of cells that express , or do not express Vpu , as Tetherin alters the ratios of p24/p55 Gag ( Fig . 5A ) . When we co-cultured T cells with R3A- ( + ) , - ( − ) , and –Udel-infected MDMs , and continued to incubate the T cells separately for two days , only the Vpu-containing R3A- ( + ) efficiently infected the T cells ( Fig . 9B ) . This observation indicated that Tetherin can inhibit cell-cell transmission of HIV . Control experiments were performed to confirm that the p55 Gag detected in the T cells after the co-culture was synthesised in these cells , and did not derive from the MDMs . As expected , no p55 Gag was detected in T cells that were harvested immediately after the co-culture with MDMs , or exposed to NVP during and after the co-culture , and no viral or cellular proteins were detected in the recovered media when the T cells were omitted ( Fig . 9B ) . Further control experiments showed that Vpu expression in R3A-infected MDMs did not influence their adhesion to T cells ( Fig . S7 ) . To demonstrate that Tetherin inhibits the transmission of HIV-1 from MDMs to T cells , and not the subsequent replication of the virus in T cells , we limited replication to a single round , either by adding NVP to the T cells immediately after the co-culture , or by harvesting the T cells after only one day . We still observed efficient infection of the T cells only with R3A- ( + ) ( Fig . 9C ) . Notably , when we depleted R3A-infected MDMs of Tetherin before the co-culture ( Fig . 9A ) , T cell infection with the Vpu-negative R3A- ( − ) and –Udel was rescued ( Fig . 9D ) . We next sought to investigate the mechanism by which Tetherin inhibits cell-cell transmission of HIV from macrophages to T cells . Hardly any p17 Gag accumulations were observed between R3A-infected MDMs and T cells , rendering us unable to quantify VS ( Fig . S8A ) . We hypothesise that the p17 Gag accumulations at MDM-T cell interfaces are more transient when MDMs are infected with the dual-tropic R3A than with the CCR5-tropic BaL , as significantly more primary CD4+ T cells express CXCR4 than CCR5 at their surface ( Fig . S8B , C ) , which may accelerate R3A entry into T cells . We next used Gag qPCR to investigate the early events of T cell infection following cell-cell transmission from MDMs . Intriguingly , similar levels of HIV DNA were detected in T cells immediately after their co-culture with R3A- ( + ) , - ( − ) or -Udel-infected MDMs , although only R3A- ( + ) -infected T cells from the same experimental samples contained significant HIV protein levels two days later ( Fig . 10A ) . Addition of NVP to the T cells during the co-culture inhibited the accumulation of HIV DNA ( Fig . 10A ) . Since high Tetherin levels retain mature HIV particles on MDMs , we hypothesised that during cell-cell transmission in the absence of Vpu , infectious virus clusters may be transferred from MDMs to T cells , and lead to high HIV DNA levels in a few T cells , but overall a low proportion of infected cells . To test this hypothesis , we co-cultured R3A- ( + ) , - ( − ) , or –Udel-infected MDMs with T cells , immunostained the T cells for p17 Gag immediately after the co-culture and analysed them by flow cytometry . We found that T cells carried significantly more HIV clusters in the absence of Vpu than in its presence , and the difference was particularly pronounced for medium and large clusters ( Fig . 10B , C ) . Moreover , when we incubated the T cells for longer times following their co-culture with MDMs , we found that R3A- ( + ) DNA accumulated much faster than R3A- ( − ) or –Udel DNA , and significantly higher R3A- ( + ) DNA levels were detected in the T cells after three days ( Fig . 11A ) . Consistently , when we quantified the proportion of HIV-infected T cells by flow cytometry five days after their co-culture with infected MDMs , only T cells that had been co-cultured with R3A- ( + ) -infected MDMs contained a significant proportion of Gag-positive cells ( Fig . 11B , C ) . Together these data show that only in the presence of Vpu , do MDMs transmit sufficient HIV to initiate a spreading infection in T cells . In the absence of Vpu , endogenous Tetherin inhibits the cell-cell transmission of HIV from primary MDMs to autologous CD4+ T cells , presumably by promoting the transfer of infectious virus clusters to a limited number of target cells .
In recent years , an increasing number of cellular proteins that inhibit , or restrict , virus replication have been identified . Of these , Tetherin ( HM1 . 24/CD317/Bst-2 ) stands out in that its efficient counteraction appears to have been crucial for the global spread of HIV [4] . Though SIV has crossed the species barrier from apes to man on at least four different occasions , giving rise to HIV-1 group M , N , O and P viruses , only group M HIVs , which have a fully functional antagonist of Tetherin , the Vpu protein , have spread globally [4] , [33]–[35] . In the absence of an antagonist , Tetherin restricts cell-free propagation of HIV by physically linking mature virus particles to the surface of infected cells [2] , [3] , [7] , [8] . Tetherin can also activate the NFκB signalling pathway , which may contribute to restriction [36] . A recent study showed that in addition to the full-length protein , cell lines and primary cells express a short isoform of Tetherin that lacks 12 N-terminal residues , is less sensitive to antagonism by Vpu , and cannot activate the NFκB pathway [37] . However , there is an increasingly strong view that direct cell-cell transmission of HIV is more efficient than cell-free propagation [13] . In this study we found that Tetherin can also restrict cell-cell transmission of HIV from macrophages to CD4+ T cells . Macrophages , including neural microglia , are targets of HIV infection in vivo and , at least partially , responsible for HIV-associated dementia and neuropathy [38] . Several aspects of HIV replication in macrophages differ from other cell types: Whereas T cells are rapidly depleted early after HIV infection , macrophages appear to be more resistant to the cytopathic effects of HIV , and can survive for weeks to months following infection . This has led to the suggestion that macrophages may serve as reservoirs for HIV , particularly at the late stages of AIDS , when T cells are largely depleted [38] . Although both T cells and macrophages are major targets for HIV infection , the cell biology of virus replication in macrophages can differ to that seen in T cells . In infected tissue culture macrophages at least , the assembly of new virions is thought to occur predominantly in IPMCs ( or virus containing compartments [VCC] ) , and not at the cell surface as seen in T cells [24] , [25] . Some controversy exists as to whether IPMCs can transiently detach from the PM [39] , but most data indicate that the majority of these compartments are contiguous with the cell surface [24] , [25] , [28] . IPMCs are thought to be largely impermeable to antibodies [40] , [41] . In vivo , this may shield sites of HIV assembly from circulating neutralising antibodies , which may help HIV-infected macrophages to evade detection by the host immune system , and contribute to their long-term survival . Virus assembly and budding into IPMCs may also allow HIV release to be regulated , for example through VS [15] , [16] . Because of the influence of Tetherin on the pandemic spread of HIV , the contribution of macrophages to HIV pathogenesis , and the cell type-specific differences in HIV replication , it is imperative to understand the effects of Tetherin on HIV replication in macrophages . To address this issue , we have relied almost entirely on primary human cells , i . e . macrophages derived from monocytes isolated from HIV-negative donors , and CD4+ T cells separated from the peripheral blood mononuclear cells of the same donors . This approach ensured that we studied cells expressing endogenous levels of Tetherin , thus avoiding possible effects of aberrant glycosylation and trafficking inherent to overexpression [7] . Moreover , we relied exclusively on HIV Env-mediated infection , avoiding possible artefacts that might result from the use of aberrant entry pathways and/or high multiplicities of infection of pseudotyped HIV . By analysing both mRNA and protein , we found that type I IFNs upregulate the expression of Tetherin in MDMs even at low concentrations , and to an extent similar to that seen in IFN-treated T cells ( Fig . 1 ) . In the course of our study , we observed striking differences between western blots of Tetherin under non-reducing and reducing conditions: When we lysed MDMs in Laemmli buffer devoid of any reducing agent , Tetherin appeared as a prominent smear at ∼60–100 kDa and a weaker smear at ∼40 kDa ( Fig . S9 ) . These bands likely correspond to dimeric and monomeric forms of glycosylated Tetherin , respectively . Consistently , both bands completely disappeared upon Tetherin RNAi ( Fig . 5B , 9D ) . Blotting the same lysates in the presence of 2-mercaptoethanol revealed a sharp , prominent band at ∼24 kDa , and a higher molecular weight smear appeared only with longer exposures of the blots ( Fig . S9 ) . We propose that the sharp band seen after reduction masks changes in Tetherin levels observed under non-reducing conditions , and may explain why a recent study found only moderate upregulation of Tetherin protein when treating MDMs with high levels of IFN [21] . Our immunofluorescence studies show that in uninfected MDMs , endogenous Tetherin localises to the cell surface , IPMCs , and TGN , without any obvious enrichment in IPMCs ( Fig . 2 ) . In cells infected with Vpu-deleted HIV , Tetherin retains virus in IPMCs ( Fig . 6 ) , which may result in a passive enrichment of Tetherin ( Fig . 7A ) . As we know that accumulating virus expands IPMCs [29] , tethered HIV will also cause a passive expansion of the size of the assembly compartments . These data , and the observation that less mature HIV is associated with MDMs following Tetherin RNAi ( Fig . 5B ) , explain why a recent study detected smaller and fewer virus-filled IPMCs when depleting MDMs of Tetherin [21] . When investigated on uninfected MDMs , where IPMCs are not passively expanded by accumulating virus , we find no evidence that Tetherin plays an active role in the formation and/or maintenance of IPMCs ( Fig . 7 ) . The improved Tetherin immunofluorescence labelling we achieved allowed us to detect cell surface Tetherin on MDMs , and Vpu-induced downregulation ( Fig . 2A , Fig . 4 ) , which was not observed in a recent study [21] . Low cell surface levels of Tetherin in the presence of Vpu corresponded with decreased overall levels ( Fig . 3B ) . However , even in the presence of Vpu , HIV-infected MDM populations showed higher overall Tetherin levels than uninfected cells ( Fig . 3B ) . We believe that this is caused by an IFN response to long-term HIV infection . Vpu would partially counteract the increased Tetherin expression in infected cells , but released IFN would lead to high Tetherin levels in the uninfected MDMs of the same population . In this situation , western blot analysis would show an increase in the overall Tetherin levels in the population ( Fig . 3B ) , whereas flow cytometry , gated to the infected cells only , would detect decreased levels of cell surface Tetherin ( Fig . 4B , C ) . Consistently , cell surface levels of Tetherin on uninfected cells within infected MDM populations were higher than on completely untreated cells ( Fig . 4B , C ) , and Tetherin levels in R3A-infected MDMs decreased when we prevented activation of the IFN-α/β receptor using antibodies ( Fig . S2 ) . Nevertheless , our data show that HIV-1 Vpu efficiently antagonises endogenous Tetherin in primary macrophages . Residual Tetherin restriction may occur even in the presence of Vpu , as even less HIV was retained ( Fig . 5B ) , and more released ( Fig . 5D ) , upon Tetherin RNAi than in the presence of Vpu alone , but these differences were less pronounced and not statistically significant . VS between infected and uninfected cells have been suggested to facilitate the cell-cell spread of HIV [13] . Although VS between infected macrophages and T cells have been observed [15] , evidence that these are involved in HIV transmission is missing . In this study , we report that structures reminiscent of VS form between HIV-infected MDMs and autologous CD4+ T cells ( Fig . 8A , Fig . S4 ) . The temporal appearance of these VS is consistent with them mediating cell-cell transmission of virus , i . e . they succeed MDM-T cell interaction , but precede the appearance of HIV DNA in T cells ( Fig . 8B , C ) . However , we cannot rule out the possibility that other modes of cell-cell transfer , including filopodial bridges and membrane nanotubes , may also contribute to HIV transmission from macrophages to T cells . We have developed a novel co-culture assay to examine the effects of Tetherin on the cell-cell spread of HIV from infected MDMs to T cells ( Fig . 9A ) . In contrast to other studies , in which infected and target cells were co-cultured for up to several days [18] , [19] , [21]–[23] , we limited the co-culture to only 6 h . This approach prevented the accumulation of cell-free virus , and thus strongly favoured cell-cell transmission ( Fig . 8D , Fig . S6 ) . Our assay was sensitive enough to reliably detect even low levels of infection , as seen in our experiments as a result of the short co-culture and the use of primary cells . Finally , and again in contrast to other studies [17] , [18] , [21] , [22] , our assay allowed us to detect infection using the levels of p55 Gag only . Using this assay , we found that Tetherin can inhibit cell-cell transmission of HIV from MDMs to autologous CD4+ T cells , and this effect was independent of whether or not the virus was allowed to replicate in the T cells ( Fig . 9 , Fig . 10 , Fig . 11 ) . On-going replication led to increasing levels of T cell infection at one and two days after their co-culture with R3A- ( + ) -infected MDMs , and intermediate infection levels were detected when NVP was added to the T cells for the two day-incubation after the co-culture . However , efficient T cell infection always depended on the downregulation of Tetherin in the MDMs , either by Vpu ( Fig . 9B , C ) , or by RNAi ( Fig . 9D ) . Immediately after their co-culture with R3A- ( + ) , - ( − ) or –Udel-infected MDMs , all T cells contained similar levels of HIV DNA , but only in the presence of Vpu was significant infection detected two days later ( Fig . 10A ) . We hypothesise that , in the absence of Vpu , high Tetherin levels on MDMs promote the transfer of infectious clusters of HIV to T cells , which lead to high HIV DNA levels in a few T cells , but overall a low proportion of infected cells . Consistently , immediately after their co-culture with infected MDMs , we detected more and larger HIV clusters on T cells in the absence of Vpu than in its presence ( Fig . 10B , C ) , and when HIV DNA and protein levels were assessed three or five days later , respectively , significantly more HIV DNA and a higher proportion of infected T cells were detected when Tetherin was antagonised by Vpu ( Fig . 11 ) . These observations are consistent with a previous study , which showed that in the absence of Vpu , HIV clusters are transferred from infected to uninfected T cells , but fail to initiate productive infection [17] . Notably , similar DNA levels in T cells immediately after their co-culture with infected MDMs confirmed that R3A- ( − ) and -Udel are as infectious as R3A- ( + ) . Recent evidence suggests that an accumulation of HIV DNA in activated or resting T cells may trigger innate immune responses , and lead to cell death by apoptosis or pyroptosis , respectively [42]–[44] . Therefore , during macrophage-T cell transmission of Vpu-deficient HIV , the accumulation of viral DNA in target cells may promote cell death , which could contribute to inefficient T cell infection . However , when we labelled T cells with a dead cell stain 0 , 6 , 18 , and 30 h after their co-culture with HIV-infected MDMs , we did not detect increased T cell death in the absence of Vpu ( Fig . S10 ) . In vitro at least , cell-cell transmission of HIV is thought to be more efficient than cell-free propagation . The high evolutionary pressure on SIV/HIV to maintain a Tetherin antagonist suggests that Tetherin inhibits both the cell-cell and cell-free spread of HIV . Although our data are consistent with this notion , there may be cell type-specific differences . For example , VS between T cells are thought to involve polarised budding of HIV into the synaptic cleft [14] , whereas VS between monocytic cells and T cells may form by re-localisation of virus-filled IPMCs to the site of VS formation [16] . HIV that accumulates in IPMCs before reaching the VS may be more susceptible to clustering by Tetherin than newly budded virions in the T cell-T cell synapse . Consistently , most studies using monocytic cells , i . e . MDMs and monocyte-derived dendritic cells , found that Tetherin restricts cell-cell transmission of HIV [21] , [22] . Similarly , Vpu-deficient HIV-1 , as well as virus strains encoding mutated Vpu proteins , have been shown to inefficiently spread in macrophage populations [45] . Whether Tetherin also inhibits T cell-T cell spread remains controversial . A recent study suggested that Tetherin increases the number of VS formed between T cells , and thereby enhances target cell infection [18] . Consistently , in a previous study a Vpu-deficient HIV-1 clone emerged during selection of viruses that efficiently spread in a rapid-turnover culture of T cells [46] . However , other studies argue that Tetherin restricts the direct T cell-T cell transmission of HIV . In one study , clusters of Vpu-deficient HIV particles were seen to be transferred from infected to uninfected cells , but impaired in their ability to fuse with and thus infect target cells [17] . Still , Tetherin did not seem to perturb the formation of VS [17] . Overall our study shows that in MDMs Tetherin is upregulated even by low concentrations of type I IFNs , and localises to the cell surface , TGN , and IPMCs . Vpu efficiently antagonises Tetherin and , in the absence of Vpu , mature HIV accumulates in IPMCs . Although Tetherin-bound virus may expand IPMCs , there is no indication that Tetherin plays an active role in the formation and/or maintenance of the HIV assembly compartments . Finally , we find that Tetherin can restrict cell-cell transmission of HIV from MDMs to T cells , and the assay applied in this study may help elucidate whether the restriction factor also inhibits transmission between other cell types . Thus , this study provides crucial insight into one of the most potent HIV restriction factors identified to date , in one of the main target cells for HIV infection .
Tissue culture media and supplements were purchased from Life Technologies ( Paisley , UK ) , Fetal Calf Serum ( FCS ) Gold from PAA ( Yeovil , UK ) , human AB serum from PAA and Sigma-Aldrich ( Dorset , UK ) , tissue culture plastic from Thermo Fisher Scientific ( Waltham , USA ) and TPP ( Trasadingen , Switzerland ) , and chemicals from Sigma-Aldrich , unless specified otherwise . IFN-β was provided by M . Noursadeghi ( UCL , London , UK ) , and nevirapine obtained from the AIDS Research and Reference Reagent Program ( NIAID , Bethesda , USA ) . Antibodies to CD4 ( Q4120 ) , HIV-1 p24/p55 Gag ( 38:96K and EF7 ) and HIV-1 p17 Gag ( 4C9 ) , as well as an antiserum to HIV-1 p24/p55 Gag ( ARP432 ) , were obtained from the NIBSC Centre for AIDS Reagents ( South Mimms , UK ) . Anti-CD68 ( KP1 ) was provided by R . da Silva ( University of Oxford , Oxford , UK ) , anti-CD81 ( M38 ) by F . Berditchevski ( University of Birmingham , Birmingham , UK ) , anti-CD81 ( 1 . 337 ) by J . Grove ( UCL , London , UK ) , anti-CXCR4-Alexa Fluor 488 ( 12G5 ) by J . Hoxie ( UPenn , Philadelphia , USA ) , anti-VSV-G ( P5D4 ) by T . Kreis ( UNIGE , Geneva , Switzerland ) , and antisera to Env gp120 , HIV-1 p17 Gag ( UP595 ) and Vpu ( U2-3 ) by N . Haigwood ( OHSU , Portland , USA ) , M . Malim ( KCL , London , UK ) and K . Strebel ( NIAID , Bethesda , USA ) , respectively . Anti-HIV-1 p24/p55 Gag ( Kal-1 ) was purchased from Dako ( Ely , UK ) , anti-CD9 ( MCA469G ) and anti-TGN46 ( AHP500G ) from AbD Serotec ( Kidlington , UK ) , anti-CD18 ( MEM-48 ) and anti-CD3 ( MEM-57 ) from Abcam ( Cambridge , UK ) , anti-γ-adaptin ( 88/Adaptin γ ) , anti-CD3-PerCP ( SK7 ) , anti-CD3 ( UCHT1 ) , anti-CD4-FITC ( RPA-T4 ) , anti-CD195-PE ( 2D7/CCR5 ) , anti-CD14-APC ( M5E2 ) and anti-LAMP-1 ( H4A3 ) from BD Biosciences ( Oxford , UK ) , anti-actin ( I-19 ) from Santa Cruz Biotechnology ( Santa Cruz , USA ) , anti-IFN-α/β R2 ( MMHAR-2 ) and mouse IgG2A isotype control ( 20102 ) from R&D Systems ( Abingdon , UK ) , monoclonal and polyclonal anti-Bst-2 ( M15 and B02P , respectively ) from Abnova ( Taipei , Taiwan ) , Alexa Fluor-conjugated antibodies from Life Technologies , and HRP-conjugated antibodies from Thermo Fisher Scientific . The Nef-negative HIV-1 molecular clone NL4 . 3-R3A , here referred to as R3A- ( + ) , was provided by J . Hoxie ( UPenn , Philadelphia , USA ) [47] , and used to avoid Nef-specific effects on Tetherin . To obtain R3A- ( − ) , the Vpu start codon of R3A- ( + ) was mutated using the primers 5′-CTCTC TATCA AAGCA GTAAG TAGTA CATGT AGTGC AATCT TTACA AATAT-3′ and 5′-ATATT TGTAA AGATT GCACT ACATG TACTA CTTAC TGCTT TGATA GAGAG-3′ and the QuikChange II XL Site-Directed Mutagenesis Kit ( Agilent Technologies , Wokingham , UK ) according to the manufacturer's instructions . To obtain R3A-Udel , two unique XbaI restriction sites were introduced into the Vpu gene of R3A- ( + ) using the primers 5′-GTAAG TAGTA CATGT AATGC AATCT TTACA AATTC TAGAA ATAGT AGCAT TAGTA GTAGC AGC-3′ and 5′-GCTGC TACTA CTAAT GCTAC TATTT CTAGA ATTTG TAAAG ATTGC ATTAC ATGTA CTACT TAC-3′ , and 5′-GTATG GTCCA TAGCA CTCAT AGAAT ATAGG AAAAT ATCTA GACAA AGAAA AATAG ACA-3′ and 5′-TGTCT ATTTT TCTTT GTCTA GATAT TTTCC TATAT TCTAT GAGTG CTATG GACCA TAC-3′ , and the QuikChange II XL kit as described above . The resulting plasmid was digested with XbaI ( Promega , Southampton , UK ) and re-ligated without the 82 bp fragment of Vpu . To produce virus stocks from molecular clones , HEK 293T cells were transfected with proviral DNA using FuGENE HD ( Promega ) . Culture supernatants were harvested after two days and cleared of cell debris by centrifugation and filtration ( 0 . 45 µm ) . Viruses were pelleted through 25% sucrose cushions for 2 h at 100 , 000 g and 4°C and resuspended in complete medium ( RPMI 1640 , 100 U/ml penicillin , 0 . 1 mg/ml streptomycin , and 10% human AB serum ) . Stocks of HIV-1 BaL were prepared as described previously [24] . p24 levels in cell-free supernatants from HIV-1-infected cells were quantified using the HIV-1 p24CA Antigen Capture Assay Kit ( AIDS and Cancer Virus Program , National Cancer Institute , Frederick , USA ) , or the QuickTiter HIV Lentivirus Quantitation Kit ( Source BioScience , Nottingham , UK ) , according to the manufacturers' instructions . The single-cycle infectivities of virus stocks were determined using TZM-bl indicator cells ( provided by J . Martin-Serrano , KCL , London , UK ) . Cells were infected for 2 h at 1 , 300 g with dilutions of virus stocks containing 0 . 5–2 ng of p24 Gag or reference virus of known titres . β-galactosidase expression was quantified 24 h later using the Galacto-Star β-Galactosidase Reporter Gene Assay System ( Life Technologies ) and a PHERAstar Plus microplate reader ( BMG LABTECH , Aylesbury , UK ) . Single-cycle infectivities of MDM-derived R3A were determined by incubating TZM-bl indicator cells with cell-free virus-containing culture supernatants containing 2–5 ng of p24 Gag and quantifying β-galactosidase expression 24 h later as described above . MDMs were prepared from peripheral blood mononuclear cells ( PBMC ) , isolated from buffy coats from HIV-negative blood donors ( National Blood Service , Essex , UK ) , as described previously [48] , and differentiated in complete medium containing 10 ng/ml of M-CSF ( R&D Systems , Abingdon , UK ) for two days . Where indicated , seven day-old MDMs were infected with HIV-1 ( 3 MOI/cell ) by spinoculation for 2 h at 1 , 300 g and cultured for a further seven days . Unless specified otherwise , the MDMs were used after 14 days in culture . To obtain autologous CD4+ T cells , the non-adherent fraction of the PBMCs was frozen and defrosted after nine days , unless specified otherwise . The cells were activated for three days with 1 µg/ml lectin from Phaseolus vulgaris ( Sigma-Aldrich ) and 5 ng/ml IL-2 ( R&D Systems ) in complete medium . CD4+ T cells were isolated using the CD4+ T Cell Isolation Kit ( Miltenyi Biotec , Bisley , UK ) according to the manufacturer's instructions , and cultured for a further two days in complete medium containing 5 ng/ml IL-2 . For co-cultures of MDMs and autologous CD4+ T cells , 3 T cells/MDM were added to the MDMs in complete medium and , unless specified otherwise , incubated for 6 h at 37°C and 5% CO2 . The T cells were separated from the MDMs , residual T cells washed off with PBS , and all T cells lysed immediately or cultured in complete medium containing 5 ng/ml IL-2 . MDMs were transfected with 60 nM Stealth siRNA targeting Tetherin ( oligo ID HSS101115 , Life Technologies ) , or Stealth siRNA Negative Control Med GC ( Life Technologies ) , using Lipofectamine RNAiMAX ( Life Technologies ) according to the manufacturer's instructions . Transfection complexes were removed one day after transfection . Total DNA was isolated from T cells using the DNeasy Blood and Tissue Kit ( QIAGEN , Manchester , UK ) according to the manufacturer's instructions . 20–40 ng of DNA were used to quantify the levels of Gag and GAPDH using 500 nM of the previously characterised primers 5′-ACATC AAGCA GCCAT GCAAA T-3′ and 5′-ATCTG GCCTG GTGCA ATAGG-3′ , and 5′-ACCAC AGTCC ATGCA TCACT-3′ and 5′-GGCCA TCACG CCACA GITT-3′ [49] , respectively , the DyNAmo Flash SYBR Green qPCR Kit ( Thermo Fisher Scientific ) , and a Mastercycler ep realplex 2 ( Eppendorf , Stevenage , UK ) with the following programme: 95°C for 7 min , and 40 cycles at 95°C for 10 s and 65°C for 30 s . Serial dilutions from one experimental sample were prepared for the standard curve . For MDM-T cell co-culture experiments , the levels of contaminating MDM-derived Gag and GAPDH DNA were subtracted from the total DNA levels . To determine the levels of MDM-derived DNA , medium only was added to HIV-infected MDMs and subsequently treated as the co-cultured T cells . MDM-derived Gag DNA levels were typically between 5 and 27% of the total levels for HIV-1 BaL , and between 5 and 17% for HIV-1 R3A . MDM-derived GAPDH levels were max . 2% of the total levels . Gag DNA levels were normalised to GAPDH . Total RNA was isolated from cells using the RNeasy Plus Mini Kit ( QIAGEN ) , and 50 ng of RNA reverse transcribed using the QuantiTect Reverse Transcription Kit ( QIAGEN ) according to the manufacturer's instructions . qPCR was performed as described above using 200 nM of IFIT1 and GAPDH primers from the IFNr qRT-Primer Set ( Source BioScience ) , 200 nM of Bst-2 primers from the RT2 qPCR Primer Assay ( QIAGEN ) , and the following cycler programme: 95°C for 10 min , 40 cycles at 95°C for 15 s , 60°C for 30 s and 72°C for 30 s , and 72°C for 10 min . No reverse transcriptase was added to control samples to confirm the complete elimination of genomic DNA . IFIT1 and Bst-2 RNA levels were normalised to GAPDH . For western blot analysis cells were washed in PBS and lysed in Laemmli Sample Buffer ( Sigma-Aldrich ) for 10 min at 95°C . The lysates were separated on SDS-polyacrylamide gels and transferred to Immobilon-P PVDF membranes ( Millipore , Watford , UK ) at 20 V for 1 h under semi-dry blotting conditions . Blots were quenched in 0 . 1% Tween/5% non-fat milk/PBS for 1 h at room temperature , incubated with primary antibody at 4°C overnight , washed three times with 0 . 1% Tween/PBS , and incubated with the appropriate HRP-conjugated secondary antibody for 1 h at room temperature . After five washes with 0 . 1% Tween/PBS , membranes were briefly incubated in SuperSignal West Pico/Dura/Femto Chemiluminescent Substrate ( Thermo Fisher Scientific ) and signals detected with Amersham Hyperfilm ECL ( GE Healthcare Life Sciences , Little Chalfont , UK ) . For the comparison of Tetherin levels in T cells and MDMs , cells were lysed in non-reducing Laemmli buffer without bromophenol blue ( 10% SDS , 15% glycerol , 0 . 2 M Tris-HCl pH 6 . 8 ) , total protein concentrations determined using the Bio-Rad DC Protein Assay ( Bio-Rad , Hemel Hempstead , UK ) according to the manufacturer's instructions , and 5 µg protein used for western blot analysis as described above . All Tetherin blots were performed under non-reducing conditions and using the polyclonal Bst-2 antibody B02P . Blots were scanned and quantified with Fiji . For immunofluorescence , MDMs were washed with PBS , fixed in 4% PFA , quenched with 50 mM NH4Cl and permeabilised with 0 . 1% Triton X-100/0 . 5% BSA/6 µg/ml human IgG/PBS . Cells were labelled for 1 . 5 h with primary antibodies diluted in 0 . 5% BSA/6 µg/ml human IgG/PBS , washed in 0 . 5% BSA/PBS and incubated for 1 h with appropriate combinations of fluorescent secondary antibodies . Samples were washed , DNA stained with 10 µg/ml Hoechst 33258 in PBS , and coverslips mounted in Mowiol . Confocal images were acquired with an inverted Leica TCS SP5 confocal microscope , 63× oil objective ( NA 1 . 4 ) and LAS AF software , and processed using Fiji . Where indicated live , unpermeabilised MDMs were incubated for 1 h on ice in complete medium containing the appropriate primary antibodies before fixation . For immunostaining of live , permeabilised MDMs , cells were incubated for 20 min on ice in 0 . 05% saponin/0 . 5% BSA/6 µg/ml human IgG/PBS containing the appropriate primary antibodies , washed with ice-cold PBS , fixed , and labelled with secondary antibodies in the presence of 0 . 1% saponin as described above . Where indicated , live , permeabilised MDMs were immunostained , fixed , and immunostained with additional primary antibodies in the presence of 0 . 1% Triton X-100 as described above . For flow cytometry analysis of cell surface Tetherin levels on infected MDMs , cells were incubated for 1 h on ice in complete medium containing 10 µg/ml polyclonal Bst-2 antibody ( B02P ) . Cells were washed once with ice-cold PBS , fixed in 4% PFA , scraped off the tissue culture dish , permeabilised with 0 . 1% saponin/1% human AB serum/6 µg/ml human IgG/2 mM EDTA/0 . 05% sodium azide/PBS , labelled for 1 h with α-p24/p55 rabbit serum ( ARP432 ) , washed three times in 0 . 1% saponin/1% human AB serum/2 mM EDTA/0 . 05% sodium azide/PBS , incubated for 30 min with appropriate Alexa Fluor-conjugated secondary antibodies , washed three times and analysed on an LSR II flow cytometer ( BD Biosciences ) . For flow cytometry analysis of T cells and MDMs following their co-culture , cells were washed with PBS , fixed in 4% PFA , and immunostained with the appropriate primary and subtype specific Alexa Fluor-conjugated secondary antibodies as described above . For flow cytometry analysis of cell surface proteins on primary CD4+ T cells , cells were incubated for 30 min at 4°C in 1% FCS/6 µg/ml human IgG/2 mM EDTA/0 . 05% sodium azide/PBS , labelled for 1 h at 4°C with primary antibodies conjugated to fluorescent dyes , and washed three times in 1% FCS/2 mM EDTA/0 . 05% sodium azide/PBS . Data were analysed using FlowJo software ( TreeStar , Ashland , USA ) . To analyse the proportion of dead cells , primary T cells were washed with PBS , labelled with Violet Dead Cell Stain ( Life Technologies ) according to the manufacturer's instructions , washed with PBS , fixed in 4% PFA , and analysed by flow cytometry . Unless specified otherwise , p values were calculated using an unpaired Student's t-test . | Tetherin is a cellular protein that inhibits ( or restricts ) a broad range of enveloped viruses , including HIV , by physically “tethering” nascent particles to the plasma membrane of infected cells . CD4+ T cells and macrophages are the main targets of HIV in vivo , and both cell types express Tetherin . Although the mechanisms of Tetherin-mediated restriction in model cell lines and T cells are increasingly well understood , experimental data from macrophages are sparse , and partially contradict observations made in other cell types . Here we investigate the sensitivity of Tetherin expression to interferon , and the subcellular localisation of the restriction factor in primary human macrophages . We find that Tetherin inhibits HIV release by retaining nascent particles in macrophage HIV assembly compartments , and can also restrict the transmission of HIV across intercellular contacts between macrophages and T cells . Finally , we demonstrate that the HIV protein Vpu efficiently counteracts Tetherin in macrophages , and thereby ensures viral propagation . Our results , together with other published data , show that Tetherin can efficiently inhibit viral replication in both major target cell types of HIV , regardless of the mode of transmission . These data support the view that efficient counteraction of Tetherin was a crucial factor for the global spread of HIV . | [
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"viral... | 2014 | Tetherin Can Restrict Cell-Free and Cell-Cell Transmission of HIV from Primary Macrophages to T Cells |
Leptospirosis is an important but neglected bacterial zoonosis that has been largely overlooked in Africa . In this systematic review , we aimed to summarise and compare current knowledge of: ( 1 ) the geographic distribution , prevalence , incidence and diversity of acute human leptospirosis in Africa; and ( 2 ) the geographic distribution , host range , prevalence and diversity of Leptospira spp . infection in animal hosts in Africa . Following Preferred Reporting Items for Systematic Reviews and Meta-analyses ( PRISMA ) guidelines , we searched for studies that described ( 1 ) acute human leptospirosis and ( 2 ) pathogenic Leptospira spp . infection in animals . We performed a literature search using eight international and regional databases for English and non-English articles published between January 1930 to October 2014 that met out pre-defined inclusion criteria and strict case definitions . We identified 97 studies that described acute human leptospirosis ( n = 46 ) or animal Leptospira infection ( n = 51 ) in 26 African countries . The prevalence of acute human leptospirosis ranged from 2 3% to 19 8% ( n = 11 ) in hospital patients with febrile illness . Incidence estimates were largely restricted to the Indian Ocean islands ( 3 to 101 cases per 100 , 000 per year ( n = 6 ) ) . Data from Tanzania indicate that human disease incidence is also high in mainland Africa ( 75 to 102 cases per 100 , 000 per year ) . Three major species ( Leptospira borgpetersenii , L . interrogans and L . kirschneri ) are predominant in reports from Africa and isolates from a diverse range of serogroups have been reported in human and animal infections . Cattle appear to be important hosts of a large number of Leptospira serogroups in Africa , but few data are available to allow comparison of Leptospira infection in linked human and animal populations . We advocate a ‘One Health’ approach to promote multidisciplinary research efforts to improve understanding of the animal to human transmission of leptospirosis on the African continent .
Endemic zoonotic diseases affect impoverished and developing communities worldwide but are frequently overshadowed in public and clinician awareness by high profile infections such as malaria and HIV/AIDS [1 , 2] . In Africa , zoonotic infections are both directly responsible for human illness and death and indirectly impact human well-being as a result of reduced livestock productivity and food security [3–5] . However , bacterial zoonoses including leptospirosis remain under-diagnosed and under-reported in Africa , and as a result are overlooked as public health priorities [1 , 2 , 6] . Leptospirosis is one of the most common and widespread zoonotic infections in the world and is recognised as a neglected disease by the World Health Organisation ( WHO ) [7] . Human leptospirosis is caused by infection with pathogenic strains of Leptospira spp . bacteria [8 , 9] . More than 250 pathogenic Leptospira serovars are known to exist worldwide , which are classified into 25 serogroups on the basis of their serological phenotype [10 , 11] . Recent species determination by DNA homology has identified 13 pathogenic Leptospira spp . , and seven of these ( L . interrogans , L . borgpetersenii , L . santarosai , L . noguchii , L . weilli , L . kirschneri and L . alexanderi ) are considered as the foremost agents of human and animal disease [10 , 12] . Both serological and DNA-based classification systems are currently in use for clinical diagnosis and in understanding the pathogenesis and epidemiology of the disease [11 , 13 , 14] . A wide range of animals can carry pathogenic Leptospira bacteria and act as a source of infection [8 , 11] . Leptospira serovars often demonstrate a degree of animal host preference and some common relationships between serovars and their hosts are reported [9 , 15] . Following infection , the bacteria colonise the renal tubules and urogenital tract and are shed in the urine of infected animals . Animal species may be asymptomatic carriers of infection ( maintenance hosts ) or develop clinical disease ( accidental hosts ) depending on the infecting serovar [11 , 16] . In food producing animals , cattle and pigs are relatively susceptible to clinical infection resulting in production losses including reduced milk yield , reproductive failure and abortions [16 , 17] . In people , disease occurs through direct or indirect contact with infected urine from an animal host [8 , 9 , 15] . Good knowledge of Leptospira serovars circulating in local animal populations is important to determine sources and transmission routes for human infection [8] . In the early stages , human leptospirosis manifests most commonly as a non-specific febrile illness that is hard to distinguish from other aetiologies of febrile disease particularly in tropical areas [11 , 18 , 19] . Infection can result in severe secondary sequelae including renal failure and pulmonary haemorrhagic syndrome , and a case fatality ratio of up to 50% has been reported in complicated cases [15 , 19] . Leptospirosis is particularly common in the tropical areas where people and animals live in close contact , and warm and humid conditions favour environmental survival and transmission of the pathogen [8 , 9] . In South-East Asia and South America , leptospirosis is recognised as an important cause of renal failure and febrile disease [18–22] . However , despite its global importance , large gaps persist in our knowledge of the burden and epidemiology of leptospirosis in Africa . Reports from the WHO Leptospirosis Epidemiology Reference Group ( LERG ) indicate that leptospirosis incidence may be high in Africa , but also highlight the lack of available data [7 , 23] . Although reported seroprevalence data demonstrates widespread exposure to Leptospira spp . in humans and animals in Africa , [24] little is known about the extent of human disease or the epidemiology of Leptospira infection in different animal species in Africa . To tackle these gaps in current understanding and awareness of human and animal Leptospira infection in Africa , we performed a systematic review of peer-reviewed and grey literature following the Preferred Reporting Items for Systematic Reviews and Meta-analyses ( PRISMA ) guidelines [25] . Our aims were summarise and compare: ( 1 ) current knowledge of the geographic distribution , prevalence , incidence and diversity of acute human leptospirosis in Africa; and ( 2 ) the geographic distribution , host range , prevalence and diversity of Leptospira spp . infection in animal hosts in Africa .
A detailed protocol for this study can be found in the supplementary material ( S1 File ) . Following the PRISMA guidelines and checklist ( S1 Checklist ) references for this review were identified through searches of eight international and regional databases ( Table 1 ) using the search string ‘Leptospirosis’ OR ‘Leptospira’ and ‘Africa*’ for articles published between January 1930 and October 2014 inclusively . Additional articles for inclusion were identified by bibliography hand searches of relevant articles [26] . Abstracts and titles were compiled in EndNote ( Thomson Reuters , Philadelphia , PA , USA ) and reviewed independently by two researchers ( KJA , HMB ) to determine whether each article met pre-determined abstract inclusion and exclusion criteria ( S1 File ) . A third researcher ( JEBH ) served as a tiebreaker for any discordant decisions . Citations were included if they presented data on human or animal Leptospira spp . infection from any country within the United Nations ( UN ) definition of Africa [27] . We excluded abstracts that did not refer to original human or animal leptospirosis research data , or did not describe naturally occurring cases of leptospirosis in human or animal populations . We included case reports but excluded reports of returned travellers because of potential uncertainty around the specific location where infection was acquired . Articles classified as eligible for inclusion were retrieved in full text format and assessed against pre-defined case definitions ( Table 2 ) of human acute leptospirosis and carrier animal status agreed upon by three authors ( KJA , HMB , JEBH ) . Rigorous diagnostic criteria were specified in accordance with WHO and international reference laboratory guidelines ( Table 2 ) [7 , 11 , 16] . Serological diagnostics were not included in the case definition for carrier animals because of the inability to differentiate between previous exposure and current infection status . We also excluded articles describing studies that used laboratory animal inoculations as a diagnostic test for leptospirosis because of concerns over the risk of false positive results as a consequence of pre-existing infection in experimental animal colonies , diagnostic sensitivity and cross-contamination [16] . Full text articles were reviewed by two authors ( KJA , HMB ) and were excluded if they failed to meet case definitions , if results from the same cohort were presented more comprehensively in another eligible article , or if insufficient information was provided in the study methodology to determine whether the case definitions were met . Non-English language articles identified for full text review ( n = 97 ) included French language articles translated by KJA with assistance from a native language speaker ( n = 83 ) ; German language articles translated by a native language speaker ( n = 7 ) ; Italian articles translated by a native language speaker ( n = 4 ) ; Afrikaans ( n = 2 ) and Dutch language articles ( n = 1 ) , which were translated using online translation software with support from a Dutch language speaker [28] . Two reviewers ( KJA , HMB ) independently extracted pre-determined qualitative and quantitative data from each included article . Data on infection prevalence and incidence for comparable studies ( i . e . similar study inclusion criteria and diagnostic methodologies ) were compiled , and ranges were presented by study type ( human studies ) , location or host species ( animal studies ) if three or more citations reporting comparable data were identified . Data on serological and genetic typing of leptospiral isolates from people and animals were compiled and summarised by country and by animal species . Additional data on serogroup and genetic species of reported serovars was obtained from the Leptospirosis Library , maintained by the Leptospirosis Reference Centre , Royal Tropical Institute ( KIT ) , Netherlands [29] . The risk of bias in included studies such as selection or reporting bias was assessed following the Cochrane guidelines for systematic reviews of medical interventions [30] . Full text study validity and methodological quality was assessed by comparison to pre-determined case definition criteria to control for heterogeneity in study design and diagnostic methodology ( Table 2 ) . Studies classified as high-risk for bias were not included in quantitative analysis of leptospirosis prevalence and incidence data .
Acute human leptospirosis was reported in 46 eligible studies from 18 African countries ( Fig 2 ) [31–76] . South Africa was the most frequently represented country with a total of six articles [43 , 47 , 54 , 57 , 65 , 71] , followed by Egypt [45 , 55 , 56 , 58 , 59] and Kenya [31 , 37–39 , 42] with five included articles . Twenty-one articles described acute human leptospirosis in hospital or health centre-based cohort studies ( Table 3 ) . Five articles described data from passive population-based surveillance [35 , 41 , 64 , 70 , 73] , and two articles described active case-finding in the setting of an outbreak of acute febrile illness [31 , 72] . Non-specific febrile illness was the most common clinical criteria described for cohort or surveillance study inclusion . Jaundice was stated as a primary inclusion criterion in three hospital-based cohort studies [44 , 61 , 66] . Haemoglobinuria was stated as the only inclusion criterion in one study conducted in the Democratic Republic of the Congo ( DRC ) [40] . The majority of studies ( n = 41/46 ) used microscopic agglutination test ( MAT ) as a primary method to diagnose human cases of acute leptospirosis . IgM enzyme linked immunosorbent assay ( ELISA ) testing was the only diagnostic method used in three studies [31 , 40 , 63] , but was more commonly used as part of a multi-faceted diagnostic approach ( n = 6/46 ) [44 , 45 , 58 , 64 , 68 , 73] . Fifteen ( 32 6% ) of 46 eligible human studies demonstrated leptospirosis infection by blood culture in combination with serological diagnostics [34 , 35 , 37–39 , 41 , 44 , 54–56 , 58 , 59 , 64 , 69] , and nine ( 19 5% ) studies also used PCR detection as well as culture and serology [34 , 35 , 41 , 56 , 58 , 59 , 64 , 70 , 73] . Genetic targets for diagnostic PCR assays included lbf1 , [34 , 35] lipL32 [34 , 35] , rrs [34 , 35 , 70] , and ligA [58 , 59] . No culture-independent genetic typing of Leptospira spp . was reported in any included human studies . Leptospirosis prevalence varied by study design and inclusion criteria ( Table 3 ) . In hospital-based prospective cohort studies in mainland Africa that enrolled patients with non-specific febrile illness and used MAT serology for diagnosis of acute leptospirosis with or without adjunct diagnostics , prevalence ranged from 2 3% to 19 8% ( n = 11; number of patients: median = 166; range = 39–2441 ) [33 , 36–39 , 42 , 44 , 45 , 55 , 58 , 68] . A hospital-based prospective cohort study of febrile patients in Mayotte that diagnosed acute leptospirosis by PCR and culture without serology reported a prevalence of 13 7% ( number of patients = 2523 ) [34] . In hospital-based cohort studies that used jaundice as the main study enrolment criterion , prevalence of acute leptospirosis ranged from 2 0% to 16 1% ( n = 3; number of patients: median = 102; range = 99–392 ) [44–46] . Acute leptospirosis was also reported in one patient ( 2 3% ) of 38 with haemoglobinuria [40] , three patients ( 25 0% ) of 12 involved in an outbreak of acute febrile disease in a pastoralist community in northern Kenya [31] , and eight patients ( 9 8% ) of 82 involved in an outbreak of acute pulmonary disease ( pneumonia ) in a mining camp in DRC [72] . Incidence estimates were calculated in five population-based surveillance studies [35 , 41 , 64 , 70 , 73] and two hospital-based prospective cohort studies [63 , 74] . The only estimate of incidence from mainland Africa came from northern Tanzania , where regional incidence of 75 to 102 cases per 100 , 000 people per year was reported . This estimate was obtained by combining data on leptospirosis prevalence from hospital-based surveillance of febrile disease with multipliers derived from a population-based health-care utilisation survey [74] . For the Indian Ocean islands , incidence estimates were available for the Seychelles where the average annual incidence was estimated as 60 to101 cases per 100 , 000 [63 , 70]; Réunion where the average annual incidence reported in three studies using a variety of data sources ranged from 3 1 to 12 0 cases per 100 , 000 [41 , 64 , 73] and Mayotte , where the average annual incidence calculated from cases identified through four years of active hospital-based surveillance between 2007 and 2010 was reported as 25 cases per 100 , 000 [35] . Sixteen case reports describing acute leptospirosis in a total of 34 individuals were considered eligible for study inclusion . A wide range of clinical manifestations were reported including febrile illness , jaundice , meningitis , and acute respiratory distress syndrome . Case reports described confirmed or probable acute leptospirosis in patients in South Africa ( n = 6 ) [43 , 47 , 54 , 57 , 65 , 71] , Gabon ( n = 3 ) [48 , 62 , 76] , Morocco ( n = 3 ) [50 , 52 , 53] , Algeria ( n = 1 ) [32] , Mali ( n = 1 ) [51] , Réunion ( n = 1 ) [75] , and Senegal ( n = 1 ) [60] . With the exception of Réunion and Senegal , case reports were the only eligible data on acute human leptospirosis from these countries . Naturally occurring Leptospira spp . infection in animal hosts was reported by 51 eligible citations describing studies performed in 17 African countries ( Fig 2 ) [77–127] . South Africa [84 , 100 , 101 , 104 , 117 , 120–122] and Zimbabwe [83 , 93–99] were the most frequently represented countries with a total of eight included articles per country , followed by Tanzania with seven articles [106 , 110–112 , 114–116] . Wild animal surveys were most commonly described ( n = 21/51 ) followed by strain typing of Leptospira spp . previously isolated from naturally infected animal hosts ( n = 13/51 ) , livestock disease outbreaks ( n = 7/51 ) and abattoir surveys ( n = 7/51 ) . Four citations ( n = 4/51 ) reported human leptospirosis outbreaks as the inciting cause for investigations into animal carrier status [86 , 109 , 117 , 123] . Leptospira spp . infection was demonstrated in a wide range of animal hosts ( S1 Table ) , including cattle ( Bos spp . ) [85 , 87 , 89–91 , 93–102 , 111 , 114 , 119 , 121 , 127]; pigs ( Sus scrofa domestica ) [78 , 79 , 84 , 85 , 100 , 104 , 106 , 122]; goats ( Capra aegagrus hircus ) [85]; Rusa deer ( Rusa timorensis ) [85]; dogs ( Canis lupis familiaris ) [85 , 113 , 116]; cats ( Felis catus ) [85 , 113 , 116]; rodents including the African grass rat ( Arvicanthus niloticus ) [87 , 88] , African giant pouched rat ( Cricetomys gambianus ) [110 , 112] , lesser tufted-tailed rat ( Eliurus minor ) [125] , fringe-tailed Gerbil ( Gerbilliscus robustus ) [77 , 88] , rusty-bellied brush-furred rat ( Lophuromus sikapusi ) [109] , multimammate mouse ( Mastomys sp . ) [83 , 87 , 103 , 115] , house mouse ( Mus musculus ) [80 , 81 , 83 , 85 , 118 , 120 , 124] , brown rat ( Rattus norvegicus ) [82 , 85 , 103 , 108 , 117 , 118 , 120 , 124] , black rat ( Rattus rattus ) [83 , 85 , 86 , 92 , 103 , 118 , 120 , 124] , South African pouched mouse ( Saccostomys campestris ) [88]; and a range of other free-living mammal species including shrews ( Crocidura spp . and Suncus murinus ) [86 , 103 , 115 , 118]; mongoose ( Herpestes ichneumon , Mungo mungo and Paracynictic selousi ) [80 , 105]; Egyptian fox ( Vulpes vulpes niloticus ) [80]; shrew tenrecs ( Microgale cowani , Microgale dobsoni , Microgale longicaudata , Microgale majori , Microgale principula ) [125]; streaked tenrecs ( Hemicentetes nigriceps , Hemicentetes semispinosus ) [125]; and various bat species ( Chaerephon pusillus , Miniopterus gleni , Miniopterus goudoti , Miniopterus griffithsi , Miniopterus griveaudi , Miniopterus mahafaliensis , Miniopterus majori , Miniopterus soroculus , Mormopterus francoismoutoui , Mormopterus jugularis , Mytotis goudoti , Otomops madagascariensis , Rousettus obliviosus , Triaenops furculus , Triaenops menamena ) [107 , 125] . Studies demonstrating infection in cattle were most common ( n = 20/51 ) followed by pigs ( n = 8/51 ) , black rats ( n = 8/51 ) , brown rats ( n = 7/51 ) and house mice ( n = 7/51 ) . Culture and isolation was the most common detection method for Leptospira infection in animal studies ( n = 43/51 ) . PCR assays were used to demonstrate Leptospira spp . infection in 13 ( 25 5% ) out of 51 studies [85 , 86 , 92 , 103 , 105 , 107 , 115 , 118 , 120 , 123–126] . In three studies , culture and PCR were used in combination to determine infection status [92 , 115 , 118] . As with human studies , a variety of genetic targets were used in PCR assays to detect pathogenic leptospiral DNA , including lipL32/hap1 , [85 , 86 , 118] , secY , [103] , rrl [105] , and rrs [107 , 115 , 120] . PCR was predominantly used to demonstrate Leptospira spp . infection in rodents and wild animal species . Only one study in Réunion also used PCR assays to demonstrate infection in domestic animals [85] . Leptospira infection prevalence varied widely by target animal species and diagnostic methodology ( S1 Table ) . Studies that used PCR diagnosis reported higher infection prevalence than studies that relied on Leptospira culture and isolation . Overall Leptospira infection prevalence reported in black rats tested by PCR ranged from 11 0% to 65 8% ( n = 6; number of animals: median = 79 , range = 33–141 ) [85 , 86 , 92 , 103 , 118 , 124] . In two studies where black rats were tested by both PCR and culture , prevalence was higher by PCR ( 11 0% , n = 100; and 28 . 7% , n = 94 ) than by culture ( 4 0% and 3 2% ) in Egypt [92] , and Madagascar respectively [118] . A similar relationship was observed in brown rats , house mice and Asian house shrews tested in Madagascar [118] . Cattle and brown rats were the most common species tested by culture . Prevalence in brown rats ranged from 2 7% to 8 5% by culture ( n = 3; number of animals: median = 256 , range = 130–919 ) [82 , 108 , 117] but was considerably higher in three studies that used PCR to detect infection ( 10 0% to 4 7%; number of animals: median = 11 , range = 10–96 ) [103 , 118 , 124] . In four abattoir-based surveillance studies of cattle from Egypt , Nigeria and Zimbabwe [87 , 89 , 93 , 127] , renal Leptospira spp . carrier status was detected by culture in 1 1% to 10 4% of sampled animals ( number of animals: median = 480 , range = 74–625 ) , compared to 18 2% ( number of animals = 77 ) in a single PCR-based study from Mayotte [85] . Serological typing of Leptospira spp . isolates from patients with acute leptospirosis was described in cohort studies conducted in the DRC [69] , Egypt [55 , 56] , Ghana [44] , Kenya [37–39] and Mayotte [34 , 35] , and in a case report from South Africa [54] . Isolates belonging to 15 serogroups were reported ( Table 4 ) . Mini and Icterohaemorrhagiae were the most commonly reported serogroups . Isolates that were equally cross-reactive with representative serovars from more than one serogroup ( Mini/Hebdomadis and Pyrogenes/Ballum ) were reported by two studies in Mayotte [34 , 35] . In animal studies , isolates belonging to 12 serogroups were reported from 33 articles . At least one animal host was identified within Africa for 11 ( 73 3% ) out of the 15 human-infecting serogroups identified in this review ( Table 4 ) . However , only six of these serogroups were detected in human and animal populations from the same country . These were serogroup Autumnalis in Kenya [39 , 88]; and serogroups Canicola [56 , 92 , 113] , Grippotyphosa [56 , 80 , 81 , 92] , Icterohaemorrhagiae [55 , 80 , 92 , 127] , Pomona [55 , 56 , 127] and Pyrogenes [56 , 92] in Egypt . Serogroups associated with human febrile illness were frequently isolated from multiple animal hosts . One of the most commonly reported serogroups isolated from patients in Africa , serogroup Icterohaemorrhagiae , was isolated from cattle , brown rats , Egyptian mongoose and an Egyptian fox . Cattle were identified as carrier hosts for the widest range of Leptospira serogroups ( n = 9 ) but several other animal species , such as African grass rats and black rats were also identified as carrier hosts for multiple serogroups . Leptospira spp . isolated from human patients with acute leptospirosis belonged to five pathogenic Leptospira species ( Table 5 ) . L . interrogans was the most widespread and common species reported in either human or animal studies in Africa . Multiple animal hosts were identified for L . interrogans as well as the other common species , L . borgpetersenii and L . kirschneri , from a variety of countries . The widest diversity in Leptospira spp . was reported from two Kenyan studies of acute human leptospirosis , where isolates belonging to five species were identified ( L . borgpetersenii , L . interrogans , L . kirschneri , L . noguchii and L . santarosai ) [38 , 39] . However , L . noguchii and L . santarosai were not detected in any other studies . Four Leptospira species: L . borgpetersenii , L . borgpetersenii-like , L . interrogans and L . kirschneri; were identified in two human studies on Mayotte , as well as by a concurrent study of black rats performed during the same period [34 , 35 , 86] . Divergent Leptospira spp . described as L . borgpetersenii-like and L . borgpetersenii Group B were detected in human and animal studies respectively in Mayotte , and in a study of indigenous small mammals in Madagascar [34 , 35 , 86 , 125] . Sequencing and alignment of the atypical isolates from rat kidneys in Mayotte [86] showed perfect identity with isolates derived from people [35] .
This systematic review is the first to synthesize and compile data on the epidemiology of acute human leptospirosis and pathogenic Leptospira spp . infection in animals in Africa . Leptospirosis remains amongst the neglected tropical diseases and is frequently overlooked in research priorities for African countries [1] . Yet , through this systematic review we have revealed a wealth of scientific evidence for acute human infection demonstrating that acute leptospirosis is an important cause of febrile illness in hospital patients across the African continent . Few studies providing population-level data on leptospirosis incidence in Africa were identified but available estimates indicate that the disease incidence is high in both island and mainland populations . In reports of human disease and animal infection , three predominant species , Leptospira borgpetersenii , L . interrogans and L . kirschneri , and a variety of Leptospira serogroups were diagnosed . Leptospira infection was reported in a wide range of domestic and wild animal species from across Africa but studies linking data on animal infections with studies of acute human disease were rare . Acute leptospirosis was diagnosed in up to 19 8% of inpatients with non-specific febrile illness in hospital-based cohort studies conducted in several countries identified by this review . In sub-Saharan Africa , recent studies have highlighted that clinical over-diagnosis of malaria may conceal other aetiologies of febrile illness [20 , 128] . Consistent with findings in other resource-limited tropical settings ( e . g . South America [15 , 129] and South-East Asia [130–132] ) , the evidence synthesised here demonstrates that acute leptospirosis infection is geographically widespread across the continent and should be considered as an important differential diagnosis for non-specific febrile illness in Africa . Few estimates of leptospirosis incidence in Africa were identified by our review , revealing a key gap in research and surveillance outputs to date . The majority of incidence estimates identified came from the Indian Ocean islands where reports of annual incidence ranged from 3 1 to 101 cases per 100 , 000 people . In the African continent , the western Indian Ocean Islands appear to be the best-characterised region with regards to the human leptospirosis burden , possibly as a consequence of greater access to public health laboratories through French Territorial links [133] . We identified only one report of annual leptospirosis incidence from mainland Africa . This estimate of 75 to 102 cases per 100 , 000 people [74] was calculated from Tanzanian hospital-based surveillance data and is consistent with the WHO leptospirosis burden epidemiology reference group ( LERG ) predicted median African incidence of 95 5 cases per 100 , 000 [7] . At present , given the lack of population level data highlighted by LERG and by this review , estimates of the incidence of leptospirosis in Africa should be interpreted with care . However the data that are available from the continent indicate that the overall leptospirosis burden is likely to be high relative to other global regions . If the incidence figures identified by this review are close to the true burden of disease , up to 750 , 000 people in Africa will develop acute leptospirosis each year , representing a substantial disease burden that would far exceed current worldwide estimates ( 500 , 000 annual cases worldwide ) [23] . Our review has revealed three predominant Leptospira species and a considerable diversity in reported pathogenic Leptospira serogroups in people and animals across the continent . Animal hosts , including livestock and invasive and indigenous rodent species , were reported for the majority of species and serogroups detected in human cases . However , there was poor geographical overlap in serogroup reporting between human and animal studies . Based on the findings of this review , we suggest that the major animal hosts of human-infecting serovars may vary across Africa and that both livestock and rodents may play important roles in human disease transmission . Few data were identified that described Leptospira spp . diversity in human cases and animal populations from the same country , and few studies attempted to link data on acute human leptospirosis with evidence of Leptospira infection in local animal populations . Studies on the Indian Ocean Islands of Mayotte and Madagascar were the exception to this . Isolates with unusual patterns of genetic and serological diversity , recently reclassified as a new pathogenic species Leptospira mayottensis [134] , were detected from both human and black rat infections , implicating the black rat as the source of these human infections [35 , 86] . These studies demonstrate the value of integrated human and animal research to identify sources and transmission routes of human leptospirosis , which can in turn help prioritise investment in disease prevention and control efforts . The data included in this review most likely represents only the tip of the leptospirosis iceberg in Africa . Underreporting of leptospirosis is thought to be substantial and an overall lack of awareness about the disease and poor accessibility of diagnostic facilities are likely to contribute to this underreporting in Africa populations [135–137] . Patterns in reporting characteristics such as over-representation of study areas with greater research infrastructure , logistical connections or prior knowledge of a disease burden may also have resulted in reporting bias , particularly in assessing the geographic distribution of reports . We observed patient selection bias in some human studies , which limited the usefulness of reported prevalence data from these sources . Methodological limitations identified in this review include the use of the broad geographical search term ‘Africa’ rather than individual country names in our initial database searches . This approach may have missed eligible citations that are not indexed to the term ‘Africa’ in our selected databases . Our inclusion criteria may have created a bias towards more recent citations because of diagnostic technological advancements since the early era of our search period . Marked heterogeneity in methods and reporting criteria for serological diagnostic data prevented the meaningful synthesis and analysis of data on the reactive serogroups in human studies . We chose to include non-English language articles to allow inclusion of articles published in the colonial era , or in local language journals . Wherever possible , a proficient language speaker , in partnership with a study author , performed article translation . However , it is possible that some eligible studies may have been overlooked due to translation limitations . Addressing the neglect of leptospirosis in Africa will be a major challenging for the future of leptospirosis research . Systematic review studies such as this can help to raise awareness of the human health threat of leptospirosis in Africa among researchers and policy makers . For medical clinicians , the non-specific presenting signs of acute leptospirosis in patients poses a substantial diagnostic challenge in developing countries where laboratory capacity rarely exists to diagnose the infection [18–20] . Hence , increasing clinician awareness and the development of treatment guidelines for the management of febrile patients should be a priority in resource-limited settings [138] . Integration of risk factor analysis in human cohort studies of febrile disease is also strongly advocated and would be a valuable next step in identifying groups at high risk of infection , and defining important animal to human transmission routes . Knowledge of reservoir or carrier animal hosts is considered essential to understanding the epidemiology , transmission and control of leptospirosis in each setting [9 , 11] , yet our review has revealed that the linkages between Leptospira infections in people and animals are rarely addressed in the existing literature . Human and animal Leptospira infections are inextricably linked , and the multi-host epidemiology of leptospirosis means that there may be many potential sources of infection in a given setting . In the future , greater emphasis should be placed on performing multidisciplinary human and animal leptospirosis studies in the same geographical settings . Connecting investigations of animal reservoir populations with confirmed human cases would improve our understanding of the role that different animal species play in the transmission of pathogenic Leptospira serovars in a variety of geographic and environmental settings [8] [139] . Using an integrated ‘One Health’ approach to explore the relationship between human and animal Leptospira infection in areas where human disease is identified would also provide invaluable evidence to quantify the direct and indirect impacts of leptospirosis on human and animal populations in Africa [140 , 141] . Control measures to prevent human leptospirosis often focus on rodent hosts of the disease . However , this review reveals that livestock are also important hosts of Leptospira infection in Africa , and may play a more substantial role in human disease transmission than is widely recognised . The clinical and sub-clinical productivity impacts of Leptospira infection in domestic animal populations in Africa are poorly understood . Around the world , several Leptospira serovars are considered to be of economic importance and cause production losses in a variety of livestock farming species including cattle , sheep , goats and pigs [17 , 100 , 142 , 143] . More than 300 million of the world’s poorest people live in Africa , and at least 60% of these are in some part dependent on livestock for their livelihood [144] . Therefore , we consider that evaluating the impact of Leptospira infection on livestock health and productivity is also an important priority for prospective research in Africa . In the future , control of Leptospira infection in livestock species may have considerable scope to directly and indirectly improve human health and well-being in Africa , through reduced human leptospirosis transmission and increased productivity in livestock that subsistence farming communities [8 , 142 , 143 , 145] . Finally , in 1967 , the German leptospirosis researcher Kathe commented that ‘The world map of leptospirosis is , in fact , the world map of leptospirologists’ [67] . This is particularly true with regards to Africa . With this systematic review , we have started to outline the map of African leptospirosis; it is now time to fill in the gaps . | Leptospirosis is an important bacterial zoonosis that affects people and animals worldwide . It is common in tropical areas where people and animals live in close contact , but the disease has been widely neglected in Africa . In this study we aimed to demonstrate the extent of leptospirosis in Africa and describe the diversity of the causative agent Leptospira spp . in human and animal infections across the continent . Through a systematic literature review , we identified 97 studies from 26 African countries that described human disease or animal infection and met inclusion criteria . Leptospirosis was the cause of illness in 2 3% to 19 8% of hospital patients with a fever . Where population-level data were available , leptospirosis was estimated to affect 3 to 102 people per 100 , 000 every year . A variety of animal hosts of Leptospira spp . were identified . Cattle were reported as carriers of a variety of serological types of Leptospira spp . infection . The role of cattle and many other different animal hosts in human disease transmission remains unclear . Our review demonstrates that leptospirosis is a substantial cause of human illness in Africa , and we recommend integration of human and animal studies in the future to help us understand the epidemiology of leptospirosis on this continent . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Epidemiology of Leptospirosis in Africa: A Systematic Review of a Neglected Zoonosis and a Paradigm for ‘One Health’ in Africa |
In the present study , the frequency , the activation and the cytokine and chemokine profile of HTLV-1 carriers with or without dermatological lesions were thoroughly described and compared . The results indicated that HTLV-1-infected patients with dermatological lesions have distinct frequency and activation status when compared to asymptomatic carriers . Alterations in the CD4+HLA-DR+ , CD8+ T cell , macrophage-like and NKT subsets as well as in the serum chemokines CCL5 , CXCL8 , CXCL9 and CXCL10 were observed in the HTLV-1-infected group with skin lesions . Additionally , HTLV-1 carriers with dermatological skin lesions showed more frequently high proviral load as compared to asymptomatic carriers . The elevated proviral load in HTLV-1 patients with infectious skin lesions correlated significantly with TNF-α/IL-10 ratio , while the same significant correlation was found for the IL-12/IL-10 ratio and the high proviral load in HTLV-1-infected patients with autoimmune skin lesions . All in all , these results suggest a distinct and unique immunological profile in the peripheral blood of HTLV-1-infected patients with skin disorders , and the different nature of skin lesion observed in these patients may be an outcome of a distinct unbalance of the systemic inflammatory response upon HTLV-1 infection .
Human T-lymphotropic virus 1 ( HTLV-1 ) was the first retrovirus discovered with human clinical importance [1] . While the majority of patients infected with HTLV-1 may remain asymptomatic ( 95% ) , some patients ( 0 . 2–5% ) can develop severe clinical symptoms [2] , [3] . In certain endemic regions , the prevalence and incidence of these clinical symptoms among persons infected with HTLV-1 is significantly elevated [4] . Among the many disorders that HTLV-1 carriers may develop , two are paramount: the adult T-cell leukemia/lymphoma ( ATL ) and a chronic neurological disease , the HTLV-1-associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) . Contrasting with HIV , HTLV-1 has poor infectivity of CD4+ T cells in vitro and the viral particles are scarce in the peripheral blood of HTLV-1 carriers . In spite of that , HTLV-1 proviral DNA is detected particularly in CD4+ T cells of the host and the virus is able to induce strong activation and proliferation of many subsets of cells , which allow this retrovirus , together with other factors , to persist by clonal expansion of CD4+ T cells of the infected host [5] , [6] , [7] . There are clear evidences that the immunological profile of HAM/TSP patients is composed by a robust hyperimmune response [8] , contrasting with ATL patients . Increased levels of Type-1 cytokines such as IFN-γ , TNF-α , IL-1β , IL-2 , IL-6 , IL-9 and IL-13 found in HAM/TSP patients are examples of such evidences [9] , [10] , [11] , [12] . Asymptomatic HTLV-1 carriers , however , seem to present a balanced hyperimmune response characterized by altered frequency of proinflammatory IL-12-positive neutrophils and TNF-α-positive monocytes , which is modulated by high frequency of IL-10-secreted by CD4+ and CD8+ T cells according to Brito-Melo and collaborators ( 2007 ) [13] . There is still many unresolved questions on the context of the immunological status of HTLV-1 carriers who develop inflammatory symptoms , but there is no doubt that HAM/TSP is evidently a clinically and immunologically distinct entity that should be considered as unique among other inflammatory disorders associated with HTLV-1 . The dermatological disorders associated with HTLV-1 , however , have been poorly investigated , which poses a challenge on the management – prognosis and treatment - of patients with such disorders . Contrasting with the lack of scientific findings on the mechanisms that lead to dermatological disorders in HTLV-1 carriers , infectious and autoimmune dermatitis have been increasingly reported on those subjects , especially in endemic areas [14] , [15] , [16] , [17] , [18] , [19] . Regarding the immunological context of dermatological disorders in HTLV-1-infected individuals , Nascimento and collaborators ( 2009 ) [20] reported that infective dermatitis has similar immunological features to HAM/TSP and could be considered as a risk factor for development of myelopathy . In face of the elevated prevalence of dermatological disorders among HTLV-1-infected individuals and the scarce knowledge of their immunological status , this study aimed at evaluating frequency , activation and the cytokine and chemokine profile of HTLV-1 carriers with or without dermatological diseases .
For FACS immunostaining , antibodies including: anti-human CD3-FITC , anti-human CD4-FITC , anti-human CD8-FITC , anti-CD14-FITC , anti-human CD16-PE , anti-human CD19-FITC , anti-human CD56-FITC , and anti-human HLA-DR-PE ( Pharmingen , San Diego , CA , USA ) were utilized . Whole blood staining of individual samples was performed according to manufacturer's instructions and adapted as described [22] . For acquisition and analysis , identification of the subsets was performed by the dual-color immunophenotyping method within the lymphocyte or monocyte scatter gate . All results were expressed as percentage of positive cells for the different subsets of cells analyzed in this study . To assess the levels of the cytokines – TNF-α , IL-12 , IL-10 , IL-6 and IL-1β – and chemokines – CCL2 ( MCP-1 ) , CCL5 ( RANTES ) , CXCL8 ( IL-8 ) , CXCL9 ( MIG ) , CXCL10 ( IP-10 ) – in the sera from HTLV-1 carriers and controls , Cytometric Bead Array kits ( BD Biosciences , California , USA ) were utilized according to manufacturer's protocol and adapted as described [23] . Analysis of raw data was performed using the FlowJo cytometry analysis software ( FlowJo , Stanford , USA ) and the median fluorescence intensity ( MFI ) of each bead cluster was evaluated to calculate the cytokine concentration in the sera of patients . Cytokines concentrations were extrapolated according to the standard curve created by serial dilutions of the positive control . The concentrations of cytokines were expressed in pg/mL . To quantify the HTLV-1 proviral load of the HTLV-1 seropositive individuals , peripheral blood was collected from the patients in tubes containing EDTA anticoagulant . DNA was isolated from peripheral blood by column extraction ( QIAamp DNA Blood kit; Qiagen GmbH , Hilden , Germany ) and HTLV-1 proviral load was quantified by a real-time SYBR Green PCR method as previously described [24] . The value for the HTLV-1 proviral load was reported as [ ( pol average copy number ) / ( albumin average copy number/2 ) ]×104 and expressed as the number of HTLV-1 copies/104 cells . The ANOVA one-way with Dunnet's post-test was utilized to compare the groups for all the immunological parameters evaluated . Pearson's and Spearman's correlation tests were utilized to compare cytokine levels with HTLV-1 proviral load of the patients . The Prism GraphPad Software version 5 . 0 ( San Diego , CA , USA ) was applied for the statistical analysis , and differences between groups with P values <0 . 05 were considered as statistically significant and indicated in the figures as letters or a line connecting the two different groups .
Figure 1 shows the frequency of the subset of cells evaluated in HTLV-1 carriers with skin lesions . The data demonstrated that the two types of skin lesions present different immunophenotypic profiles . The HTLV-1-infected group with infectious dermatological lesions presented statistically decreased frequency of B cells , increased frequency of CD8+ T cells , and increased T/B cell ratio when compared to the HTLV-1-infected group with autoimmune skin lesions . The HTLV-1-infected group with autoimmune skin lesions showed increased levels of CD4+ HLA-DR+ T cells when compared to the HTLV-1-infected group without lesions . Regarding the innate immune response of HTLV-1 carriers with skin disorders , Table 3 shows the frequency of the innate and regulatory cell subsets evaluated . Macrophage-like ( CD14+CD16+/CD14+ ) , pro-inflammatory monocytes ( CD14+CD16+HLA-DR++/CD14+CD16+ ) , NK cells ( CD3−CD16+/−/CD56+/−/CD3− ) , NKT cells ( CD3+CD16+/−/CD56+/−/CD3+ ) and regulatory T cells ( CD4+CD25+ High ) were analyzed in the peripheral blood of HTLV-1 carriers with and without lesions as well as control ( Table 3 ) . The results demonstrated a statistically significant increase in the macrophage-like subset in the HTLV-1-infected group with skin lesions when compare to uninfected control with skin lesions , while the proinflammatory monocytes were decreased in the HTLV-1-infected group with and without skin lesions when compared to their respective controls . The NK subset showed increased percentage in the HTLV-1-infected group with and without skin lesions when compared to their respective controls . Interestingly , the NKT subset showed statistically significant increase in the HTLV-1-infected group with skin lesions when compared to the uninfected controls with skin lesions . The same difference was not observed in the HTLV-1-infected group without skin lesions when compared to its respective control . To verify whether the nature of skin lesion was associated with altered chemokine and cytokine levels , the levels of these molecules in the infectious skin lesions group and the autoimmune skin lesions group were evaluated and contrasted ( figure 2 ) . The chemokines CCL5 and CXCL8 are significantly increased in the HTLV-1-infected group with autoimmune dermatological lesions when compared to the HTLV-1-infected group with infectious skin lesions . The chemokine CXCL10 was statistically increased in the HTLV-1-infected group with autoimmune skin lesions when compared to HTLV-1-infected group without lesions . The cytokine ratio analysis showed a decrease in the IL-12/IL-10 ratio in the HTLV-1-infected group with infectious dermatological lesions when compared to the HTLV-1-infected group without skin lesions , which indicates that the decrease in this ratio observed previously is attributed to the group with infectious skin lesions . Figure 3 shows the panoramic chemokine and cytokine profile of high producers from the HTLV-1-infected and control groups displayed in radar graphs . The altered chemokine and cytokine profile identified before was sustained when the high producers from each group were compared . Remarkably , CXCL9 shows evident decrease associated to HTLV-1 infection . The high producers of the HTLV-1-infected group with infectious skin lesions demonstrate a clear and statistically significant increase of TNF-α and IL-6 when compared to the HTLV-1-infected group with autoimmune skin lesions . This group also shows a significant decrease in IL-12 when compared to the HTLV-1-infected group without lesions . Radar graphs with cytokine profile of high producers from the Control groups are displayed in Figure S1 . The proviral load of peripheral blood mononuclear cells from HTLV-1-infected patients with or without skin lesions was evaluated . Figure 4 shows that the mean of proviral load in the groups with and without skin lesions or in the groups with infectious or autoimmune skin lesions did not differ statistically ( figure 4 ) . On a different analysis , these groups were merged and the patients were classified as possessing: high ( ≥1000 proviral copies/104 cells ) , medium ( >100 and <1000 proviral copies/104 cells ) or low proviral load ( ≤100 proviral copies/104 cells ) . The frequency of patients with high , medium or low proviral load from each group demonstrated that HTLV-1 carriers without skin lesion contained the lowest percentage of patients with high-medium proviral load and the highest percentage of patients with low proviral load . To evaluate whether the immunological features screened in the present study correlated with presence of the HTLV-1 provirus , the chemokines and cytokines levels were compared among patients of each group subdivided previously as high-medium and low proviral load carriers ( figure 5 ) . Among all the chemokines and cytokines/IL-10 ratio tested , TNF-α/IL-10 and IL-12/IL-10 ratios were statistically higher within HTLV-1-infected patients with infectious and autoimmune skin lesions , respectively , bearing high-medium proviral load . Spearman correlation's analysis confirmed significant correlation between proviral load and TNF-α/IL-10 ratio ( r = 0 . 8827; p = 0 . 0333 ) in patients with infectious skin lesions , and proviral load and IL-12/IL-10 ratio ( r = 0 . 4777 p = 0 . 0285 ) in patients with autoimmune skin lesions .
Dermatological alterations are quite common in HTLV-1-carriers . Several reports have described dermatological diseases such as infective dermatitis as a prodromic manifestation of HAM/TSP and ATLL progression [14] , [19] , [25] , [26] , [27] . Although these cutaneous disorders are common in HTLV-1 carriers , it is still unclear how HTLV-1 and its machinery could induce dermatological lesions . The HTLV-1 genome in skin biopsies may be difficult to detect even by highly sensitive methods [28] , [29] , [30] , which could be partially explained by the evidence of deleted HTLV provirus in the cutaneous lesions of mycosis fungoides patients [29] , [31] , [32] . However , the association between HTLV and Mycosis fungoides has remained controversial since few patients with mycosis fungoides are seropositive for antibodies to structural components of HTLV 1 and 2 virions and not all of the patients present evidence of tax sequences [33] . Other studies support this controversy but challenge the findings of HTLV proviral sequences in patients with Mycosis fungoides after evaluation of these patients's samples by the same molecular probes and techniques utilized in previous reports . These studies have indicated a possible misdiagnosis of Mycosis fungoides or little association with HTLV provirus [34] , [35] , [36] , [37] , [38] . These findings suggest that the association of HTLV provirus and Mycosis fungoides is still unclear and should be considered with care . In addition , the tissue damage may not be caused by a direct effect of the virus , but actually originated by the systemic and local deregulation of the immune response in cutaneous disorders induced by the HTLV-1 . Brito-Melo ( 2002 and 2006 ) and Coelho-dos-Reis ( 2007 ) and collaborators proposed that , among the many altered subset of cells on HTLV-1 carriers from Brazil , the decreased frequency of B cells , the increased frequency of activated CD8+ T cells and the T/B cell ratio are characteristic of HAM/TSP patients [13] , [22] , [39] . In the present study , these immunological markers together with others were evaluated in HTLV-1 carriers with skin lesions . The results indicated that HTLV-1-infected carriers with dermatological disorders also display alterations similar to the ones found in HAM/TSP patients from Brazil . However , HTLV-1-infected patients with skin lesions showed other immunological features that suggest a distinct and unique immunological profile in their peripheral blood . Figure 6 illustrates a schematic representation of these unique immunological profiles in the peripheral blood of HTLV-1-infected patients with infectious or autoimmune skin disorders and how these profiles could be associated with each particular skin lesion . In the present study , it was identified that patients with infectious dermatological lesions had similar frequency and activation status to HAM/TSP patients previously described , however , the chemokine profile seems to be divergent considering previous findings . Previous studies have demonstrated that levels of serum chemokines discriminate clinical HAM/TSP disease from HTLV-1 carrier state [40] . Increased serum levels of CXCL10 was previously observed in HAM/TSP patients when compared to asymptomatic carriers from a Brazilian cohort of HTLV-1-infected patients [40] , which converges with the results of the present study that also showed an increase of CXCL10 in HTLV-1-infected patients with autoimmune skin lesions . In the present study , a decrease in CXCL9 was also found in HTLV-1-infected patients with skin lesions regardless of the lesion type when compared to uninfected controls . Contrasting with this finding , high levels of CXCL9 were found to be features of patients with HAM/TSP when compared to asymptomatic carriers [40] . HTLV-1 patients with autoimmune skin lesions presented elevated CCL5 and CXCL8 . In vitro evidence of HTLV-1 induction of chemokine secretion were reported showing that patients as well as cell lines infected with HTLV-1 express chemokines such as CCL2 , CCL3 , CCL4 , CCL5 , CXCL8 , CXCL10 and CCL22 [41] , [42] , [43] , [44] , [45] and some of these chemokines may be induced by the transactivator Tax protein [41] , [45] . Many types of cells are able to produce chemokines and their levels are variable , even in healthy subjects , therefore future studies performed in vitro and ex vivo should be carried out to understand whether HTLV-1 patients with different dermatological lesions indeed have a distinct chemokine secretion profile and also comparing the chemokine profile of HAM/TSP specially regarding their serum levels of CXCL9 . The monocytic populations seem to be altered in HTLV-1 patients with skin lesions . The macrophage-like monocytes ( CD14+CD16+/CD14+ ) were increased in the HTLV-1 group with skin lesions while the proinflammatory monocytes ( CD14+CD16+HLA-DR++/CD14+CD16+ ) were decreased in both HTLV-1-infected groups with and without skin lesions . These results are in agreement with previous results that showed downregulation of CD14 exerted by HTLV-1 infection and impairment of differentiation of monocytes into macrophages or dendritic cells from HTLV-1-infected patients [46] , [47] . This unbalance of the monocytic subsets in the peripheral blood could ultimately affect migration and homing of monocyte-derived subsets , such as dendritic cells in the tissues , which may breach the skin defenses and allowing for lesion formation in HTLV-1 infected patients [47] . On the proviral load analysis , considering the low number of the patients with high proviral load ( #6 – 2 without skin lesions and 4 with skin lesions ) , we merged the patients with high and medium proviral load to allow for a better understanding of the association of proviral load and skin lesion . In this analysis , the group with skin lesions presented higher frequency of patients with the higher-medium proviral load ( 14/27 ) as compared to the group without lesion ( 3/9 ) . The balance of cytokines also demonstrated to be uniquely altered in HTLV-1 patients with skin lesions . The association of elevated proviral load and exacerbated immune response namely by increase of CD8+ T cell frequency and cytokines such as IFN-γ and TNF-α was comprehensively described in patients with HAM/TSP . However , little is still known about this equilibrium in other HTLV-1-associated diseases . In agreement with previous findings on patients with myelopathy , the elevated proviral load in HTLV-1 patients with infectious skin lesions correlated significantly with TNF-α/IL-10 ratio , while IL-12/IL-10 ratio seems to be the ratio driving the unbalanced cytokine response in patients with autoimmune skin lesions . The reason that determines why the cytokine balance is different in the infectious versus autoimmune skin lesions still remains to be investigated . One hypothesis to explain this difference in cytokine balance could be the presence of different T cell subsets in the peripheral blood of patients with skin lesions . While the HTLV-1-infected group with infectious dermatological lesions presented increased frequency of CD8+ T cells , the HTLV-1-infected group with autoimmune skin lesions showed increased levels of CD4+ HLA-DR+ T cells . These different T cell populations could provide a microenvironment that modulates the production of TNF-α and especially IL-12 from macrophage-like monocytes , which are increased in patients with skin lesions ( Figure 6 ) . These results show that the two types of skin lesions , infectious and autoimmune , seem to be associated with two distinct immunological cytokine balances in patients with higher proviral load . Understanding the different systemic inflammatory environments pictured in the peripheral blood of HTLV-1 carriers with skin lesions may be useful to unveil the systemic and local pathogenesis directly or indirectly induced by HTLV-1 as well as for future application of these immunological biomarkers in clinical investigations . | In the present study , the immunological profiles of HTLV-1 carriers with or without dermatological lesions were thoroughly described and compared . The results indicated that HTLV-1-infected patients with dermatological lesions have distinct frequency and activation status than asymptomatic carriers . Alterations in cells and molecules that are important for immune cell function were observed in the HTLV-1-infected group with skin lesions . Additionally , HTLV-1 carriers with dermatological skin lesions have elevated frequency of high proviral load as compared to asymptomatic carriers , which indicates that the virus may be present in higher frequency in those patients . Patients with different skin lesions , autoimmune or infectious , also demonstrated differences in their immunological profile . All in all , these results suggest a distinct and unique immunological profile in the blood of HTLV-1-infected patients with skin disorders , and the different nature of skin lesion observed in these patients may be an outcome of a distinct unbalance of the systemic inflammatory response upon HTLV-1 infection . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"immunology",
"biology"
] | 2013 | Immunological Profile of HTLV-1-Infected Patients Associated with Infectious or Autoimmune Dermatological Disorders |
Increasing evidence indicates that microRNAs ( miRNAs ) are contributing factors to neurodegeneration . Alterations in miRNA signatures have been reported in several neurodegenerative dementias , but data in prion diseases are restricted to ex vivo and animal models . The present study identified significant miRNA expression pattern alterations in the frontal cortex and cerebellum of sporadic Creutzfeldt-Jakob disease ( sCJD ) patients . These changes display a highly regional and disease subtype-dependent regulation that correlates with brain pathology . We demonstrate that selected miRNAs are enriched in sCJD isolated Argonaute ( Ago ) -binding complexes in disease , indicating their incorporation into RNA-induced silencing complexes , and further suggesting their contribution to disease-associated gene expression changes . Alterations in the miRNA-mRNA regulatory machinery and perturbed levels of miRNA biogenesis key components in sCJD brain samples reported here further implicate miRNAs in sCJD gene expression ( de ) regulation . We also show that a subset of sCJD-altered miRNAs are commonly changed in Alzheimer’s disease , dementia with Lewy bodies and fatal familial insomnia , suggesting potential common mechanisms underlying these neurodegenerative processes . Additionally , we report no correlation between brain and cerebrospinal fluid ( CSF ) miRNA-profiles in sCJD , indicating that CSF-miRNA profiles do not faithfully mirror miRNA alterations detected in brain tissue of human prion diseases . Finally , utilizing a sCJD MM1 mouse model , we analyzed the miRNA deregulation patterns observed in sCJD in a temporal manner . While fourteen sCJD-related miRNAs were validated at clinical stages , only two of those were changed at early symptomatic phase , suggesting that the miRNAs altered in sCJD may contribute to later pathogenic processes . Altogether , the present work identifies alterations in the miRNA network , biogenesis and miRNA-mRNA silencing machinery in sCJD , whereby contributions to disease mechanisms deserve further investigation .
Creutzfeldt-Jakob disease ( CJD ) is a human transmissible spongiform encephalopathy characterized by behavior changes , progressive dementia , loss of coordination and myoclonus . At the molecular level , CJD is associated with the conversion of the normal , cellular prion protein ( PrPC ) to an abnormal conformation ( PrPSc ) and further accumulation of PrPSc in the brain in the form of protein aggregates [1] . Despite the established role of PrPC in several neuronal functions such as synaptic plasticity , neurotransmission and neuronal development , the molecular mechanisms triggering the PrPC to PrPSc conversion and the cellular pathways unchained by prion infection leading to neuronal damage and cell death remain elusive . Sporadic CJD ( sCJD ) is the most common human prion disease , presenting a high degree of heterogeneity . sCJD is classified into six subtypes , based on variations at codon 129 of the prion protein gene ( PRNP Met or Val ) and on the size of protease resistant PrPSc ( type 1 or 2 ) . Among these subtypes MM1 and VV2 are the most prevalent [2 , 3] and give rise to unique clinical and neuropathological features , such as specific gliosis , neuroinflammation , spongiosis and synaptic loss signatures [2 , 4–7] . A widespread regional and subtype-specific mRNA and protein deregulation leading to the alteration of multiple biological functions and signaling pathways is also associated with sCJD [5 , 8–10] . Transcriptomic and proteomic patterns are regulated by several factors including miRNAs; these have been recognized as key regulators of gene expression . miRNAs are small ( 21–25 nucleotides long ) , non-coding RNAs , that regulate gene expression through partial complementary binding to their mRNA targets in the RNA-induced silencing complex ( RISC ) . This miRNA-mRNA interaction usually leads to gene silencing through a variety of forms , including mRNA cleavage , translational repression and de-adenylation [11 , 12] . Several miRNAs are selectively expressed in the central nervous system ( CNS ) and have been reported to be involved in CNS development , function and pathogenesis [13 , 14] . In addition , specific miRNA signatures have been proposed for Alzheimer’s ( AD ) , Parkinson’s ( PD ) and Huntington’s ( HD ) disease , as well as for Fronto-temporal dementia ( FTD ) [15–20] , supporting the idea that miRNA deregulation is a common hallmark of neurodegenerative diseases . While the study of miRNAs in relation to prion pathogenesis has gained experimental momentum since several miRNAs were found to be altered in in vivo and ex vivo models of prion diseases [21–25] , the miRNA signature in sCJD has not been reported so far . A potential link between miRNAs and prion diseases has been suggested based on the co-localization of PrPC within RISC components in endosomes and multivesicular bodies . Binding of PrPC to the type III RNase Dicer ( Dicer ) and Argonaute ( Ago ) proteins , which represent essential components of the RISC loading complex , has been proposed as a requirement for effective repression of several miRNA targets [26] . Hence , miRNA deregulation could be triggered by many aspects of sCJD pathology , including replacement of the physiological PrP forms with pathological ones . Simultaneously , miRNA deregulation may have drastic consequences in sCJD gene expression patterns and may act as a contributing factor in the cascade of events leading to fast disease progression . In order to increase our understanding of the miRNA contribution to sCJD pathogenesis , we performed small RNA-Sequencing ( small RNA-Seq ) in the two most affected brain regions in sCJD , frontal cortex ( FC ) and cerebellum ( CB ) , in the two most prevalent sCJD subtypes ( MM1 and VV2 ) , which are linked to region specific clinical and pathological outcomes [2 , 27] . We demonstrate a strong regional and subtype-specific alteration of miRNA expression in sCJD and molecular alterations in miRNA biogenesis and silencing machinery; we further show that a subset of sCJD enriched miRNAs are actively incorporated in the RISC complex . Additionally , we detected the presence of commonly changed miRNAs in other neurodegenerative dementias such as AD , dementia with Lewy bodies ( DLB ) and fatal familial insomnia ( FFI ) . Further , sCJD-related miRNA alterations were studied in a temporal manner , utilizing a sCJD mouse model; sCJD miRNA profiles were validated in the utilized animal model at clinical disease stages , whereas most of those miRNAs were not found to be regulated at earlier disease points suggesting diverse and dynamic miRNA expression programs during disease progression . Finally , we profiled selected miRNAs in the CSF of sCJD cases , which ruled out the presence of a major correlation between miRNA levels in CSF and brain tissue . Altogether , our results show a significant deregulation of miRNA expression , activity and biogenesis in sCJD and they highlight the potential role of miRNAs in the pathology of prion diseases and alternative neurodegenerative conditions .
miRNA expression signatures were determined by small RNA sequencing in the frontal cortex ( FC ) and in the cerebellum ( CB ) of sCJD MM1 and VV2 and in age and gender matched controls . We selected these brain regions , because they are strongly affected in sCJD and display differential neuropathological patterns between MM1 and VV2 subtypes [2 , 27] . Obtained sequences were annotated based on the overlap with publicly available genome annotations , including miRNAs , tRNAs , rRNAs , other small RNAs and genomic repeats . miRNAs represented an average of 27% of total counts ( S1 Table ) . Total number of reads on the FC and CB mapping onto miRNAs with at least 2 counts in a given sample are shown ( S2 Table ) . Two independent pipelines were used for the analysis of the differential miRNA expression , Seqbuster [28] and OASIS [29] . Both pipelines showed a high level of agreement in the detection of differentially expressed miRNAs ( 89% ) . Seqbuster analysis revealed the presence of 70 miRNAs with altered expression in the FC of sCJD MM1 and 27 in sCJD VV2 compared to controls ( Fig 1A , S3 Table ) . In the CB , 22 miRNAs were changed in sCJD MM1 and 69 in sCJD VV2 compared to controls ( Fig 1A , S3 Table ) . The majority of the differentially altered miRNAs were expressed in both tissues , suggesting that the changes on their levels are tissue specific ( S2 and S3 Tables ) . The miRNA signature in sCJD was highly dependent on the brain region and sCJD subtype ( Fig 1B and S3 Table ) . Regarding sCJD subtype alterations , a high percentage of miRNAs were commonly regulated between both subtypes in the FC ( 31% ) ( Fig 1B ) . In the CB , the percentage of commonly altered miRNAs between subtypes was lower ( 10% ) ( Fig 1B ) . Isoforms of a mature miRNA have been referred as isomiRs [30] . They are functionally active and highly abundant in brain tissue , both in control and in neurodegenerative diseases [15 , 31 , 32] . In the present study , 2883 and 4075 different isomiRs were found in the CB and FC , respectively ( S4 Table ) . Furthermore , most of the sequences mapping onto miRNA database ( reference miRNAs and IsomiRs ) showed 3–50 counts . No major differences were detected between control and sCJD subtype cases regarding their isomiRs profiles ( S2 Fig ) , suggesting that isomiR processing is not significantly altered in sCJD . A subset of miRNAs found to display altered expression in sCJD based on small RNA-Seq analysis was further validated by qPCR analysis . miRNAs were selected according to number of counts and fold change alterations in the RNA-seq analysis and/or their previous association with prion disease pathogenesis and/or other neurodegenerative diseases [21 , 23 , 33 , 34] . A total of 18 miRNAs were analyzed in both regions . The alterations in the levels of 15 and 10 miRNAs were validated in a regional specific manner in the FC and CB , respectively ( Fig 2A ) . In sCJD FC , miRNAs 29b-3p , 342-3p , 146a-5p , 154-5p , 195-5p , 26a-5p , 16-5p , 449a , 142-3p , let7i-5p and 135a-5p were increased , while miRNAs 124-3p , 331-3p , 877-5p and 125a-5p were decreased compared to controls , in agreement with RNA-seq data . miRNAs 378a-3p and 5701 , which expression was only altered in CB did not present changes in the FC . In CB , miRNAs 146a-5p , 154-5p , 26a-5p , 378a-3p , 449a , 142-3p , let7i-3p and 5701 were increased , and miRNAs 124-3p and 877-5p were decreased in sCJD , in agreement with RNA-seq data . The rest of miRNAs , which expression was only altered in FC did not present changes in the CB . Finally , miRNA-204-5p , a miRNA presenting no alterations in the FC and CB of sCJD by RNA-seq analysis , was used as negative control showing no changes among groups at qPCR level . Therefore , while most of the qPCR validated miRNAs were altered in both disease subtypes , a subset of them presented subtype-specific changes , which were in agreement with small RNA-Seq data ( S3 Table ) . To confirm that upregulated miRNAs in sCJD brain tissue were functionally active , we performed RISC immunoprecipitations in the FC of control and sCJD MM1 cases using two different Argonaute ( Ago ) antibodies detecting Ago-2 ( 11A9 ) and Ago1-4 family members ( H-300 ) . These antibodies were able to immunoprecipitate Ago-containing miRNA complexes from brain tissue ( Fig 2B ) . RNA extraction from immunoprecipitates and further qPCR analysis allowed us to detect miRNA enrichment for a subset of miRNAs with increased expression in sCJD MM1 brain tissue according to qPCR analysis ( miRNA-146a-5p , miRNA-26a-5p , miRNA-195-5p and miRNA-154-5p ) . As negative controls , miRNA-204-5p and miRNA-5701 were tested . According to RNA-seq and qPCR data , miRNA-204-5p was regulated neither in the FC nor in the CB of sCJD , while miRNA-5701 was upregulated only in the CB of sCJD cases . In agreement with this , miRNA-204-5p and miRNA-5701 levels were unchanged in RISC immunoprecipitates between control and sCJD cases ( Fig 2B ) . To rule out the possibility that the differences in RISC-miRNA enrichment were due to alterations in Ago-2 expression between controls and sCJD , Ago-2 levels were analyzed by qPCR and western blot . No alterations were found in Ago-2 protein and mRNA levels between controls and sCJD cases , either in the FC or in the CB ( Fig 2C ) . Similarly , no changes on the expression levels of GW182 , an Ago binding protein essential for miRNA-mediated gene silencing [35] were detected between control and sCJD cases ( S3A and S3B Fig ) . Next , we investigated potential alterations in the miRNA-mRNA silencing complexes that could explain the alterations in sCJD miRNA signatures previously detected . As a first step , we performed gel filtration chromatographic assays of control and sCJD brain homogenates . Ten fractions containing different proteomic patterns according to their molecular weight were obtained . PrP levels were homogeneously distributed along the chromatographic fractions as described before [36 , 37] ( S4A and S4B Fig ) . Western blot analysis revealed the presence of Ago-2 in higher molecular weight fractions in sCJD compared to control samples , suggesting that Ago-2 in sCJD is interacting with a different subset of partners ( Fig 3A ) . This is in agreement with a different subcellular localization of Ago in sCJD brain tissue as revealed by immunohistochemistry analysis . Indeed , using two different antibodies , we detected an increased nuclear localization of Ago in the FC of sCJD MM1 and VV2 cases , in contrast to controls , where staining was mainly detected in the cytoplasmic compartment ( Fig 3B and S5A and S5B Fig ) . While Ago-2 expression was mainly detected in neurons , both in controls and sCJD cases , double immunofluorescence analysis revealed the presence of Ago-2 positive microglial cells ( S5 Fig ) . In contrast , no differences on subcellular localization were detected for GW182 between control and sCJD cases ( S3 Fig ) . PrP and Ago-2 are interacting proteins in physiological conditions [26] , but nothing is known about the potential role of PrPSc in the RISC complex . Since the endosomal compartment , in which RISC assembly and turnover occurs , has been proposed as a site of prion conversion [38] we aimed to investigate the presence of PrPSc in Ago-2 complexes , which could be one of the contributors to their altered chromatographic Ago-2 patterns in sCJD . To this end , Ago-2 immunoprecipitates from the FC of controls and sCJD MM1 were subjected to RT-QuIC analysis . Positive signal was detected in immunoprecipitates from sCJD samples , but not from controls ( Fig 3C ) , indicating the presence of pathogenic PrP in Ago-2 complexes , in agreement with the presence of RT-QuIC signal and protein oligomers in Ago-2 containing chromatographic fractions ( S4C and S4D Fig ) . The alteration of RISC components in sCJD and the well-known presence of reticulum stress in models of prion disease [39–41] raised the possibility that stress granules ( SG ) , which are normally transient structures , form stable complexes in sCJD . Immunohistochemical and immunoblot analysis of the SG markers eukaryotic initiation factor 3 ( eIF3 ) and T-cell-restricted intracellular antigen-1 ( Tia-1 ) revealed that , in sCJD , neither their subcellular localization nor their expression levels were altered ( Fig 3D and 3E ) . In agreement with this , we did not detect hyper-phosphorylation of the SG activator eIF2α in sCJD ( Fig 3D ) . Similarly , levels of the SG and p-bodies marker DEAD-Box Helicase 6 ( p54/rck ) showed no alterations between controls and sCJD . However , increased expression levels of the specific p-bodies marker decapping mRNA 1a ( dcp1a ) was detected in sCJD MM1 and VV2 ( Fig 3E ) . Altogether , our findings suggest the presence of alterations in the p-bodies-dependent mRNA decay mechanisms without the activation of stress granule responses . Disruption of the miRNA biogenesis pathway components might cause alteration of miRNA homeostasis and neurodegeneration [42 , 43] . miRNA alterations in sCJD prompted us to consider possible alterations in the miRNA biogenesis pathway . The expression levels of three key components of miRNA biogenesis , the ribonucleases Dicer and Drosha and the microprocessor complex DGCR8 , a cofactor of Drosha , were studied in sCJD brain samples . mRNA expression analysis revealed decreased DGCR8 levels in the CB of sCJD cases ( Fig 4A ) . At the protein level , Drosha levels were significantly lower in the FC of sCJD MM1 and in the CB of sCJD VV2 , resembling the regional and subtype pathological involvement of the disease . Decreased Dicer levels were detected in the FC of sCJD MM1 , while reduced levels of DGCR8 were found in the CB of sCJD VV2 ( Fig 4B ) . The absence of direct regional and/or subtype-specific alterations among the main components of the miRNA biogenesis pathway suggests the presence of a complex impairment of the miRNA biogenesis machinery in sCJD . Additionally , we investigated the expression levels of Exportin 5 , a RanGTP-dependent dsRNA-binding protein mediating pre-miRNAs nuclear export [44 , 45] , which expression is deregulated in AD , but not in PD or Down’s syndrome dementia [46] . Exportin 5 levels in sCJD were altered neither at mRNA nor at protein levels compared to controls ( S6 Fig ) . A prominent hallmark in sCJD pathogenesis is the concomitant increase of neuronal loss and gliosis [2 , 47] . Since cell-type specific miRNA signatures have been described in neural populations [34 , 48 , 49] , we aimed to investigate the neural-type miRNA expression profiling in sCJD . First , sCJD-regulated miRNAs were compared to those reported to be enriched in neurons , microglia and astrocytes [34] . The expression of neuron-enriched miRNAs was not significantly altered in sCJD ( Fig 5A ) , indicating that the sCJD-related miRNA signature is not a mere consequence of neuronal death . On the other hand , most microglia and astrocyte-enriched miRNAs presented deregulated levels in sCJD , most likely as a result of glial proliferation and activation . To gain insight into subcellular and neural-type localization of sCJD-associated miRNAs in human brain tissue , in situ hybridizations were performed for the following miRNAs: miRNA-124-3p , miRNA-26-5p and miRNA-146a-5p ( Fig 5B and 5C ) . The three miRNAs were localized in the cytoplasm of neurons . Additionally , miRNA-146a-5p labeling was also detected in capillary walls and in some small cells compatible with glial morphology . Decreased miRNA-124-3p staining detectable in sCJD was associated with a reduced number of neurons . However , we also detected less signal intensity in surviving sCJD neurons , both in the FC and in the CB ( Purkinje and granular cells ) regions ( Fig 5B and 5C ) , in agreement with the idea that lower expression of neuronal-related miRNAs in sCJD is not exclusively associated with neuronal loss . Several neurodegenerative disorders share pathological hallmarks such as accumulation of protein aggregates and self-propagation , and common pathways seem to contribute to the neurodegenerative mechanisms in different diseases [50–52] . Thus , we speculated that an overlap between miRNA sCJD profiling and other dementia-related conditions could exist . In order to select the most appropriate miRNAs we compared the miRNA signatures in the FC of sCJD obtained from the small RNA-seq analysis from this study with the one reported in the pre-frontal cortex ( PFC ) of AD cases by Lau et al . [20] . Eight ( 22 . 8% max . coincidence ) and seven miRNAs ( 14% max . coincidence ) were commonly increased and decreased respectively in both datasets ( Fig 6A ) . Among these , miRNA-195-5p , 877-5p and 323a-5p were previously validated in sCJD from our small RNA-Seq dataset and miRNA-195-5p and 877-5p were validated by qPCR ( Fig 2A ) . To perform a cross-validation study with the corresponding sCJD brain regions and methodologies we extracted RNA from the FC of AD and DLB cases and age-matched controls . Six miRNAs were selected: i ) miRNA-195-5p , 877-5p and 323a-5p , commonly regulated in the FC of sCJD and in the PFC of AD , ii ) miRNA-146a-5p and miRNA-342-3p , reported to be altered in AD and prion disease models [22 , 23 , 53 , 54] and iii ) miRNA-5701 . The latter was used as a negative control due to its exclusive enrichment in the CB of sCJD . In AD samples , we detected coincident gene expression regulations with sCJD for miRNA-195-5p ( increased ) and for miRNA-877-5p and 323a-5p ( decreased ) ( Fig 6B ) . Rapid progressive forms of AD ( rpAD ) mimicking the disease progression and cognitive decline of sCJD [55 , 56] were included in our study . No significant differences in the expression levels of the six analyzed miRNAs were detected between AD and rpAD ( Fig 6B ) . In DLB , we detected coincident gene expression regulations with sCJD for miRNA-877-5p and miRNA-323a-5p ( both with decreased expression levels ) ( Fig 6C ) . Finally , we extended our analysis to cases of fatal familial insomnia ( FFI ) , a genetic prion disease presenting mild cortical and cerebellar affection [57 , 58] . A subset of four sCJD-regulated miRNAs in the FC and CB were tested . Only miRNA-195-5p showed common expression profiles in sCJD and FFI , with increased expression in both brain regions compared to age-matched control ( Fig 6D ) indicating a lack of complete specificity of miRNA patterns between neurodegenerative diseases from same etiology . To gain insights into the temporal-dependent sCJD miRNA profiles we took advantage of the sCJD MM1 mouse model tg340-PRNP129MM ( tg340 ) inoculated with sCJD MM1 brain homogenate . Mice were sacrificed at pre-clinical ( 120 dpi ) , early clinical ( 160 dpi ) and clinical ( 180 dpi and 210 dpi ) disease stages . Survival time was 199 ± 7 . 5 days ( Fig 7A ) . To confirm the disease-specific regional and subtype neuropathological and biochemical alterations in the tg340 mice , several prion hallmarks were assessed . Increased PrPSc deposition ( Fig 7B ) , synaptic damage ( Fig 7C and S7 Fig ) , neuroinflammation ( Fig 7D ) and spongiform degeneration ( Fig 7E ) were detected in the cortex compared to the CB of the tg340 infected mice . These data confirmed the region specific alterations of sCJD MM1 subtype in the tg340 , resembling the most prominent cortical pathology in human sCJD MM1 [5 , 59] . Next , the expression levels of the qPCR-validated miRNAs in sCJD were analyzed in a temporal manner ( Fig 7F ) . Ten and eleven miRNAs were validated in the cortex and in the CB at one of the clinical stages ( 180 and/or 210 dpi ) , respectively . Among these , seven were commonly changed in both regions , in agreement with data from sCJD MM1 . An interesting observation from our qPCR panel was the temporal specific alterations of the sCJD-related miRNAs , since only miRNA-16a-5p ( increased ) and miRNA-124-3p ( decreased ) showed altered levels at early clinical stages of the disease in the cortex ( Fig 7F and S8 Fig ) . This indicates that qPCR validated miRNAs are reflecting late pathogenic alterations , while miRNA-16a-5p and miRNA-124-3p may also participate in early pathogenic mechanisms . In agreement with this , functional enrichment analysis from small RNA-Seq indicates that the main common functions related to the sCJD-regulated miRNAs are cell death and survival ( S5 Table ) . These results suggested that diverse deregulated miRNA , rather than a specific miRNA deregulation , could contribute to the pathological mechanisms in sCJD . Therefore , we highly purified miRNAs from the FC of control , sCJD MM1 and VV2 brains and transfected them into neuroglioma ( H4 ) and differentiated neuroblastoma ( SH-SY5Y ) cells . Five miRNAs were analyzed with qPCR in both cell lines , resembling the disease subtype profiling in human sCJD brain ( S9A Fig ) . Overexpression of sCJD-MM1 purified miRNAs lead to an increased cell death in neuroblastoma , but not in neuroglia cells ( S9B Fig ) , indicating that the overexpression of the sCJD regulated miRNAs is able to induce subtype specific cell death in neuron-like cells . Finally , to gain insight into the potential upstream regulators of differential miRNA expression in sCJD a motif enrichment analysis was performed for data generated from FC of sCJD MM1 sequencing . Among the known sCJD related pathways , our analysis revealed a significant enrichment of a STAT3-binding motif for miRNAs with increased expression in sCJD ( S6 Table ) . This suggests that the expression of sCJD-specific miRNAs is under STAT3 regulation , which has been described as activated not only in experimental models of prion diseases [60 , 61] , but also in sCJD post-mortem tissue [5] . CSF miRNAs have been suggested as a source of biomarkers in neurodegenerative disease mirroring alterations in the brain tissue [62 , 63] . Thus , we aimed to investigate whether the detected miRNA alterations in the brain tissue of sCJD patients could be reflected in the CSF . CSF RNA was extracted from twelve control and twelve sCJD cases and was subjected to qPCR analysis for the following miRNAs: 154-5p , 204-5p , 378a-3p , 331-3p , 26a-5p , 195-5p , 124-3p , 7i-3p , 143-3p , 449a and 5701 . For normalization we used the non-coding small nuclear RNA U6 , which showed stable levels between control and sCJD cases ( Fig 8A ) . Detectable signal ( 35<Cts ) was obtained for miRNAs 378a-5p , 26a-5p and 204-5p , with miRNA-204-5p showing significantly decreased levels in sCJD compared to controls ( Fig 8B ) .
In the present study , we report the first systematic analysis of miRNA populations in two brain areas and two disease subtypes of sCJD cases , utilizing small RNA-seq analysis . We detected marked alterations in miRNA patterns with the presence of regional and sCJD-subtype specific signatures , with highest deregulations in the FC of MM1 and in the CB of VV2 cases , two brain regions and subtypes showing high pathological affection in sCJD [1] . In this regard , the low overlap between sCJD subtypes altered miRNAs in CB and FC could be explained by the singular pathology of sCJD VV2 in CB , where characteristic PrPSc aggregates ( synaptic and plaque-like ) and degree of spongiform degeneration , neuronal loss and neuroinflammation profiling are detected [5–7] , following classical well-known sCJD regional and subtype-dependent molecular neuropathology [2 , 47 , 64] . Small RNA-seq provides a blind and unbiased approach to the study of the small RNA transcriptome . However , a limitation of this technique is the presence of potentially biased fold changes when number of counts is low . Therefore , confirmatory analysis by qPCR is indispensable to consistently validate the regulation of selected targets . The enrichment of regulated miRNAs in Ago-containing complexes , as well as a severe , global reduction of miRNA expression levels in sCJD compared to controls described herein , supports the idea that alterations in expression levels are translated into the functional miRNA silencing machinery . Small RNA-seq also revealed length and sequence heterogeneity for the vast majority of miRNAs . However , the fact that the proportion of different miRNA variants detected by isomiR profiling was similar in all cases indicates that the molecular mechanisms involved in isomiR generation are not altered in sCJD , similarly to the situation previously reported for HD [15] . Data on miRNA alterations in sCJD are limited to two targeted studies , including very small cohorts of cases . Upregulation of miRNA-146a-5p in the neocortex of sCJD cases ( n = 3 sCJD , n = 3 controls ) [65] and upregulation of miRNA-342-3p in sCJD brain tissue ( n = 2 sCJD , n = 1 control ) [23] are in agreement with our observations . In contrast , several studies have been devoted to analysing the miRNAome in prion animal models . A lack of major correlation between regulated miRNAs in sCJD and scrapie-infected mice was detected in high-throughput studies . However , miRNAs: 146a-5p , 342-3p , 142-3p , 26a-5p , 124a-3p ( RNA-seq altered and qPCR validated in sCJD ) and 338-5p , 18a-3p , 455-5p , 182-5p ( RNA-seq altered in sCJD ) are commonly altered in at least one of the studies where scrapie miRNA profiles have been investigated [21–23 , 25 , 33 , 66] . Additionally , two miRNAs upregulated in the basis pontis of bovine spongiform encephalopathy-infected macaques , miRNA-342-3p and miRNA-494-3p [23] , were also enriched in sCJD , but exclusively in the FC region . Although low co-occurrence on miRNA profiles may be due to different methodologies , the absence of detailed regional studies in mouse models and the specific prion-related pathology in humans may explain this divergence . Altogether , these data highlight the importance of detailed regional and disease-subtype studies in prion diseases . Yet , intra-species comparisons are now achievable through the study of humanized PRNP mouse models , which not only fully recapitulate pathological hallmarks of human disease [5 , 59] but also , as reported in the present study , resemble human miRNA profiling . In this regard , alterations in miRNA signatures in tg340 at clinical stages are not detectable at pre-clinical stages . This finding , along with the presence of enriched miRNA-target genes related to cell death and survival , indicates that sCJD-regulated miRNAs may play a role in the molecular mechanisms related to the neurodegenerative process and that a different population of miRNAs , would be responsible for the primary causative events of the disease . Relative decreased mature miRNA levels in sCJD are consistent with decreased expression levels of Dicer , Drosha and DGCR8 . The miRNA biogenesis pathway is highly conserved and its disruption is a well-reported cause of neurodegeneration . Loss of Dicer levels provokes neuronal dysfunction in Purkinje cells [67] , dopaminergic neurons [68] and motor neurons [69] , and increased excitability of CA1 pyramidal neurons [70] while compromising axonal integrity in Schwan cells [71] . In addition , Dicer protein levels have been found to be decreased in temporal lobe epilepsy patients with hippocampal cell loss , with about half of the miRNAs in the tissue displaying reduced levels [72] . Finally , similar to our observations , impaired miRNA biogenesis at Dicer level associated with downregulation of miRNA levels and reorganization of Dicer and Ago-2 complexes has recently been described in amyotrophic lateral sclerosis [73] suggesting that miRNA malfunction could be a contributor to pathogenesis associated with protein-misfolding associated diseases . Interestingly , lack of expression changes in Exportin 5 suggests that changes detected in the miRNA expression profiles in sCJD are most likely not due to alterations in the nuclear export of pre-miRNA . Whether alterations of miRNA biogenesis and homeostasis in sCJD are primary factors in the neurodegenerative phenotype of the disease due to dysfunctional miRNA maturation , or are a consequence of the pathology , deserves further studies . Besides highlighting alterations in miRNA biogenesis and network , our results also reveal a remodelling of the miRNA-mRNA silencing complexes in sCJD . This is sustained by partial re-distribution of Ago-2 to higher molecular weight chromatographic fractions , increased Ago nuclear reactivity , presence of Ago-2 positive microglial cells and increased expression of p-body marker dcp1 in sCJD brain tissue . Ago-2 and RISC components have recently been found in the nucleus of humans and Drosophila and associated in multi-protein complexes with functional silencing activity over nuclear targets [74 , 75] , as well as with additional functions in pre-mRNA splicing and transcriptional repression [75] . The role of Ago proteins , especially Ago-2 , in prion pathology deserves attention not only due to its differential localization in sCJD , potentially altering its physiological functions , but also because PrPC has been described as an Ago-2 interacting partner , promoting the accumulation of miRNA-RISC effector complexes [26] . PrPC is internalised into the endocytic recycling pathway and most of the molecules are recycled intact to the cell surface [76] . Since late endosomes and/or multivesicular bodies are the main site for intracellular conversion of PrPC to PrPSc , and RISC formation and/or turnover depends on the endosomal pathway [77 , 78]; the detection of PrPSc in Ago-2 complexes provides a link between RNA silencing and membrane trafficking in sCJD pathogenesis . Additionally , a translocation of Ago-2 from cytoplasm to nuclear fractions in sCJD would alter , and potentially impair , the silencing of cytoplasmic mRNA targets by the RISC complex . As additional modifiers of the miRNA-mRNA silencing complexes in sCJD we investigated the presence of SG , which appear in the cell under stress conditions such as oxidative and endoplasmic reticulum stress [79 , 80] , two well-known sCJD hallmarks [41 , 81] . Although both p-bodies and SG may support overlapping cellular functions and share components , they are not equivalent and they are spatially distinct . SG are thought to be responsible for mRNA storage as these sites lack the decapping enzyme [82] , and their formation is mediated through phosphorylation of eIF2α and aggregation of Tia-1 [83 , 84] . Neither increased eIF2α phosphorylation nor altered levels of SG markers were detected , suggesting that SG are not specific structures in sCJD . A surprising finding of our study was the absence of massive reduction of neuronal-enriched miRNAs levels , indicating high miRNA stability in brain after neuronal death . An exception to this was the decreased levels of miRNA-124-3p , also decreased in experimental models of prion diseases [21 , 22 , 66] . In situ hybridization supports the idea that miRNA-124-3p is not merely decreased as a consequence of neuronal death , since surviving neurons express less miRNA-124-3p compared to those from controls , pointing towards a specific role for this miRNA in sCJD pathology . miRNA-124-3p is the most abundant miRNA in the brain and promotes neuronal differentiation and maintenance of neuronal identity [85] . In AD , its expression is decreased in the anterior temporal cortex [86] , decreased in the dentate gyrus , and upregulated in the locus coeruleus [87] . In an ex vivo model of PD , miRNA-124-3p is decreased regulating apoptosis and impaired autophagy [88] . This plethora of evidence denotes a functional role of miRNA-124-3p in neurodegeneration . In fact , our study details the existence of common miRNA traits in the cortical region of AD , DLB and sCJD . There is virtually no information on the functions of the two commonly regulated miRNAs in the brain of the three dementias ( miRNA-877-5p and miRNA-323a-5p ) . On the contrary , miRNA-195-5p , elevated in AD and sCJD , downregulates Aβ production by targeting APP and BACE1 , and protects against chronic brain hypoperfusion-mediated dementia [89] . Its overexpression also led to reduced BACE1 and decreased Aβ levels in an independent study [90] . Although the precise role of miRNA-195-5p in prion diseases is unknown , we also detected increased levels in the FC of fatal familial insomnia ( FFI ) , a genetic prion disease with moderate cortical involvement [91] . Based on the conception that one miRNA can target multiple mRNAs and one mRNA can be targeted by multiple miRNAs , our results support multiple lines of evidence indicating that the result of the intricate alterations in miRNA networks and clusters , rather than representing a change in the expression of a single miRNA , are responsible for pathological phenotypes [92–94] . Thus , the precise miRNA homeostasis in sCJD brain would underlie the spectrum of molecular and phenotypic cues , in agreement with the acquisition of a disease-related phenotype by a neuronal-like cell line upon transfection with the sCJD-associated miRNA transcriptome . CSF miRNAs may reflect alterations in brain pathology of neurodegenerative diseases . Indeed , miRNA profiling in AD and PD correlates with disease status and pathological features [63 , 95] but less is known about the levels of brain-regulated miRNAs in the CSF . Our targeted study revealed that only miRNAs with reported high CSF expression levels were detectable [96] . Of these , only miRNA-204-5p displayed decreased levels in sCJD cases . Interestingly , this miRNA was not statistically regulated in sCJD brain . Our findings are in line with those reported in AD , where no obvious relationship between altered miRNAs in CSF and pathologically affected brain regions was found [17 , 95] . As the reason for this lack of correlation is unknown , it is tempting to speculate that different disease stages between CSF and brain sample collection ( time of diagnosis for CSF versus post-mortem for brain tissue ) may contribute to these differences . Additionally , as the origin of CSF miRNAs is not well understood , CSF miRNAs may originate not only from brain , but also from extracraneal tissues . In summary , our study presents , for the first time , comprehensive miRNA signatures in human prion diseases and unravels the complex network of regional and disease-subtype miRNA alterations in sCJD , as well as revealing the presence of a disturbed miRNA biogenesis pathway and miRNA-mRNA silencing machinery . It also highlights the existence of time-dependent miRNA profiles along disease duration and identifies commonly altered miRNAs between several dementias sharing a partial clinical overlap . Taken together , the present data shed light on the potential role of miRNAs as a contributing factor and/or transmitters of pathogenic molecular traits in sCJD .
List of Taqman probes assays , Exiqon primer sets and antibodies is given in S7 Table . Lipofectamine 2000 was from Thermo Fisher Scientific . Thioflavin and Propidium Iodide were from Sigma . WST-1 was from Roche . TruSeq Small RNA Sample Preparation Kit was from Illumina . CHROMA SPIN-200 spin columns were from Clontech and Protein G magnetic beads were from Invitrogen . Brain tissue processing has been described before [5–7 , 97] . Mean ages and gender for studied control and sCJD cases , for RNA-seq , qPCR and western blot analysis are detailed in S8 Table . Information on the mean ages and gender for the AD , DLB and FFI samples that were analysed with RT-qPCR in the present study are as below: For AD analysis: Control = 75 ( 3M/2F ) , AD = 76 ( 4M/4F ) and rapid progression AD ( rpAD ) = 77 ( 3M/3F ) . rpAD cases were AD cases with disease duration shorter than 2 years . For DLB analysis: Control = 71 ( 3M/2F ) , DLB = 75 ( 3M/2F ) . For FFI analysis: Control = 58 ( 2M/1F ) , FFI = 52 ( 2M/1F ) . Biochemical studies including sCJD and FFI cases were performed in biosafety rooms ( S3 level ) . mRNA and miRNA levels were associated neither to RNA integrity number ( RIN ) values nor to post-mortem time . Protein levels were not associated to post-mortem time . CSF samples were obtained from an unrelated series of patients with sCJD and from controls . sCJD patients diagnosed with probable or definite sCJD according to established criteria were considered for the sCJD group [98] . The control group was composed of patients suffering from neurological conditions ( S9 Table ) . The presence of neurodegenerative diseases in the control cohort was excluded in the follow-up clinical diagnostic , and CSF neurodegenerative biomarkers ( 14-3-3 , tau , p-tau and Aβ42 ) were negative at the time of diagnosis . PRNP codon 129 genotyping ( Met or Val ) was performed after genomic DNA isolation from blood samples according to standard methods [99] . Western blot PrPSc profile was classified as type 1 ( un-glycosylated PrPSc at 20 kDa ) or type 2 ( un-glycosylated PrPSc at 19 kDa ) , based on electrophoretic mobility after proteinase K ( PK ) digestion [4 , 64] . The purification of RNA from FC and CB of CJD and age-matched controls was performed using the mirVana isolation kit ( Ambion , US ) according to the manufacturer’s instructions . After purification , samples were treated with the RNase-free DNase set ( Ambion , US ) for 30 min to avoid carry over and subsequent amplification of genomic DNA . The concentration of each sample was determined using the NanoDrop 2000 spectrophotometer ( Thermo Scientific , US ) . RNA integrity number ( RIN ) was verified with the Agilent 2100 Bioanalyzer ( Agilent , US ) . The threshold for further sample selection was set to RIN value equal to or greater than 5 . 5 . Starting from 1 μg of total RNA , libraries were prepared following the TruSeq Small RNASample Preparation Guide protocol from Illumina ( Part # 15004197 Rev . E ) . Library quality was assessed on the Agilent Technologies 2100 Bioanalyzer . DNA was loaded into a lane of a single-read flow cell at a concentration of 3–3 . 5 pM for cluster generation using a single-read cluster generation kit ( Illumina ) . From 13 to 15 barcoded samples were sequenced per lane . The sequencing primer ( 5′-CGACAGGTTCAGAGTTCTACAGTCC GACGATC-3′ ) was annealed to the clusters and the flow cell was then mounted on a Hiseq 2000 instrument ( Illumina ) for sequencing , and 36–41 sequencing cycles were performed . A PhiX control lane loaded at a concentration of 2 pM was used to monitor run quality . Image processing and base calling was performed using Illumina sequencing analysis pipelines v0 . 3 . 0 or v1 . 3 . 2 . A total of 72 samples were analyzed by small RNA-seq: For FC 13 controls , 13 sCJD MM1 and 13 sCJD VV2 were analyzed for CB: 12 controls , 12 sCJD MM1 and 9 sCJD VV2 . Reads were trimmed to 36 nt and ligation adapters were removed using the adrec . jar program from the seqBuster suite ( http://github . com/lpantano/seqbuster ) [28] with the following options: java -jar adrec . jar 1 8 0 . 3 . Sequences were mapped to the hg19 genome with the command line: bowtie -f -v 1 –a–best–strata . For summing up miRNA read counts we mapped the reads against miRBase version 21 hairpins with the miraligner . jar tool with these options: java–jar miraligner . jar 1 3 3 . Out of a total of several million reads , we discarded any reads without a minimum of 10-nt linker subsequence directly adjoining the insert , showing two or less mismatches . Then sequences were mapped to human pre-miRNA and mature miRNA databases provided in the miRBase ( http://miRNA . sanger . ac . uk/sequences/ , Release 14 ) , as well as mRNA , ncRNA , repeats and genome databases available at ( http://hgdownload . cse . ucsc . edu/goldenPath/hg18/bogZips/ ) , using Mega BLAST . For motif discovery , deregulated miRNAs were searched for the genomic coordinates of their primary miRNAs . Upstream regions of 1kb size from each miRNA were extracted and exported to BED files and the script findMotifsGenome . pl in HOMER suite was used to find transcription factor binding motifs within promoter regions using genome assembly hg38 [100] . For differential expression analysis we used DESeq2 analysis and log2 transformation of the count data . Padj value was <0 . 15 and nominal p values were in all cases <0 . 05 . We used the count matrix generated by Seqbuster . Only miRNAs consistently expressed ( counts > 10 ) in at least 10 samples out of the 21–26 were included in each analysis ( controls versus MM1 or VV2 cases ) . IsomiRs were annotated and analyzed using the SeqBuster tool [28] . For miRNA annotation the following parameters were configured: one mismatch , 3 nts in the 3’ or 5’-trimming variants , 3 nts in the 3’-addition variants . These options permitted annotations of the following types of alignment: ( i ) perfect match , where the sequence is completely identical to the reference sequence; ( ii ) trimming at the 3’-end of the reference miRNA sequence , which is an miRNA variant several nucleotides shorter or longer that matches to the mature or precursor reference sequence , respectively; ( iii ) trimming at the 5’-end of the sequence , an analogous case as to ( ii ) but focused on the 5’-end of the miRNA; ( iv ) nucleotide additions at the 3’-end of the sequence and ( v ) nucleotide substitutions , showing nucleotide changes with respect to the reference sequence . The parameters for the alignment to the mRNA and genome databases allowed up to one mismatch and up to three nucleotide additions in the 3’-terminus . For deep characterization of IsomiRs we applied several filters . First , the sequences considered in the analysis presented a frequency above 3 . Second , 10 was chosen as the ‘Contribution Cut-Off’ parameter , meaning that every isomiR considered in the analysis contributes by more than 10% to the total number of variants annotated in the same miRNA locus . Third , we applied the Z-score option to exclude sequencing errors as the possible cause of the nucleotide changes observed in some variants . RNA samples extracted with the mirVANA isolation kit ( Ambion ) using the specific protocol for small RNA isolation were run on a 6% Urea–PAGE . The bands containing miRNAs ( 15–30 nt ) were excised from the gel and incubated for 1 hour with 250 mM NaCl and 1 mM EDTA . miRNAs were then precipitated with 2 . 5 volumes of 100% ethanol ( v/v ) over night at −80°C , washed twice with 70% ethanol and re-suspended in nuclease-free water . H4 and SH-SY5Y cells ( American Type Culture Collection ) were cultured at 37°C in a 95%/5% Air/CO2 water-saturated atmosphere in Dulbecco’s modified Eagle’s medium ( DMEM , Thermo Fisher Scientific ) containing 10% heat inactivated fetal bovine serum ( FBS , Thermo Fisher Scientific ) , 2 mM L-glutamine and 100U/ml Penicillin/streptomycin ( Gibco ) . SH-SY5Y cells were differentiated with DMEM containing 3% FBS and 10 μM all-trans retinoic acid ( RA , Sigma ) for 72 hours . Differentiation medium was replenished after 48 hours . Cells were transfected with 250 ng of highly purified miRNAs with Lipofectamine 2000 ( Invitrogen ) following the manufacturer’s instructions . Analysis of protein fractions according to their molecular weight was performed as described before [36] using CHROMA SPIN-200 ( Clontech , USA ) spin columns . Columns were pre-spun at 200xg for 3 min to remove storage buffer . Buffer exchange was made by the addition of 500 μl lysis buffer followed by centrifugation at 200 xg for 3 min . This step was then repeated . 75 μl of 1% brain homogenates were applied to the gel bed . After spinning at 120 xg for 2 min elution fractions were collected , and 40 μl of extraction buffer was added after each centrifugation step . The tg340 mouse line expressing about 4-fold level of the human PrP M129 on a mouse PrP null background was generated as described elsewhere [59] . Control and sCJD MM1 brain tissues ( 10% ( w/v ) homogenates ) were used as inocula . Individually identified 6–10 week-old mice were anesthetized and inoculated in the right parietal lobe using a 25-gauge disposable hypodermic needle . Additionally , MM1 inoculum dilutions were performed to study prolonged disease times; animals were sacrificed at 210 dpi ( 10–1 dilution ) . Mice were observed daily and their neurological status was assessed weekly . The animals were euthanized at pre-symptomatic ( pre-clinical: 120 dpi ) and symptomatic ( early clinical: 160 dpi and clinical: 180 dpi ) stages and the brain was removed . A part of the brain was fixed by immersion in 10% buffered formalin , to quantify spongiform degeneration and perform immunohistological analysis . The other part was frozen at −80°C , for extraction of protein and RNA . Paraffin-embedded tissue blots from tg340 mice samples was carried out as described previously [5] . For each tissue sample , serial sections , 4 mm thick for PET blot , were collected on membranes . Serial sections were stained with hematoxylin and eosin . SHa31 antibody was used for PrP immunodetection . In order to confirm the direction of the miRNA alterations detected by RNA-seq in sCJD cases ( increased or decreased levels compared to controls ) by an independent methodology qPCR analysis of selected miRNAs was performed . Quantitative real time PCR for miRNAs was performed using the miRCURY LNAmiRNA PCR System ( Exiqon ) following Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines . RNA was extracted with the mirVana isolation kit ( Ambion ) following the manufacturer’s instructions . PCR amplification and detection were performed with the Roche LightCycler 480 detector , using 2x SYBR GREEN Master Mix . The reaction profile was: Polymerase Activation/Denaturation ( 95°C for 10 min ) followed by 40 amplification cycles ( 95°C-10 sec , 60°C-20 sec ) . miRNA levels were calculated using the LightCycler 480 software . Samples were normalized by the relative expression of the housekeeping small nuclear RNAs U6 and U5 . Housekeeping genes showed no variability between analyzed groups . CT values obtained from the miRNA qPCR analysis ranged from 18 to 31 . Quantitative real time PCR for mRNAs was performed using Taqman probes ( Life Technologies ) on total RNA extracted with mirVana’s isolation kit ( Ambion ) following the manufacturer’s instructions . PCR assays were conducted in duplicate using cDNA samples obtained from the retrotranscription reaction and diluted 1:15 in 384-well optical plates . PCR amplification and detection were performed with the Roche LightCycler 480 detector , using Taqman Universal PCR Master Mix , following the manufacturer’s instructions . The reaction profile was as follows: denaturation–activation cycle ( 95°C for 10 min ) followed by 40 cycles of denaturation–annealing–extension ( 95°C , 10 min; 72°C , 1 min; 98°C , continuous ) . mRNA levels were calculated using the LightCycler 480 software . Samples were normalized based on the relative expression of a housekeeping gene ( glyceraldehyde-3-phosphate dehydrogenase [GAPDH] ) . The housekeeping gene showed no variability between analyzed groups . Human tissues were lysed in lysis buffer: 100 mM Tris pH 7 , 100 mM NaCl , 10 mM EDTA , 0 . 5% NP-40 and 0 . 5% sodium deoxycolate plus protease and phosphatase inhibitors . After centrifugation at 14 , 000g for 20 min at 4°C , supernatants were quantified for protein concentration ( BCA , Pierce ) , mixed with SDS-PAGE sample buffer , boiled , and subjected to 8–15% SDS-PAGE . Gels were transferred onto nitrocellulose membranes and processed for specific immunodetection by chemiluminescence ( ECL Amersham , US ) using the indicated antibodies . Densitometries were carried out with the ImageJ software and values were normalized using β-actin levels . Protein G magnetic beads were pre-equilibrated in lysis buffer ( 150 mM NaCl , 50 mM Tris , 0 . 5% NP40 , protease and phosphatase inhibitors ) and mixed with 1 mg of human FC brain homogenate from either control or sCJD MM1 cases and with 4 μg of Ago antibodies 11A9 or H-300 . As a control , 4 μg of an unspecific IgG antibody was used . Complexes were incubated overnight at 4°C with gentle end-to-end shaking . To extract the immunoprecipitated RNA , beads were washed three times in lysis buffer and resuspended in phenol-chloroform . The RNA in the aqueous phase was precipitated for 1 h at -80°C after addition of 2 . 5-fold volume ethanol and 0 . 1-fold volume NaAc ( 3mol/l ) ; precipitated RNA was pelleted by centrifugation for 25 min at 4°C at 20 , 000xg . After washing in cold 70% ethanol , centrifugation and air drying , RNA was re-suspended in 10 μl of RNase-free water . The miRCURY LNA Universal RT miRNA PCR kit ( Exiqon ) was used for miRNA reverse transcription . For this , 6 . 5 μl of re-suspended RNA was applied in a total RT reaction volume of 10 μl ( 2μl 5x reaction buffer , 1μl enzyme mix , 0 . 5μl nuclease free water ) . cDNA was synthesized as described before for miRNA RT . A 1:80 cDNA dilution was used for miRNA quantification via real-time PCR amplification and miRNA LNA primer sets . For miRNA recognition locked nucleic acid ( LNA ) modified probes combined with signal amplification technology using enzyme-labeled immunoassay were obtained from Exiqon ( Vedbaek , Denmark ) . The following double digoxigenin ( DIG ) -labelled sequences of the LNA probes were used: hsa-miRNA-124: 5’-DIG/ggcattcaccgcgtgcctta/DIG-3’ , hsa-miRNA-146a 5’-DIG/aacccatggaattcagttctca/DIG-3’ , has-miRNA-26a 5’-DIG/agcctatcctggattacttgaa/DIG-3’ . The sequence of the U6 snRNA positive control probe was: 5’-DIG/cacgaatttgcgtgtcatcctt/-3’ . 6 μM-thick brain tissue sections were deparaffinised , deproteinized with Proteinase K ( 15μg/ml ) at 37°C for 10 min , washed in PBS and dehydrated in increasing concentrations of ethanol . Probe hybridization was performed over night at 55°C with 100 nM ( hsa-miRNA-146a , hsa-miRNA-26a ) , 40 nM ( miRNA-124 ) , or 1 nM ( U6 snRNA ) of LNA probe diluted in hybridization mix . After stringent washing in salt sodium citrate ( SSC ) buffer and blocking with 2% sheep serum/1% bovine serum albumin , probe-target complex was visualized immunologically with anti-DIG antibody ( Roche , 1:800 ) conjugated to alkaline phosphatase acting on the chromogen NBT/BCIP . In some cases , slides were counterstained with nuclear fast red ( Vector laboraties ) . For quantification of miRNA-124-3p , 3 controls and 2 sCJD MM1 cases were used . For immunofluorescence analysis in brain tissues , de-waxed sections , 4 microns thick , were stained with a saturated solution of Sudan black B ( Merck , DE ) for 15 min , to block the auto-fluorescence of lipofuscin granules present in cell bodies , and then rinsed in 70% ethanol and washed in distilled water . The sections were boiled in citrate buffer to enhance antigenicity and blocked for 30 min at room temperature with 10% fetal bovine serum diluted in PBS . Then , the sections were incubated at 4°C overnight with primary antibodies . After washing , the sections were incubated with Alexa488 or Alexa546 ( 1:400 , Molecular Probes , US ) fluorescence secondary antibodies against the corresponding host species . The sections were mounted in Immuno-Fluore mounting medium ( ICN Biomedicals , US ) , sealed , and dried overnight . Sections were examined with a Leica TCS-SL confocal microscope . RT-QuIC was performed as previously described [101] with minor modifications . Briefly , recombinant PrP ( 10 μg ) was seeded with 15 μl of Ago-2-Immunoprecipitates in 85 μl of reaction buffer . Reaction was set in a final volume of 100 μl and placed in a 96-well black optical bottom plate ( Fisher Scientific ) . Each sample was run in duplicate . Prepared plates were sealed and incubated in a FLUO Star OPTIMA plate reader ( BMG Labtech Ortenberg , GE ) at 42°C for 80 h , with intermittent shaking cycles consisting of 1 min double orbital shaking at the highest speed ( 600 rpm ) followed by a 1 min break . Lumbar punctures were performed for diagnostic purposes at the time point of the first diagnostic work-up and samples were stored at −80°C until analysis . 14-3-3 protein was analyzed as described previously [102] and total tau was quantitatively measured using the enzyme-linked immunosorbent assay kits INNOTEST-hTAU-Ag from Fujirebio according to the manufacturer’s instructions . RT-QuIC analysis was performed as described before [101] . RNA purifications from CSF were performed using miRCURY RNA Isolation Kit–Biofluids ( Exiqon ) following manufacturer-provided protocol with minor modifications . 200 μl CSF input volume was used and treated with 2 μg/μl Proteinase K in order to optimize the RNA yield . As an inert RNA carrier 2 μg Glycogen per CSF sample was added . The miRCURY LNA Universal RT miRNA PCR kit ( Exiqon ) was used for miRNA reverse transcription . For this , 4 μl of re-suspended RNA was applied as input in a total RT reaction volume of 10 μl ( 2 μl 5x reaction buffer , 1 μl enzyme mix , 3 μl nuclease free water ) and cDNA was synthesized as described before for miRNA RT . A 1:80 cDNA dilution was used for miRNA quantification via real-time PCR amplification and miRNA LNA primer sets . The small RNA U6 revealed to be stable in CSF samples from sCJD and control samples and was used as reference gene for miRNA quantification . For comparisons of the two groups , the Mann-Whitney test was used . In multiple comparisons , the Kruskal-Wallis test was used . Dunn's multiple comparison test was used for post hoc analysis . Statistical analyses and calculations were carried out using GraphPad Prism 5 software . Statistical significance was set at *p<0 . 05 . Brain tissue samples were obtained from the Institute of Neuropathology Brain Bank ( HUB-ICO-IDIBELL Biobank ) and the Biobank of Hospital Clinic-IDIBAPS , following pertinent guidelines of the Spanish legislation and the local ethics committee . The present study was conducted according to the revised Declaration of Helsinki and Good Clinical Practice guidelines and was approved by the local ethics committees ( University of Göttingen -No . 9/6/08 , 19/11/09 and 18/8/15 ) . Informed written consent was given by all study participants or their legal representative . All participants were adults , and samples were anonymized . For animal investigation , principles of laboratory animal care ( NIH publication No . 86–23 , revised 1985 ) were followed . All animal experiments were performed in compliance with the French , national guidelines , in accordance with the European Community Council Directive 86/609/EEC . The protocols comply with the Animal Research: Reporting In Vivo Experiments ( ARRIVE ) guidelines . The experimental protocol was approved by the INRA Toulouse/ENVT ethics committee ( Permit number: 310955547 ) . | miRNAs are small non-coding RNAs that regulate gene expression through complementary binding to their mRNA targets . Specific miRNA signatures have been proposed for several neurodegenerative diseases supporting the idea that miRNA deregulation is a common disease hallmark . Here we present the comprehensive miRNA signature in sporadic Creutzfeldt-Jakob disease ( sCJD ) . Our study unravels the complex network of regional and disease-subtype miRNA alterations , and the presence of a disturbed miRNA biogenesis pathway and miRNA-mRNA silencing machinery . We also highlight the existence of time-dependent miRNA profiles and identify commonly regulated miRNAs between several dementias with cortical pathology sharing a partial clinical overlap and pathological involvement with sCJD . The present data shed light on the potential role of miRNAs as a contributing factor of pathogenic molecular traits associated with sCJD . | [
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"infe... | 2018 | Regional and subtype-dependent miRNA signatures in sporadic Creutzfeldt-Jakob disease are accompanied by alterations in miRNA silencing machinery and biogenesis |
Cellular processes are “noisy” . In each cell , concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations . While noise varies with time , it is often measured at steady state , for example by flow cytometry . When interrogating aspects of a cellular network by such steady-state measurements of network components , a key need is to develop efficient methods to simulate and compute these distributions . We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first , an approach to modeling intrinsic noise via solution of the chemical master equation , and second , a convolution technique to account for contributions of extrinsic noise . We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network . Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits , as well as a signaling network underlying the mammalian cell cycle entry .
Cellular processes are “noisy” . In each cell , concentrations of molecules ( e . g . , mRNAs , proteins ) are subject to random fluctuations ( noise ) due to the small numbers of these molecules and to environmental perturbations [1] , [2] . Cellular noise impacts on information transmission involved in cell signaling dynamics [3]–[5] , while cells may take advantage of such variability in adapting to changing environments or for cell-fate decisions [6]–[15] . Improved understanding of how noise influences and is modulated by cellular processes will greatly benefit from efficient , streamlined computational tools to quantify noise , and to use noise to probe properties of the underlying regulatory networks [16]–[19] . To date , stochastic modeling of gene expression has typically relied on forward simulations of time courses , for example via Gillespie algorithms [20] , [21] or numerical solution of stochastic differential equations ( SDEs ) [4] , [22] , [23] . Flow cytometry and fluorescence microscopy currently allow for access to increasingly rich data on approximately steady-state distributions of gene expression . These distributions arise biologically when a set of reactions proceeds much faster than environmental changes , and observing such data provides a step towards understanding some aspects of the underlying cellular network . To assess how such data can be informative , we need to compute or simulate aspects of the steady-state distribution . Forward simulation can be time-consuming , and new approaches are needed . Approaches such as umbrella sampling [24] and coupling-from-the-past [25] have been introduced , but the sampling biases of the former and substantial computational expenses of the latter leave areas for improvement . Mechanistic modeling of noise is complicated by its diverse sources , which have been classified as intrinsic or extrinsic [26] , [27] . Intrinsic noise results from the stochasticity of chemical kinetics when the numbers of interacting molecules are sufficiently small; it can be described by the chemical master equation ( CME ) . In essence , intrinsic noise represents deviation of known reactions with known rates from their results as predicted by classical chemical kinetics [28] . In contrast , extrinsic noise results from other reactions and from fluctuations in rate constants , and it is often the dominant source of variability in a system [26] , [29] . Extrinsic noise may result from any process not represented in the network model itself . A direct route to model intrinsic noise is to calculate steady-state solutions to the CME , often by using an approximation . An analytical solution based on a continuous master equation describing protein production in bursts has been formulated by Friedman and colleagues [30] , while Fourier and colleagues [31] present analytical solutions for several other networks . Walczak and colleagues [32] investigate another solution approach based on using an eigenbasis from a simpler system to solve the massive linear equation resulting from setting the CME to steady-state . The approximation here lies in the difference between each system's eigenbases , and its suitability for a specific system needs to be determined on an ad hoc basis . More general methods have been investigated as well . The Hartree approximation [33] assumes probabilistic independence of molecule numbers for each species; this approximation greatly reduces the dimensionality of the system , but tends to break down seriously in multimodal systems , unless the joint distribution has a mode at each combination of the one-dimensional distributions' modes ( this is frequently not the case ) . Cao and colleagues [34] , [35] investigate accurate though computationally costly numerical solution methods for the CME , such as efficient exhaustive enumeration of microstates . Munksy and Khammash [36] , focusing on the application of methods for solving the master equation , investigate the necessary data for obtaining reaction parameters in a system dominated by this type of noise . A related approach is to calculate an “energy landscape” for a network . Ao [37] assumes an SDE model and derives a potential that yields the probability distribution as its Boltzmann distribution . Wang and colleagues [38] also use an SDE model and then construct a potential landscape based on a Hodge decomposition of the flux vector in the system . Both approaches are useful for a wide range of SDEs , including the chemical Langevin equation . However , they are thus subject to the inaccuracies of that equation—most importantly , the inaccuracy at low molecule numbers—and may also lack computational tractability for complex systems . Qian and Beard [39] , in constructing potential landscapes for non-equilibrium systems based on chemical potentials , provide an approximation for the probability distribution that follows the Hartree approximation . In contrast to intrinsic noise , extrinsic noise lacks a unique modeling framework and is often determined by empirical inference of distributions from data . One approach that accounts for some of these effects is to perturb the rate constants while modeling intrinsic fluctuations using a Gillespie algorithm-type simulation strategy [40] . This approach may also produce extrinsic fluctuations that could be produced by other sources , such as other reactions and measurement noise . However , direct steady-state calculations can instead pool together results from many extrinsically perturbed distributions , thus preventing the need to perform calculations for many parameter sets and many time points . Analytical inclusion of extrinsic noise is also possible , and indeed the use of exponentially distributed burst sizes in modeling protein production in [30] amounts to this . In addition , extrinsic noise can be accounted for by addition of random noise to molecule numbers in each time step of a timecourse simulation based on a stochastic differential equation [4] , [23] . Recent single-molecule fluorescent measurements have allowed experimental determination of molecule number distributions in Escherichia coli , thus measuring both intrinsic and extrinsic noise [41] . Despite these progresses , a major challenge lies in the lack of well-defined computational framework for thorough , systematic evaluation of these methods with experimental data . As a step to address these issues , we have developed an integrated framework for modeling steady-state distributions in the context of both intrinsic and extrinsic noise sources . As an illustration , we have applied these methods to the analysis of two well-characterized bistable switches and evaluated the methods against experimental data . Furthermore , we also demonstrated the applicability of these methods to a more complex signaling network , the Myc/Rb/E2F network , which underlies the control of mammalian cell cycle entry .
In general , the observed distribution of molecular counts ( Pobserved ) can be treated as the combination of an intrinsic component ( Pintrinsic ) and an extrinsic component ( Pextrinsic ) ( Figure 1A ) . The intrinsic component is uniquely determined by the reaction mechanisms and the corresponding rate constants . Our approach ( Figure 1B ) takes a list of species , reactions with known rate information , and known extrinsic noise parameters , and at the first step calculates the steady-state distribution based on the chemical master equation . This first step accounts for intrinsic noise implicitly and can be done analytically for systems with a sufficiently small number of states . When the CME is too complicated to solve analytically , it can be solved numerically to generate the steady-state distributions , up to the size and dimension limits imposed by computational capabilities . The CME is of the form MP = 0 , where M is a matrix and P is the steady-state probability vector . With many reacting species , as the matrix size may imply prohibitive computation cost , we can rescale the CME or sample approximately from the solution . For scaling , the dimension of the space of distributions is reduced by approximating the CME in terms of directional derivatives and then re-sampling . The scaled CME is then solved by linear algebra . Even with scaling , however , the matrix computations needed to solve the CME become prohibitive when more than a few species are present or when the distribution is complex . We address these limitations by developing a modified Gibbs sampling ( MGS ) method to generate the steady-state solution to the CME . Gibbs sampling provides a set of samples from a distribution by sampling one dimension of the distribution ( in this case , the molecule number for a given species ) at a time , using the conditional distribution for that species given the current molecule numbers of all the other species . In our MGS method , detailed balance is assumed for different sets of reactions at each iteration , generating approximate conditional distributions from which exact sampling is possible . The MGS method scales much more favorably than the direct CME solution with increased numbers of species . Its scaling property is similar to that of ordinary Gibbs sampling . Importantly , it overcomes some caveats associated with alternative approximations , especially in multimodal systems . In particular , it avoids the restrictions on the distribution space caused by the Hartree approximation . Also , it overcomes the difficulties in sampling multiple local minima that occur with the standard Gibbs sampler . The second step of our approach is to model extrinsic noise by convolution . Typically , representing extrinsic noise as perturbations to rate parameters [40] can present significant difficulties in their application to experimental data . Sampling from a parameter sample space would lead to high computational cost because of the need to redo calculations for many different parameter sets . Methods based on adding noise at each time step similarly bring the cost of calculation at many unnecessary points in time . To this end , we have developed a convolution approach to represent extrinsic noise by averaging many effects , which allows more direct application to experimental data . It is well suited to combining analysis of the modes by a deterministic model , allowing rapid and accurate estimation of reaction parameters , with estimation of further noise parameters based on the observed distribution . Combining MGS , CME rescaling , and convolution model for extrinsic noise defines an integrated framework for efficient computation of the steady-state distribution of gene expression for a given set of parameters . To illustrate their use , we consider the application of the overall framework to several examples; aspects of these have been mentioned in the previous section .
Two key challenges in stochastic modeling of cellular networks are computational efficiency in describing intrinsic noise and adequate description of extrinsic noise . This study provides a modular approach that makes such computations more tractable . To compute intrinsic variability , a range of approaches for predicting intrinsic noise , ranging from modified Gibbs sampling to scaled CME solution to direct CME solution in order of increasing accuracy and decreasing efficiency , is presented . Our methods provide an efficient alternative to previous time-stepping and analytical methods for modeling noise in cellular networks . These techniques can implement a model quite accurately for certain systems . However , the time-stepping method can require great computational cost , especially in its most accurate form ( the Gillespie algorithm ) , and does not necessarily provide an accurate representation of extrinsic noise . The direct analytical approach is desirable because it accounts exactly for intrinsic noise , but it is only feasible for the simplest biological networks . In principle , our approximate methods are generally applicable to cellular networks with arbitrary complexity . Likewise , representation of extrinsic noise by convolution provides significant advantages both in its intuitive relationship to the final distribution and in its computational tractability ( e . g . small number of parameters ) . Because extrinsic noise is a heterogeneous phenomenon with multiple sources , it is likely to have some components best modeled as variation in parameters and others best modeled in other ways . Applicability of each method can be evaluated by its ability to produce similar distributions to other methods , and more generally to account for different sources of noise . The convolution method , with its ability to mimic results from parameter perturbation methods , is useful in this regard . Also , we expect the convolution method to be highly flexible . Though it is developed in the context of analyzing steady-state distributions , it may also be applied to incorporate contributions of extrinsic noise into time-course simulations of stochastic network dynamics , for example by the Gillespie algorithm . The convolution method is also not constrained to the Gaussian form of the extrinsic noise distribution used here: if appropriate in a system , different extrinsic noise distributions with different numbers of parameters could be used . The Gaussian was used here because it was likely the best distribution with a manageably low number of parameters in these cases , but other distributions , such as mixture models , could provide more realistic final distributions at the expense of larger parameter sets . In theory , this pattern could be continued to the point of using an unconstrained function as the extrinsic noise distribution to exactly fit the observed final distribution; while , as noted above , this would compromise the mechanistic insight from the analysis , it may be useful in characterizing the system in other ways , especially if the reaction parameters are known from other measurements . Importantly , our study has defined a general , streamlined framework where one can derive unknown parameters from a distribution using fitting algorithms . For instance , our framework for extrinsic noise aids in obtaining initial estimates of reaction parameters based on the modes of the distribution , since these correspond well to the best-fit parameters . We have illustrated the basic concept of this approach through the analysis of two simple synthetic gene circuits as well as the feasibility of its application to a more complex cell cycle entry model . Due to the wide variety of perturbations that extrinsic noise can induce in all parameters and variables , however , we caution that apparent agreement with experiment could be seen for different models . To overcome this challenge , prior knowledge and alternative measurements are helpful for constraining the model , in terms of both reaction mechanisms and corresponding parameters . It is likely that this general framework is applicable for any biological network where a sufficiently mechanistic reaction mechanism is available . However , specific interpretations and applications of the fitted parameters , including those for the extrinsic noise distribution , will be context dependent . Ad hoc constraints and prior knowledge of the Markov chain describing the network dynamics , such as irreducibility , may sometimes be required , which usually demands no more mechanistic insights of the system than what's already required to carry out the actual sampling scheme .
The following model is adapted from that of Gardner and colleagues [47] . Let the two proteins be U and V with molecule numbers u and v respectively . Based on Hill kinetics for synthesis and linear kinetics for degradation , the system can be described as: ( 20 ) ( 21 ) A point in state space for this system is denoted ( u , v ) . With appropriate parameters , the system can be bistable . In such a case , the deterministic steady states are at ( umin , vmax ) and ( umax , vmin ) , where umin<umax and vmin<vmax . The number of states in this system that could potentially have nonnegligible probability is small enough that the CME at steady state can be solved analytically using linear algebra , provided the molecule numbers are small enough: ( 22 ) Scaling allows solutions for larger amounts of protein . The circuit can be induced by adding the antibiotic NFX . Adding antibiotic to induce protein U's high state involves initiating an SOS response , which degrades protein V [48]; thus it brings about the perturbation ( 23 ) where dv0 is the basal degradation rate of V , dv0+dv1 is its maximal degradation rate , A is the antibiotic concentration , and kA is the half-maximal constant for the enhanced degradation . For experimental flow cytometry data , ON and OFF fractions of the data were determined by fitting the points to a mixture model consisting of two Gaussians using Mixmod 2 . 1 . 1 . The theoretical data were partitioned based on which protein had a higher molecule number . Let n denote the number of T7 RNAP molecules in a given cell and let the cell have M promoters producing it , with m of them in an inactive state ( O0 ) and M-m in an active state ( O1 ) . Tan et al . [49] modeled this system using the Gillespie algorithm with six reactions . Five are normal chemical reactions: synthesis of a T7 RNAP molecule from O0 , with propensity k0 m , or from O1 , with k1m; conversion of an O0 and a T7 RNAP molecule to an O1 , with propensity kfmn , or the reverse , with kb ( M−m ) ; and degradation of T7 RNAP , with propensity dx0n . The sixth is cell division , which distributes the T7 RNAP molecules according to a binomial distribution and resets all the promoters to O0 , and has propensity ( 24 ) Where μ0 and θ are constants , C is the carrying capacity of the system , and N is the number of cells in it . When tracking molecule numbers in a single cell for purposes of determining steady state , one thus assumes , according to the binomial distribution , that cell division moves the molecule number from n′ to n with probability ( 25 ) For n′≥n . It is useful to apply a somewhat different definition of steady state for this system than in more typical reaction systems . If all the reactions are required to reach steady state , then the system must be at carrying capacity , and thus cell division can be eliminated from the analysis; this results in a monostable circuit . However , provided that the intracellular reactions are fast on the timescale of the growth curve , temporary quasi-steady states at other points along the growth curve , for example at log phase , can exhibit significant additional properties , including bistability; thus steady-state analysis at these times can replicate the features observed by the Gillespie algorithm . To do this , let ( 26 ) be the effective growth rate , leading to the steady-state master equation ( 27 ) The network can be induced by IPTG , effectively increasing k0 and k1 . Different steady states can be investigated by examining the system at different OD levels; in each case N/C is estimated as the ratio of the current OD to the carrying-capacity OD . ON/OFF fractions for this network were found by identifying the two most prominent peaks in the histogram of the protein being monitored and then defining the bin in between those peaks with the lowest value as the border between ON and OFF . When peaks were found to blur together ( a problem in the theoretical distributions ) , the point on a shoulder with the lowest derivative ( approximated as a finite difference between points ) was designated as the border between the main peak and the shoulder “peak . ” E . coli , JM2 . 300 was transformed with two plasmids , pTSMa and pCIRa [48] . The cells were cultured overnight at 37°C with 2 mM IPTG to ensure OFF state . The cells were then washed twice with fresh media , diluted 1000 fold , and cultured at 37°C . After three hours , cells were treated with various concentrations of NFX and further cultured at 37°C for 5 hours . Samples were then collected and subjected to flow cytometry analysis . LB medium was used throughout the experiment . The construction and characterization of the T7 RNAP* positive feedback circuit were described by Tan et al . [49] . MC4100z1 cells ( from Michael Elowitz ) were used throughout the study . As an example to evaluate the feasibility of extending the proposed computational framework to a more complex model , we adopt a previously developed stochastic model for this network [51] . It consists of a set of stochastic differential equations , which has the general form of ( 28 ) where Xi ( t ) represents the number of molecules of a molecular species i ( i = 1 , … , N ) at time t , and X ( t ) = ( X1 ( t ) , . . , XN ( t ) ) is the state of the entire system at time t . X ( t ) evolves over time at the rate of aj[X ( t ) ] ( j = 1 , … , M ) , and the corresponding changes in the number of individual molecules are described in vji , Γj ( t ) and ωi ( t ) are temporally uncorrelated , statistically independent Gaussian noises . Γj ( t ) is the standard normal distribution with mean 0 and variance 1 . ωi ( t ) tunes the level of empirical additive extrinsic noise [51] . When ωi ( t ) is set to 0 , the SDE simulation gives an approximation to the exact solution of the discrete stochastic chemical reaction system [52] , against which the MGS distributions are compared . The inclusion of either low or high levels of extrinsic noise is realized by setting ωi ( t ) to 15 or 50 , respectively . Based on the reactions involved in this system [51] , we can write down the following CME: ( 29 ) where • represents the state of interest for the molecular species not specified , as in ( [M] , [E] , [CD] , [CE] , [R] , [RP] , [RE] ) , which represent molecular number . Refer to Lee et al [51] for detailed description of the reaction mechanism and the corresponding rate constants . | Variability from one cell to another is a pronounced and universal trend in living organisms; much of this variability is related to varying concentrations of proteins and other chemical species across the cells . Understanding this variability is necessary if we are to fully understand cellular functions , particularly the ways in which cells differ from each other and in which cells with the same origin behave in different ways ( e . g . in human development and cancer ) . When using a chemical model for some aspect of cellular function , one needs to consider two sources of variability: intrinsic variability , which results from the reactions proceeding as in the model but naturally varying because of the finite number of molecules in the cells and their random behavior; and extrinsic variability , which results from other kinds of variation not accounted for in the specific , deterministic model . We present new methods to model and compute both kinds of variability , to facilitate the study of cellular variability as a whole . Our methods provide advantages in speed , accuracy , and scope of mechanisms modeled , and we apply them to experimental data , demonstrating the nature of intrinsic and extrinsic noise in those systems . | [
"Abstract",
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"Results",
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"Methods"
] | [
"biology",
"computational",
"biology"
] | 2011 | Computation of Steady-State Probability Distributions in Stochastic Models of Cellular Networks |
The Yorkie/Yap transcriptional coactivator is a well-known regulator of cellular proliferation in both invertebrates and mammals . As a coactivator , Yorkie ( Yki ) lacks a DNA binding domain and must partner with sequence-specific DNA binding proteins in the nucleus to regulate gene expression; in Drosophila , the developmental regulators Scalloped ( Sd ) and Homothorax ( Hth ) are two such partners . To determine the range of target genes regulated by these three transcription factors , we performed genome-wide chromatin immunoprecipitation experiments for each factor in both the wing and eye-antenna imaginal discs . Strong , tissue-specific binding patterns are observed for Sd and Hth , while Yki binding is remarkably similar across both tissues . Binding events common to the eye and wing are also present for Sd and Hth; these are associated with genes regulating cell proliferation and “housekeeping” functions , and account for the majority of Yki binding . In contrast , tissue-specific binding events for Sd and Hth significantly overlap enhancers that are active in the given tissue , are enriched in Sd and Hth DNA binding sites , respectively , and are associated with genes that are consistent with each factor's previously established tissue-specific functions . Tissue-specific binding events are also significantly associated with Polycomb targeted chromatin domains . To provide mechanistic insights into tissue-specific regulation , we identify and characterize eye and wing enhancers of the Yki-targeted bantam microRNA gene and demonstrate that they are dependent on direct binding by Hth and Sd , respectively . Overall these results suggest that both Sd and Hth use distinct strategies – one shared between tissues and associated with Yki , the other tissue-specific , generally Yki-independent and associated with developmental patterning – to regulate distinct gene sets during development .
The regulation of gene expression is a complex , multilayered process , but at its core lays the interaction between transcription factors ( TFs ) and DNA . TFs regulate gene expression by binding their target DNA sequences , which are generally organized into groups of regulatory motifs known as enhancers or cis-regulatory modules ( CRMs ) [1]–[3] . Understanding how TFs interact with DNA is crucial for our understanding of gene regulatory networks , and genomic approaches – chromatin immunoprecipitation followed by microarray or sequencing analysis ( ChIP-chip or ChIP-seq , respectively ) – have now given us the ability to monitor TF-DNA interactions on a genome-wide scale [4]–[6] . However , understanding the regulatory impact of the observed interactions remains a challenge , especially in light of the fact that many TFs appear to bind to thousands of genomic regions [7]–[9] . Thus one of the key questions now faced by those attempting to map regulatory networks is how regulatory specificity is achieved within this sea of TF-DNA binding . It is likely that only a subset of the thousands of binding events observed for most TFs regulate gene expression . Work on the Drosophila early embryo TF network suggests that functional binding can be distinguished from neutral binding based simply on ChIP signal strength , and studies exploring the fly embryonic mesoderm TF network indicate that temporally dynamic binding is more likely to be functional [10]–[13] . While the former study is based on a single developmental time point ( the blastoderm stage of embryogenesis ) , the latter studies suggests developmentally dynamic TF-DNA interactions play a crucial role in defining the gene regulatory networks at later stages of development . Furthermore , additional studies have highlighted the importance of tissue and chromatin context in impacting TF-DNA interactions in Drosophila [14]–[18] and mammals [19]–[21] . Clustered binding events – possibly representing ‘shadow’ or ‘distributed’ enhancers – have also been highlighted as enriched in functional binding [13] , [22]–[25] and , accordingly , regions of clustered ChIP peaks are more likely to be developmentally dynamic [25] . Indeed , the regulatory networks of later developmental stages may be more complex than those of the early embryo . As the development of multicellular organisms proceeds , cell fates are progressively refined , generating numerous cell and tissue types throughout the organism; growth and patterning of these unique tissues often requires the reiterative use of a largely overlapping set of TFs [26] . If the same TFs are reused in different tissue types to carry out distinct functions , precise mechanisms must be in place for these factors to achieve regulatory specificity . One possible scenario for tissue-specific TF functions is that the same TF binds to distinct DNA sequences in a tissue-specific fashion . In this model , tissue-specific CRM activities are directed by tissue-specific TF-DNA interactions . Conversely , tissue-shared TF-DNA interactions would drive tissue-nonspecific CRM activity across tissues . Tissue-specific binding could be regulated through direct or indirect interactions with other transcription factors , or through tissue-specific differences in chromatin landscape , such as binding site accessibility or histone modifications [1] , [2] , [27]–[30] . In an alternate model , the tissue-specific regulatory activity of a TF is regulated at a step subsequent to DNA binding [26] . In this case , binding events shared between tissues can drive tissue-specific expression patterns , with regulatory specificity provided by direct or indirect interactions with another transcription factor or cofactor . Although tissue-specific binding is thought to reveal functional enhancers [2] , [11] , [12] , [31] , it remains an open question whether tissue-nonspecific binding of TFs is functional and , if so , whether it can also lead to tissue-specific enhancer activity . Regardless of whether a TF's activity is regulated at the level of DNA binding or beyond , chromatin landscape has the potential to modulate regulatory output . The histones that make up nucleosomes can be subject to significant posttranslational modification , and certain posttranslational modifications are associated with active or inactive CRMs ( e . g . histone 3 lysine 27 acetylation or trimethylation , respectively ) [32] , [33] . A recent genome-wide study of >50 chromatin-associated proteins found that Drosophila chromatin can be broken down into five distinct chromatin states: YELLOW , RED , BLUE , BLACK , and GREEN [34] , [35] . The YELLOW and RED states represent generally ‘active’ chromatin , while the other three represent various ‘repressive’ states . This five state model is based on the DamID ( DNA adenine methyltransferase identification ) method for characterizing in vivo protein-DNA interactions , but is highly consistent with a similar model based on genome-wide ChIP data [34] , [36] , [37] . Although much is yet to be explored regarding the interplay of TFs and these chromatin types , the five DNA-binding factors tested in the chromatin state study preferentially bound RED chromatin , suggesting this chromatin state might positively modulate DNA interactions for these factors [34] . However , as these studies were conducted in cell lines , the influence of chromatin type on tissue-specific binding and regulatory activity in vivo remains untested . To begin exploring the mechanisms underlying tissue-specific gene regulation , we focus here on three Drosophila transcriptional regulators that have been implicated downstream of the Hippo signaling pathway: Yorkie ( Yki ) , Scalloped ( Sd ) , and Homothorax ( Hth ) . The Hippo tumor suppressor pathway is a key regulator of cellular proliferation in both invertebrates and mammals [38]–[42] . The pathway centers around two serine-threonine kinases , Hippo and Warts , and downstream of these kinases the Hippo pathway regulates gene transcription [43] , [44] . A direct target of Warts , the transcriptional coactivator Yki is an essential mediator of Hippo-regulated proliferation [45] . As a coactivator , Yki lacks a DNA binding domain and must partner with sequence-specific DNA binding proteins in the nucleus to regulate gene expression . Multiple TFs have been implicated in the recruitment of Yki to DNA; in Drosophila , two well-characterized Yki binding partners are Sd and Hth [46]–[51] . Yki promotes tissue growth in a tissue-nonspecific manner across imaginal discs , and Sd and Hth are necessary for these functions in the wing and eye , respectively [48]–[50] . Ectopic Yki activity , whether driven by targeted overexpression or through mutations that compromise Hippo signaling , drives tissue overgrowth without changing tissue identity [45] . Sd and Hth , on the other hand , are required for both tissue identity and tissue growth: in addition to their roles regulating proliferation together with Yki , Sd and Hth also have important Yki-independent developmental roles . For example , Sd , in conjunction with Vestigial ( Vg ) , specifies wing fate [52]–[54] . Hth specifies antennal fate , participates in patterning the proximal-distal axis of the wing and leg , and maintains cells in an undifferentiated state in the developing eye [55]–[59] . Additionally , Yki and Sd play a role in specifying non-retinal fates in the eye imaginal disc [60] . Thus , these three factors are ideal for studying context-specific gene regulation: all three factors promote tissue growth ( cell proliferation and survival ) , while Sd and Hth also carry out highly tissue-specific functions . Because of their unique and shared roles in the wing and eye-antennal imaginal discs , we performed genome-wide ChIP experiments for Sd , Hth , and Yki in both of these tissues . Strong , tissue-specific binding patterns are observed for Sd and Hth , while Yki binding is remarkably similar between these two tissues . Tissue-specific binding events for Sd and Hth are located at genes consistent with their known developmental roles , are significantly enriched in Polycomb-associated ( BLUE ) chromatin , and are associated with enhancers that are active in the corresponding tissue . Binding events common to the eye and wing are also observed for Sd and Hth; these tissue-shared binding events are generally associated with genes regulating cell proliferation and other “housekeeping” functions . Interestingly , the tissue-shared Hth and Sd binding events account for the majority of Yki occupancy . We also identified and characterized separate but adjacent wing and eye enhancers from the bantam ( ban ) gene , a previously described direct target of the Hippo pathway , and show that their activities are dependent on direct Sd and Hth binding , respectively . Overall these results suggest that the TFs Sd and Hth use at least two binding strategies – one context-independent and associated with Yki binding , the other tissue-specific and associated with developmental patterning – to regulate different gene sets during development .
The transcriptional coactivator Yki is required for cell survival in all imaginal discs [45] . Two of Yki's partner TFs , Hth and Sd , are required for cell survival in the eye and wing imaginal discs , respectively , yet these TFs also have important developmental roles beyond the control of cell proliferation and survival . To explore tissue specific gene regulation by these TFs at the downstream end of the Hippo pathway , we performed genome-wide chromatin immunoprecipitation ( ChIP-chip ) experiments for each factor in both the wing ( W ) and eye-antenna ( EA ) imaginal discs . For Hth and Yki we used polyclonal antibodies raised against the native proteins , and for Sd we used a GFP protein trap line , which is wild type as a hemi- or homozygote , and polyclonal anti-GFP to immunoprecipitate bound chromatin fragments from wild type eye-antenna or wing imaginal discs of wandering stage 3rd instar larvae [61]–[63] . Immunoprecipitated fragments were hybridized to high-density , whole-genome tiling arrays to generate a global , tissue-specific view of genomic binding for all three factors ( Figure 1A , B ) . An overview of the binding events for these factors is provided in Figure 1A and extensive lists are provided in Dataset S1 . To explore the tissue specificity of Yki , Sd and Hth binding , we defined tissue-specific peaks as those that are called at a False Discovery Rate ( FDR ) of 1% in the tissue of interest and not called at a less stringent FDR of 25% in the other tissue ( Figure 1A ) [64] . This dual-threshold method avoids calling a peak as tissue-specific if it falls just below the significance threshold of FDR1 in one of the two tissues ( i . e . , a peak that would be called at FDR1 in the eye and an FDR of 5% in the wing ) . Although small differences in binding strength may also be important for tissue specific gene regulation , our initial goal was to characterize robust tissue specific binding events . For simplicity , W>EA will be used to refer to bound regions called as FDR1 peaks in the wing and not called as FDR25 peaks in the eye-antenna; the converse will be referred to as EA>W . Regions called as FDR1 peaks in one tissue and at FDR25 in the other tissue are considered as shared binding events in both tissues , and referred to as EA≈W binding events . Using this thresholding scheme ( Figure 1A ) , it is immediately apparent that Sd and Hth specifically bind a large number of genomic regions in the wing disc ( approximately 2000 W>EA for both factors ) compared to the eye-antennal disc ( <200 EA>W for both factors ) . In contrast , for both tissues , tissue-specific binding by Yki is limited to a few hundred events , a small fraction of the total ( less than 6% ) . Thus , the tissue-specific binding events observed for both Sd and Hth distinguish these factors from Yki , which displays little tissue-specific binding ( Figure 1A ) . These results suggest that the site specific TFs Sd and Hth target the genome in a way that is fundamentally distinct from the coactivator Yki; in these two imaginal tissues , Sd and Hth binding is exquisitely sensitive to cellular context , whereas Yki binding is relatively insensitive to cellular context . As Yki lacks a DNA binding domain , DNA binding TFs such as Sd and Hth are needed for recruitment of Yki to regulatory loci . To determine the extent to which Sd and Hth can account for Yki binding we compared the genome-wide binding site overlap between Yki and these two TFs . In total , Sd and Hth can account for ∼70% of Yki binding in wing , and ∼50% of Yki binding in the eye-antenna ( Figure S1 , see also Figure 1C discussion below ) . Because of the difficulties inherent in comparing independently thresholded binding site calls , this is likely to be a conservative estimate . Indeed , if we instead ask how many Yki binding sites overlap Sd and Hth peaks called at an FDR of 25% , we find that these two factors overlap 82% in the wing and 73% in the eye ( not shown ) . Regardless of the peak-calling threshold used , Yki's overlap with Sd is more prevalent than its overlap with Hth , suggesting Sd is used more frequently than Hth to recruit Yki in both tissues ( Figure S1 ) . Consistent with the finding that the majority of Yki binding is shared between the wing and eye-antenna discs , Yki's EA≈W peaks overlap most significantly with EA≈W peaks for Sd and Hth ( Figure 1C ) . In fact , the EA≈W binding events for all three factors are highly correlated . For Yki over 25% of EA≈W peaks overlap both Sd and Hth EA≈W peaks ( all three factors bound to the same location ) , and two-thirds overlap with at least one of the two TFs . The pattern is more dramatic for Sd and Hth . Approximately 37% of Sd EA≈W peaks overlap Yki+Hth peaks and ∼88% overlap Yki or Hth peaks . For Hth , almost half ( 49% ) of the EA≈W peaks overlap Yki+Sd peaks and 72% overlap Yki or Sd . The high overlap of tissue-shared binding is reminiscent of previously described ‘hotspots’ of TF colocalization , or HOT ( high-occupancy target ) regions [37] , [65] , [66] . Indeed , the EA≈W binding events for all three factors significantly overlap embryonic HOT regions , with 57% , 53% , and 37% of HOT regions overlapping EA≈W Yki , Sd , and Hth , respectively ( all p<10−50 , hypergeometric test ) . Thus , the bound regions shared by these three factors in the imaginal discs are also significantly bound by other TFs at a very different stage of development . On the other hand , overlap between Yki binding with tissue-specific Sd and Hth binding events is not nearly as significant ( Figure 1C ) . These results indicate that when Yki , Sd , and Hth are bound to the same genomic locations , this co-occupancy is independent of tissue context . It is clear from the results described above that two distinct types of binding are observed for the TFs Sd and Hth: binding that is shared between the wing and the eye-antenna , and binding that is specific to one of the two tissues . To better understand the variables influencing tissue-specific binding and , presumably , regulatory specificity , we explored the differences between these two modes of genomic binding . We first sought to further characterize the DNA targeted by tissue-specific and tissue-shared Sd and Hth binding . EA>W binding events were left out of these analyses because it is difficult to compare patterns from the small number of binding events in this set to patterns from the thousands of binding events in the tissue-shared and W>EA sets . We looked at three additional characteristics – genomic location ( TSS proximal , intergenic , intronic , etc . ) , DNA motif enrichment , and DNA conservation – and , again , found striking differences between the tissue-shared and W>EA binding sites . First , tissue-shared binding is much more likely to fall at proximal promoter regions , with >46% of EA≈W for both Sd and Hth falling within 1 kb of a transcription start site . In contrast , W>EA binding is much more likely to occur in intronic or intergenic regions , with >70% of W>EA binding for both Sd and Hth falling within intergenic or intronic DNA ( Figure 2A ) . With regard to potential DNA motifs influencing Sd and Hth binding , the most significant centrally enriched motifs in EA≈W peaks do not match characterized Sd or Hth DNA binding sites , but instead are GATA-like motifs and AT-rich motifs , respectively ( Figure 2B , C ) . Interestingly , Sd and Hth sequences matching the consensus DNA binding sites are the most enriched in the W>EA binding regions for each factor , respectively ( Figure 2B , C ) [53] , [67]–[71] . A complete list of enriched motifs is provided in Dataset S2 ( see also Figure S2 ) . We also find that , for both Sd and Hth , sequences at W>EA binding events are more likely to be evolutionarily conserved compared to sequences at EA≈W binding events ( Figure 2D , E ) . Thus , binding events that are dependent on tissue context are distinct from shared binding events in multiple ways – they are more likely to be distal to the transcription start site , associated with expected DNA motifs , and more conserved . All of these qualities are consistent with tissue-specific binding events occurring at CRMs targeting genes with complex regulatory inputs . In terms of target genes , for both Sd and Hth EA≈W regions significantly target housekeeping genes , but additional non-housekeeping ( i . e . , developmental ) gene classes are also targeted ( Dataset S3 ) , which is not surprising considering the thousands of Sd and Hth EA≈W binding events . RNA-seq data from WT wing discs reveal that the majority of genes with Sd and Hth EA≈W are highly expressed ( Dataset S4 ) . We also asked whether tissue-specific and tissue shared events ever target the same loci , or whether these two modes of binding are always separable . For both Sd and Hth , we separated target genes into those targeted only by an EA≈W binding event ( termed ‘EA≈W only’ ) , those targeted by both EA≈W and W>EA binding ( termed ‘EA≈W+W>EA’ ) , and those targeted by only W>EA binding ( termed ‘W>EA only’ ) . The majority of EA≈W binding for both Sd and Hth ( 74% and 68% , respectively ) falls into the ‘EA≈W only’ category . The genes targeted in this way – no tissue-specific input – are enriched for housekeeping gene ontology categories like ‘cellular metabolic process’ ( Figure 2E ) ; Sd ‘EA≈W only’ also targets cell cycle genes ( Figure 2E ) . Consistent with these GO categories , the ‘EA≈W only’ binding events for both TFs are also the most significantly associated with Yki binding ( Figure S1C ) . In contrast , for both Sd and Hth , the ‘EA≈W only’ events do not significantly target developmental genes . The remaining ∼25% of EA≈W binding for Sd and Hth are associated with loci that also receive tissue-specific input . Genes targeted in this fashion are enriched for categories associated with developmental patterning and morphogenesis ( Figure 2E ) . Thus , both housekeeping genes and developmental genes are associated with tissue-shared input , but developmental genes also have tissue-specific input at distinct locations , perhaps reflecting different CRMs . These results highlight the differences in regulatory logic across unique gene sets , and are consistent with patterned developmental gene expression requiring more complex cis-regulatory input . Transcriptional regulators influence gene expression by binding to CRMs , or enhancers . To identify potential CRMs regulated by Yki , Sd , and Hth , we compared our genome-wide binding data to the recently described FlyLight resource cataloging DNA regions with cis-regulatory activity in imaginal discs [72] . In total , Sd and Hth each bound >200 DNA fragments that drive expression in the wing disc ( 248 and 233 , respectively ) , and 170 DNA fragments that drive expression in the eye disc ( Dataset S5 ) . Yki binding to FlyLight enhancers was much lower , with Yki peaks overlapping 98 wing enhancers and 84 eye enhancers . In contrast to Sd and Hth , overall Yki binding is not enriched relative to random expectation at FlyLight enhancers . Strikingly , the pattern of TF-CRM colocalization is significantly greater for tissue-specific binding events compared to tissue-shared binding events ( Figure 3A ) . For both Sd and Hth , W>EA binding events are most significantly enriched for enhancers that drive expression in the wing disc , relative to enhancers that drive expression in the eye , antenna , or leg . For example , 164 FlyLight enhancers include Sd W>EA binding peaks and 147 ( 89 . 6% ) of these are active in the wing . For comparison , only 89 ( 55% ) of the 164 Sd W>EA bound enhancers are active in the eye , and the vast majority of these ( 80/89 , 90% ) also drive expression in the wing . A similar pattern is observed for Hth: 131 enhancers have Hth W>EA peaks and 116 ( 88 . 6% ) of these are active in the wing . On the other hand , 72 ( 54% ) are active in the eye , and 90% ( 65/72 ) of these are also active in the wing . Thus , for both Sd and Hth W>EA binding is strongly enriched for enhancers that drive expression in the wing disc . CRMs that are active in the wing but do not overlap with Sd or Hth W>EA binding may be targeted by factors not analyzed here . Although based on a much smaller number of binding events , EA>W binding events are enriched for CRMs driving eye expression ( Figure 3A ) . In total , W>EA Sd binding events overlap 147 FlyLight DNA fragments that drive expression in the wing ( p<10−95 ) . Because some of the FlyLight fragments are partially overlapping , this amounts to 115 unique CRMs . For Hth , W>EA binding overlaps 116 enhancer fragments ( p<10−50 ) , representing 92 unique CRMs . For example , W>EA Sd and Hth binding sites at the wingless ( wg ) locus overlap two CRMs that drive expression matching the known wg pattern ( Figure 3B ) . Sd is necessary for the dorsal-ventral ( DV ) stripe of wg expression in the wing , and Hth positively regulates wg expression in the hinge [54] , [59] . Interestingly , one of these wg CRMs is bound by both Sd and Hth and captures robust wing DV stripe and hinge expression; only Sd binds the other CRM , which drives DV stripe expression but very weak hinge expression . The numbers are much smaller , but still significant , in the eye-antenna disc due to the smaller number of EA>W binding events . Hth EA>W binding events overlap 8 CRMs that drive expression in the eye ( p<10−3 ) , and Sd EA>W binding events overlap 4 eye CRMs ( p<10−2 ) . Though small in number , interesting patterns are driven by these eye CRMs . Sd , for example , binds 4 CRMs , but all are near genes that play key roles in photoreceptor specification: anterior open ( aop , also known as yan ) , scabrous ( sca ) , and the Bar genes B-H1 and B-H2 ( Figure 3C ) [73] , [74] . Hth EA>W binding is associated with CRMs targeting key regulators of eye disc development such as pointed ( pnt ) , odd paired ( opa ) , eyes absent ( eya ) , which is known to be repressed by Hth in the anterior eye , and wg , which is positively regulated by Hth in the ventral eye [55] , [73] , [75]–[77] . Importantly , the wg CRM with Hth EA>W binding captures the dorsal and ventral expression domains of wg in this tissue ( Figure 3B ) . This CRM is distinct from the wing CRM with Hth W>EA binding ( see above ) and is consistent with the known role for Hth in the regulation of the wg locus [75] . Taken together , these data demonstrate that , in comparison to Yki binding and tissue-shared Sd and Hth binding , tissue-specific binding for both Sd and Hth is more significantly associated with developmentally regulated CRMs often located within intricately regulated loci . The above findings indicate that tissue-specific binding is a key variable influencing the regulatory specificity of Sd and Hth . Still , a significant fraction of binding for both TFs is tissue-nonspecific , at least when comparing entire eye-antenna and wing imaginal discs; some of the binding that appears to be ‘shared’ could be a consequence of specific binding in distinct cell types within these discs . For example , we observe Sd , Hth , and Yki binding to several well-characterized transcriptional targets of the Hippo pathway in a primarily tissue-nonspecific manner ( Figure 4A , Figure S3 ) , including the microRNA ( miR ) encoding gene bantam ( ban ) [48]–[50] , [78]–[80] . ban both promotes proliferation and prevents apoptosis , and ban is essential for Yki-driven overproliferation across imaginal tissues [78]–[80] . Moreover , Sd and Hth regulate bantam expression in the wing and the eye , respectively [48]–[50] . Although ban expression is patterned in both the wing and eye , the regulatory enhancers that direct these expression patterns have not been previously identified . Based on transcriptome data and position of putative insulator elements [81] , [82] , the small ban hairpin is derived from a ∼40 kb locus , and a 12 kb primary transcript ( Figure 4A ) . Yki , Sd , and Hth binding is extensive across this locus , especially within a large intergenic region 5′ to the start of the primary transcript . Two Gal4 enhancer traps near the promoter of this primary transcript each capture bantam's expression pattern ( Figure 4A–C and data not shown ) . In the eye , expression is high in the proliferative domain anterior to the morphogenetic furrow; in the wing , expression is high in the pouch and hinge , with regions of repression at the dorsal-ventral ( DV ) and anterior-posterior ( AP ) compartment boundaries ( Figure 4B , C ) . The fact that both enhancer traps are inserted >12 kb upstream of the hairpin , close to the putative start of transcription , suggests that regulatory inputs driving ban expression may be in the large 5′ intergenic region , consistent with the Yki , Sd , and Hth binding patterns . We used transgenic reporter constructs to identify the regulatory modules directing bantam's wing and eye expression , ultimately scanning >40 kb of the ban locus ( Figure 5A ) . Although the Gal4 enhancer traps described above are inserted in opposite directions flanking a region of Yki , Sd , and Hth binding , the DNA fragment separating these enhancer traps does not drive ban-like expression patterns ( not shown ) . Further searches identified a 3 . 5 kb region >30 kb upstream of the ban hairpin ( ∼17 . 5 kb upstream of the putative transcription start site ) , bound by Sd , Hth , and Yki in both the eye-antenna and wing discs , that recapitulates ban eye and wing expression ( Figure 5A ) . None of the other regions tested , including fragments that show strong binding and one that is activated when Hippo signaling is compromised [47] , drove a bantam-like expression pattern in wing or eye discs . The 3 . 5 kb region that drives expression in the eye and wing discs was further broken down into distinct eye and wing enhancers ( Figures 5B , C ) . The minimized eye and wing enhancers are 670 bp and 591 bp , respectively , and both are highly conserved across all 12 sequenced Drosophila species ( Figure 5A ) . Expression of ban in the anterior eye progenitor domain is dependent on Hth [48] . Based on ChIP-PCR studies , we previously suggested that Hth and Yki directly activate ban in the anterior eye [48] . The identification of the eye enhancer ( ban-eye ) allowed us to further test this hypothesis . The ChIP-chip data indicate that Hth binds this enhancer in vivo . Although Yki is also present , it falls below a FDR of 25% ( Figure 4A ) . Nevertheless , the importance of these interactions is demonstrated by additional genetic experiments . Expression driven by the ban-eye enhancer is lost in hthP2 clones ( Figure 6A ) but is unaffected in sdΔB clones ( not shown ) . Similar loss of expression was also seen using a hth allele ( hth100 . 1 ) that only expresses homeodomain-less isoforms of Homothorax , suggesting that full-length Hth is required for activation of bantam in the anterior eye ( Figure S4A ) . Expression is also lost in clones of cells lacking extradenticle ( exd ) , an obligate Hth binding partner ( Figure S4B ) [83]–[85] . Additionally , the enhancer is strongly activated in clones ectopically expressing Hth posterior to the morphogenetic furrow , in regions of the disc where neither hth nor bantam are normally expressed ( Figure 6B ) . Importantly , the ban-eye enhancer contains a single sequence that matches a Hth binding site , and a 3 bp mutation of this motif ( from GACAG to GGGGG ) abolished its activity ( Figure 6C ) . The ban-eye enhancer also contains a DNA binding motif for Exd , and mutation of this motif ( from TGAT to GGGG ) resulted in a similar ablation of expression in the eye imaginal disc ( Figure S4C ) . Finally , ban-eye expression is also dependent on yki , as it was lost in ykiB5 clones ( Figure 6D ) . Together with the ChIP data , these genetic and enhancer mutagenesis experiments support a model in which Hth+Exd+Yki directly activate bantam expression in the progenitor domain of the eye via an enhancer more than 30 kb upstream of the bantam hairpin . We carried out similar experiments on the newly identified bantam wing enhancer ( ban-wing ) . Sd and Yki are required for expression of bantam in the wing imaginal disc [49] . Expression driven by the ban-wing enhancer is lost in sdΔB clones ( Figure 7A ) . To test whether Sd regulation of this enhancer is direct , putative Sd binding sites in the wing enhancer were mutated . Altogether , the bantam wing enhancer contains seven putative Sd binding sites , and mutation of all seven eliminated the vast majority of expression in the wing pouch and wing hinge ( Figure 7B ) . Mutation of fewer than seven of the Sd motifs led to more subtle decreases in expression ( not shown ) . Despite the significant loss of wing expression when Sd sites are mutated , residual expression remains in cells flanking the AP compartment boundary in the wing pouch , suggesting that this enhancer may also integrate Decapentaplegic ( Dpp ) input independently of Sd . These observations are consistent with a previous report showing that Dpp is an activator of bantam expression in a Yki-dependent manner [47] . In addition , similar to the ban-eye enhancer , expression driven by the ban-wing enhancer is lost in ykiB5 clones , in all regions of the wing pouch ( Figure 7C ) . Finally , unlike ban-eye , mutating the only recognizable Hth binding site had no effect on the activity of the ban-wing enhancer ( not shown ) . Together these results suggest that Sd+Yki directly regulate the bantam wing enhancer and that Dpp+Yki independently regulate this element close to the AP compartment boundary . Genome-wide TF-DNA interactions take place in the context of chromatin , which has the potential to significantly impact a TF's ability to bind DNA [1] , [17] , [86] . An analysis of dozens of chromatin-associated proteins and histone modifications in Drosophila Kc cells generated a high-resolution view of various chromatin states across the genome [34] . Five states were defined that included highly ‘active’ regions ( the YELLOW and RED chromatin states ) , a Polycomb bound region ( the BLUE state ) , and two transcriptionally silent regions ( the BLACK and GREEN states ) . To determine if there is a correlation between chromatin state and Yki , Sd , and Hth binding we looked at the significance of overlap between the binding of these factors and the five chromatin states . Although the small number of EA>W binding events prevented us from finding any significant patterns in the eye-antenna , some interesting patterns emerged when comparing EA≈W and W>EA binding patterns ( Figure 8A ) . All EA≈W binding events are highly enriched for binding in the YELLOW and RED chromatin types ( 64% , 58% , and 44% of EA≈W Yki , Sd , and Hth sites , respectively , overlap YELLOW chromatin; and 20% , 27% , and 30% of EA≈W Yki , Sd , and Hth sites , respectively , overlap RED chromatin . ) Although distinct , both RED and YELLOW are transcriptionally active chromatin states in Kc cells . YELLOW and RED chromatin types are also enriched for Yki W>EA binding events , albeit to a lesser degree than EA≈W , and a significant overlap with RED chromatin is seen for Sd and Hth W>EA peaks . Interestingly , however , for both Sd and Hth , W>EA binding events are not enriched for the YELLOW chromatin state but are instead enriched for binding in BLUE chromatin regions ( discussed below ) : 32% and 30% of Sd and Hth W>EA binding events , respectively , occur in BLUE chromatin . Thus , the tissue-specific and tissue-shared binding for Sd and Hth correlate with distinct chromatin landscapes . The correlations between TF binding and distinct chromatin types become more interesting when considering the properties of YELLOW , RED , and BLUE chromatin . YELLOW chromatin , which is preferred by all three factors but only at sites with tissue-shared binding , is associated with active chromatin modifications and genes that are highly expressed in a ubiquitous manner ( ribosomal components , DNA repair machinery , etc . ) [34] . The DNA in RED chromatin , which is enriched in both tissue shared and W>EA binding by Sd , Hth , and Yki , is highly accessible , as measured by FAIRE ( formaldehyde assisted isolation of regulatory elements ) [87] , and associated with genes expressed in a patterned fashion , such as genes involved in signal transduction and those encoding transcription factors [34] . BLUE chromatin , where only W>EA Sd and Hth binding events are highly enriched , is marked by Polycomb group ( PcG ) proteins and PcG-associated repressive histone modifications ( histone H3 lysine 27 trimethylation ) ; genes associated with BLUE chromatin tend to encode exquisitely controlled developmental master regulator genes ( i . e . , selector and selector-like genes ) . The above patterns are also apparent when looking at enriched Gene Ontology ( GO ) categories ( Dataset S6 ) . In addition , GO analysis reveals that the small number of EA>W binding events for Hth and Sd are also associated with selector-like genes: retinal determination and photoreceptor specification genes , respectively ( Dataset S6 ) . Thus , for Sd , Hth , and Yki , EA≈W binding is strongly associated with genes that are highly and ubiquitously expressed . Tissue-specific Sd and Hth binding , on the other hand , is uniquely enriched in highly regulated selector-like gene loci ( see Dataset S6 for examples ) . Although tissue-specific binding is abundant for both Sd and Hth , the bantam wing and eye enhancers direct tissue-specific , patterned expression even though overall Sd/Hth/Yki binding is similar between both tissues at these regions ( Figure 4 ) . These enhancers may provide an example of how context-independent input from Sd and Hth can drive developmentally patterned expression . Alternatively , because our ChIP experiments were carried out with whole wing and whole eye-antenna discs , it is also possible that the observed ChIP signals come from distinct cell types within individual discs ( e . g . Hth may bind to the ban-wing enhancer in the hinge and Sd may bind to the ban-wing enhancer in the pouch ) . Consistent with this notion , these CRMs are in BLUE chromatin , where tissue-specific binding is typically observed ( Figure 8 ) . In fact , although Sd and Hth tissue-shared binding is most enriched at YELLOW chromatin , ∼10–20% of these binding events occur in BLUE chromatin and ∼30% occur in RED chromatin . Finally , we explored the properties of tissue-shared Sd- or Hth-bound DNA across different chromatin types . First , the DNA bound by these factors in BLUE and RED chromatin is much more conserved than that falling within YELLOW chromatin ( Figure 8C and 8E ) . Second , when we search for the DNA motifs identified in the W>EA binding events ( Figure 2 ) we find that these motifs are significantly more enriched at peaks in BLUE and RED chromatin compared to YELLOW chromatin ( Figure 8C and 8E ) . For Sd , the core GGAATG sequence is significantly more enriched in tissue-independent binding events occurring in BLUE and RED chromatin ( Figure 8C ) . Similar patterns are seen with the more degenerate sequence RGAATG , with >39% of BLUE peaks containing the motif and <35% of YELLOW peaks containing the motif ( p = 0 . 0257 ) . For Hth , neither of the identified consensus sequences ( TGAC or TGAT ) is differentially enriched in the various chromatin states ( not shown ) . However , as mentioned above , Hth binds DNA together with a second homeodomain-containing factor , Exd . The core overrepresented sequences from Figure 2 , TGAC and TGAT , represent Hth and Exd consensus motifs , respectively , and adjacent copies of these motifs are significantly enriched in BLUE chromatin binding events ( Figure 8E ) . A similar pattern is observed for the more degenerate sequence ( WGAY{N0–2}WGAY ) , with >51% of BLUE peaks and >37% of YELLOW peaks containing this sequence ( p<0 . 0001 ) . Interestingly , the same patterns are seen when comparing W>EA binding across BLUE , RED , and YELLOW chromatin ( Figure 8D , F ) , indicating that many of the overall differences between tissue-specific and tissue-shared binding are based on the properties of Sd and Hth binding in YELLOW versus BLUE and RED chromatin . These findings suggest that a subset of tissue-nonspecific binding events , particularly those falling within regions of BLUE chromatin , is likely to regulate developmentally patterned gene expression .
The control of gene expression in multicellular eukaryotes depends on a limited set of transcription factors that are reused in different contexts and combinations to execute a diverse array of cellular functions . To gain insight into this process we used tissue-specific , genome-wide ChIP to explore the global DNA targeting properties of three transcriptional regulators – Yki , Sd , and Hth . Yki is a transcriptional coactivator that regulates tissue growth in all tissues , and it does so in part through interactions with the DNA binding TFs Sd and Hth [41] , [48]–[51] . However , in addition to their Yki-dependent roles in promoting tissue growth , Sd and Hth also have highly tissue-specific developmental roles [52]–[59] . Thus , this group of regulators provides an ideal starting point for addressing the logic by which TFs execute both tissue-specific and -nonspecific gene regulatory functions in vivo . Below we discuss the implications of the differences we uncovered between these modes of binding for Hth and Sd , as well as the unexpectedly large number of shared binding sites for Yki . Drosophila Yki was initially identified as an essential transcriptional coactivator in the Hippo tumor suppressor pathway [45] . Loss of function clones of yki grow very poorly , while gain of function Yki clones result in tissue overgrowths that are similar to those generated when the upstream kinases ( Hippo and Warts ) are compromised . These observations suggested that Yki , with the help of DNA binding proteins , would target genes required for cell proliferation and survival , including the known Hippo pathway targets cycE and diap1 . Consistent with this expectation , we observe Yki binding to these and other genes that are regulated by the Hippo pathway ( Figure S3 ) . Unexpectedly , however , in addition to known Hippo pathway genes we observe Yki binding to several thousands of genes in both the eye-antenna and wing imaginal discs , implying that Yki targets many more genes than those regulated by the Hippo pathway , or that the Hippo pathway targets many more genes than previously thought . Consistent with the latter possibility , over 1000 of the genes identified as tissue-shared Yki targets in this study are upregulated >2-fold in wts− wing discs relative to wild-type based on recently published RNA-seq data [88] . In addition , Yki was recently shown to bind and activate several genes required for mitochondrial fusion [89] . Moreover , the mammalian homologs of Yki , Yes-associated protein ( YAP ) and TAZ ( transcriptional coactivator with PDZ-binding motif ) are thought to regulate many genes in a wide variety of contexts , including human embryonic stem cells and several adult human tissues [39] , [41] , [42] . Taken together , these results suggest that Yki may be a widely used transcriptional coactivator in Drosophila and vertebrates . The severe cell proliferation defects associated with yki mutant clones may have obscured its other functions in other pathways . These results are consistent with the idea that Yki and its vertebrate orthologs interact with a wide variety of transcription factors [47] , [88] , [90] , [91] . Together , the data imply that DNA binding proteins in addition to Sd and Hth may recruit Yki to a large number of broadly active CRMs . The view that Yki is recruited to DNA by factors other than Sd was recently questioned by experiments suggesting that , in the eye imaginal disc , sd yki double mutant clones proliferate better than yki single mutant clones [92] . These observations were interpreted to suggest that Sd is a default repressor of proliferation and survival-promoting genes . However , this conclusion is complicated by the observation that both Sd and Yki are also important for specifying non-retinal ( peripodial epithelium ) fates in the eye imaginal disc [60]: thus , the partially rescued growth of sd yki clones could in part be due to a fate transformation . Further , we found that the activity of the ban-eye enhancer is not affected in sd clones , but is lost in hth clones , arguing that at least for this direct Hippo pathway target Hth , not Sd , is the primary activator . It is noteworthy that although their activities can be separated , the ban wing and eye enhancers identified here are adjacent to each other in the native ban locus . It is plausible that Sd+Yki input provides a basal level of activity in both tissues and that Hth and Sd boost this level in the eye and wing , respectively . Regardless , the improved growth of sd yki clones does not argue against the idea that Yki is recruited to survival genes by Hth in wild type eye discs . Taken together with our genome-wide binding and ban enhancer studies , we suggest that the absence of Sd results in both a fate change and some derepression of survival genes , but that wild type proliferation and gene regulation in the eye disc requires the recruitment of Yki to the DNA by Hth . In contrast to the widespread and largely tissue-nonspecific binding we observe for Yki , Sd and Hth exhibit both tissue-specific and tissue-shared binding events . Multiple characteristics distinguish these types of binding . First , tissue-shared binding by both Sd and Hth is frequently associated with Yki binding and often close to cell cycle and housekeeping genes , while tissue-specific binding is not . These observations are consistent with previous studies showing that Yki controls cell survival and proliferation in all imaginal discs , an activity that is regulated by the Hippo pathway [45] , [62] . Second , compared to tissue-shared binding , DNA sequences bound by Sd and Hth in a tissue-specific manner are more conserved , more likely to contain the TF's consensus binding site , less likely to be promoter proximal , and more likely to be associated with key developmental regulatory loci . Third , tissue-specific Sd and Hth binding events are more likely to overlap with enhancers active in the corresponding tissue . To illustrate this point , the newly identified tissue-specific TF-CRM interactions at wg match the known roles for Sd and Hth ( Figure 3B ) . Taken together , these results suggest that regulation at the level of TF-DNA binding is a significant mechanism by which Sd and Hth regulate tissue-specific gene expression . Tissue-specific binding could be regulated through direct or indirect interactions with additional transcription factors , through tissue-specific differences in DNA accessibility , or through a combination of these factors . We also found that distinct chromatin types are differentially correlated with tissue-specific and -nonspecific binding , even though these chromatin categories were defined in Kc cells . All tissue-shared binding events have a strong tendency to occur in actively transcribed chromatin states ( YELLOW and RED ) . Tissue-specific ( W>EA ) Sd and Hth binding is also enriched in RED chromatin but is uniquely enriched in BLUE chromatin . BLUE chromatin is associated with Polycomb-mediated repression . The W>EA Sd and Hth binding in Polycomb-associated chromatin indicate that these factors target tissue-specific enhancers that are also regulated by PcG proteins during development . Despite the importance of tissue-specific binding as a regulatory mechanism for Sd and Hth activity , both factors also displayed a significant amount of tissue-shared binding . We found that these tissue-shared binding events can be broken down into distinct groups based on the local chromatin environment ( Figure 8G ) . The majority of tissue-shared binding occurs in YELLOW chromatin and is associated with ubiquitously expressed housekeeping genes . However , binding that occurs in BLUE chromatin , and to a lesser extent in RED chromatin , is more conserved and more likely to be associated with a TF's motif , both characteristics of tissue-specific binding . In the case of the bantam eye and wing enhancers , Sd and Hth binding in BLUE chromatin is direct and apparently able to drive tissue-specific , rather than ubiquitous , expression patterns . Other examples of enhancers in RED or BLUE chromatin that drive patterned expression and have tissue shared binding are shown in Figure S5 . These observations suggest that gene regulation by Sd and Hth may also be controlled at a step beyond DNA binding , perhaps via interactions with additional transcription factors at a given enhancer . Alternatively , some of the binding events called as tissue-shared may turn out to be specific binding events in distinct cell types within each imaginal disc ( e . g . hinge , notum , and pouch in the wing disc and antenna , eye progenitor domain , and photoreceptors in the eye-antenna disc ) . Regardless , the hundreds of Sd- and Hth-CRM interactions identified in this study ( Dataset S5 ) provide a tremendous resource for further dissecting the mechanisms by which Sd and Hth regulate patterned gene expression . Notably , few of the above conclusions would have been clear had genome-wide binding been measured in only one of the two tissues . Tissue-specific binding is not the most highly enriched ( that is , the signal is generally weaker compared to tissue-shared events ) ( Figure S6 ) and might have been overlooked had we just characterized one tissue , where the strongest peaks are generally the focal point [7] , [10] , [93] . The tissue-specific binding events detected here may also occur in subsets of cells in the wing or eye-antennal discs , which are also heterogeneous in cell type . This would explain why tissue-specific binding signals may be weaker , because the ChIP data represent an average of all cell types in a single imaginal disc type . If correct , it would be an error to focus on only the strongest peaks when analyzing in vivo TF binding , particularly in heterogeneous tissues . It is possible that ChIP signal is more biologically meaningful in highly homogenous tissues like the blastoderm Drosophila embryo , or in cell culture . Still , distinct TF-DNA binding mechanisms ( long residence time versus rapid binding turnover ) with different functional outcomes can lead to indistinguishable , strong ChIP peaks , making it difficult to interpret ChIP data on strength of signal alone [94] . Despite their lower intensity , many biologically relevant binding events , such as those identified here , may only stand out when looking at the influence of tissue context on binding .
Imaginal disc ChIPs were performed as described previously [63] , [95] . Briefly , imaginal discs were dissected from wandering third-instar larvae and placed in PBS on ice . Discs were then fixed with 1 . 8% formaldehyde , and chromatin was sonicated to an average size of 500 bp . Immunoprecipitations were performed with goat anti-Hth ( dG-20 , Santa Cruz Biotechnologies; 1 . 5 µg/ml for IP ) , rabbit anti-GFP ( ab290 , Abcam; 1∶300 dilution for IP ) , and rabbit anti-Yki ( [62]; 1∶300 dilution for IP ) . ChIP and input DNA were amplified using the GenomePlex WGA4 Whole Genome Amplification Kit ( Sigma ) , and then labeled according to Affymetrix protocols and hybridized on Affymetrix GeneChip Drosophila Tiling 2 . 0R Arrays . Tiling array data were processed with MAT ( Model-based Analysis of Tiling-arrays ) , with peaks called at 1% FDR ( false discovery rate ) and 25% FDR as described in the results section [64] . FlyLight enhancers were described previously [72] , [96] . The significance of overlap between genomic regions ( ChIP peaks versus ChIP peaks; ChIP peaks versus FlyLight enhancers; ChIP peaks versus chromatin states ) were calculated using the mergePeaks program within the HOMER ( Hypergeometric Optimization of Motif EnRichment ) Suite [97]; expected overlap and co-occurrence p-values are calculated based on the hypergeometric distribution . Breakdown of binding events by genomic region was performed using the CEAS ( Cis-regulatory Element Annotation System ) program within the Cistrome platform [98] . Motif analysis in Figure 2 was performed using Centrimo , which uses a binomial test to identify non-randomly distributed motifs within ChIP peaks ( i . e . , motifs selectively enriched near peak centers ) [99] . The JASPAR Core database [100] was used for motif scanning by Centrimo , with the default significance threshold of E-value ≤10; E-value is the enrichment p-value ( binomial test ) multiplied by the number of motifs in the JASPAR Core database ( 460 motifs ) . Two Gal4 enhancer trap insertion lines , coupled with UAS-GFP , were used to assess bantam expression pattern: NP3256 and NP0016 ( DGRC , Kyoto ) . Ectopic Hth expression was examined on larvae with genotype: yw , hs-Flp1 . 22; ban-eye ( 51D ) /UAS-Hth; actin>stop>Gal4 , UAS-GFP/+; larvae were heat-shocked for 7 min at 37°C and dissected 48 h later at crawling stage ( UAS-Hth is described in [101] ) . All mutant clones were performed on Minute background and in some cases in Df ( 3L ) H99 ( hid- , rpr- , grim- ) /+ background [102] in order to alleviate the growth disadvantage of these mutant cells . hth mutant clones were analyzed in imaginal discs from non-Tb larvae that resulted from the cross between males with genotype yw;; FRT82B hthP2/TM6B , Tb or yw;; FRT82B hth100-1/TM6B , Tb and females with genotype yw , hs-Flp1 . 22; ban-eye ( 51D ) ; FRT82B , M , hs-GFP/TM6B , Tb . exd mutant clones were analyzed in discs from female larvae from the progeny of males with genotype yw , exd1 , FRT19A/Y; tub-Exd/CyO ( the tub-Exd transgene rescues the mutant exd1 [103] ) and females with genotype yw , M , Ubi-GFP , FRT19A/FM7; ban-eye ( 51D ) ; hs-Flp . exd mutant clones were verified by lack of staining with anti-Exd Ab . sd mutant clones were analyzed in discs from Tb female larvae that resulted from the cross between males with genotype yw , sdΔB , FRT19A/Y;; Dp ( 1;3 ) DC523/TM6B , Tb ( Dp ( 1;3 ) DC523 rescues the sd mutant ) and females with genotype yw , M , Ubi-GFP , FRT19A/FM7; ban-wing ( 51D ) ; hs-Flp . yki clones were analyzed in discs from larvae resulting from the cross between males with genotype yw , hs-Flp1 . 22; FRT42D , ykiB5; Df ( 3L ) H99/C ( 2L;3R ) , Tb and females with genotype yw , hs-Flp1 . 22; FRT42D , M , hs-GFP/Cyo; ban-lacZ ( 86Fa ) /TM2 , where ban-lacZ is either the ban-wing or ban-eye enhancer driving lacZ . All mitotic clones where generated by a 45 min heat shock at 37°C and because of the Minute background larvae were dissected 72 h after heat shock at crawling stage . To induce GFP expression in larvae marked with hs-GFP , those were heat-shocked again 1 h before dissection for 20 min at 37°C . All enhancer-reporter in vivo assays were performed using the PhiC31 attB/attP system [104] . Overlapping DNA fragments covering >40 kb of the bantam locus were PCR amplified and introduced in lacZ-bearing reporter vectors . Two attB reporter vectors were used for assaying enhancer activity: one marked with mini-white+ gene ( pRVV54 ) and the other marked with mini-yellow+ gene ( pRVV212 ) . Both vectors carry a multiple cloning site for enhancer introduction , a minimal Drosophila synthetic core promoter [105] , followed by nuclear lacZ [106] and the late SV40 transcriptional terminator sequence [107] . Transgenes were inserted in either 51D or 86Fa attP sites [108] . Once minimized the ban eye and wing enhancers were inserted in both 51D and 86Fa attP sites and used for genetic experiments . Mutant ban enhancer transgenes were inserted in site 51D and compared to the corresponding ( eye or wing ) wildtype ban enhancer transgene inserted in the same site . The bantam eye enhancer is delimited by primers: GCTTCGCATCGTAGTCGTCCCCC and TAAAAAAAAAAAACAGAAGCACCTTTG . The bantam wing enhancer is delimited by primers: GTTTGCTCTGCTCTACGCCACC and AACTTTCAACTTTTTTTTTTAGTTG . Primers used for enhancer mutagenesis are listed in Table S1 . The following antibodies were used for tissue stainings: rabbit anti-β-galactosidase ( Cappel ) , guinea pig anti-Hth [85] , rabbit anti-Exd ( 8857540 ) , rabbit anti-Yki ( gift from D . Pan ) , mouse anti-Dlg ( Developmental Studies Hybridoma Bank ) . Imaginal discs were immunostained by standard procedures . AlexaFluor488 , AlexaFluor555 , and AlexaFluo647 conjugates with secondary antibodies from Invitrogen were used at 1∶1000 dilution . | The Hippo tumor suppressor pathway controls proliferation in a tissue-nonspecific fashion in Drosophila epithelial progenitor tissues via the transcriptional coactivator Yorkie ( Yki ) . However , despite the tissue-nonspecific role that Yki plays in tissue growth , the transcription factors that recruit Yki to DNA , most notably Scalloped ( Sd ) and Homothorax ( Hth ) , are important regulators of developmental patterning with many tissue-specific functions . Thus , these three transcriptional regulators – Yki , Sd , and Hth – provide a model for exploring the properties of protein-DNA interactions that regulate both tissue-shared and tissue-specific functions . With this goal in mind , we identified the positions in the fly genome that are bound by Yki , Sd , and Hth in the progenitors of the wing and eye-antenna structures of the fly . These data not only provide a global view of the Yki gene regulatory network , they reveal an unusual amount of tissue specificity in the genomic regions targeted by Sd and Hth , but not Yki . The data also reveal that tissue-specific binding is very likely to overlap tissue-specific enhancer regions , provide important clues for how tissue-specific Sd and Hth binding occurs , and support the idea that gene regulatory networks are plastic , with spatial differences in binding significantly impacting network structures . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Divergent Transcriptional Regulatory Logic at the Intersection of Tissue Growth and Developmental Patterning |
The widespread distribution of lentiviruses among African primates , and the lack of severe pathogenesis in many of these natural reservoirs , are taken as evidence for long-term co-evolution between the simian immunodeficiency viruses ( SIVs ) and their primate hosts . Evidence for positive selection acting on antiviral restriction factors is consistent with virus-host interactions spanning millions of years of primate evolution . However , many restriction mechanisms are not virus-specific , and selection cannot be unambiguously attributed to any one type of virus . We hypothesized that the restriction factor TRIM5 , because of its unique specificity for retrovirus capsids , should accumulate adaptive changes in a virus-specific fashion , and therefore , that phylogenetic reconstruction of TRIM5 evolution in African primates should reveal selection by lentiviruses closely related to modern SIVs . We analyzed complete TRIM5 coding sequences of 22 Old World primates and identified a tightly-spaced cluster of branch-specific adaptions appearing in the Cercopithecinae lineage after divergence from the Colobinae around 16 million years ago . Functional assays of both extant TRIM5 orthologs and reconstructed ancestral TRIM5 proteins revealed that this cluster of adaptations in TRIM5 specifically resulted in the ability to restrict Cercopithecine lentiviruses , but had no effect ( positive or negative ) on restriction of other retroviruses , including lentiviruses of non-Cercopithecine primates . The correlation between lineage-specific adaptations and ability to restrict viruses endemic to the same hosts supports the hypothesis that lentiviruses closely related to modern SIVs were present in Africa and infecting the ancestors of Cercopithecine primates as far back as 16 million years ago , and provides insight into the evolution of TRIM5 specificity .
The lentiviruses comprise a genus within the family Retroviridae [1] . These include viruses of horses , small ruminants , cows and felids , as well as some 40 or more species of primate lentiviruses- the latter including HIV-1 , HIV-2 and the simian immunodeficiency viruses ( SIVs ) of Old World African primates [2] . The primate lentiviruses form a distinct branch within the lentivirus genus , and share a number of derived features including several unique accessory genes [3] . Endogenous sequences related to the modern lentiviruses have been discovered in the genomes of mustelids ( weasels and ferrets ) [4 , 5] , lagomorphs ( rabbits and hares ) [6 , 7] , colugos ( flying lemurs ) [8] and multiple species of lemur [9 , 10] . These ancient lentivirus ERVs ( endogenous retroviruses ) interleave with modern lentiviruses in phylogenetic trees , and molecular clock analyses indicate that they range in age from 3 to 12 million years [3] . One of these , the pSIVgml ERV of lemurs , shares features with both non-primate and primate lentiviruses , and therefore represents a transitional form bridging the primate and non-primate lentiviruses [3 , 9] . These observations indicate that lentiviruses very similar to modern lentiviruses have existed for at least several million years . However , it remains an open question as to when the common ancestors of the modern primate lentiviruses first emerged in the ancestors of extant African primates , and whether emergence of these viruses influenced evolution of host antiviral defense genes . There are several observations suggesting that lentiviruses have been endemic to African primates going back many generations . For example , there is a general trend in which pathogenic infections are associated with a recent acquisition of a primate lentivirus . Thus , higher mortality rates are observed among humans infected with HIV-1 or HIV-2 , chimpanzees infected with SIVcpz , rhesus macaque species experimentally infected with SIVmac or SIVsmm , and pig-tailed macaques infected with SIVagm [11–18] . In contrast , primates believed to be long-standing hosts of a particular SIV are less likely to present overt clinical symptoms of infection during a normal lifespan [12 , 13 , 19] . However , the timescales required for coevolution to result in non-pathogenic interactions between lentiviruses and hosts are unknown; therefore , while such observations are suggestive of long-term coevolution , they are not useful for dating the origins of these viruses . Comparisons of extant SIVs on mainland Africa and Bioko Island suggest that modern SIVs were present for at least the past 30 , 000 years [20] . In contrast , endogenous lentivirus sequences establish that lentiviruses existed 3–15 million years ago , and the identification and functional analysis of positively selected sites in certain host-encoded restriction factors also provides compelling , indirect evidence for the existence of ancient lentiviruses [21–28] . TRIM5 is unique among well-characterized restriction factors in specifically targeting retroviruses via direct binding to the viral capsid after entry into the cytoplasm of the infected cell [29–31] . As a consequence , evolutionarily derived changes in TRIM5 of modern species should include adaptations selected by retroviruses encountered by the ancestors of those same species [32] . We therefore reasoned that reconstructing the evolution of the TRIM5 gene of African Catarrhine primates ( Old World monkeys and apes ) should reveal patterns consistent with long-term interactions with primate lentiviruses , and that host lineage-specific adaptations in TRIM5 should correlate specifically with recognition and restriction of extant primate lentiviruses . Here , we used additional sampling of TRIM5 sequences from Old World primates , phylogenetic reconstruction , and restriction of a panel of retroviruses representing multiple retroviral genera to establish a correlation between 1 ) adaptations unique to the TRIM5 gene of Cercopithecine monkeys ( macaques , mangabeys , baboons , guenons , African green monkeys , and other related species ) but not other primates , and 2 ) specificity for only that subset of primate lentiviruses endemic to modern Cercopithecinae hosts . The distribution of these changes on a phylogeny of African primates indicates that ancestral lentiviruses closely related to modern SIVs began colonizing the primate lineage in Africa as far back as 11–16 million years ago . Furthermore , using a panel of previously described amino-acid substitutions in the SIVmac239 capsid protein ( CA ) , we found that the TRIM5 proteins of two different Cercopithecinae lineages evolved to target an interface unique to the capsid proteins of lentiviruses .
TRIM proteins are named for their shared tripartite domain structure comprising RING , B-box and coiled-coil domains [33 , 34] . The α isoform of TRIM5 encodes a C-terminal PRYSPRY domain that acts as the viral recognition domain [29 , 30] . Among primates , the TRIM5α PRYSPRY domain has evolved under strong positive selection with a majority of positively selected sites clustered within four variable domains ( V1 to V4 ) [25 , 35 , 36] . These variable domains are thought to directly mediate contacts with retroviral capsids ( CA ) [35–52] . Notably , the length of the V1 sequence has remained constant in all primate lineages except the Cercopithecinae [29 , 36 , 53 , 54] . The subfamily Cercopithecinae includes two tribes , the Cercopithicini ( including guenons , Patas monkeys and African green monkeys ) and the Papionini ( which includes macaques , baboons , and mangabeys ) ; V1 length variation in the Cercopithecines differs between the two tribes , with some Papionini TRIM5α V1s having been lengthened by two amino acids , while some Cercopithicini TRIM5α V1s have been lengthened by 20 amino acids [29 , 36 , 53] . To reconstruct the evolution of V1 sequences in Cercopithecinae primates , we generated an alignment that included TRIM5 sequences retrieved from public databases and by sequencing of previously unreported TRIM5 genes representing five additional Cercopithecinae species , for a total of 22 Catarrhini species and subspecies . These included new sequences representing four guenon species , Cercopithecus wolfi ( Wolf’s Guenon , n = 3 ) , Cercopithecus cephus ( mustached guenon , n = 1 ) , Cercopithecus ascanius ( Schmidt’s guenon n = 2 ) , Cercopithecus neglectus ( De Brazza's monkey , n = 1 ) , and the mangabey Cercocebus torquatus ( red-capped mangabey , n = 1 ) . We then used this alignment to trace the origins of the V1 length variants and map evolutionary events onto the established phylogeny of Old World primates ( Fig 1 ) . Consistent with previous reports , we found that length variation in the TRIM5 V1 region is unique to the Cercopithecinae . Among some cercopithecine primates , at least two independent duplication-insertion events occurred at or adjacent to TRIM5α amino acid position 339 . Due to the length polymorphisms in this region we will refer to this site , centered on cercopithecine TRIM5α position 339 , as the V1-patch ( Fig 1 ) . From our analysis we established that a Q ( henceforth “V1:Q” ) corresponding to position 339 in cercopithecine TRIM5αs represents the state that was present in the ancestor of all Old World primates ≥30 million years ago ( Fig 1 and Table 1 ) . Strikingly , over the course of catarrhine evolution , V1:Q has remained unmodified in all other primate lineages except the Cercopithecinae . In contrast , among the latter we found TRIM5 variants with the evolutionarily derived V1 modifications at this site , in addition to TRIM5 orthologs that had retained the ancestral V1:Q residue ( Fig 1B ) . The presence of the ancestral ( V1:Q ) and derived residues in the Cercopithecinae indicates that selection favored the maintenance of multiple TRIM5α variants in this primate lineage , a possible indication of long-term balancing selection [54] . All V1-patch variants representing the Cercopithecini tribe either have a Q at position 339 ( or the homologous position ) , or else they share a common V1:Q-to-G substitution ( which we call V1:G ) . In addition , the cercopithecin V1:G variant was further modified by a duplication of adjacent sequence resulting in the insertion of 20 additional amino acids ( which we call V1:G+20 ) ; examples of extant species with the V1:G+20 variant include African green monkeys and some Patas monkeys ( Fig 1 ) [36 , 53] . Interspecies and intraspecies differences within these 20 residues indicate that the inserted sequences have continued to evolve after the initial duplication event . The presence of a G at the homologous position in TRIM5α of guenons , African green monkeys and some Patas monkeys allowed us to date both evolutionary events ( Fig 1A ) . Thus , we infer that V1:G arose first , between ~11 and 16 million years ago , while the insertion event leading to V1:G+20 likely occurred between ~3 . 5 and 14 million years ago ( Fig 1B and Table 1 ) . In the Papionini , all variants of V1:Q share a two-amino-acid duplication ( Fig 1B ) ; among the Papionini , there are examples of TRIM5αs with this two-amino-acid insertion in every genus . This indicates that the original insertion was present in the last common ancestor of all extant papionin species ~8–15 million years ago ( Fig 1B ) . Following the insertion event , this modified patch continued to evolve , resulting in the V1:SFP , V1:TFP , V1:PFP , V1:LFP , and V1:MFP derivatives found in extant papionin species ( we will use “V1:+2” when referring to these collectively ) ( S1A Fig ) . These changes to the two-amino-acid duplication have obscured the sequence of the initial insertion and its potential evolutionary intermediates . From an examination of known papionin TRIM5α sequences and the papionin phylogeny , we have inferred one possible evolutionary pathway ( illustrated in S1 Fig ) . Briefly , the insertion most likely arose from a six-base pair duplication of adjacent sequence resulting in V1:QFQ , which subsequently underwent a number of substitutions ( S1B and S1C Fig ) . We and others have previously reported that Asian macaques have a third TRIM5 variant , in which the PRYSPRY domain of a V1:Q ortholog has been replaced by a cyclophilin A domain ( Fig 1B ) ; thus far , the TRIM5-cyclophilin A fusion has not been shown to restrict any viruses other than lentiviruses [59–63] . Diversity within the V1-patch is unusual among other Old World primates , making the appearance of multiple V1-patch modifications in the Cercopithecinae remarkable . In contrast to V1:Q , which has remained unmodified outside the Cercopithecinae for more than 30 million years , within the Cercopithecinae the V1-patch was modified by evolution at least twice , in two independent lineages ( the Cercopithecini and the Papionini tribes ) in the space of about 1–4 million years , about 8–16 million years ago ( Fig 1 ) . These V1 modifications then continued to evolve , with further modifications occurring at or adjacent to position 339 in V1 ( Fig 1 ) . The emergence of V1 patch variants in two independent Cercopithecinae lineages suggests that these early modifications may have conferred a selective advantage . We therefore next sought to determine the impact , if any , that these modifications have on the restriction of retroviruses . To do this , we assayed for restriction by a panel of TRIM5 orthologs , including a recreated ancestral TRIM5α sequence representing the last common ancestor of all cercopithecine TRIM5 sequences . First , in order to reconstruct the sequence of an ~11–16 million year old TRIM5α protein to represent the last common ancestor of all extant TRIM5s of the subfamily Cercopithecinae , we created a comprehensive sequence alignment that included 60 full-length TRIM5α sequences from 22 primate species and subspecies . A maximum-likelihood tree was generated from this alignment ( S2 Fig ) . Both the alignment and tree were used to predict the sequence of the last common ancestral sequence of all cercopithecine TRIM5αs using the FastML server ( http://fastml . tau . ac . il/ ) [64–66] . This reconstructed TRIM5α , which we refer to as ancTRIM5αV1:Q , is predicted to reflect the sequence that predates the selective events at TRIM5α V1 position 339 ( includes the ancestral Q at position 339 ) ( S2 and S3 Figs ) . AncTRIM5αV1:Q approximates the ancestral TRIM5α sequence in which the initial V1 adaptations occurred , and importantly , provides a single , isogenic backbone for directly comparing the functional consequences of individual V1 adaptations in the context of an otherwise identical protein sequence . In combination with the panel of naturally occurring TRIM5 orthologs bearing these adaptations , this allows us to make a thorough assessment of the impact of these changes on restriction of a diverse panel of retroviruses . Thus , we also modified ancTRIM5αV1:Q to compare the impact of each of the cercopithecine V1 adaptations , including the V1:G and V1:G+20 variants , which we refer to as ancTRIM5αV1:G and ancTRIM5αV1:G+20 . The 20 amino-acid insertion recreates the original duplication ( that is , the duplicated sequences are identical ) without the additional diversification seen in extant TRIM5α orthologs . We also generated ancTRIM5α derivatives with the additional V1-patch modifications found in Papionini species ( ancTRIM5αV1:SFP and ancTRIM5αV1:TFP ) and their predicted evolutionary intermediates ( ancTRIM5αV1:QFQ , ancTRIM5αV1:PFP ) ( Fig 2 and S1 Fig ) . We next generated stable cell lines expressing each of the HA-tagged ancTRIM5αV1:Q derivatives , as well as cell lines stably expressing HA-tagged versions of TRIM5α orthologs cloned from extant cercopithecine species . The latter included TRIM5α orthologs with a naturally occurring G at position 339 from mustached guenons ( mus ) , De Brazza’s monkeys ( deb ) and two alleles from Schmidt’s guenon ( Sch1 and Sch2 ) , and TRIM5α orthologs bearing the 339G and the 20-amino-acid insertion from African green monkey-derived Vero cells ( AgmV ) and COS-1 cells ( AgmC ) . TRIM5αs with the V1:SFP modification came from sooty mangabeys and red-capped mangabeys ( sm ( ceat-1 ) and rcm , respectively ) . Rhesus ( rh ) TRIM5α ( mamu-1 ) is a modern day V1:TFP allele . Cercopithecine TRIM5α orthologs with an unmodified V1:Q were included from rhesus macaque ( mamu-5 ) and Wolf’s guenon ( wlf ) TRIM5αs . Human ( hu ) TRIM5α ( V1:Q ) was included as a non-cercopithecine control ( Fig 2 ) . To ask whether adaptations in TRIM5 V1 unique to cercopithecine primates may have been selected specifically for recognition of Cercopithecine retroviruses , we sought to determine whether the V1:G , V1:+2 and V1:G+20 variants and their derivatives specifically affect restriction of cercopithecine SIVs , or whether they have more general effects , either positive or negative , on the ability to restrict other retroviruses . Specifically , to assay restriction by our panel of 19 TRIM5α proteins , we assembled a representative panel of cercopithecine SIVs , as well as two human lentiviruses ( HIV-1nl4 . 3 and HIV-2rod ) , two non-primate lentiviruses ( the feline immunodeficiency virus , FIV , and the equine infectious anemia virus , EIAV ) ; an avian alpharetrovirus ( RSV ) ; two murine gammaretroviruses ( N-tropic and B-tropic strains of murine leukemia virus , N-MLV and B-MLV ) ; and a betaretrovirus ( Mason-Pfizer monkey virus , M-PMV ) . Infectious SIV virus stocks were produced either by transfection of the respective molecular clones or of HIV-1-based molecular clones engineered to encode the CA domains of other Cercopithecine SIVs ( S4–S6 Figs ) . These included the SIVs from species in the tribe Cercopithicini—mustached guenons ( SIVmus ) and African green monkeys ( SIVagmTan-1 , SIVagmVer , and SIVagmGrv ) —and from the Papionini tribe- macaque-passaged sooty mangabey SIV ( SIVsmE543-3 ) , the rhesus macaque SIV isolate ( SIVmac239 ) , a stump tailed macaque SIV ( SIVstm ) and an SIV isolate from a red-capped mangabey ( SIVrcm ) ( Fig 2 ) . The 19 TRIM5-expressing cell lines were assayed for the ability to restrict each of the 16 viruses in the panel . Although expression levels of the different TRIM5α derivatives varied , we did not observe a correlation between expression level and restriction ( Fig 2 and S7 Fig , S1 Dataset ) . Importantly , all TRIM5α constructs , including the synthetic ancestral constructs , expressed functional TRIM5α proteins , as each of the 19 cell lines was able to restrict three or more viruses representing two or more retroviral genera ( Fig 2 ) . Other than the avian alpharetrovirus ( RSV ) and the mouse gammaretrovirus B-MLV , which were not restricted by any of the 19 TRIM5α proteins tested , all other viruses were restricted by at least one TRIM5-expressing cell line . Except for the Cercopithecine SIVs , all of the other retroviruses were either almost always resistant ( RSV , M-PMV , B-MLV ) or almost always sensitive ( N-MLV , EIAV , FIV and HIV-1 ) to restriction by the majority of the TRIM5α proteins tested ( Fig 2 ) . For example , N-MLV was restricted by all 19 TRIM5α proteins in the panel , whereas B-MLV was resistant to all 19 . Therefore , the capacity to restrict the non-Cercopithecine retroviruses ( N-MLV , B-MLV , MPMV , FIV , EIAV , and HIV-1 ) is an ancestral and conserved property of all Cercopithecine TRIM5αs , and most importantly , restriction of these retroviruses was not determined by the presence or the absence of the V1:G , V1:+2 and V1:G+20 adaptations in V1 ( in other words , adding or removing these specific modifications from V1 did not alter the restriction of any non-cercopithecine retrovirus tested , regardless of context ) . In contrast , viruses with CA domains from cercopithecine SIVs were only restricted by the subset of TRIM5-V1 variants bearing lineage-specific adaptations in V1 ( Fig 2 ) . Specifically , there was a clear correlation between restriction of the eight cercopithecine SIVs in our panel and the presence of those adaptive changes found exclusively in the TRIM5 V1-patch of cercopithecine primates . That these specific adaptions in V1 are sufficient for restriction of cercopithecine viruses is demonstrated by the gain of SIV restriction by the ancestral TRIM5α proteins modified to carry three adaptive changes ( ancTRIM5αV1:TFP , ancTRIM5αV1:G and ancTRIM5αV1:G+20 ) compared to the unmodified version ( ancTRIM5αV1:Q ) ( Fig 2 ) . Moreover , the patterns of restriction associated with each of the reconstructed ancestral TRIM5α proteins resembled those of the modern TRIM5α orthologs naturally bearing the same adaptations ( i . e . , the V1:G , V1:+2 and V1:G+20 adaptations ) ( Fig 2 ) . For example , both ancestral reconstructions and modern cercopithecine TRIM5α orthologs with V1:Q or V1:SFP failed to restrict SIV of cercopithecine hosts , whereas both ancTRIM5αV1:TFP and rhTRIM5αV1:TFP from rhesus macaques gave nearly identical patterns of restriction . Similarly , substituting the Q339 residue with a G in the ancTRIM5α to produce ancTRIM5αV1:G only resulted in a gain of ability to restrict a subset of SIVs that was also restricted by modern orthologs from mustached guenons and Schmidt’s guenons , species which naturally bear a G at the homologous position in V1 ( musTRIM5αV1:G and sch2TRIM5αV1:G in Fig 2 ) . Finally , ancTRIM5α with the G and the 20 amino-acid insertion ( ancTRIM5αV1:G+20 ) only restricted a subset of SIVs that was also restricted by TRIM5 orthologs cloned from African green monkey cell lines , a species which naturally bears the V1:G+20 modification ( referred to as agmCTRIM5αV1:G+20 and agmVTRIM5αV1:G+20 in Fig 2 ) . It is also noteworthy that TRIM5αs representing each tribe generally did not restrict viruses from the same tribe . Thus , cercopithecin TRIM5αs did not restrict cercopithecin SIVs and , with the exception the TFP alleles , papionin TRIM5αs did not restrict papionin SIVs . This observation is consistent with the possibility that these viruses have been co-evolving with their respective tribes with little or no inter-tribe transmission or recombination between viruses of the two clades . The specificity conferred by adaptations unique to TRIM5-V1 of cercopithecine monkeys was demonstrated by three additional observations: first , if a reconstructed ancestral TRIM5α protein failed to restrict a virus , modern TRIM5αs naturally bearing the same adaptations were also unable to restrict that virus; second , not every V1 modification led to SIV restriction; third , restriction was usually not observed when an SIV was tested for restriction by the TRIM5α from its established host , regardless of modifications found in V1 ( for example , SIVmac was not restricted by the rhesus TRIM5V1:TFP allele , SIVrcm was not restricted by the red-capped mangabey ortholog , and HIV-1 was not restricted by human TRIM5α ) ( Fig 2 ) . Finally , we note that M-PMV was previously reported to be resistant to restriction by TRIM5α cloned from COS cells [67] , whereas we found this virus was restricted greater than 100-fold by agmCTRIM5aV1:G+20 . The explanations for this discrepancy include the possibility that different alleles were cloned from the COS cells , or the fact that the reported restriction was performed in HeLa cells ( which are capable of expressing endogenous human TRIM5 ) whereas our assays were done in TRIM5-null CRFK cells . This result does not affect the overall conclusion that adaptations in V1 uniquely affect restriction of cercopithecine SIVs , and sensitivity or resistance of all other viruses to agmCTRIM5αV1:G+20 were in agreement with previously published results [36 , 53 , 67–70] . All of the naturally occurring TRIM5α orthologs with V1:TFP , V1:G or V1:G+20 adaptions restricted at least three different SIVs ( SIVsmE543 , SIVstm , SIVrcm ) ( Fig 2 ) . ancTRIM5αV1:TFP , ancTRIM5αV1:G and ancTRIM5αV1:G+20 restricted SIVsmE543 and SIVrcm ( Fig 2 ) . Taken together , these observations suggest that these TRIM5α variants may recognize the same or similar target ( s ) in the capsids of these viruses . We next sought to determine which regions of the CA protein determined the resistant or sensitive phenotypes . To do this , we assayed the ability of our TRIM5-expressing cell lines to restrict a previously described panel of HIV-SIV chimeric viruses in which sequence ( s ) from the CA domain of HIV-1nl4 . 3 were introduced into the CA domain of SIVmac239; this panel was successfully used to map determinants of rhesus TRIM5α specificity [25] . We chose 10 TRIM5α variants from species in the subfamily Cercopithecinae to test against this panel of viral mutants in order to identify gain-of-sensitivity CA mutations; 9 of these restrict HIV-1 but not SIVmac239 , and a 10th , wlfTRIM5αV1:Q , does not restrict either HIV-1 or SIVmac239 , and was included as a control . We have previously reported that surface features of CA ( β-hairpin , 4–5 loop , helix-6 and the 6–7 loop ) largely govern sensitivity/resistance to rhesus TRIM5α alleles , and that the TRIM5α sensitive phenotype can be transferred between HIV-1 and SIVmac239 by the exchange of these features [25] . Similarly , to establish whether surface features of capsid are the primary determinants of restriction by our diverse panel of TRIM5 proteins we assayed restriction of four viruses: these included the two parental viruses ( HIV-1nl4 . 3 , 2 and SIVmac239 ) ; a modified SIVmac239 virus bearing the CA surface features of HIV-1 ( SIV-HIVsurface ) ; and a modified HIV-1nl4 . 3 virus bearing the CA surface features of SIVmac239 ( HIV-SIVsurface25 ) ( S5 and S6 Figs ) [25] . For the nine TRIM5αs that differentially restrict HIV-1 and SIVmac239 we found that the restricted phenotype was governed by the CA surface and that these phenotypes could be exchanged between viruses by swapping the surface CA features ( S8 Fig and S2 Dataset ) . As predicted the 10th TRIM5α , wlfTRIM5αV1:Q , did not restrict the parental viruses or the CA-chimeric viruses . There are 25 amino acid differences between the CA proteins of the parental HIV-1nl4 . 3 and HIV-SIVsurface25 ( the chimeric virus in which the CA surface features are derived from SIVmac239 ) . To identify specific sites that modulate the TRIM5α-sensitive phenotype , we next tested a series of SIVmac239 variants in which the amino acid at each of these 25 positions was substituted with the amino acid found at the corresponding position in HIV-1nl4 . 3 ( Table 2 ) [25] . Of these viruses , 23 were infectious and were assayed for gain-of-sensitivity to restriction by the 10 TRIM5αs ( Fig 3A , S5 and S6 Figs ) . Interestingly , we observed that the ancestral TRIM5α proteins with the V1:G , V1:+2 and V1:G+20 adaptations did not restrict as many of the SIVmac239-derived capsid mutants as modern TRIM5αs with the same V1 features ( Fig 3A ) . This may indicate that restriction specificity is influenced by multiple determinants in capsid and/or other sites within TRIM5α ( including co-evolving sites ) such that effects of single point mutations on a particular TRIM5 may be context dependent . We identified three CA mutations that were individually sufficient to make the SIVmac239 CA sensitive to ancTRIM5αV1:Q-mediated restriction ( SIVmac239Q3V , SIVmac239G6L and SIVmac239V111L ) . As expected , none of the CA mutations resulted in a gain of restriction by wlfTRIM5αV1:Q ( which did not restrict either of the parental viruses ) ( Figs 2 and 3A ) . Six of the eight remaining TRIM5α proteins also restricted the SIVmac239Q3V and SIVmac239G6L mutant viruses , and a seventh , sch2TRIM5αG , restricted SIVmac239G6L but not SIVmac239Q3V ( Fig 3A and S2 Dataset ) . Both of these mutations ( Q3V and G6L ) map to the β-hairpin , a structural feature that is conserved among the CA proteins of orthoretroviruses [71–76] . AncTRIM5αV1:Q and four additional TRIM5α variants restricted the SIVmac239V111L mutant , which has a substitution in helix-6 ( Fig 3A ) . Consistent with the notion that modern day TRIM5αs evolved from ancTRIM5αV1:Q , we found that extant TRIM5αs largely maintained the capacity to restrict the same SIVmac239 CA-mutant viruses as ancTRIM5αV1:Q ( Fig 3A ) . Many of these extant TRIM5αs restricted a unique subset of the SIVmac239 mutants , indicating that adaptions which alter capsid recognition occurred over the 11+ million years separating the extant TRIM5αs from ancTRIM5αV1:Q . We found that only TRIM5αs which restricted cercopithecine SIVs ( Fig 2 ) were capable of restricting more mutant SIVmac239 viruses than ancTRIM5αV1:Q . There were four CA mutations , ( Δ7Q , P87H , S100R , D112Q ) that were restricted by at least one Cercopithecini TRIM5α and one Papionini TRIM5α . For example , the P87H and D112Q mutants were restricted by musTRIM5αV1:G , sch2TRIM5αV1:G and rhTRIM5αV1:TFP ( Fig 3A ) . Similarly , both rhTRIM5αV1:TFP and sch2TRIM5αV1:G restricted SIVmac239Δ7Q , and SIVmac239S100R ( Fig 3 ) . Thus , these two TRIM5αs , from two different Cercopithecine tribes , had highly similar patterns of restriction ( Fig 3 ) . The simplest explanation for this observation is that specific Papionini and Cercopithecini TRIM5α orthologs have independently evolved to target Cercopithecine SIVs by targeting similar CA features .
The TRIM5 proteins of primates have a collective capacity to recognize and restrict highly divergent retroviruses from multiple genera , and indeed , some individual orthologs can restrict multiple , distinct retroviruses [77] . Molecular evolutionary analysis also reveals that positive selection , measured as dN/dS ratios , varies significantly in timing and intensity between branches of the primate phylogenetic tree [36] , indicating that TRIM5 can evolve at different times in response to the viruses uniquely encountered by different host lineages . Thus , correlating lineage-specific patterns of evolution with specificity for particular types of viruses can provide insight into past virus-host relationships [21] . However , attributing past selective events to a specific type of virus is difficult for several reasons—first , because of positive selection , the phylogenetic tree of a restriction factor may not faithfully recapitulate host phylogeny; second , the viruses responsible for selection may have no extant relatives known to science; and third , serial bouts of selection by different viral agents can alter or obscure the effects of prior adaptations in the restriction factor locus . We reasoned that the TRIM5 gene of cercopithecine primates should reflect selection due to the emergence of the subset of primate lentiviruses whose descendants are currently endemic to many African monkeys of the Cercopithecinae subfamily . Our analysis was aided by the fact that the phylogenetic relationships of Old World primates are very well established , and by the existence of SIV sequences and isolates from multiple cercopithecine hosts . We identified a small subset of adaptations that arose exclusively in Old World primates of the Cercopithecinae subfamily lineage ( including both tribe Papionini and tribe Cercopithecini monkeys ) , centered on position 339 in V1 ( numbering is based on accession NM_001032910 . 1 as a reference ) . This includes a Q-to-G substitution at position 339 itself and two independent insertion events at or immediately adjacent to position 339 , as well as some subsequent , lineage-specific substitutions that occurred within these inserted sequences . Based on the established phylogenetic relationships among Old World primates , we estimate that these adaptations in V1 began to appear between 11 and 16 million years ago ( Fig 1 and Table 1 ) . In stark contrast , the V1 regions of the TRIM5 proteins of all the other Old World primates ( Hominoidea species and Colobinae species ) are of uniform length , and retain the conserved , ancestral Q at position 339 . Using a reconstructed ancestral TRIM5 protein engineered to contain these cercopithecine-specific adaptations in V1 , we show that the changes affect only restriction of extant lentiviruses ( SIVs ) of cercopithecine monkeys , but do not affect restriction of other lentiviruses , or of retroviruses representing three additional retroviral genera . Likewise , among extant , naturally occurring TRIM5α orthologs , only those containing identical or similar adaptive changes in V1 consistently restricted cercopithecine lentiviruses . In other words , restriction of all other viruses tested was independent of the presence or absence of these adaptations in V1 , demonstrating that V1 adaptations unique to the cercopithecine TRIM5 locus were most likely selected by viruses closely related to the SIVs currently endemic to these hosts ( the exceptions occur when both the virus and TRIM5 represent the same host species , reflecting host-specific adaptation ) . While these changes affect restriction of cercopithecine SIVs , it is interesting that they did not affect restriction of other sensitive viruses , such as MLV , which are known to be affected by sequences in the V1 loop [38 , 43 , 49–51 , 78 , 79] . Thus , these specific changes resulting in gain-of-specificity for cercopithecine lentiviruses did not overwrite or alter the ability of the PRYSPRY domain to interact with the capsids of the other retroviruses tested . As mentioned before , a Q at position 339 ( or its homologs ) in TRIM5 V1 reflects the ancestral state of all catarrhine primates , which last shared a common ancestor ~24–34 million years ago ( Table 1 ) . While in non-cercopithecine lineages the ancestral Q has remained unmodified during ~24–34 million years of primate evolution , this position was twice modified by evolution in two independent cercopithecine lineages within a span of approximately 1–4 million years ( Fig 1 and Table 1 ) . A reconstructed ancestral TRIM5α representing the last common ancestor of all Old World primates has been reported , and its ability to restrict gammaretroviruses and lentiviruses assayed [80] . Like our somewhat younger ( 11–16 million year old ) ancTRIM5αV1:Q ( Fig 2 and S1 Dataset ) , this ancestor also has a Q in the V1-patch , and like our ancTRIM5αV1:Q , this much “older” variant also restricted N-MLV and HIV-1 , weakly restricted HIV-2 ( ~2–4 fold ) , and did not restrict B-MLV , SIVmac239 or SIVagmTan [80] . This report , along with our results , strengthens the conclusion that adaptations in the V1-loop in the cercopithecine lineage arose in response to a virus or viruses related to the modern SIVs found in these species . The most parsimonious explanation for our observations is that adaptations in V1 that specifically affect restriction of SIVs from cercopithecine hosts , but not of other retroviruses , reflects selection by lentiviruses related to the modern cercopithecine SIVs . This conclusion is consistent with other observations regarding the prehistory of the lentiviruses . For example , distinct cercopithecine SIV lineages are believed to have existed prior to the isolation of Bioko Island from the African continent at least 10 , 000 years ago [20] , and endogenous lentivirus sequences in the genomes of several mammalian species indicate that viruses related to extant lentiviruses existed at least 3–15 million years ago [4–6 , 8 , 9] . Evidence for ancient lentiviral infection in the ancestors of Old World primates also comes from the study of patterns of selection in genes with known anti-viral activity , such as tetherin ( BST-2 ) and the APOBEC3 enzymes , and their interactions with viral antagonists of these factors , such as the Nef , Vpu and Vif accessory proteins of modern lentiviruses [21 , 22 , 81 , 82] . Our results lend strong support to these studies by virtue of extending the conclusions to a third , unrelated restriction factor gene ( TRIM5 ) , which operates via a distinct mechanism and targets a different stage in the retroviral replication cycle . Furthermore , because we determined that adaptations found exclusively in the TRIM5 genes of cercopithecine species affect only restriction of those lentiviruses naturally found in cercopithecine hosts , but had little effect ( either positive or negative ) on restriction of any other retrovirus tested ( including other primate and non-primate lentiviruses , as well as retroviruses of other genera ) , we can also extend the results of previous studies by concluding that adaptations in cercopithecine TRIM5 were selected by lentiviruses closely related to the subset of simian immunodeficiency viruses currently found in modern cercopithecine monkeys . Our observations also suggest that the TRIM5αs representing species in the Papionini and Cercopithecini tribes have independently evolved to restrict endemic lentiviruses through recognition of common or closely overlapping sites on the CA protein , suggesting convergent evolution to target the same feature ( s ) of the lentiviral capsid core ( Fig 3 ) . Specifically , we found that the restriction-sensitive and restriction-resistant phenotypes are largely determined by the CA surface features and that a handful of single amino-acid substitutions within these surfaces are sufficient to render a once resistant virus sensitive ( Fig 3 and S8 Fig , S2 Dataset ) . We identified seven such capsid mutations that affected restriction by both papionin and cercopithecin TRIM5αs , of which four specifically affected restriction by modern papionin and cercopithecin TRIM5αs but not ancTRIM5αV1:Q ( Fig 3 ) . Importantly , cercopithecine TRIM5αs which retained the ancestral V1:Q did not restrict any of the four mutant viruses . The fact that the TRIM5αs from Papionini and Cercopithecini monkeys that restricted a common subset of extant cercopithecine SIVs were also similarly affected by the same single-amino acid substitutions in capsid argues that TRIM5α from both tribes recognize similar or overlapping features in CA . The locations in CA of the mutations that sensitize SIVmac239 to papionin and cercopithecin TRIM5α-mediated restriction may be significant . These map to two different regions of the CA protein . The first is the β-hairpin ( mutants Q3V , G6L and Δ7Q ) , which is a structural feature conserved across Orthoretrovirinae retrovirus capsids . Mutations at two of these sites , Q3V and G6L , were independently sufficient to render the SIVmac239 capsid sensitive to nearly all of the TRIM5αs assayed , including those that otherwise did not restrict SIVs ( Figs 2 and 3 ) . This observation is in agreement with previous proposals that the β-hairpin represents a conserved feature of all retrovirus capsids that is widely exploited by catarrhine TRIM5α proteins [25 , 78 , 79] . The second set of mutations ( V111L , P87H , S100R , and D112Q ) cluster in a region responsible for mediating important interactions with host cofactors ( S9 Fig ) . Intriguingly , P87H , S100R and D112Q only affected restriction by the subset of TRIM5α proteins with Cercopithecinae-specific adaptations in V1 ( Figs 2 , 3 and 4 , S9 Fig ) . Specifically , these sites are found at the base of the 4–5 loop , which for some lentiviruses mediates contacts with Cyclophilin A and Nup-358 [83–85] , and are also directly above the binding pocket for CPSF6 and Nup-153 ( Fig 4 and S9 Fig ) [86–89] . Together , interactions with these cofactors facilitate efficient nuclear import of the viral genome , and are thought to shield the reverse transcription complex from innate immune sensors [84–88 , 90 , 91] . The broader implication of these observations is that papionin and cercopithecin TRIM5αs may have both adapted to the emergence of lentiviruses by exploiting critical , lentivirus-specific interactions with host-encoded cellular cofactors . It is plausible that these two regions of CA- the β-hairpin and the junction between cofactor binding sites constitute genuine sites of interaction between TRIM5α and CA . Structures of the TRIM5 region encompassing the B-box and coiled-coil domains indicate that this part of the protein exists as an anti-parallel dimer [92 , 93] . Using overlapping residues between this structure [92] and a structure of the PRYSPRY domain [94] , a model of the B-box-coiled-coil-PRYSPRY dimer has been generated [92] . This model suggests that the PRYSPRY domains are tucked under the coiled-coils in an arrangement that would position the two PRYSPRY domains such that their variable loops extend in opposite directions [35 , 92] . If this model is correct ( and barring large-scale conformational rearrangements of TRIM5α upon CA binding ) , the TRIM5 binding site ( s ) would also be predicted to face in opposite directions . When mapped onto a HIV-1 CA hexamer , the sites we identified that modulate sensitivity to papionin and cercopithecin TRIM5α proteins are consistent with this prediction , with a spacing that is in general agreement with the published B-box-coiled-coil-PRYSPRY model ( Fig 4 ) . Remarkably , when this TRIM5 model is placed over a two-fold axis of symmetry at the center of the CA hexamer , TRIM5 variable loops 2 and 3 sit above the β-hairpins and V1 is oriented towards the junction between cofactor binding sites ( Fig 4 ) . While we cannot exclude alternative models , our findings are consistent with models in which TRIM5α engages lentiviral CA through two sets of contacts , one in the structurally conserved β-hairpin and the second at the junction between binding sites of at least four cellular cofactors [25 , 78 , 79] . Confirmation or rejection of this model will ultimately require structural determination of the PRYSPRY domain in complex with its cognate capsid target .
TRIM5α variants were isolated from: African green monkey kidney cell lines COS-1 and Vero were obtained from the American Type Culture Collection ( Manassas , VA ) and grown in DMEM/10% FBS . Skin fibroblast cell lines derived from the following primate species were obtained through Coriell Cell Repositories ( Camden , NJ ) and cultured according to specification: Cercocebus torquatus ( red-capped mangabey , PR00485 ) , Cercopithecus cephus ( mustached guenon , PR00531 ) , Cercopithecus ascanius ( black-cheeked white-nosed monkey PR00566 and PR00634 ) , Cercopithecus neglectus ( De Brazza's monkey , PR01144 ) , Cercopithecus wolfi ( Wolf's guenon PR00486 , PR00530 and PR01241 ) . Crandell-Rees feline kidney ( CRFK ) cells and human embryonic kidney 293T/17 ( HEK293T/17 ) cells were obtained from American Type Culture Collection ( Manassas , VA ) and grown in DMEM/10% FBS . CRFK cell lines stably expressing N-terminally HA-tagged TRIM5 orthologs were maintained in DMEM/10% FBS supplemented with 5 μg/ml Puromycin . RNA was extracted using Trizol reagent ( Ambion/Life Technologies ) . cDNA was prepared using a Transcriptor First Strand cDNA Synthesis Kit ( Roche ) using an anchored-oligo ( dT ) 18 primer . TRIM5α cDNAs were amplified and N-terminally HA-tagged using TRIM5-F-GCGGAATTCGCCACCATGTACCCATACGACGTCCCAGACTACGCTGGCGGCGCTTCTGGAATCCTGCTTAATGTAAAG AND TRIM5-R-ACCATCGATGGCTCAAGAGCTTGGTGAGCACAGAGTC primers . PCR amplicons were directly coloned into pLPCX ( Clonetech ) using EcoRI and ClaI sites . TRIM5αs were cloned into pLPCX using EcoRI and ClaI sites . Retroviral GFP-reporter viruses were produced from the following plasmids: HIV-1 was produced from the following reagent that was obtained through the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH: pNL4-3-deltaE-EGFP ( Cat# 11100 ) from Drs . Haili Zhang , Yan Zhou , and Robert Siliciano [95] . The pV1EGFP derivatives encoding the 5’ region of SIVmac239 , SIVsmE543 , SIVstm/37 . 16 were previously described [96] . N-tropic or B-tropic MLVs from either pCIG-N or pCIG-B and pLXIN-EGFP ( gifts of Jonathan Stoye , Medical Research Council , London , U . K . ) . Rous sarcoma virus ( RSV ) [97] ( Addgene plasmid 13878 , courtesy of Constance Cepko , Harvard Medical School , MA ) , Equine infectious anemia virus ( EIAV ) pEV53D and pEIAV-SIN6 . 1 CGFPW ( Addgene plasmids 44168 and 44171 courtesy of John Olsen , University of North Carolina ) [98 , 99] . Feline immunodeficiency virus ( FIV ) pFP93 and pGINSIN ( gifts from Eric Poeschla , Mayo Clinic ) [100 , 101] . The first 205 amino acids of the pNL4-3-deltaE-EGFP CA were replaced with the equivalent stretch of the following SIV CAs similar to previously published reports [102 , 103]: SIVrcm ( AF349680 ) , SIVagmVerv ( L40990 ) , SIVagmGrv ( M66437 ) and SIVmus-1 ( AY340700 ) . These CA were synthesized as Strings by GeneArt/Life Technologies and cloned into pNL4-3-deltaE-EGFP using a previously described shuttle vector [25] . HIV-1nl4 . 3-SIVmac239 chimeric viruses were previously described [25] . All single-cycle viruses were produced in HEK293T/17 cells by cotransfection of the appropriate viral plasmid ( s ) and pVSV-G ( Clontech Laboratories , Mountain View , CA ) , using GenJet ( SignaGen; Ijamsville , MD ) . Viral supernatants were titered on CRFK cells; supernatant volumes resulting in approximately 25% GFP/EGFP+ CRFK cells were used for infectivity assays on the cell lines expressing the indicated ortholog of TRIM5α . Stably expressing TRIM5 CRFK cells were seeded at a concentration of 5×104 cells per well in 24-well-plates and infected with the appropriate amount of VSV-G pseudotyped , single-cycle , GFP/EGFP expressing viruses . After 2 days , expression of GFP/EGFP was analyzed by fluorescence-activated cell sorting ( FACS ) performed on a FACSCaliburTM flow cytometer ( BD , Franklin Lakes , NJ ) , and data were analyzed using FlowJo software ( Tree Star , Inc . , Ashland , OR ) . To predict a TRIM5α amino-acid sequence representing the last common ancestor of all extant Cercopithecinae species , we generated an alignment of 60 unique catarrhine TRIM5α sequences , including those first reported in this study and those obtained from publicly available databases . A maximum likelihood phylogenetic tree was generated in Geneious ( Biomatters Limited , Auckland New Zealand ) using the PhyML plugin [104]; the TRIM5α tree topology approximated the established relationships of Old World primates ( S2 Fig ) . This tree and corresponding alignment were used for ancestral node reconstruction via the FASTML server ( http://fastml . tau . ac . il/ ) [64–66] . The sequence of ancTRIM5αV1:Q corresponds to the predicted nodal sequence for the last common ancestor of all cercopithecine TRIM5αs ( S3 Fig ) . A synthetic version of this sequence including a N-terminal HA tag was generated ( Genescript , Piscataway , NJ ) and subcloned into pLPCX ( Clonetech Laboratories , Mtn . View , CA ) . The 339Q/G substitution and the 2 amino-acid insertions were generated by mutagenic PCR using this plasmid as a template . The 20 amino-acid insertion was ordered as part of smaller fragment ( Genescript , Piscataway , NJ ) and subcloned into the ancTRIM5αV1:Q vector to create ancTRIM5αV1:G+20 . Constructs were then used to generate stable cell lines as described . Cells were lysed in M-PER reagent ( Pierce Biotechnology , Rockford , IL ) and mixed with an equal volume of 2x Laemmli sample buffer ( Sigma , St . Louis , MO ) and solubilized by boiling for 10 min at 99°C . Protein was separated by SDS/PAGE . β-actin was detected using a mouse monoclonal antibody ( 20272 ) ( Abcam , Cambridge England ) . HA was detected with a rabbit polyclonal sera ( PA1-29751 ) ( Pierce Biotechnology , Rockford , IL ) using dilutions recommended by the manufacturer . HIV-1 p17 was detected using the following reagent which was obtained through the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH: Anti-HIV-1 p17 Polyclonal ( VU47 ) from Dr . Paul Spearman [105] . The GenBank accession numbers for TRIM5αs sequenced for this study are KP743973-KP743978 . | Old World primates in Africa are reservoir hosts for more than 40 species of simian immunodeficiency viruses ( SIVs ) , including the sources of the human immunodeficiency viruses , HIV-1 and HIV-2 . To investigate the prehistoric origins of these lentiviruses , we looked for patterns of evolution in the antiviral host gene TRIM5 that would reflect selection by lentiviruses during evolution of African primates . We identified a pattern of adaptive changes unique to the TRIM5 proteins of a subset of African monkeys that suggests that the ancestors of these viruses emerged between 11–16 million years ago , and by reconstructing and comparing the function of ancestral TRIM5 proteins with extant TRIM5 proteins , we confirmed that these adaptations confer specificity for their modern descendants , the SIVs . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"Methods"
] | [] | 2015 | Evolutionary and Functional Analysis of Old World Primate TRIM5 Reveals the Ancient Emergence of Primate Lentiviruses and Convergent Evolution Targeting a Conserved Capsid Interface |
Tungiasis ( sand flea disease ) is a neglected tropical skin disease caused by female sand fleas ( Tunga spp . ) embedded in the skin of the host . The disease is common in sub-Saharan Africa and predominantly affects children living in impoverished rural communities . In these settings tungiasis is associated with important morbidity . Whether tungiasis impairs life quality has never been studied . The study was performed in 50 children with tungiasis , living in resource-poor communities in coastal Kenya . Based on the Dermatology Life Quality Index ( DLQI ) a tool was developed to determine life quality impairment associated with tungiasis in children , the tungiasis-related Dermatology of Life Quality Index ( tungiasis-related-DLQI ) . Pain and itching were assessed using visual scales ranging from 0–3 points . The intensity of infection and the acute and chronic severity of tungiasis were determined using standard methods . Seventy eight percent of the patients reported a moderate to very large effect of tungiasis on life quality at the time of the diagnosis . The degree of impairment correlated with the number of viable sand fleas present in the skin ( rho = 0 . 64 , p < 0 . 001 ) , the severity score of acute clinical pathology ( rho = 0 . 74 , p < 0 . 001 ) , and the intensity of pain ( rho = 0 . 82 , p < 0 . 001 ) . Disturbance of sleep and concentration difficulties were the most frequent restriction categories ( 86% and 84% , respectively ) . Four weeks after curative treatment , life quality had improved significantly . On the individual level the amelioration of life quality correlated closely with the regression of clinical pathology ( rho = 0 . 61 , p < 0 . 001 ) . The parasitic skin disease tungiasis considerably impairs life quality in children in rural Kenya . After effective treatment , life quality improves rapidly .
Tungiasis ( sand flea disease ) is a parasitic skin disease caused by female sand fleas ( Tunga spp . ) penetrated into the skin of humans or animals [1] . It belongs to the ever-growing group of neglected tropical diseases ( NTDs ) which are infectious diseases prevalent in the tropical and subtropical regions and characterized by affecting the health of the world's poorest people and limiting their productivity . Tungiasis is particularly neglected in the sense that hitherto little research has been carried out with regard to disease burden and that health care providers in endemic areas commonly ignore the condition . Sand flea disease predominantly affects people living in poverty: in shanty towns at the periphery of metropolitan areas , in the rural hinterland or in isolated communities at the coast in South America , the Caribbean and sub-Saharan Africa including Madagascar [2–9] . Children and the elderly bear the highest disease burden [5 , 6 , 10] . In these population groups prevalence may be as high as 65% . Children frequently carry dozens of embedded sand fleas simultaneously [5 , 10] . In endemic areas 95 to 98% of all tungiasis lesions occur at the feet [10 , 11] . The toes , the sole and the heel are typical predilection sites [12] . Once embedded in the skin , the female sand flea undergoes a massive hypertrophy , and within two weeks reaches the size of a pea . Through an opening of about 250 μm the parasite remains in contact with the environment [13] . Being a continuously enlarging and biologically active foreign body located in the epidermis , embedded sand fleas cause an intense inflammatory response [12 , 14 , 15] . Bacterial superinfection is common and intensifies the inflammation [16] . Intense pain and itching are almost constant [13] . Frequent sequels are suppuration , ulcers , deep fissures , periungual oedema as well as deformation of nails and toes [12 , 14] . Although an effective and safe treatment exists it is not yet available in endemic countries [17–19] . Therefore , embedded sand fleas are removed by inappropriate sharp and non-sterile instruments such as needles , safety pins or razor blades [10 , 19] . This further increases the risk of bacterial superinfection and intense inflammation . Constant re-infection–as is the rule in endemic settings—impairs mobility , eventually leads to mutilation of the feet and immobilization of the patient . Anecdotal observations suggest that the restricted mobility may have a detrimental effect on household economics and impair school performance in children , mainly due to high absenteeism [19] . It is reasonable to assume that tungiasis causes mental strain and distress . In a setting where people rarely wear closed shoes the disease cannot be hidden in public and , since it is associated with poverty , it stigmatizes its victims [19] . In school , children are teased and ridiculed . Whether tungiasis has an impact on life quality has never been investigated in a systematic manner . This study , therefore , aimed at assessing life quality in children living in a tungiasis-endemic area in rural Kenya .
The study was performed in Kakuyuni Sublocation , Kilifi County , coastal Kenya , from September to October 2014 . In the area , tungiasis is endemic with prevalences ranging from 30 to 85% in school-age children as determined in a school survey prior to the study ( S1 Appendix ) . Demographic data were collected within the larger survey addressing the epidemiology of tungiasis in the area . In Kilifi county communities are small and consist of two to five homesteads—clusters of houses—which are located about 100 m from each other , separated by fields or bushland . Usually four to six children sleep together with their parents in the same room , frequently on rugs put on the floor , more seldom on a dilapidated mattress , and rarely in a bed . Eighty five percent of the rooms do not have a solid floor , facilitating the propagation of the parasite inside the house . Eighty eight percent of the households have domestic animals such as goats , chicken , cats and dogs . In 89% of the homesteads the income is less than the official minimum wage of US$ 2 per day and thus fall in the lowest income bracket . School- age children very rarely own shoes and make their long way to school barefooted [20] . Community health workers were asked to identify households with tungiasis or to find out where they knew tungiasis had occurred previously . Children aged 5 to 14 years with at least six tungiasis lesions ( viable , non-viable or manipulated ) were eligible for the study . A total of 50 patients–between 2 and 15 children in each case—were recruited from five villages . In order to avoid family-related inclusion bias only the first child in a household identified to have tungiasis was eligible for the study . The intensity of infection ( number of embedded sand fleas ) and severity of tungiasis was determined by standardized procedures [5 , 21] . Since in endemic areas female sandfleas almost constantly penetrate the skin of the feet , the examination was limited to this topographic site [10 , 11] . Eligible patients were explained the procedure and a caregiver ( usually the mother ) was asked for informed written consent . The feet of the patient were carefully washed with soap in a bucket . Thereafter , the feet were thoroughly examined by the principal investigator ( SW ) in a room in which the privacy of the patient was guaranteed . Lesions were staged according to the Fortaleza classification and counted [13]: Stage I to III are viable sand fleas; in stage IV the parasite is dying or already dead [12 , 21] . Lesions manipulated with a sharp instrument , such as a needle , a safety pin , a thorn or a razor blade were documented as manipulated lesions . Patients were not asked who had tried to remove embedded sand fleas . Clinical pathology was assessed semi-quantitatively , using previously established severity scores for acute and chronic tungiasis ( SSAT; SSCT ) [21] . The SSAT varies from 0–30 points , the SSCT from 0–32 points . Pain and itching were assessed using visual scales ranging from 0–3 points ( S2 Appendix ) . Deliberately , the figures were kept very simple to make them understandable even for small children with little school education . The DLQI is a simple tool widely used to determine skin-associated life quality impairment . The English original was developed by Finlay and Khan [22] , and is available at http://www . cardiff . ac . uk/dermatology/quality-of-life/ . The DLQI is validated for an array of skin diseases of infectious and non-infectious origin [23 , 24] . In a first step , we modified the original questionnaire such that the tool measures the characteristic sequels of an inflammatory parasitic skin disease located at the feet . Next , we adapted the wording of the questions to the vocabulary and attitudes of Kenyan children . This resulted in a tungiasis-related DLQI with six categories of impairment and a score ranging from 0 to 18 points ( S3 Appendix ) . Categories of impairment are as follows: feeling of shame , impairment of leisure activities , difficulty in walking , impairment of concentration during classes , social exclusion and sleep disturbances . The last four of these six categories were assessed using visual analogue scales ( S2 Appendix ) , the first two just verbally . The tungiasis-related DLQI was translated into Swahili , pre-tested in children , back-translated into English , then refined and translated into Swahili again . The questions were read out loudly and explained to the patients in a standardized manner by one of the native Swahili-speaking community health workers . Children were shown the visual scales , depicting the categories of impairment one by one and were asked to point to the corresponding figure with their fingers . Answers were categorized as follows: no restriction perceived = 0 , only a small restriction perceived = 1 point , important restriction perceived = 2 points , severe restriction perceived = 3 points . The points for each category were added up to form the tungiasis-related DLQI ( Table 1 ) . Immediately after examination and interview the patients were referred to the local health centre for treatment . There they were treated according to national guidelines ( http://www . jigger-ahadi . org/National Policy Guidelines for Prevention and Control . pdf; accessed November 29 , 2016 ) . Patients were asked to present themselves at a determined location , usually the school , four weeks after treatment for follow up . The data were entered into an Excel database ( Excel Version 2013 , Microsoft , Redmont , Washington , USA ) and checked for errors which might have occurred during data entry . The data analysis was carried out using the Analysis ToolPack Add-In ( Microsoft , Redmont , Washington , USA ) . The median and the interquartile range were calculated as indicators of central tendency and dispersion of the data , respectively . Since the data did not follow a normal distribution , non-parametric tests were used . The Mann-Whitney-U test was used to compare the modified Dermatology Life Quality Index ( mDLQI ) between subgroups of patients , and the Wilcoxon matched pairs signed rank test for the comparison of variables before and after treatment . The Spearman rank correlation coefficient was calculated to determine the significance of correlations . Relative frequencies were compared with the Chi-squared and Fisher exact tests . The sample size estimate was based on the assumption of a 25% difference in tungiasis-related life quality impairment before and after treatment . Estimating a dropout rate of 5% , 45 patients were needed for complete data analysis ( probability = 0 . 95; power of the test = 0 . 80 ) . The study was approved by the Ethics Review Committee at Pwani University , Kilifi County; approval number ERC/PhD/010/2014 . The custodians and their protégés were informed about the objectives and procedures of the study in Swahili . The right to deny participation and withdraw consent at any given time was clearly explained . The informed consent form was read out loud word by word in Swahili and explained further when required; questions of the custodian and the children were discussed and answered by a community health worker . Informed assent was obtained from the patients before examination and treatment . Written consent was obtained via fingerprint or signature from the legal guardian or the headmaster of the school in which the patient was enrolled . Participants were only examined in the presence of their mother or the headmaster . For other illnesses requiring treatment a referral form was prepared by a community health worker , and patients were referred to the Health Facility in Kakuyuni . Treatment was also made available for household members with tungiasis who did not participate in the study .
Fifty patients were included in the study , 35 of them male and 15 female . The demographic and clinical characteristics are summarized in Table 2 . The median age was 8 years ( range 5–14 ) . Fifty four percent of the patients had more than six viable lesions ( boys 57% , girls 47% ) . The maximum number of lesions was 457; 162 of them viable . Severity scores for acute and chronic tungiasis were: median SSAT 10 ( maximum 27 ) and median SSCT 6 ( maximum SSCT 11 ) , respectively . Of the total 3 , 556 lesions present at baseline 58% had been manipulated by the patient or the caregiver . All participants showed at least one manipulated lesion , the median number being 33 ( maximum 196 ) . Overall , the intensity of tungiasis and the severity of disease was higher in boys than in girls . The tungiasis-related DLQI showed that 78% of the patients reported a moderate to a very large restriction of their life quality ( Table 3 ) . The majority of the patients ( 56% , Fig 1 ) showed a moderate impairment corresponding to a median tungiasis-related DLQI of 6 points ( 25th and 75th percentile 4–8 . 5 for boys and 3 . 5–8 for girls , respectively ) . Sleep disturbance and concentration difficulties in class were the impairment categories most commonly reported ( Table 4 ) . None of the restriction categories differed between boys and girls . There was a strong correlation between the severity of the acute clinical pathology as measured by the SSAT and the impairment of the life quality ( rho = 0 . 74 , p < 0 . 001 , Fig 2 ) . The intensity of pain ( as assessed by the visual scale ) showed an even stronger correlation with the tungiasis-related DLQI ( rho = 0 . 82 , p < 0 . 001 ) . The intensity of itching was less strongly correlated ( rho = 0 . 61 , p < 0 . 001 ) . There was only a weak correlation between tungiasis-associated chronic pathology as measured by the SSCT and the tungiasis-related DLQI ( rho = 0 . 27 , p = 0 . 06 ) . Whereas the total number of lesions and the number of manipulated lesions did not correlate with the tungiasis-related DLQI ( rho = 0 . 23 , p = 0 . 10 and rho = 0 . 004 , p = 0 . 98 , respectively ) , the number of viable lesions did ( rho = 0 . 64 , p < 0 . 001 ) . Of the 50 patients , 46 presented again four weeks after treatment . In these patients acute clinical pathology had decreased significantly: median SSAT at baseline = 10 ( interquartile range 7 . 25–12 ) versus 7 at follow up ( interquartile range 3 . 25–8; p<0 . 001 ) . This was accompanied by a slight though significant decrease of the tungiasis-related -DLQI: median = 6 ( interquartile range 4–8 ) versus 5 ( interquartile range 1 . 25–6; p<0 . 001 , Table 5 ) . The patients noted a clear amelioration in the restriction categories; concentration difficulty in class and impairment of leisure activities ( p = 0 . 001 and p < 0 . 001 , respectively ) as well as feeling of shame and walking difficulties ( p = 0 . 007 and p = 0 . 003 ) . On the individual level there was a highly significant correlation between the reduction of acute clinical pathology and the amelioration of life quality ( rho = 0 . 61 , p < 0 . 001; Fig 3 ) .
The group of 50 tungiasis patients enrolled in the study exhibited a broad spectrum of tungiasis-associated pathology , from moderate to severe disease . The fact that all children had manipulated lesions emphasizes that the embedded fleas were causing suffering , and in an act of despair , either they themselves or their caregivers tried to relieve that suffering by physical removal of the flea . In fact , 96% of the patients perceived their life quality to be impaired and 78% considered the impairment moderate to severe according to our mDLQI . This frequency of life quality impairment is similar to those reported from studies in patients with other neglected tropical parasitic skin diseases such as scabies , hookworm-related cutaneous larva migrans and cutaneous leishmaniasis [25–27] . However , patients with these other skin diseases classified the impairment as severe less frequently than patients with tungiasis . Other than social exclusion , all impairment categories were reported in similar frequencies ( range 62 to 86% ) to each other . Sleep disturbances due to pain and itching were perceived as particularly impairing . This is plausible , because itching usually intensifies at night , and pain is perceived more intensive in a quiet environment when a person tries to fall asleep . A lack of sufficient and re-generative sleep leads to tiredness , bad mood and concentration difficulties in class , another impairment category noted by the patients . In the long term , sleep disorders might cause the development of psychological problems such as anxiety [28] . To alleviate the pain patients avoid placing the whole foot on the ground while walking leading to a classical gait which is readily recognized as a tungiasis patient from a long distance [29] . Obviously , impaired mobility limits the typical leisure activities of children in rural Africa . Since children with tungiasis are ridiculed at school and because it is widely known that tungiasis affects the poorest of the poor , it is understandable that the feeling of shame and stigmatization are perceived as important restrictions . Probably , both restriction categories are also interlinked with social exclusion . Since the skin alterations are located on visible body parts , they are difficult to conceal and , on the long run , may lead to withdrawal and/or exclusion from society . Leprosy is paradigmatical for such sequels [30] . Patients may be confronted with ignorance or misconceptions regarding the aetiology of their skin disease , such as the fear that the condition is contagious or caused by poor personal hygiene–assumptions eventually leading to stigmatization [31 , 32] . This is the case , for instance , in lymphatic filariasis , a parasitic skin disease leading to gross lymphoedema of legs , arms and the genitals [33 , 34] . Stigmatization is detrimental to the well-being of the patient , causing distress and potentially inducing mental disorder [32 , 35] . We did not find any significant difference in impairment quality and frequency between boys and girls . A similar observation was made by Schuster et al . in patients with hookworm-related cutaneous larva migrans living in a slum in Manaus , Brazil [25] . The importance of this study is multifold . For the first time , it was shown that tungiasis—a parasitic skin disease still considered to be a nuisance rather than an important parasitic disease in standard text books–impairs life quality . The impairment categories perceived by the patients do not only reflect the clinical pathology caused by the embedded parasite ( such as itching , pain and restricted mobility ) but also confirm the mental strain and distress it causes . Since the disease cannot be concealed in the setting where the patients live , children are exposed to ridicule and stigmatization . Besides , the impaired mobility is presumably responsible for restricted social interactions and hinders typical leisure activities of children living in rural Africa . Second , the study clearly shows that a cause-effect-relationship exists between tungiasis and impaired life quality . At baseline the intensity and severity of tungiasis as well as the degree of pain were all positively correlated to the degree of life quality impairment and the degree of association was strong . After effective treatment life quality ameliorated or was restored . On the individual level there was a highly significant correlation between the reduction of disease severity and the amelioration of life quality after treatment . Finally , life quality was not restored in the patients who became re-infected during the observation period ( Fig 3 ) . Whereas in previous studies on life quality in neglected parasitic skin diseases it remained unknown to which degree the setting ( such as living in poverty ) contributed to the impairment categories perceived by the patients , this study convincingly shows that it is the disease—and not the setting—which causes an important impairment of life quality in children . Third , the results of this study are a strong argument that tungiasis is an important health hazard—on the individual level as well as on the public health level—and that health care providers and regulators should give it the priority it deserves . Effective treatment and prevention do exist and should be made accessible for all patients in endemic areas [17 , 36] . The study has a couple of limitations: First , an observation time of four weeks is too short to completely reverse clinical pathology and , hence , it is also too short to determine whether life quality will be completely restored after all lesions have healed . However , a prolongation of the follow up period would have increased the risk of re-infection . In this case , it would have been impossible to distinguish between inflammation still persisting from an original infection and clinical pathology resulting from newly penetrated sand fleas . Second , a higher power of the study would have been preferable . However , due to financial and logistic constraints , it was impossible to increase the study size and to include a control group . Third , there is an over-representation of males in the study population . This sex imbalance reflecting the higher prevalence of tungiasis in males in the endemic areas [4 , 5] might have been caused by a selection bias . Taken together , a simple tool enabled the demonstration of a cause-effect-relationship between the presence of a wholly neglected tropical disease and impaired life quality in children living in an impoverished setting in rural Africa . This work highlights the urgent need for international donors to support the development and registration of curative and preventive interventions , and for policy makers and health officials in endemic countries to address tungiasis to avoid this suffering . | Although tungiasis ( sand flea disease ) is associated with important morbidity and affects millions of people in South America , the Caribbean and sub-Saharan Africa , it has been largely ignored by health care providers up to now . In this study we show that the restriction of life quality due to tungiasis goes far beyond physical aspects and that the disease is also a mental and emotional strain on affected children in rural Kenya . Almost eighty percent of the diseased individuals reported a moderate to very large effect of tungiasis on their life quality . The degree of perceived life quality impairment correlated with the number of embedded sand fleas and pain . Sleep disturbance and concentration difficulties were the most frequent impairment . Effective treatment led to a rapid improvement of pathology and , subsequently , to an improved quality of life . The results of this study give substantial evidence that tungiasis is an important health hazard deserving more attention from policy makers and international donors . | [
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"... | 2018 | Tungiasis-related life quality impairment in children living in rural Kenya |
Cryptosporidium is a major cause of severe diarrhea , especially in malnourished children . Using a murine model of C . parvum oocyst challenge that recapitulates clinical features of severe cryptosporidiosis during malnutrition , we interrogated the effect of protein malnutrition ( PM ) on primary and secondary responses to C . parvum challenge , and tested the differential ability of mucosal priming strategies to overcome the PM-induced susceptibility . We determined that while PM fundamentally alters systemic and mucosal primary immune responses to Cryptosporidium , priming with C . parvum ( 106 oocysts ) provides robust protective immunity against re-challenge despite ongoing PM . C . parvum priming restores mucosal Th1-type effectors ( CD3+CD8+CD103+ T-cells ) and cytokines ( IFNγ , and IL12p40 ) that otherwise decrease with ongoing PM . Vaccination strategies with Cryptosporidium antigens expressed in the S . Typhi vector 908htr , however , do not enhance Th1-type responses to C . parvum challenge during PM , even though vaccination strongly boosts immunity in challenged fully nourished hosts . Remote non-specific exposures to the attenuated S . Typhi vector alone or the TLR9 agonist CpG ODN-1668 can partially attenuate C . parvum severity during PM , but neither as effectively as viable C . parvum priming . We conclude that although PM interferes with basal and vaccine-boosted immune responses to C . parvum , sustained reductions in disease severity are possible through mucosal activators of host defenses , and specifically C . parvum priming can elicit impressively robust Th1-type protective immunity despite ongoing protein malnutrition . These findings add insight into potential correlates of Cryptosporidium immunity and future vaccine strategies in malnourished children .
Malnutrition affects an estimated 165 million children worldwide [1] , contributes to an estimated 45% of early childhood mortality [2] , and interferes with immune responses to enteric pathogens and mucosally delivered vaccines [3] . Cryptosporidium sp . , a ubiquitous waterborne apicomplexan intestinal protozoan , is a prototypic pathogen that is more severe in malnourished children . Independent of socioeconomic status , early childhood Cryptosporidium infection associates with excess mortality in West Africa ( hazard ratio 2 . 9; 1 . 7–4 . 9 ) , sub-Saharan Africa , and South Asia ( HR 2 . 3; 1 . 3–4 . 3 ) where malnutrition prevalence remains high . Cryptosporidium infection associates with up to a 4-fold risk for persistent diarrhea ( >14 days ) [4–7] increases likelihood of recurrent diarrheal episodes , and associates with growth decrements [8 , 9] . Even non-diarrheal Cryptosporidium infections can acutely impair growth [10] , and sustained linear growth shortfalls may persist for months following infection [11 , 12] . While severe manifestations of Cryptosporidium infection in patients living with advanced HIV/AIDS [13] and studies in animal models confirm an undisputed role for Th1-effector mediated clearance of Cryptosporidium [14–16] , whether and how malnutrition increases susceptibility to Cryptosporidium in children is not well understood . Unlike the protective effect of IFN-γ seen in jejunal tissues of sensitized healthy volunteers who rapidly clear Cryptosporidium [17] , fecal IFN-γ levels are paradoxically lower in malnourished children infected with Cryptosporidium than uninfected controls [18 , 19] . In contrast , stool cytokines in malnourished children with active cryptosporidiosis demonstrate increased TNF-α , IL-8 , and IL-13 , and serum IgE , but not IgG is elevated [19] . Also , whereas circulating CD4+ and CD8+ T-cells from infected individuals produce IFN-γ upon re-stimulation with Cryptosporidium antigens [20] , cell-mediated immune ( CMI ) responses are generally impaired during infection in malnourished children , but serum and fecal antibodies are increased [21] . Whether this apparent skew in the immune response is characteristic not only of active , but also responses to recurrent Cryptosporidium infection in malnourished children has yet to be determined . Although reduced systemic IFN-γ has been documented in some malnourished children [2] , CD4+ T-cell quantity and activation is not consistently impaired , and one follow-up study in malnourished children demonstrated partial reconstitution of CMI through six weeks post-Cryptosporidium infection [21] . We have previously reported that undernourished neonatal and weaned mice have enhanced susceptibility to Cryptosporidium [22–24] infection concurrent with diminished baseline mucosal IFN-γ secretion [23] . Restoration of IFN-γ levels via systemic exposure to the TLR9 agonist CpG immediately prior to infection can partially attenuate Cryptosporidium susceptibility during malnutrition [24] . Despite apparently diminished basal IFN-γ responses during malnutrition , however , mice vaccinated with the Salmonella enterica serovar Typhi strain CVD 908-htr intranasal vector expressing the Cryptosporidium sporozoite antigen Cp15 [25] had unexpectedly preserved splenocyte CMI , including IFN-γ recall responses , through two weeks post vaccination [26] . This finding coupled with a rise in IFN-γ at later timepoints post-infection [27] suggests that despite constitutively diminished IFN-γ secretion , these malnourished hosts could produce IFN-γ in adaptive responses . In the present study , we dissected how protein malnutrition influences both primary and secondary immune responses to natural C . parvum infection and tested whether mucosal delivered strategies could overcome the resultant immunodeficiency . We first established that severe protein malnutrition ( PM ) differentially recapitulates short and long-term features of severe childhood cryptosporidiosis , and combines with C . parvum to worsen intestinal epithelial cell tight-junction disruption and mucosal architecture . These changes were co-incident with a fundamentally altered basal ( or primary ) immune response to Cryptosporidium antigens in both the systemic and mucosal compartment that resembled findings in malnourished children ( i . e . increased IL13 , decreased IFNγ ) . Surprisingly , however , Th1-type secondary responses were not only preserved , but enhanced during PM . Even at low ( 106 ) doses in this model , priming with viable C . parvum oocysts was sufficient to provide protective immunity . CD8+ T-cells predominated the secondary mucosal immune response , and were accompanied by Th-1 type cytokines ( IFNγ , IL12p40 ) along with the lymphocyte chemoattractant , CCL5 . Vaccination with either of two Cryptosporidium sporozoite antigens expressed in the S . Typhi vector , however , was unable to provide protective immunity in malnourished mice , despite robust boosted responses in nourished hosts . We also found that while remote non-specific mucosal exposures to either S . Typhi or CpG could partially attenuate the course of cryptosporidiosis during PM , neither alone nor in combination was as effective as C . parvum priming .
The severity of Cryptosporidium infection in malnourished children directly correlates with quantitative fecal parasite burden [28] , altered gut function [9] , and growth impairments that persist beyond the period of active parasite shedding [11] . To optimize a malnutrition model that best recapitulated these features , we simultaneously performed Cryptosporidium challenge in weaned mice maintained on three different isocaloric diets: a full nutrient control diet ( CD ) ; a 7% protein , 5% fat , reduced vitamin diet ( Regional Basic Diet-RBD ) [29 , 30]; or a 2% protein , 15% fat , vitamin sufficient diet ( isolated protein deficient-PD ) [23 , 24 , 31] ( Fig 1A ) . We found that 5 x 107 C . parvum oocysts administered after 5 days of dietary acclimation led to weight loss and greater parasite shedding in PD-fed mice ( Fig 1B ) . Severe zinc deficiency , which increases susceptibility to other enteric infections [32] , did not further enhance the C . parvum infection phenotype ( S1 Fig ) . Intestinal damage as measured by villus:crypt ratios was most significantly altered in Cryptosporidium-infected PD-fed mice ( Fig 1B ) . Live oocysts were necessary to cause weight loss , confirming that the resultant pathology was due to active C . parvum infection and not a maladaptive response to parasite products in the malnourished host ( Fig 1C ) . Both the PD diet and C . parvum disrupted epithelial tight-junction expression . Specific tight-junction protein expression was measured using immunofluorescence four days after C . parvum challenge ( Fig 1D ) . Neither expression nor localization of the tight junction scaffolding protein ZO-1 was altered under any of the conditions studied , providing a useful internal control . In contrast , occludin expression was reduced by PD alone . C . parvum infection also affected occludin , primarily by inducing redistribution to the intracellular vesicular pool . C . parvum infection with PD resulted in both reduced expression and enhanced internalization of occludin . As reported previously [33] , little claudin-2 expression was present under basal conditions . This was not affected by PD alone . C . parvum infection upregulated claudin-2 expression , though only part of the claudin-2 expressed localized to the tight junction . In contrast , C . parvum infection with PD resulted in markedly increased claudin-2 expression , nearly all of which was concentrated at the tight junction . Finally , similar to the long-term growth impairments that are greater in symptomatic cryptosporidiosis than asymptomatic infections [10] , challenge with 107 C . parvum compared with 106 C . parvum , led to more sustained concentration-dependent growth impairments through 21 days post-challenge ( Fig 1E ) , despite a similar duration of shedding ( Fig 1F ) . We hypothesized that increased severity of cryptosporidiosis during PD was due to impaired Th1-type immunity . To compare basal immune responses to Cryptosporidium antigens during malnutrition , we stimulated splenocytes from uninfected CD or PD-fed mice ( S1 Fig ) with two different immunogenic recombinant Cryptosporidium sporozoite antigens ( Cp15 and CApy ) [34] . Primary responses to either C . parvum antigen were fundamentally different between naïve CD and PD-fed mice . Rather than IFN-γ , IL17A predominated in PD-fed mice along with a relatively IFN-γ and with tendency toward Th2-type cytokines ( Fig 2A ) . Serum IgG titres were also constitutively lower in PD-fed mice ( Fig 2B ) . Secondary immune responses to either Cryptosporidium antigen at 13–15 days after C . parvum challenge were also diet-dependent , but strikingly opposite of the primary response in PD-fed mice . Nourished infected mice cleared parasites with little evidence of a serological response to either antigen . Serum IgG geometric mean titre ( GMT ) was attenuated both at baseline and after infection in all PD-fed mice ( Fig 2C ) . Only the more heavily infected PD-fed C . parvum challenged mice , however , demonstrated robust secondary cytokine responses in mesenteric lymph nodes ( MLNs ) , including IFN-γ ( Fig 2C ) . Splenocytes of infected CD-fed mice mirrored the minimal cytokine responses in MLNs ( Fig 2D ) . C . parvum challenged PD-fed animals unexpectedly demonstrated a 14- ( CApy ) and 41-fold ( Cp15 ) increase in post-stimulation IFN-γ secretion , whereas the increases in IL-13 , IL-5 , and IL-4 seen in primary responses were reversed ( Fig 2D ) . Since we previously observed decreased IFN-γ in ileal tissues of PD-fed mice during peak infection [23] , we hypothesized that despite robust systemic IFN-γ recall following C . parvum challenge , PD would interfere with effective mucosal immune responses to serial C . parvum exposures . PD-fed mice were challenged with either a priming dose of 106 C . parvum oocysts or the standard challenge dose of 107 oocysts similar to re-challenge models in gnotobiotic piglets [35] . We confirmed clearance of parasite shedding within 11 days and through day 22 post-challenge in both groups prior to re-challenge with C . parvum 107 oocysts on day 22 ( Fig 1F ) . Despite ongoing PD , prior C . parvum exposure , unexpectedly , completely protected against weight loss ( Fig 3A ) and diminished parasite burden ( Fig 3B ) . In subsequent experiments , even in the absence of Cryptosporidium antigen expression , intranasal S . Typhi vector exposures at 6 and 4 weeks prior to 107 C . parvum challenge partially reduced disease severity ( S4 Fig ) . The effect was likely not due to S . Typhi-enhanced IL17A responses . While IL17 expression in the ileal mucosa remained elevated several weeks after S . Typhi exposure in some mice , IL17RA mice , which lack IL17 signaling , were relatively less susceptible to C . parvum challenge than wild-type controls ( S4 Fig ) . Rather , the benefit of S . Typhi more resembled the partial benefit seen in PD-fed mice receiving an intraperitoneal TLR9 agonist CpG-ODN 1668 at the time of infection ( S5 Fig ) . Intriguingly , remote intranasal CpG , though incompletely protective , was more effective than intraperitoneal CpG delivered at the time of infection . There was no apparent additional benefit to S . Typhi combined with CpG ( S5 Fig ) . Since either CpG-ODN in PD-fed weanling mice [24] or oral CpG in neonates [38] restores mucosal IFN-γ , we hypothesized that remote intranasal CpG might be as efficacious as C . parvum priming . To test this hypothesis we first confirmed that only viable C . parvum priming , but not heat-inactivated C . parvum , established protective immunity ( Fig 8A and 8B ) and increased Th1-type cytokines ( S6 Fig ) . While even a single mucosal exposure to intranasal CpG-ODN 1668 ( CpG-C . parvum ) and even S . Typhi ( S . Typhi-C . parvum ) 21 days prior to C . parvum challenge facilitated post-infection recovery , only the single 106 C . parvum priming afforded complete protection against weight loss and rapid parasite clearance ( Fig 8C and 8D; S5 Fig ) . Thus , while enhancing host defenses through transient exposures during PD has a lasting impact on cryptosporidiosis severity , only C . parvum priming , which activated secondary Th1-type effector responses , resulted in protective immunity .
The epidemiology of malnutrition and Cryptosporidium are intimately associated , but mechanisms whereby malnutrition increases severity of cryptosporidiosis are poorly understood . In order to improve understanding of how malnutrition-induced mucosal immune deficits influence Cryptosporidium infection and immune responses , we isolated which nutritional deficiencies have the greatest impact on mucosal host defenses against Cryptosporidium . We then determined which of several anti-cryptosporidial preventive strategies would most effectively overcome the resultant immunodeficiency . Protein malnutrition ( PM ) in mice selectively replicated clinical , histological , and immunological features of active Cryptosporidium infection in malnourished children . Using remote exposures to non-specific mucosal defense enhancers ( S . Typhi vaccine vector and CpG ) , vaccination with Cryptosporidium antigens expressed in an S . Typhi vector , and viable C . parvum priming we demonstrated that while PM fundamentally alters Th1-type basal and vaccine-boosted immune responses at the time of infection , the resultant immunodeficiency is not insurmountable . Rather , robust and protective adaptive Th1-type effector responses can occur when priming with viable C . parvum . These findings , summarized in Fig 9 , provide new insights into nutrient-dependent mucosal immune deficiencies relevant to Cryptosporidium outcomes and a viable model in mice for future comparisons of Cryptosporidium prevention strategies . Cryptosporidium is a challenging parasite to study and there is a lack of established models that confidently mimic human disease . Thus , we first wanted to establish which model would best recapitulate known clinical features of active Cryptosporidium infection in malnourished children . While we and others have used various malnutrition protocols to intensify enteric infections [23 , 24 , 31 , 29 , 39] it was not known which nutrients were most essential for Cryptosporidium susceptibility . Only isolated protein malnutrition , but not multinutrient deficiency , sufficiently replicated dose-dependent disease severity evidenced by weight loss and persistent growth impairment , intestinal villus injury and epithelial tight junction disruption , and the elevated IL13 but paradoxically diminished IFNγ response seen in malnourished children . Amino acid dependent immune pathways therefore may be most relevant for Cryptosporidium outcomes . In contrast , isolated zinc deficiency less markedly enhanced cryptosporidiosis even though zinc can shorten duration of diarrhea and lessens severity and virulence of enteroaggregative Escherichia coli infection in our models [32] . Indeed , current standard WHO-recommended oral rehydration therapy and zinc treatment appears to be less effective for cryptosporidiosis than other diarrheal pathogens in children [6] . Rather , targeted amino acid therapies such as alanyl-glutamine [24 , 40] or arginine [41] may more selectively diminish severity of Cryptosporidium infection . Prolonged PM in our model , like total caloric restriction [42] , leads to progressive depletion in multiple IFNγ-producing effector cell populations ( CD8+ T-cells , CD11c+CD103+ dendritic cells , and NKT cells ) that should otherwise be steadily increasing in maturing young mice [43] . Consequently , like in malnourished children with active cryptosporidiosis [18] , either peak primary C . parvum challenge during PM in mice or stimulation of their naïve lymphocytes with C . parvum antigens elicits a basal IL13 response rather than IFNγ . Consistent with this observation , a systematic review concluded that malnutrition most significantly impairs effector T-cell responses and Th1-type cytokines but not total T- and CD4+ T-cell numbers . Malnutrition also associated with a tendency toward Th2-type cytokines [2] . We find that PM alone is biologically sufficient to recapitulate a similar immunological alteration . These data contribute to increasing recognition that immune effector function is differentially altered , rather than globally suppressed , by select nutrient deficiencies . Vitamin A deficiency , for example , disproportionately alters ratios but not absolute numbers of mucosal innate lymphoid subsets , increasing susceptibility to Citrobacter rodentium while paradoxically enhancing immunity against intestinal nematodes [44] . In the case of Cryptosporidium infection , a skewed response during PM may not only increase susceptibility to primary infection , but also contribute to the pathogenesis . Our studies confirm for the first time in vivo tight-junction disruption seen in in vitro Cryptosporidium models [45 , 46] . Of the studied epithelial cell proteins , the most marked disruptions were seen in the cytokine-inducible tight-junction proteins occludin and claudin-2 . Both internalization of occludin and IL-13 induced claudin-2 upregulation have also been reported in human and experimental inflammatory bowel disease models [33 , 47] . Internalization of occludin occurs during acute TNF-α induced barrier loss and diarrhea [48] . In vitro and in vivo studies have shown that removal of occludin from the tight junction , either by endocytosis or reduced expression , facilitates paracellular flux of small and large molecules via the ‘leak pathway’ . This complements the increased ‘pore pathway’ permeability to water and small cations following increases in claudin-2 expression [49] . In addition to potentially promoting diarrhea , the inflammatory response to Cryptosporidium during PM corresponds with persistent growth impairment , and may resembles the chronic T-cell activation seen in the environmental enteropathy associated with malnutrition [50] . Thus , not only heavier Cryptosporidium infection , but potentially aberrant host responses during malnutrition contribute to the greater severity of diarrhea and subsequent post-infection sequelae . While PM-induced depletion of several cell types may be independently important for anti-cryptosporidial defenses , CD11c+CD103+ cells that co-express the CXCR3 receptor were recently shown to be more consequential for early Cryptosporidium clearance in primary infection in neonatal mice than loss of either NK cells or T-cells [43] . Although CD11c+CD103+ cells did increase following C . parvum priming in the current study , the influx of CD3+CD8+ T-cells was even more striking . Sensitized CD8+ T-cells have a direct role in clearing Cryptosporidium from human epithelial cells [51] as well as in experimental models of C . muris infection [52 , 53] and have been shown to accumulate after infection in neonatal mice [54 , 55] . Preceding the influx CD3+CD8+ T-cells in this model was an increase in Th1-type chemoattractants CXCL9 , CXCL10 , CCL3 and CCL5 at peak primary infection . Epithelial cells are a major source of these chemokines in response to exposure to Cryptosporidium [43 , 56 , 57] , and appear to be functional despite PM . Interestingly , however , CCL5 but not CXCL9 or CXCL10 , dominated re-challenge responses during PM . This is in contrast with the predominance of CXCL10 rather than CCL5 in patients with HIV/AIDS during Cryptosporidium infection , and is consistent with the likelihood that the influx of T-cells after C . parvum priming in this model contributed to elevated CCL5 [56] . Ours is the first study to identify that the majority of CD3+CD8+ also co-express the αE:β7 marker CD103+ indicative of intestinal honing [58] in response to Cryptosporidium . A subpopulation of these cells has recently been classified as tissue resident effector memory ( Trm ) cells that produce higher amounts of IFNγ than CD3+CD8+CD103- cells , accumulate locally following intracellular infections at mucosal sites [59 , 60] and thus represent an emerging target for mucosal vaccine development [61 , 62] . Further elucidation of the role of Trms in Cryptosporidium infections may therefore further inform vaccine development [13] . Together with our previous findings [26] , our collective vaccine studies show that PM during the priming phase of intranasal vaccination does not impair immunogenicity to Cryptosporidium antigens expressed in an S . Typhi vector , but PM at the time of infection impairs vaccine-boosted immunity and does not protect against Cryptosporidium challenge . Similar effects were seen during PM and a multistage antigen-based Mycobacterium tuberculosis vaccine-challenge model in mice [63] . Since our investigation began , it has been recognized that an oral route may more optimally target vaccine responses to the small intestine than the intranasal route used in our studies , including Cryptosporidium antigens expressed in an attenuated S . Typhimirium vector [61 , 64] . Thus , oral rather than intranasal Cryptosporidium vaccine delivery might better mimic C . parvum priming . Intranasal vaccination with either Cryptosporidium antigen in fully nourished hosts leads to a robust enhancement in the immune response , similar to human volunteers that demonstrated serum IgG antibodies only after secondary , but not primary C . parvum infection [65] . As fully nourished mice appear to clear C . parvum infection via innate defenses , there was no apparent decrease in parasite clearance following vaccination . These findings raise important implications in the limitations of inferring correlates of immunity in healthy volunteers and desired effective responses in malnourished target populations . Similarly , Maier et al . demonstrated that malnourished mice infected with rotavirus demonstrated diminished seroconversion , but were unexpectedly protected against viral re-challenge [66] . We present a series of experiments designed to compare the effect of both specific ( sporozoite antigen vaccine and homologous re-challenge ) and non-specific ( the attenuated S . Typhi vector and the TLR9 agonist CpG ) antigen exposures . In this model , we demonstrate that while less effective than C . parvum priming , even remote non-specific exposures can diminish disease severity , even if not reducing parasite shedding . Others have reported partial efficacy of vector-based anti-Cryptosporidium vaccines in animal models [67] , including apparent unexplained phenomena of the vector alone [68 , 69] . In the present study S . Typhi enhanced IL-17A production , most strikingly during PM . IL-17A responses are also increased in naturally acquired S . Typhi [70] and Ty21a vaccination [71] . While the loss of IL-17A producing lymphocytes promotes disease severity in simian immunodeficiency virus and Cryptosporidium co-infection [72] a protective role of IL-17A against Cryptosporidium remains undefined . Alternatively , we speculate that S . Typhi provided broad activation of innate immunity and thus modulated mucosal responses to microbial products such lipopolysaccharide [26] , or influenced claudin protein expression and barrier function [33 , 47 , 73 , 74] . Oral immunization with Salmonella vaccine strains has also been shown to protect against non-Salmonella bacterial infections via sustained changes in TLR expression and non-specific macrophage activation [75] . Signaling TLR3 in combination with TLR5 [76 , 77] , TLR4 [78] and TLR9 [24 , 38] independently influence Cryptosporidium infection outcomes . While prior studies have focused on TLR activation immediately prior to infection , we found that even remote selective TLR activation with CpG mimicked the S . Typhi effect and was more effective than CpG administration immediately prior to infection . Such observations are aligned with the concept of “trained innate immunity” with potential for enhanced responses to repeated exposures to non-specific microbial products [79] . These concepts may be highly relevant in malnourished children given potentially divergent microbiota and frequent exposures to enteropathogens associated with childhood malnutrition [80] . Important questions remain regarding primary and secondary immune responses to Cryptosporidium in malnourished children . First , although many features of our model overlap with childhood cryptosporidiosis during malnutrition , murine immunology is not a surrogate for children , and further investigations are needed to delineate whether malnourished children similarly demonstrate such contrasting immune responses to primary and secondary infection . Indeed , despite our finding robust protective immunity after even low-inoculum C . parvum exposure , recurrent and multiple Cryptosporidium infections are well documented in malnourished children [8 , 81] . The prevalence of asymptomatic infections is only beginning to be defined as detection of these exposures may require highly sensitive molecular diagnostics [28] , and when applied outnumber diarrheal infections ~10:1 [12] . If asymptomatic exposure does induce a protective response , the duration of immunity in children may be short lived . While recurrence was unlikely within one month of infection in one cohort [82] , by nine weeks post-infection only 54% of children in a different study demonstrated antibody to an immunodominant Cryptosporidium antigen gp15 through nine weeks [83] . Although humoral and cell-mediated responses to specific Cryptosporidium antigens are not universally concordant [20 , 84] , these findings suggest that immunity in children may rapidly wane . Also , our findings are restricted to homologous C . parvum re-challenge and may as such be strain-specific . We did not have access to C . hominis and important anthroponotic strains of Cryptosporidium parvum [5 , 85] that when given to gnotobiotic piglets reveal incomplete heterologous protection [35] . This is important since sequential infections with different Cryptosporidium spp . subtype are documented [82 , 86] . Finally , the role of passive maternal immunity in infants [87] and the role of intestinal microbiota remain important considerations not addressed in the present study . In conclusion , in a PM model that replicates several clinical and immunologic features of Cryptosporidium infection in malnourished children , our findings raise important future directions for understanding Cryptosporidium pathogenesis and immunity during malnutrition . First , the stark contrasts in immune responses in natural C . parvum infection and post vaccine-boosted immunity during PM compared with fully nourished animals reinforces the need for further investigation to distinguish primary from secondary responses to Cryptosporidium sp . in malnourished children . Second , elucidating the independent roles of direct consequences of Cryptosporidium-induced damage from a potentially deleterious host inflammatory response on tight-junction alterations may advance understanding of cryptosporidiosis pathogenesis and raise novel candidate therapeutics . Finally , since alterations in basal immune responses during malnutrition appear to have the greatest impact at the time of infection , a successful anti-Cryptosporidium vaccine for malnourished children may need to consider not only systemic correlates of immunogenicity , but a careful examination of which mucosal effector responses are most indicative of future protection . Thus , overcoming defective mucosal T-cell effector responses , such as enriching intestinal CD3+CD8+CD103+ cells population , may be a component of successful strategies to eliciting protective anti-cryptosporidial immunity during malnutrition .
This study included the use of mice . This study was conducted in strict accordance with recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the the International Animal Care and Use Committee at the University of Virginia ( Animal Care and Use Committee Protocol number: 3315 ) . Tissue procurement was performed under anesthesia that was induced and maintained with ketamine hydrochloride and xylazine , and all efforts were made to minimize suffering . All animal experiments were performed at the University of Virginia ( UVa ) . Weaned 21-day-old female C57Bl/6 wild-type mice were purchased from Charles River for all experiments . On arrival , mice were acclimated for at least 48 hours prior to handling . Mice were randomly distributed in weight-matched groups prior to any interventions . Experimental isocaloric diets consisted of either: a full nutrient ( 20% protein ) control diet ( CD ) ( TD . 08678 , Harlan; or Research Diets ) , a multinutrient deficient ( 7% protein , 5% fat , vitamin reduced diet ) Regional Basic Diet ( RBD , Research Diets ) , a 2% protein , 15% fat , vitamin sufficient , isolated protein deficient diet ( PD ) ( TD . 08679 , Harlan ) , or isolated zinc deficient but otherwise full nutrient diet ( ZD ) ( Research diets ) . Diets were either upon arrive of mice to the facility , or for S . Typhi vaccine experiments at protocolized timepoints prior to infection . All mice remained on their respective diets diets throughout the remainder of the experiment . Mice in experiments designed for testing boosted vaccine responses were originally on in-house chow ( Harlan ) prior to transitioning to respective diets between 5–12 days prior to Cryptosporidium parvum infection , a range that shows a consistent phenotype in this model [23 , 24] . IL17RA-/- mice on a C57Bl/6 background were obtained through a material transfer agreement with Amgen . Oocysts of C . parvum ( Iowa isolate ) were purchased from Waterborne , Inc . ( New Orleans , LA ) at a concentration of 1 x 109/50 mL PBS . Oocysts were rinsed in PBS using centrifugation at 650 g prior to infection [24] . The final concentration of the challenge inoculum was determined using a hemacytometer and the pellet was re-suspended in PBS to yield a final concentration of 1–5 x 107 oocysts in 100 μl or as a 1:10 dilution ( 1–5 x 106 oocysts/100 μl ) for low-dose inoculum experiments . For experiments using heat-inactivated Cryptosporidium , lot-matched oocysts were placed in a dry block shaker ( Thermomixed , Eppendorf ) at 90°C for 10 minutes with 300 rpm shaking per prior protocols [88] . Ileal tissue histology was performed at 14 days post-C . parvum infection . Three-cm segments of ileum were cut in cross section and fixed in 10% zinc-formalin for 48 hours prior to transfer into 70% ethanol . Ileal villus length and crypt depth ( ≥10 villus:crypt pairs/mouse ) were measured in a blinded manner ( HH ) as previously described using Image J software [23] . Ileal tissues from 4-week-old mice fed either 20% control diet or 2% protein deficient diet for 7 days prior to C . parvum 107 challenge were harvested on day 4 post-infection . Ileal tissue was cut into 0 . 5 cm segments and embedded in an optimum cutting temperature ( OCT ) media-filled cryomold on dry ice . Embedded tissues were stored at -80°C . Frozen sections ( 5 μm ) were fixed in 1% paraformaldehyde in phosphate-buffered saline and immunostained with mouse anti–ZO-1 ( Invitrogen ) , rabbit anti–claudin-2 ( Abcam ) , or mouse anti–occludin ( Invitrogen ) followed by Alexa Fluor 488– or Alexa Fluor 594–conjugated secondary antibodies ( Invitrogen ) , along with Hoechst 33342 ( Invitrogen ) . Stained sections were mounted in ProLong Gold ( Invitrogen ) and images were captured using a Coolsnap HQ camera ( Roper Scientific ) mounted on an Axioplan 2 epifluorescence microscope equipped with a Plan-Neofluar 63× NA 1 . 3 objective ( Zeiss ) and ET-sputtered single band filter sets ( Chroma Technology ) [89] . The microscope was controlled using MetaMorph 7 ( Molecular Devices ) . Exposure times were matched between conditions for each antigen , and all post-acquisition processing was standardized for each antigen . Overlays were created using MetaMorph 7 and subsequently rotated using Adobe Photoshop CS6 . The S . Typhi CVD 908-htr vector transfected with the pSEC10 plasmid expressing ClyA and ClyA-sporozoite surface antigen fusion proteins was generated at the University of Maryland Center for Vaccine Development and prepared at Virginia Commonwealth University ( VCU ) as previously published [26 , 34] . The surface sporozoite antigens Cp15 and CApy were identified using a reverse vaccinology approach and initial immunogenicity studies were performed at Virginia Commonwealth University [90] . The optimized vaccine protocol utilized sequential intranasal prime inoculation with 5x109 live S . Typhi at two week intervals followed two weeks later by an intramuscular boost with 20 μg of homologous recombinant Cp15 , CApy , or NUS control-peptide mixed 1:1 with alum adjuvant [26] . S . Tyhpi aliquots were prepared fresh for each inoculation at VCU and transported to UVa for immediate use . Mice remained on the house vivarium chow throughout the duration of vaccine protocol and for another 51 days prior to transitioning to customized diets . Each mouse was weighed periodically throughout the vaccination and malnutrition protocol . In a separate experiment interval stools were collected for Salmonella detection using TaqMan ( AgPath-ID OneStep RT-PCR kit ( Life Technologies , Cat#4387391 ) for the invA target ( 5’-3’ sequences: F- TCGGGCAATTCGTTATTGG; R- GATAAACTGGACCACGGTGACA; Probe-FAM-AAGACAACAAAACCCACCGC-MGB ) [28] to confirm absence of shedding 24 hours after the first intranasal dose and immediately before the second intranasal dose . Infection with 106 or 107 Cryptosporidium oocysts , intranasal 5x109 live S . Typhi , or 100 μg intranasal CpG-ODNs ( 1668 5’TCCATGAGCTTCCTGATGCT’3; Sigma-Aldrich ) occurred after 5–7 days of conditioning on the 2% protein diet at 26–28 days of life . The dose of CpG-ODN was determined from prior experience [24] and pilot experiments . S . Typhi for these experiments was regrown from frozen aliquots from an initial shipment from VCU using previously published methods [26] . Mice were weighed serially following initial challenge for 20–22 days . On post-challenge day 20–22 , mice were re-challenged with 107 C . parvum oocysts and weighed daily thereafter for 3–10 days . Stools were collected every other day following primary or secondary challenge . Data analyses were performed with GraphPad Prism 6 software ( GraphPad Software ) . All statistical analyses were done with the use of analysis of variance , Student t tests , and Bonferroni or Tukey post hoc analysis where applicable . Differences were considered significant at P <0 . 05 . Data are represented as means ± standard errors of the mean unless otherwise specified . | Cryptosporidium attributable morbidities in malnourished children are increasingly recognized . Exactly how malnutrition interferes with host mucosal immunity to diarrheal pathogens and mucosal vaccine responses remains unclear . Dissecting these interactions in an experimental model of cryptosporidiosis can uncover new insights into novel therapeutic approaches against a pathogen for which effective therapies and vaccines are currently unavailable . We demonstrate that although malnutrition diminishes baseline ( primary ) Th1-type mucosal immunity these deficits can be partially overcome via non-specific mucosal strategies ( S . Typhi and CpG ) and completely restored after a sub-clinical ( low-dose ) exposure to viable C . parvum . These results add insight into preventive strategies to help alleviate Cryptosporidium-specific diarrhea in children in low-resource settings and abrogate prolonged post-infection sequelae . | [
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"salmonel... | 2016 | Cryptosporidium Priming Is More Effective than Vaccine for Protection against Cryptosporidiosis in a Murine Protein Malnutrition Model |
Recent research suggests that genetic interactions involving more than two loci may influence a number of complex traits . How these ‘higher-order’ interactions arise at the genetic and molecular levels remains an open question . To provide insights into this problem , we dissected a colony morphology phenotype that segregates in a yeast cross and results from synthetic higher-order interactions . Using backcrossing and selective sequencing of progeny , we found five loci that collectively produce the trait . We fine-mapped these loci to 22 genes in total and identified a single gene at each locus that caused loss of the phenotype when deleted . Complementation tests or allele replacements provided support for functional variation in these genes , and revealed that pre-existing genetic variants and a spontaneous mutation interact to cause the trait . The causal genes have diverse functions in endocytosis ( END3 ) , oxidative stress response ( TRR1 ) , RAS-cAMP signalling ( IRA2 ) , and transcriptional regulation of multicellular growth ( FLO8 and MSS11 ) , and for the most part have not previously been shown to exhibit functional relationships . Further efforts uncovered two additional loci that together can complement the non-causal allele of END3 , suggesting that multiple genotypes in the cross can specify the same phenotype . Our work sheds light on the complex genetic and molecular architecture of higher-order interactions , and raises questions about the broader contribution of such interactions to heritable trait variation .
Understanding the genetic basis of complex traits is critical for advancing medicine , evolutionary biology , and agriculture [1] , [2] . A challenge to progress in this area is that genetic variants can interact , resulting in unexpected phenotypic consequences [3]–[7] . Most of our knowledge about these genetic interactions in natural systems comes from studies focused on two-locus interactions where at least one of the loci exhibits a measurable effect on its own ( e . g . , [8] ) . However , evidence suggests that genetic interactions involving three or more loci also occur [9] , [10] , and that loci participating in such interactions may not individually have detectable effects [11] . Determining how these higher-order interactions arise and influence phenotypic variation could help solve the ‘missing heritability’ problem faced by geneticists studying humans and model species [12] . In this paper , we describe the genetic basis of a complex trait that is influenced by higher-order interactions . We identified this phenotype , a dramatic change in the morphology of Saccharomyces cerevisiae colonies , in a cross of haploid derivatives of the lab strain BY4716 and the clinical isolate 322134S ( hereafter ‘BY’ and ‘3S’ , respectively ) . The colony morphology trait in the BY×3S cross is similar to phenotypes described in other yeast isolates and crosses ( e . g . , [13]–[19] ) . Thus , by comprehensively determining the genetic basis of colony morphology variation among BY×3S offspring , we not only generate novel insights into how higher-order interactions contribute to phenotypic variation , but also provide new information regarding the genetic basis of a frequently studied model complex trait .
Although both BY and 3S , as well as most of their haploid offspring , form smooth colonies ( Figure 1A–C ) , ∼2% of their progeny exhibited rough colonies when we examined 250 segregants ( Figure 1D ) . Previous work has shown that such heritable variation in colony morphology in S . cerevisiae can arise due to naturally occurring polymorphisms or spontaneous mutations at chromosomal loci [13] , [14] , [18] , [19] , aneuploidies [17] , and prions [15] . Unlike chromosomal loci , which should show stable inheritance across generations , aneuploidies and prions can be gained or lost , resulting in phenotypic switching . Multiple lines of evidence suggest that chromosomal loci are the primary cause of rough morphology in the BY×3S cross . Neither BY nor 3S exhibits rough morphology , indicating that the phenotype likely requires a combination of alleles from both of these strains . Consistent with this statement , we found that the frequency of rough morphology increased to 12 . 5% and 21 . 2% among recombinant haploid progeny obtained by backcrossing a rough segregant to BY and 3S , respectively ( Tables S1 and S2; Methods ) . The higher frequency of rough segregants in backcrosses is expected if alleles from both parents contribute to the trait , as fewer causative alleles should segregate in the backcrosses than in the original cross . Further supporting the argument that our observations of rough morphology were due to chromosomal loci instead of transient factors , we found no evidence for chromosome-scale aneuploidies or phenotypic switching in the backcrossed segregant ( Figure S1; Methods ) . To identify loci that contribute to rough morphology , we generated thousands of random spores from the aforementioned backcrosses and used low-coverage whole genome sequencing to selectively genotype individuals that showed the phenotype ( Methods ) . We obtained 92 and 88 rough segregants from the BY and 3S backcrosses , respectively . Using these data , we detected five genomic loci that were strongly enriched among these individuals but not among control segregants ( Figure S2 ) : three on Chromosomes IV , V , and XV inherited from 3S ( Figure 2A ) , and two on Chromosomes XIII and XIV inherited from BY ( Figure 2B ) . All of these loci , except the one on Chromosome XIV , were fixed among individuals with rough morphology . We attempted to determine causal genes underlying each of the five loci . Our initial resolution of the loci was between 4 and 14 genes ( Figure 2C–G; Table S3; Methods ) . To decrease the number of candidate genes , we performed targeted genotyping on 19 additional backcross segregants , as well as 8 multi-locus introgression strains that had been subjected to 6 rounds of backcrossing with selection for the rough phenotype ( Figure S3; Methods ) . This additional stage of genetic mapping refined the loci to between 2 and 9 genes per locus , and 22 genes in total ( Figure 2C–G; Table S4 , S5 , S6 ) . We deleted each of the 20 remaining non-essential candidate genes from one of the multi-locus introgression strains ( Methods ) . Across these deletions , a single gene at each locus showed an effect on the phenotype: TRR1 ( Chromosome IV ) , FLO8 ( Chromosome V ) , MSS11 ( Chromosome XIII ) , END3 ( Chromosome XIV ) , and IRA2 ( Chromosome XV ) ( Figure 2C–G ) . Because the two remaining candidate genes—AVO1 and TOP2—were essential , we examined them using an alternative strategy that suggested they do not contribute to the observed colony morphology variation ( Text S1 ) . We used complementation tests to determine whether the five identified genes possess functional variation ( Methods ) . Each haploid deletion strain was mated to three rough and three smooth haploid backcross progeny ( Methods ) . These matings were designed to produce diploids that were homozygous for the required alleles at four of the causal loci and hemizygous for the fifth causal locus . For END3 , FLO8 , MSS11 , and TRR1 , the experiments provided support that the parental alleles differ in their effects . All matings of deletion strains to smooth backcross progeny produced smooth hemizygotes . Further , either two ( in the cases of FLO8 and MSS11 ) or three ( in the cases of TRR1 and END3 ) of the matings of deletion strains to rough backcross progeny produced rough hemizygotes ( Figure 3A ) . However , for IRA2 , the two possible hemizygotes showed no phenotypic difference , with both exhibiting smooth morphology ( Figure 3A ) . IRA2 has been reported to show haploinsufficiency in growth rate experiments [20] , and this haploinsufficiency may also explain some of our reciprocal hemizygosity results for this gene . To provide stronger support for IRA2's role in the trait , we performed allele replacements of IRA2 in a smooth backcross segregant that carried the non-causal allele of IRA2 , as well as the causal alleles of END3 , FLO8 , MSS11 , and TRR1 ( Methods ) . While transformations with the IRA23S allele had no phenotypic effect , we found that transformations with the IRA2 allele from the rough segregant that had been backcrossed resulted in a change from smooth to rough morphology ( Figure 3B ) . Sequencing of IRA2 from 3S and the rough segregant revealed a single difference between the two alleles: a frameshift mutation that truncates the protein by 117 amino acids ( hereafter referred to as IRA23S-Δ2933; Text S2 ) . IRA2 is known to be hypermutable and spontaneous mutations in this gene have been shown to influence a variety of multicellular growth phenotypes [19] , [21] . However , our results demonstrate that the effects of spontaneous mutations in IRA2 can depend on an individual's genotype at a number of additional genes . We also checked for IRA23S-Δ2933 in the four other rough individuals that we found in our original BY×3S mapping population . Three of these rough segregants possessed the frameshift mutation , suggesting that IRA23S-Δ2933 probably arose during the outgrowth of the BY/3S diploid prior to its sporulation . Previous work by other groups identified functional polymorphisms in END3 and FLO8 that also segregate in our cross [22] , [23] . BY has a premature stop mutation in FLO8 that prevents it from undergoing many forms of multicellular growth [22] . As for END3 , a missense polymorphism in this gene contributes to variability in high temperature growth in a cross of the clinical isolate YJM789 and S288c , the progenitor of BY [23] . Of relevance to our study , this variant in END3 has effects that are strongly dependent on genetic background [24] . With respect to TRR1 , the Saccharomyces Genome Resequencing Project [25] and our own sequencing data indicate that the BY and 3S alleles of this gene differ by a single nucleotide , which is a synonymous SNP in the 52nd codon of the gene: BY has an ATC codon and 3S has an ATT codon . Although both of these codons are recognized by the same isoleucine tRNA , the ATT codon is preferred by a nearly two-to-one ratio throughout the yeast genome , suggesting that the SNP might have an effect on translational efficiency . Only lab-derived S . cerevisiae strains carry the ATC allele that confers smooth morphology , while all other sequenced S . cerevisiae and S . paradoxus strains harbor the ATT allele that is likely involved in rough morphology . Work to determine the functional variant ( s ) in MSS11 , which possesses a number of coding and noncoding polymorphisms that could have effects , is ongoing ( Table S7 ) . The causal genes encode proteins with diverse cellular functions: End3 plays a role in clathrin-mediated endocytosis [26] , [27] , Flo8 and Mss11 are transcription factors that regulate cell-cell adhesion and multicellular phenotypes in S . cerevisiae [28] , [29] , Ira2 is a negative regulator of the RAS-cAMP pathway [30] , and Trr1 is an enzyme involved in oxidative stress response [31] , [32] . Flo8 and Mss11 physically interact [33] , and IRA2 and MSS11 show a genetic interaction when both are knocked out [34] . To our knowledge , none of the other pairs of identified genes have been reported to interact at the biochemical , genetic , physical , or regulatory levels . To assess whether Flo8 and Mss11 might directly regulate the expression of the other genes , we examined existing data from calling card analyses , a technique that identifies genomic sites bound by transcription factors [16] . These results indicated that Flo8 and Mss11 are unlikely to bind the promoters of END3 , IRA2 , and TRR1 , although admittedly the study involved a different strain than our cross parents . After identifying causal genes at the five loci , we analyzed the effects of these genes in more detail by genotyping them in a panel of phenotyped segregants from dissected backcross tetrads ( Methods ) . Every individual with rough morphology possessed the 3S allele of FLO8 and TRR1 , the BY allele of MSS11 , and IRA23S-Δ2933 ( Figures 4A–B and S4A–B; Tables S8 and S9 ) . Although most individuals with rough morphology carried END3BY , a small fraction of individuals with END33S also showed the trait ( Figures 4B and S4C; Table S9 ) , indicating that alleles at additional loci complement END33S . We more deeply investigated the genetic basis of rough morphology among individuals with END33S . First , we used a gene knockout strategy to check whether END33S is necessary for these individuals to exhibit rough morphology ( Methods ) . end33SΔ strains were smooth ( Figure S5 ) , suggesting that the alternate genetic architecture for rough morphology requires END33S . Second , we tried to identify loci that complement END33S . Four rough END33S progeny were present in our sequenced mapping population from the 3S backcross . Among these segregants , we detected 11 previously unidentified genomic regions where individuals shared the same genotype ( Figure S6; Table S10; Methods ) . We were able to reduce this set to four candidate loci on Chromosomes VII , XI , XII , and XV by genotyping additional backcross progeny ( Table S11; Methods ) . To determine which of the four loci have causal roles in rough morphology , we mated a relevant backcross segregant to 3S and analyzed a panel of 51 second-generation backcross progeny ( Table S12; Methods ) . The BY alleles at the Chromosome VII and XV loci were fixed among the 39 individuals with rough morphology , while the other two loci showed no evidence of playing a role in the trait ( Figure 4C; Table S12 ) . Given only individuals that carried BY alleles at both the Chromosome VII and XV loci exhibited rough morphology , it is likely that these loci genetically interact to complement END33S . Our findings indicate that the segregant used for backcrossing carried more than one set of interacting alleles that can specify rough morphology ( Figure 4D ) . Identifying the causal genes and genetic variants underlying the Chromosome VII and XV loci can thus shed light on how these different genotypes produce the same trait . However , our ongoing efforts to clone the causal factors at these loci are limited by the crude resolution of the present data ( each locus is presently resolved to >60 kilobases; Table S10 ) . We note that initial gene deletion experiments focused on 18 candidates ( Table S13 ) , including LAS17 and YAP1802 , whose cognate proteins functionally interact with End3 [35] , [36] , have been unsuccessful . Moving forward , we plan to determine the genes that underlie the Chromosome VII and XV loci , and characterize their relationship with END3 . In summary , we have demonstrated that sets of five or more genetic variants can synthetically interact to produce major phenotypic effects . Alleles involved in these higher-order interactions may either be polymorphisms that segregate in natural populations or spontaneous mutations . Our results also illustrate that rather than functioning in a single biochemical pathway , protein complex , or regulatory circuit , the genes involved in higher-order interactions can play roles in a number of cellular processes . This finding implies that characterizing higher-order interactions using data from screens and annotations focused solely on reference genomes may be a challenge , and highlights how genetic variation can serve as a tool for detecting previously unidentified functional relationships among genes . Further , we have shown that multiple sets of alleles can interact to produce the same phenotypic effect . Additional work is necessary to determine how this latter finding is mediated at the molecular and systems levels . Overall , our study suggests that characterizing the larger-scale contribution of higher-order interactions to phenotypic variation is a necessary step in improving our basic understanding of the genotype-phenotype map .
All phenotyping experiments were performed on agar plates containing yeast extract and peptone ( YP ) with 2% ethanol as the carbon source ( YPE ) . Prior to phenotyping , strains were grown up in liquid YP with 2% dextrose ( YPD ) . Stationary-phase cultures were manually pinned onto YPE and allowed to grow for five days at 30°C , and were then imaged using a standard digital camera . Sequencing data from the rough segregant used in backcross experiments was examined at the chromosome-scale for evidence of aneuploidy . Average per base coverage of each chromosome was computed in R and compared to the genome-wide average . This segregant was also plated at low density on a large number of YPE plates . We screened tens of thousands of colonies for instances of phenotypic switching and observed no cases where an individual converted from rough to smooth morphology . Strains used in this paper contained the Synthetic Genetic Array marker system [37] , which allowed us to easily generate large numbers of recombinant MATa progeny . All segregants discussed in the paper were MATa can1Δ::STE2pr-SpHIS5 his3Δ and all backcrosses involved mating these individuals to either a BY or a 3S strain that was MATα his3Δ . In these crosses , strains with opposite mating types were mixed together on a YPD plate and incubated for four hours at 30°C . Zygotes were then obtained by microdissection . To generate segregants , diploids were sporulated at room temperature using the protocol described by Guthrie and Fink [38] . Once sporulation had completed , spore cultures were digested with β-glucuronidase and then plated onto yeast nitrogen base ( YNB ) plates containing canavanine , as described previously [39] . Spores were plated at a density of roughly 100 to 200 colonies per plate . Whole genome sequencing libraries were prepared using the Illumina Nextera kit , with each of the backcross segregants barcoded with a unique sequence tag . The libraries were mixed together in equimolar fractions and sequenced on an Illumina HiSeq machine by the Beijing Genomics Institute using 100 base pair ( bp ) ×100 bp reads . Sequencing reads were then mapped to the S . cerevisiae reference genome using the Burrows-Wheeler Aligner ( BWA ) [40] . We used data from 36 , 756 high confidence SNPs that had been identified based on comparison of Illumina sequence data for 3S to the BY genome . Similar to Andolfatto et al . [41] , we employed Hidden Markov Models ( HMMs ) to determine the haplotypes of the segregants based on the sequencing data . We computed the fraction of reads at each SNP that came from BY and used the vector of these fractions in HMMs that were implemented chromosome-by-chromosome in the HMM ( ) package of the R statistical programming environment . Any segregants producing data that showed evidence of contamination , diploidy , or aneuploidy were excluded from genetic mapping and downstream analyses . Four and eight such individuals were left out of the BY and 3S mapping populations , respectively . Genotypes inferred from the HMM were used in genetic mapping analyses . At each position in the genome , we determined the fraction of individuals that carried the allele from the parent not used in the backcross . We scanned the genome for alleles from the non-backcross parent that were detected in a large fraction of segregants . We report loci where these alleles were at 95% frequency or higher . To determine intervals in which causal genes were located , we identified the smallest region that was bounded by recombination breakpoints among individuals from a backcross that shared the same allele at a peak . Backcross diploids were sporulated and digested in β-glucuronidase to permit tetrad dissection . Standard microdissection techniques were used to isolate tetrads and separate individual spores . Haploid multi-locus introgression strains were constructed using six rounds of recurrent backcrossing with phenotypic selection , starting from the same segregant used in our backcross mapping experiment . Eight of these strains were generated , with four made by recurrently backcrossing to 3S and four made by recurrently backcrossing to BY . We also used a subset of individuals from the tetrad dissections that showed rough morphology . To conduct the fine-mapping , we typed these individuals at a number of markers in each interval using PCR and restriction digestion , or Sanger sequencing . All genes within causal loci were deleted using the CORE cassette , in the same manner described by Storici et al . [42] . Homology tails matching the 60 bases immediately up- and downstream of each gene were attached to the CORE cassette through PCR and introduced into cells using the Lithium Acetate method [43] . Selection for G418 resistance was used to screen for integration of the CORE cassette; correct integration was then checked using PCR . All deletions were performed in a haploid multi-locus introgression strain . To perform complementation tests , deletion strains were mated to multiple dissected segregants that carried either the causal or non-causal allele of the deleted gene , as well as the causal alleles at the four other involved genes . The same phenotyping methods described above were employed for these strains . To generate allele replacement strains for IRA2 , a smooth segregant with the non-causal allele of IRA2 and the causal alleles at the other four loci was transformed using a modified form of adaptamer mediated allele replacement [44] . Transformations were conducted with two partially overlapping PCR products—a full-length amplicon of IRA2 that was tailed at the 3′ end with the 5′ portion of the kanMX cassette and a copy of the kanMX cassette that was tailed on the 3′ end with part of the intergenic region downstream of IRA2 . Knock-ins were identified using selection on G418 and verified by Sanger sequencing . Sequenced strains from the backcross to 3S were partitioned based on their genotype at END3 . We then screened these individuals for sites where they all carried BY alleles . A group of additional rough segregants with END33S that had been obtained during tetrad dissections were genotyped by PCR amplification and restriction digestion of markers across each of the new loci . One of these additional backcross segregants was mated to 3S , and a panel of rough progeny from this second-generation backcross were typed at the remaining candidate loci . | Although it is well known that interactions among genetic variants contribute to many complex traits , the forms of these interactions have not been fully characterized . Most work on this problem to date has focused on relatively simple cases involving two or three loci . However , higher-order interactions involving larger numbers of loci can also occur , and may have significant effects on the relationship between genotype and phenotype . In this paper , we dissect a colony morphology trait that segregates in a cross of two yeast strains and is caused by genetic interactions among five or more loci . Our work demonstrates that higher-order interactions can have major phenotypic effects , and provides novel insights into the genetic and molecular basis of these interactions . | [
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"traits"
] | 2014 | Genetic Interactions Involving Five or More Genes Contribute to a Complex Trait in Yeast |
Neurons typically release both a small-molecule neurotransmitter and one or more neuropeptides , but how these two types of signal from the same neuron might act together remains largely obscure . For example , serotonergic neurons in mammalian brain express the neuropeptide Substance P , but it is unclear how this co-released neuropeptide might modulate serotonin signaling . We studied this issue in C . elegans , in which all serotonergic neurons express the neuropeptide NLP-3 . The serotonergic Hermaphrodite Specific Neurons ( HSNs ) are command motor neurons within the egg-laying circuit which have been shown to release serotonin to initiate egg-laying behavior . We found that egg-laying defects in animals lacking serotonin were far milder than in animals lacking HSNs , suggesting that HSNs must release other signal ( s ) in addition to serotonin to stimulate egg laying . While null mutants for nlp-3 had only mild egg-laying defects , animals lacking both serotonin and NLP-3 had severe defects , similar to those of animals lacking HSNs . Optogenetic activation of HSNs induced egg laying in wild-type animals , and in mutant animals lacking either serotonin or NLP-3 , but failed to induce egg laying in animals lacking both . We recorded calcium activity in the egg-laying muscles of animals lacking either serotonin , NLP-3 , or both . The single mutants , and to a greater extent the double mutant , showed muscle activity that was uncoordinated and unable to expel eggs . Specifically , the vm2 muscles cells , which are direct postsynaptic targets of the HSN , failed to contract simultaneously with other egg-laying muscle cells . Our results show that the HSN neurons use serotonin and the neuropeptide NLP-3 as partially redundant co-transmitters that together stimulate and coordinate activity of the target cells onto which they are released .
Drugs that selectively manipulate serotonin signaling are widely used to treat depression and other psychiatric disorders , yet these drugs are often ineffective , and no specific molecular defects in serotonin signaling have been identified as the cause of these disorders [1] . This situation suggests there is more to understand about the basic science of serotonin signaling that could help explain the cause of psychiatric disorders . One feature of serotonin signaling in the mammalian brain that remains poorly understood is that serotonin neurons appear to also release a specific neuropeptide , Substance P [2–6] . Of the ~80 billion neurons in the human brain , only about 100 , 000 make serotonin . Their cell bodies are concentrated in the dorsal raphe nuclei of the brain stem , but they extend axons throughout the brain that release serotonin to influence many brain functions [2 , 7 , 8] . Several methods have been used to measure the proportion of serotonin neurons that express Substance P in the various raphe subnuclei of human or rat brain , with results suggesting that somewhere between 25–100% of serotonin neurons also express substance P [2 , 5 , 9 , 10] . The apparent co-release of serotonin and Substance P from the same neurons is one instance of the broad but poorly studied phenomenon of co-transmission by small-molecule neurotransmitters and neuropeptides [11–14] . It remains unclear how exactly co-released serotonin and neuropeptide ( s ) might functionally interact . Clinical studies of Substance P antagonists showed that they , like selective serotonin reuptake inhibitors , can have significant anti-depressant activity [15–17] . One study in the brain stem respiratory circuit indicated that serotonin and substance P each independently stimulate activity of the circuit [10] , but the complexity of mammalian brain circuits makes precise analysis of such effects difficult . Small neural circuits of invertebrates provide the potential for more precise analysis of the functional effects of co-transmission . In these circuits , every cell can be identified , the small-molecule neurotransmitter and neuropeptide content of each cell can be determined , and the functional effects of each signal can potentially be characterized . An elegant body of work on small circuits from crustaceans has used pharmacological and electrophysiological methods to analyze the functional effects of co-transmitters [11] . However , the nature of these experimental systems typically requires that the isolated circuit be studied after dissection from the animal and often precludes genetic tests of transmitter function . C . elegans provides the opportunity to use powerful genetic methods to functionally analyze co-transmission within small circuits of intact , freely-behaving animals . Mutations and transgenes can be used to manipulate neurotransmitters , neuropeptides , and their receptors , optogenetic methods can be used to manipulate activity of presynaptic cells , and the functional consequences of all these manipulations on postsynpatic cells can be read out using genetically-encoded Ca2+ indicators and by measuring animal behavior . Despite the promise of this approach , there are so far few examples of its use to study co-transmission [18 , 19] . Here , we have applied the genetic toolbox described above to analyze the functional consequences of co-transmission by serotonin and a neuropeptide within a well-characterized small circuit of C . elegans . The C . elegans egg-laying circuit contains three neuron types that signal each other and the egg-laying muscles to generate ~2 minute active phases , during which rhythmic circuit activity induces egg-laying behavior , that alternate with ~20 minute inactive phases , during which the circuit is largely silent and no eggs are laid [20] . The two serotonergic hermaphrodite specific neurons [HSNs ) serve as the command neurons [21] within this circuit in that 1 ) worms lacking HSNs are egg-laying defective [22]; 2 ) optogenetic activation of HSNs is sufficient to induce activity of the circuit that mimics a spontaneous active phase [23–26]; and 3 ) no other cells in the circuit have these properties [23] . We show here that the HSNs use a combination of serotonin and a neuropeptide to induce the coordinated circuit activity of egg-laying active phases .
The small circuit that initiates egg-laying behavior is schematized in Fig 1A . The serotonergic Hermaphrodite-Specific Neurons ( HSNs ) , along with the cholinergic Ventral Cord type C neurons ( VCs ) , synapse onto the type 2 vulval muscles ( vm2 ) , which contract with the type 1 vulval muscles ( vm1 ) to expel eggs [20] . Loss of the HSNs results in a severe egg-laying defect: a mutation in egl-1 causes death of the HSNs and results in animals that , despite continuing to make eggs , rarely lay them [27] , resulting in the striking phenotype of adult worms bloated with accumulated unlaid eggs ( Fig 1B and 1C ) . Because addition of exogenous serotonin to worm culture media is sufficient to induce egg laying , even in worms lacking HSNs [22] , it has been suggested that HSNs induce circuit activity simply by releasing serotonin , and thereby sensitizing the egg-laying muscles to activation by the acetylcholine released by other motor neurons of the circuit [23 , 28] . Contrary to this model , we found that animals lacking serotonin had only mild egg-laying defects ( Fig 1D ) . The tph-1 gene encodes the serotonin biosynthetic enzyme tryptophan hydroxylase , and animals with a tph-1 null mutation have no serotonin detectable by anti-serotonin antibodies or HPLC analysis [29 , 30] . tph-1 mutant animals had only mild egg-laying defects ( ~18 unlaid eggs ) , appearing more similar to the wild type ( ~12 unlaid eggs ) than they did to egl-1 mutants lacking HSNs ( ~47 unlaid eggs; Fig 1B–1D ) . This result , along with previous pharmacological , genetic , and behavioral studies of the function of serotonin in egg laying [31] , is consistent with the idea that serotonin release can only partially explain how the HSNs initiate egg laying . To determine definitively if serotonin is required for the HSNs to stimulate egg laying , we optogenetically stimulated the HSNs of animals either wild-type for tph-1 or deleted for the tph-1 gene ( Fig 1E ) . Animals with channelrhodopsin ( ChR2 ) expressed in the HSNs and that are wild-type for tph-1 have been shown previously to lay eggs within a few seconds of exposure to blue light , but only if the required ChR2 cofactor all-trans retinal ( ATR ) is supplied to the worms [24 , 25] . We found that upon optogenetic activation of HSNs , tph-1 mutant animals laid a number of eggs statistically indistinguishable from the number laid by control animals wild-type for tph-1 ( Fig 1E and S1 Video ) . Thus the HSNs do not require serotonin to stimulate egg-laying behavior . Our results , along with previous studies [31–34] , lead to the hypothesis that the HSNs release a co-transmitter that allows them to stimulate egg laying even without serotonin . We hypothesized that this co-transmitter could be one or more of the neuropeptides encoded by five neuropeptide genes previously shown to be expressed in the HSNs [35–37] . To test this idea we created five types of transgenic worm strains , each overexpressing one of these neuropeptide genes under its own promoter . Each strain had an extrachromosomal transgene composed of multiple copies of a ~45 kb C . elegans genomic DNA clone containing one of the neuropeptide genes . Overexpressing a neuropeptide gene in this way can result in a gain-of-function phenotype caused by an increase in the normal signaling effects of the encoded neuropeptides [19 , 38] . Therefore , for a neuropeptide that induces egg laying , we expected overexpression to cause an increase in the frequency of egg-laying behavior . Indeed , we found that overexpressing the nlp-3 neuropeptide gene resulted in a dramatic increase in the frequency of egg-laying behavior . An increased rate of egg-laying behavior results in a decrease in the time eggs have in the mother to develop before they are laid [39] . More than 80% of the eggs laid by worms carrying the high-copy nlp-3 transgene were laid at early stages of development , compared to ~5% for control animals not overexpressing any neuropeptide ( Fig 2A ) . Overexpressing any of the other four neuropeptide genes did not increase the frequency of egg laying ( Fig 2A ) . We obtained nlp-3 null mutant animals in which the nlp-3 gene is deleted . Unlike the egl-1 mutants lacking HSNs that accumulate ~47 unlaid eggs ( Fig 1C ) , nlp-3 null mutants accumulated only ~19 unlaid eggs ( Fig 2B ) , and thus were more similar to the wild type ( Fig 1B ) or tph-1 mutant worms lacking serotonin ( Fig 1D ) . However , when we made tph-1; nlp-3 double mutants so that the HSNs lacked both serotonin and NLP-3 neuropeptides , the adult animals were bloated with ~42 unlaid eggs and thus showed a severe egg-laying defect similar to that of egl-1 animals . We obtained a second , independent deletion mutant for nlp-3 and observed the same mild defect in the single mutant and a similar severe defect in the double mutant with tph-1 ( Fig 2D ) . The above experiments demonstrated that serotonin and NLP-3 stimulate egg laying but did not examine if they do so by being released from the HSN neurons . Previous studies demonstrated that the HSNs contain serotonin , and it was inferred from indirect evidence that HSNs can release serotonin to stimulate egg laying [22 , 40 , 41] . We used the nlp-3 promoter to drive GFP expression and saw , as previously reported [42] , that nlp-3 is expressed in the HSN neurons but no other cells of the egg-laying circuit ( Fig 3A ) , consistent with the hypothesis that NLP-3 neuropeptides are released from HSNs to stimulate egg laying . To directly test what combination of transmitters the HSNs use to stimulate egg laying , we generated animals that express ChR2::YFP in the HSN neurons ( Fig 3B ) and ( as controls ) that were wild-type for tph-1 and nlp-3 , or that carried null mutations in tph-1 , nlp-3 , or both . We then tested whether optogenetic stimulation of the HSNs could induce egg laying . Both the control animals and null mutants for tph-1 laid eggs readily upon ChR2 activation , with no statistically significant differences in the number of eggs laid ( Fig 3C ) or in several other measures of the egg-laying behavior induced ( e . g . time to first egg laid , time to last egg laid , S2A–S2C Fig ) . However , whereas all wild-type and tph-1 animals tested laid eggs upon ChR2 activation , 7/20 nlp-3 mutant animals failed to lay any eggs , and the 13/20 that did lay eggs laid fewer on average than did the wild-type or tph-1 animals . No eggs were laid by any tph-1; nlp-3 double mutant animals ( Fig 3C ) . Therefore , we conclude that: 1 ) the HSN neurons release both serotonin and NLP-3 peptides to stimulate egg laying; 2 ) either signal alone is sufficient to stimulate at least some egg laying; and 3 ) when lacking both signals the HSNs have no detectable ability to stimulate the behavior . While the experiments shown in Fig 3C demonstrate that NLP-3 released from the HSN stimulates egg laying , they did not test whether NLP-3 might also be released from additional cells to stimulate egg laying . To investigate this issue , we first determined the entire set of cells in C . elegans that express the nlp-3 gene . We used a transgene to drive expression of GFP from the nlp-3 promoter , and by combining this with red fluorescent markers of various subsets of identified neurons , identified every nlp-3-expressing cell ( Fig 4 , S3 Fig and S4 Video ) . These comprise 18 neuron types , including 16 bilaterally symmetric neuron pairs plus two unpaired neurons , totaling 34 neurons . Six of these neurons express nlp-3::GFP barely above background levels , while the other 28 gave strong GFP signals ( Fig 4 ) . One muscle cell also expresses nlp-3::GFP . The HSNs are the only neurons that express nlp-3::GFP that are in the midbody or that are known to play a role in egg laying . We note that Nathoo et al . [35] had previously identified a subset of the nlp-3-expressing cells , and our results largely agree with the earlier work . To test if any cells besides the HSNs might release NLP-3 to stimulate egg laying , we crossed together the egl-1 mutation ( previously used in Fig 1 ) that results in absence of the HSNs with transgenes that overexpress nlp-3 from its own promoter . As seen in Fig 5 , egl-1 animals retain a large number of unlaid eggs , while animals carrying either of two independent nlp-3 overexpressor transgenes are hyperactive egg layers and therefore retain fewer unlaid eggs than does the wild type . Animals carrying both the egl-1 mutation and an nlp-3 overexpressor transgene had an intermediate phenotype , retaining fewer eggs than the animals carrying the egl-1 mutation alone and more eggs than animals carrying the nlp-3 overexpressor alone . This result demonstrates that , at least when nlp-3 is overexpressed , release of NLP-3 from some cells other than HSN is able to stimulate egg-laying behavior to some extent . An experiment that could help determine the extent to which NLP-3 release from the HSNs versus other cells is normally used to stimulate egg laying would be to re-express NLP-3 specifically in the HSN of nlp-3 null mutants to determine if this is sufficient to rescue the egg-laying defects of these animals . In attempting this experiment , we found that transgenes expressed in the HSN of nlp-3 mutants tend to cause defects in HSN development , similar to those shown in S1A Fig . This effect made the results of the rescue experiment uninterpretable . We conclude that the HSN does use NLP-3 to stimulate egg laying , and that it is possible that other NLP-3-expressing cells may also contribute to the activation of egg laying by releasing NLP-3 , but the extent to which they do so remains unclear . To further investigate the relationship between serotonin and NLP-3 in activating egg-laying behavior , we performed additional experiments to test if either of these transmitters is required to allow the other to stimulate egg laying . For serotonin stimulation of egg laying , we used a standard assay [22] in which worms were placed in microtiter wells with M9 buffer or M9 buffer containing 7 . 5 mg/ml serotonin , and the number of eggs laid in 60 minutes was counted . We saw , as observed previously [40 , 43–45] , that exogenous serotonin stimulates egg laying in wild-type animals , but not in animals deleted for the serotonin receptor gene ser-1 ( Fig 6A ) . Null mutants for nlp-3 were stimulated by serotonin to lay eggs at the same rate as were the wild-type controls , demonstrating that NLP-3 is not required for serotonin to stimulate egg laying . We used a converse experiment to test if NLP-3 could stimulate egg-laying in the absence of serotonin . We generated C . elegans transgenes that overexpressed nlp-3 by containing multiple copies of nlp-3 genomic DNA or control transgenes that did not overexpress nlp-3 . In a strain background wild-type for tph-1 , we observed ( Fig 6B ) , as we had seen previously in an analogous experiment ( Fig 2A ) , that overexpression of nlp-3 resulted in hyperactive egg laying as evidenced by a high percentage of early-stage eggs laid . When we carried out this same experiment in a tph-1 null mutant , nlp-3 overexpression also resulted in hyperactive egg laying , albeit at a somewhat reduced level ( Fig 6B ) . Thus serotonin is not required to allow nlp-3 overexpression to induce egg laying , but the absence of serotonin may mildly reduce the effects of NLP-3 . To understand the functional effects of the HSN co-transmitters , we recorded Ca2+ activity of the vulval muscle cells , which are postsynaptic targets of the HSNs , in animals that were wild-type , lacked serotonin , lacked NLP-3 , or lacked both . To do so , we co-expressed the Ca2+-sensitive green fluorescent protein GCaMP5 and the Ca2+-insensitive red fluorescent protein mCherry in the vm2 muscle cells , the direct postsynaptic targets of the HSNs , and also in the vm1 muscle cells , which are gap-junctioned to vm2 and have been thought to contract with vm2 to expel eggs [46] . Using methods we previously developed [23 , 47 , 48] , we carried out ratiometric fluorescence imaging of intact animals ( S2 Video ) to measure Ca2+ transients under conditions that allow egg-laying behavior to proceed as it does in standard lab culture , such that in wild-type animals ~2 minute egg-laying active phases occur about every 20 minutes . Fig 7 shows traces of one-hour recordings of Ca2+ transients in the entire ensemble of vm1 and vm2 cells together for three animals of each of the following genotypes: wild-type , tph-1 and nlp-3 single mutants , the tph-1; nlp-3 double mutant , and egl-1 animals lacking HSNs . We observed frequent vulval muscle activity in all genotypes: the recordings in Fig 7 show hundreds of Ca2+ transients for each genotype . However , less than 10% of the vulval muscle Ca2+ transients resulted in egg release in the wild type , and even fewer successful egg-laying events occurred in mutants lacking serotonin or NLP-3 ( 29 eggs released over three hours for the wild type , compared to 15 for tph-1 and 9 for nlp-3 ) . Activity in animals lacking both serotonin and NLP-3 neuropeptides ( tph-1; nlp-3 ) or lacking HSNs ( egl-1 ) was actually more frequent than in the wild type , but very rarely produced successful egg release: each genotype released just two eggs in the three hours analyzed . To identify the differences between vulval muscle contractions that did or did not release eggs , we adjusted how we collected images during Ca2+ recordings . Previously-published Ca2+ imaging of the vulval muscles used images focused at the center of the group of two vm1 and two vm2 muscles found on either the left or right side of the animal . The resulting images showed Ca2+ activity that was often found at the most ventral tip of this group of muscles , but that could not be assigned to individual muscle cells [25 , 46 , 47] . By focusing more laterally on either the left or right set of vulval muscles , we could resolve individual vm1 and vm2 cells and determine which of the four muscle cells within the set were active during any given Ca2+ transient detected ( Fig 8A , S3 Video ) . All of the data presented in Fig 8 results from use of this more lateral focus . The large majority of the muscle activity we observed in every genotype examined occurred exclusively in one or both of the vm1s imaged , with no concurrent activity detected in the vm2s , and we refer to such events as "vm1 only" ( Fig 8B ) . We never observed an event in any genotype in which a Ca2+ transient occurred exclusively in vm2 cell ( s ) without accompanying activity in vm1 cell ( s ) . In the wild type , ~15% of Ca2+ transients involved both vm1 and both vm2 cells imaged , and we refer to such events as "vm1 + vm2" . In the wild type , vm1 + vm2 vulval muscle contractions occurred exclusively within active phases , the ~2-minute intervals during which eggs were laid and that contained frequent vulval muscle transients ( Fig 7 ) . Furthermore , all 29 egg release events observed in the wild type occurred during one of the 51 vm1 + vm2 vulval muscle contractions we saw during the three hours of recordings analyzed . We conclude that simultaneous contraction of all the vulval muscle cells is necessary for egg release . Mutants lacking either serotonin , NLP-3 , or both showed a similar or even greater number of vm1 Ca2+ transients compared to the wild type , but a decreased percent of these events were accompanied by vm2 Ca2+ transients to produce vm1 + vm2 events ( Fig 8B and 8C ) . The decrease compared to the wild type in the percent of events that included vm2 was about 2-fold in tph-1 and nlp-3 single mutants , but 10- to 15-fold in the tph-1; nlp-3 double mutant and in egl-1 animals lacking HSNs . In the mutants , as in the wild-type , egg release occurred only during about half of the events that included vm2 activity ( Fig 8D ) , with no statistically significant difference in this proportion in any of the genotypes we studied ( Fig 8E ) . Thus Ca2+ activity in vm1 muscles does not require the serotonin or NLP-3 released by HSNs . However , these two signals together promote vm2 Ca2+ activity , and successful egg laying requires that vm1 and vm2 muscle cells contract simultaneously .
The principal finding of this study is that the HSN command neurons release a combination of serotonin and NLP-3 peptides to activate the egg-laying circuit . Loss of either signal , and to a much greater extent , loss of both signals , results in loss of activity of the vm2 muscle cells . The vm2s are the direct postsynaptic targets of the HSNs and their activity appears to be necessary for successful release of eggs . How do serotonin and NLP-3 peptides activate the vm2 muscle cells ? The receptor ( s ) for NLP-3 have not yet been identified in the egg-laying circuit , and it thus remains to be determined if such a receptor is expressed on vm2 and furthermore whether NLP-3 acts directly on these muscle cells . Previous studies demonstrated that serotonin activates egg laying via three G protein coupled serotonin receptors , SER-1 , SER-5 , and SER-7 [49] . Promoter::GFP transgenes for all three receptors show expression in the vm cells [43 , 45 , 33 , 49 , 50] , but the transgenic animals carrying these GFP reporters have not been examined carefully to determine which receptors are expressed in vm1 versus vm2 cells . Even so , the published images of these animals suggest that both vm1 and vm2 express one or more of these GPCRs . Thus , the anatomy of the egg-laying circuit and serotonin receptor expression patterns suggest that HSNs release serotonin at synapses onto vm2 cells to directly activate these muscles . We note , however , that serotonin and/or NLP-3 likely also activate vm2 via an indirect route . Our past Ca2+ imaging studies [25] show that the HSNs activate the cholinergic VC motor neurons , which in turn directly synapse onto vm2 ( Fig 1 ) . No serotonin receptors are known to be expressed on the VC neurons , so NLP-3 may be the HSN-released signal that activates the VCs . We therefore propose a model ( Fig 9 ) in which the HSNs activate the egg-laying circuit via two mechanisms: 1 ) Serotonin released by HSNs directly onto vm2 would act via G protein-coupled serotonin receptors to increase the excitability of the vm2 cells; and 2 ) NLP-3 peptides released by the HSNs would activate the VC neurons to release acetylcholine directly onto vm2 , triggering vm2 contractions , that when concurrent with vm1 contractions result in egg release . We note that the mechanisms in the above model remain hypothetical , and in particular it remains to be tested experimentally if NLP-3 acts on the VC neurons . A crucial step towards this would be to identify NLP-3 receptors , which is necessary to determine in what cells of the egg-laying circuit these receptors are expressed and function to stimulate egg laying . A surprising finding of this work is that activity of the vm1 muscle cells has little or no dependence on the HSNs or the signals they release . The vm1 and vm2 muscle cells are connected by gap junctions [46] and it was previously assumed that vm1 and vm2 cells would be efficiently electrically coupled and contract as a unit in response to excitatory signals released by HSNs onto vm2 . To the contrary , by adjusting our Ca2+ imaging conditions to spatially resolve vm1 and vm2 cells , we found that this assumption is incorrect . In wild-type animals , most vm Ca2+ transients occur in vm1 only , and these do not result in egg release . Only during the ~2 minute active state of the egg-laying circuit when the HSNs show rhythmic activity [25] did we observe vm1 + vm2 transients , during which all observed egg release events occurred . It may be that vm1-only transients result from release of acetylcholine by the VA7 and VB6 ventral cord motor neurons , which synapse directly onto the vm1s [23 , 47] . We note that the SER-1 , SER-5 , and SER-7 G protein-coupled serotonin receptors that promote egg laying appear to be expressed on both the vm1 and vm2 cells [43 , 45 , 33 , 49 , 50] . Thus while HSN serotonin released synaptically onto vm2 appears to act directly on vm2 to help excite these cells , it may also act extrasynaptically to promote vm1 response to both acetylcholine from VA/VB and to depolarization via gap junctions from vm2 . In this way , serotonin would promote the simultaneous contraction of both vm1 and vm2 cells . HSN-released serotonin and/or NLP-3 also may help produce simultaneous contraction of the vm cells found on the anterior and posterior sides of the vulval opening . These two sets of vm cells are not physically connected [46] except that the HSNs form synapses onto the muscle arms extending from both the anterior and posterior vm2 cells ( Fig 1 ) . By simultaneously releasing their signals onto both the anterior and posterior vm2 muscle arms , the HSNs promote the coordinated contraction of both the anterior and posterior vm cells necessary for successful egg release [51] . The apparent co-release of serotonin and NLP-3 from the HSN neurons is just one instance of the broad but poorly-studied phenomenon of co-transmission by small-molecule neurotransmitters and neuropeptides . Most neurons release both small-molecule neurotransmitters and neuropeptides . This issue has been analyzed in greatest detail within the C . elegans nervous system . There are seven known small-molecule neurotransmitters in C . elegans , and 107 of the 118 neuron types in C . elegans hermaphrodites express at least one of them [52] . At least 95 C . elegans neuropeptide genes have been described , including 23 FLP genes encoding FMRFamide-related peptides , 32 NLP genes encoding neuropeptide-like proteins , and 40 INS genes encoding insulin-like peptides . Promoter::GFP fusion transgenes have been generated for all 95 of these neuropeptide genes to analyze their expression patterns . The individual neurons expressing each FLP gene were identified , and >50% of C . elegans neurons express at least one FLP peptide gene [36] . Although the individual cells expressing each NLP and INS gene have not yet been identified , images of the expression patterns show that the large majority of these peptide genes are expressed complex subsets of neurons [35] . Thus , we can infer that the typical neuron in C . elegans releases one small-molecule neurotransmitter and one or more type of neuropeptide . Similarly , the presence of both small-molecule neurotransmitters and neuropeptides within the same individual neurons is widespread in both Drosophila [53 , 54] and in mammals [55] . The functional consequences of a neuron releasing two different types of signaling molecules have been difficult to study with precision in the complex circuits of the mammalian brain , but this issue has been the focus of many studies of small neural circuits in invertebrate model organisms [11 , 56] . In such small circuits , individual presynaptic neurons that co-release a small-molecule neurotransmitter and neuropeptides can be identified . The functional effects of each signal can be measured by bath application of neurotransmitter agonists/antagonists and/or neuropeptides followed by measurements of circuit activity using electrophysiological methods . Such work has led to a rich set of findings and many different schemes for the use of co-transmission within circuits [11 , 56] . However , the limitations of these studies include that bath application of signaling molecules does not always mimic the effects of their release from neurons [11] . The genetic approaches for analyzing co-transmission described in this work provides a useful complement to electrophysiological studies as they permit manipulation of endogenous signaling molecules with mutations and transgenes , recording of circuit activity using genetically-encoded calcium indicators , and manipulation of neural activity using optogenetics , all within intact , freely-behaving animals . We are aware of just one previous study that focused on co-transmission using this combination of genetic approaches [18] . In that pioneering study , an odor was shown to cause a C . elegans sensory neuron to co-release glutamate and a neuropeptide to act on different interneurons . The glutamate evokes a behavioral response to the odor via a complex and incompletely understood motor circuit . The neuropeptide acts via a G protein-coupled receptor to cause release a second neuropeptide back onto the original sensory neuron , limiting activity of the sensory neuron and the timescale of the behavioral response to the odor . Our studies of co-transmission focus on the C . elegans egg-laying circuit because its anatomical simplicity holds the potential that all the cells and signaling events that control this circuit can be defined , something that has yet to be accomplished for any neural circuit . In this study , we discovered that serotonin and NLP-3 peptides released from the HSN command neurons have parallel and partially redundant effects to activate coordinated , rhythmic contraction of the egg-laying muscles . This finding may be analogous to results of some previous studies of co-transmission , in which the two co-released signals act convergently to increase activity the same target cells . The most relevant example is in the mammalian brain respiratory circuit , where co-release of serotonin and the neuropeptide Substance P have parallel effects promoting rhythmic circuit activity [10] . It will be interesting to determine how mechanistically analogous these two cases of serotonin/neuropeptide co-transmission actually are , and whether the action of serotonin within the C . elegans egg-laying circuit will provide a model for the detailed workings of serotonin within neural circuits of the human brain .
C . elegans strains were cultured at 20°C on NGM agar plates with E . coli strain OP50 as a food source [57] . All strains were derived from the Bristol N2 wild-type strain . Genetic crosses and generation of transgenic strains were by standard methods [58 , 59] . Table 1 shows a list of strains , mutants , and transgenes used in this study . Gene deletion strains for nlp-3 [60] , tph-1 [61] , and ser-1 [43] were outcrossed four to ten times to the wild-type strain . The nlp-3 ( tm3023 ) allele is a 354 bp deletion that removes DNA flanked by the sequences GTCTGGACGGAAAGATCGTT…CGTGAGACTAGAAGTCCAC . The nlp-3 ( n4897 ) allele is a 1405 bp deletion that removes DNA flanked by the sequences TCCCGGATTAGTGTCCAGTC…TATGTTCAACCGAAATTAAA . Each gene deletion used removes a portion or all of the promoter and/or coding sequences of the corresponding gene such that no functional gene product is expected . The genotypes for all strains constructed using these deletions were verified by agarose gel analysis of PCR amplification products from the corresponding genes . Quantitation of unlaid eggs in adult animals and percentage of early-stage eggs laid was performed using adult animals 30 hours after staging as late L4 larvae as described in [39] . HSN neurons were optogenetically activated in animals carrying the wzIs30 transgene , which expresses a Channelrhodopsin-2::yellow fluorescent protein ( ChR2::YFP ) fusion in the HSN and a few other neurons unrelated to the egg-laying circuit from the egl-6a promoter [62 , 63] . wzIs30 also carries a lin-15 marker plasmid that rescues the multivulva phenotype of lin-15 ( n765ts ) mutant animals . All animals used in optogenetic assays were homozygous for the lite-1 ( ce314 ) mutation to eliminate an endogenous avoidance response of C . elegans to blue light . The wzIs30 transgene was homozygous for the experiment shown in Fig 1E , but we noticed that the homozygous transgene caused developmental defects in the HSNs of some animals ( S1 Fig ) that resulted in these animals being egg-laying defective . Therefore , for the experiment in Fig 1E , we examined the animals prior to optogenetic stimulation and discarded the small percentage of animals that were visibly egg-laying defective . The experiment shown in Fig 3C was carried out such that all animals were wzIs30/+ heterozygotes , which we found had morphologically normal HSNs ( S1 Fig ) . First , we constructed the strains indicated in Table 1 that were homozygous for wzIs30 and also homozygous for the other mutations required by the experiment . Then , we generated males of each of these strains and mated them to corresponding strains that were genetically identical except that they lacked wzIs30 . The cross progeny , identified by the presence of YFP-labeling , thus were heterozygous for wzIs30 but homozygous for all other mutations used in the experiment . ChR2 expressing strains were grown in the presence or absence of the ChR2 cofactor all-trans retinal ( ATR ) . ATR was prepared at 100 mM in 100% ethanol and stored at -20° C . To prepare NGM plates for behavior analysis , ATR was diluted to 0 . 4 mM with room temperature cultures of OP50 bacteria in B Broth , and 200 μl of culture was seeded onto each 60 mm NGM plate . The plates were allowed to grow for 24 hr at 25–37°C , after which late L4 worms were staged onto prepared plates for behavioral assays 24 hr later . To begin an assay , a video recording was initiated ( Flea 3 , 0 . 3 Megapixel , FireWire CCD camera , Point Grey Research ) and simultaneously a shutter was opened on a EL6000 metal halide light source generating 3 . 2 mW/cm2 of 470 ± 20 nm blue light via a EGFP filter set mounted on a Leica M165FC stereomicroscope . To overexpress neuropeptide genes ( Fig 2A ) , fosmid genomic clones including individual neuropeptide genes were selected from the C . elegans fosmid library ( Source BioScience ) . The fosmids used for four of the neuropeptide genes were: nlp-3 , WRM0633dC06; nlp-8 , WRM0614aB10; nlp-15 , WRM066cH12; flp-5 , WRM0622aF03 . For overexpression of a fifth neuropeptide gene , flp-19 , we instead PCR amplified genomic DNA containing the 746 bp flp-19 coding region along with 5015 bp upstream and 1155 bp downstream ( primers used were 5’- tcttaccaatattccggttagtgtcc-3’ and 5’-gtaatgtaagaaataattcgagccacg-3’ ) . Multicopy extrachromosomal transgenes were generated for each neuropeptide gene by microinjection , using the fosmid or PCR product at 50 ng/μl along with the lin-15 rescuing plasmid pL15EK at 50 ng/μl into lin-15 ( n765ts ) mutant animals . Negative controls were injected with pL15EK without any neuropeptide gene . Five independent transgenic overexpressor lines were generated for each injection and Fig 2A shows data averaged from these . Table 1 lists one representative overexpressor strain for each neuropeptide gene . To determine the effects of overexpressing nlp-3 in animals lacking serotonin ( Fig 6B ) , either a ~5 kb PCR product containing the nlp-3 gene ( primers used were 5'-accaagctaatcaaattttgtcaccg-3' and 5'-gcaatacaaccaatcccttttcatctc-3’ ) or as a control , E . coli genomic DNA digested to an average size of ~5 kb , was injected at 10 ng/μl along with 50 ng/μl of the lin-15 rescuing plasmid pL15EK into either lin-15 or tph-1; lin-15 animals , and transgenic lines were identified by rescue of the lin-15 phenotype . Five independent transgenic lines were established for each injection , and the early stage egg assay [39] was carried out on 50 eggs per line ( 250 eggs total per condition tested ) . One representative line for each condition is listed in Table 1 . To determine the effects of overexrpessing nlp-3 in animals lacking HSN neurons , we generated chromosomally-integrated nlp-3 overexpressing transgenes . Primers 5' cagtcagtcgacgcaatacaaccaatcccttttcatctc 3' and 5' cagtcaggtaccaccaagctaatcaaattttgtcaccg 3' were used to amplify the nlp-3 gene from fosmid genomic clone wrm0613cB03 , generating a PCR product with ~3700 bp of promoter and ~900 bp of 3' untranslated region . This was digested with restriction enzymes SalI and KpnI and inserted into Sal1 and Kpn1 digested plasmid vector pUC19 . The resulting clone pAO30 was microinjected into C . elegans at 60 ng/μl along with 10 ng/μl of pCFJ90 ( a myo-2::mCherry marker plasmid ) and 25 ng/μl of genomic DNA from E . coli strain DH5α digested to an average size of 5 kb . The resulting transgene was chromosomally integrated after psoralen/UV mutagenesis to produce the integrated transgenes vsIs275 III and vsIs276 II . To visualize nlp-3 expression , we made the plasmid pJB11 , a derivative of pJB9 [25] , which in turn is a derivative of pPD49 . 26 ( Fire lab C . elegans vector kit ) . pJB11 has a ~4 kb nlp-3 promoter region inserted into multiple cloning site I ( MCS I ) of pPD49 . 26 , followed by the GFP coding sequence inserted into MCSII , and by ~750 bp of the nlp-3 3' untranslated region inserted into MCSIII . The nlp-3::GFP plasmid pJB11 was transformed into C . elegans by microinjection at 20 ng/μl along with 25 ng/μl of genomic DNA from E . coli strain DH5α digested to an average size of 5 kb and 50 ng/μl of the lin-15 rescuing plasmid pL15EK into strains carrying the lin-15 ( n765ts ) marker mutation . The strains injected carried marker transgenes that label specific subsets of neurons with red fluorescent proteins . These transgenes were otIs356 , which expresses nuclear-localized tagRFP in all neuronal nuclei from the rab-3 promoter [64] , otIs518 , which expresses nuclear-localized mCherry in glutamatergic neurons [65] , and otIs544 , which expresses nuclear-localized mCherry in cholinergic neurons [66] . The nlp-3::GFP transgene was also transformed into a strain not expressing any red fluorescent protein , and the resulting animals were stained with DiD , a red fluorescent dye which labels a specific subset of sensory neurons [67] . The double-labeled strains were imaged using an LSM880 confocal microscope , and the GFP labeled cells were identified based on position , morphology , and comparison to the known identities of the red-labeled neurons [68] . S3 Fig shows representative head images of animals expressing nlp-3::GFP and showing labeling with each of the red fluorescent markers used . Freely-behaving animals were mounted between a glass coverslip and a chunked section of an NGM plate for imaging as described [23 , 47 , 48] . Two channels were recorded with a 20X Plan-Apochromat objective ( 0 . 8 NA ) using a Zeiss LSM 710 Duo LIVE head . Recordings were collected at 20 fps at 256 x 256 pixel , 16 bit resolution , for 1 hour . Five 1 hour recordings were collected for each genotype studied , and Figs 7 and 8 present analysis of the data from three representative 1 hour recordings per genotype . The stage and focus were adjusted manually to keep the egg-laying system in view and focused during recording periods . Care was taken to find a lateral focus that included as much of the vm1s and vm2s as possible . Ratiometric analysis for Ca2+ recordings was performed in Volocity ( version 6 . 3 , PerkinElmer ) . Volocity was also used to identify the vulval muscles as the region of interest ( ROI ) analyzed in each video frame using size and intensity parameters that varied over a small range based on individual animals . Any misidentified objects were manually excluded prior to final analysis . A ratio channel was calculated from GCaMP5 ( GFP ) and mCherry fluorescence channels within the ROI . The lowest 10% of the GCaMP5/mCherry ratio values within a 1 hour recording were averaged to establish a ΔR/R baseline using a custom MATLAB script . This script also identifies the peak of a transient based on identifying a change in prominence that was typically 0 . 25 ΔR/R over the preceding second , but this was adjusted based on the smoothness of the data for individual animals . With the experimenter blinded to the genotype of the animals being scored , video of each peak was observed in the ratio channel to determine whether the indicated activity was restricted to vm1 or present in both vm1 and vm2 and whether an egg was laid . We scored a transient as vm1-only if it was clear in the ratio channel that there was a difference of more than 50% of maximum activity between the vm1s and the adjacent regions where vm2 cells were located . Statistical analyses were performed using GraphPad Prism for Mac OS X v . 7 . 0a . 95% confidence intervals were determined and 1- or 2-way ANOVA , corrected for multiple comparisons , were performed to determine statistical significance . For egg stage assays , we used the Wilson-Brown method for determining the 95% confidence intervals for binomial data and used a Bonferroni correction to correct for multiple comparisons . | Activity of the brain results from neurons communicating with each other using chemical signals . A typical neuron releases two kinds of chemical signals: a small molecule neurotransmitter , such as serotonin , and one or more small proteins , called neuropeptides . For example , neurons in the human brain that release serotonin , a neurotransmitter thought to be involved in depression , also release the neuropeptide Substance P . Neuroscientists have typically studied the effects of neurotransmitters and neuropeptides separately , without considering how these two types of signals from the same neuron might be integrated . Here we analyzed how specific neurons in the model organism C . elegans use both serotonin and a neuropeptide together . The Hermaphrodite Specific Neurons ( HSNs ) activate a small group of neurons and muscles to generate egg-laying behavior . Killing the HSNs resulted in animals unable to lay eggs , but we found that eliminating either serotonin or the neuropeptide resulted in HSNs that still remained able to activate egg laying . However , eliminating both serotonin and the neuropeptide resulted in HSNs unable to activate coordinated contractions of the egg-laying muscles . Our results show that in a living animal , serotonin acts in concert with a co-released neuropeptide to carry out its functions . | [
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"caenorhabditis... | 2019 | Serotonin and neuropeptides are both released by the HSN command neuron to initiate Caenorhabditis elegans egg laying |
WNT signaling has been implicated in both embryonic and postnatal bone formation . However , the pertinent WNT ligands and their downstream signaling mechanisms are not well understood . To investigate the osteogenic capacity of WNT7B and WNT5A , both normally expressed in the developing bone , we engineered mouse strains to express either protein in a Cre-dependent manner . Targeted induction of WNT7B , but not WNT5A , in the osteoblast lineage dramatically enhanced bone mass due to increased osteoblast number and activity; this phenotype began in the late-stage embryo and intensified postnatally . Similarly , postnatal induction of WNT7B in Runx2-lineage cells greatly stimulated bone formation . WNT7B activated mTORC1 through PI3K-AKT signaling . Genetic disruption of mTORC1 signaling by deleting Raptor in the osteoblast lineage alleviated the WNT7B-induced high-bone-mass phenotype . Thus , WNT7B promotes bone formation in part through mTORC1 activation .
WNT proteins are a family of signaling molecules that control cell proliferation , fate decision , polarity and migration throughout metazoan evolution [1] . By engaging various receptors and co-receptors at the cell membrane , these proteins activate a context-dependent intracellular signaling network to induce diverse biological responses [2] . Deregulation of WNT signaling is frequently associated with human diseases [3] . WNT signaling was first associated with bone diseases by the finding that loss-of-function mutations in the WNT co-receptor LRP5 cause osteoporosis-pseudoglioma syndrome ( OPPG ) ( Gong et al . , 2001 ) . In contrast , deficiency in the secreted WNT inhibitor SOST , or mutations in LRP5 rendering it refractory to the WNT inhibitors such as SOST or DKK1 , results in high bone mass in patients [4] , [5] , [6] , [7] , [8] , [9] . In addition , mutations in WTX , an inhibitor of WNT/β-catenin signaling , were shown to cause X-linked sclerosing bone dysplasia known as OSCS in humans [10] , [11] . In the mouse , deletion of LRP5 either globally or specifically in bone causes osteopenia in the mouse [12] , [13] , whereas expression of the high-bone-mass forms of LRP5 increases bone accrual [13] , [14] . Moreover , mice lacking one DKK1 allele or both SOST alleles exhibit a higher bone mass [15] , [16] . Overall , genetic evidence from both humans and mice supports the importance of WNT signaling in bone formation . The intracellular signaling network mediating WNT function in bone formation is not completely understood [17] . Work in the mouse embryo has shown that deletion of β-catenin , or both LRP5 and LRP6 , in the osteogenic progenitors abolishes osteoblast differentiation , indicating that β-catenin is likely a critical component of the WNT signaling network responsible for embryonic osteoblastogenesis [18] , [19] , [20] , [21] , [22] . However , these mice die at birth , and therefore are not useful for assessing whether or not β-catenin similarly mediates WNT function in postnatal bone formation . We and others have recently shown that deletion of β-catenin in Osx-expressing cells selectively in postnatal mice reduced the life span and activity of osteoblasts , as well as increasing adipogenesis in the bone marrow [23] , [24] . Besides β-catenin , activation of PKCδ or CAMKII by WNT through phosphatidylinositol signaling has also been shown to promote osteoblast differentiation [25] , [26] . In addition , multiple WNT ligands have been reported to activate mTORC1 ( mammalian target of rapamycin complex 1 ) [27] , [28] . We have recently shown that WNT proteins also activate mTORC2 to stimulate glycolysis [29] . mTORC1 differs from mTORC2 in that it uniquely contains Raptor and is acutely sensitive to rapamycin [30] . Because mTORC1 signaling is a central mechanism integrating extracellular and intracellular cues with anabolic metabolism , it could potentially mediate WNT function during bone formation . Overall , WNT proteins may promote bone anabolism through a signaling network composed of multiple interconnecting modules . Despite a clear role for WNT signaling , the physiological WNT ligands promoting bone formation are just beginning to be elucidated . WNT1 has recently been linked to bone physiology in humans , as heterozygous or homozygous mutations have been identified in patients with inherited early-onset osteoporosis or osteogenesis imperfecta , respectively [31] , [32] , [33] , [34] . In the mouse , WNT10B has been implicated in postnatal bone formation [35] , [36] , but the low bone mass phenotype in the Wnt10b−/− mice appears later in life than the Lrp5−/− animals [37] , indicating that LRP5 may interact with other WNT ligands at the earlier stages . In the mouse embryo , WNT7B is specifically expressed within the osteogenic perichondrium; deletion of Wnt7b in the skeletal osteoprogenitors causes a delay in ossification , but the phenotype is modest and largely resolved by birth , likely due to WNT ligand redundancy [18] , [25] . In addition , WNT5A is expressed in both the perichondrium and the cartilage in the mouse embryo [18] , [38] . Studies to date have indicated that WNT5A expressed by osteoblast-lineage cells promotes both osteoblastogenesis and osteoclastogenesis , but WNT5A deficiency causes a net decrease in bone mass in postnatal mice [26] , [39] . In this study , we investigate the capacity of WNT7B versus WNT5A in regulating bone mass in vivo . We demonstrate that WNT7B dramatically enhances bone formation . Mechanistic studies identify mTORC1 as an important mediator for the bone anabolic function of WNT7B .
To study the roles of WNT7B and WNT5A , we first developed versatile mouse strains that allow these proteins to be expressed in a tissue-specific manner . Specifically , we knocked the Wnt7b or Wnt5a cDNA into the ubiquitously active Rosa26 locus so that they can be expressed upon the excision of a transcriptional stop signal by Cre ( Fig . 1A ) ( Fig . S1 ) . The resultant mouse strains , R26-Wnt7b or R26-Wnt5a , did not show any discernible phenotype in either heterozygous or homozygous state . To assess the potential role of either protein in bone formation , we activated their expression in the osteoblast lineage with either Osx-Cre targeting preosteoblasts or 2 . 3ColI-Cre targeting the more mature osteoblast-lineage cells . Mice expressing WNT5A from one or two R26-Wnt5a alleles by 2 . 3Col1-Cre appeared normal , and did not exhibit any obvious bone phenotype when analyzed by X-ray radiography or µCT at two months of age ( Fig . 1B , C ) ( Table 1 ) . The R26-Wnt5a allele was functional because its activation with Wnt1-Cre in neural crest cells caused embryonic lethality with multiple cranial facial defects ( data not shown ) . We therefore focused on WNT7B in the remainder of the study . Mice with WNT7B expression from a single R26-Wnt7b allele by either Osx-Cre or 2 . 3ColI-Cre ( hereafter Osx-Wnt7b or ColI-Wnt7b mice ) were viable without any gross abnormality . However , X-ray radiography of either mutant at two months of age detected profoundly dense bones throughout the body ( Fig . 1D–G ) ( Fig . S2 ) . The X-ray images also revealed shorter bones in the Osx-Wnt7b mice when compared to their control littermates . The mechanism for the size difference was not investigated in the present study , but to avoid size-related complications we have focused the postnatal analyses on the ColI-Wnt7b mice with a normal bone size . The severity of the high-bone-mass phenotype in ColI-Wnt7b mice was epitomized by the lack of marrow space in the long bones due to complete ossification ( Fig . 1G ) . As expected , these mice exhibited splenomegaly consistent with extramedullary hematopoiesis ( Fig . S3A–C ) . The high-bone-mass phenotype was fully penetrant in both males and females , and persisted at six months of age but was partially resolved at nine months ( Fig . S4A , B ) . The mechanism for the phenotype amelioration with aging was not fully pursued here , but appeared to track with heightened bone resorption , as indicated by the higher serum CTX-I level ( C-terminal telopeptide of type I collagen , a degradation product of type I collagen released upon bone resorption ) than the control , at nine but not six months of age ( Fig . S4C , D ) . MicroCT analyses of the two-month-old ColI-Wnt7b mice confirmed the profound high-bone-mass phenotype in both the skull and long bones ( Fig . 1H–J ) . The proximal tibial trabecular BV/TV was 13 . 7-fold elevated compared to control at two months , coupled with increased trabecular thickness and reduced trabecular spacing ( Table 2 ) . At six months of age , BV/TV in the same area was 5 . 1 fold higher in ColI-Wnt7b mice than the littermate control . Consistent with X-ray radiography , by nine months , the high bone mass in the proximal tibial trabecular area was resolved and in fact 30% less than the littermate control , even though the distal tibia and the femur maintained a high bone mass ( Fig . S4B ) ( Table 2 ) . Histology confirmed that excessive bone occupied both primary and secondary ossification centers , whereas the growth plate was largely normal in the ColI-Wnt7b mice ( Fig . 2A , B ) . Thus , WNT7B induction in osteoblast-lineage cells markedly increases bone mass throughout the body in postnatal mice . We next investigated whether WNT7B increased bone mass by altering bone formation or resorption . To assess bone formation activity , we first measured serum levels of osteocalcin , a major non-collagenous protein produced by osteoblasts . Osteocalcin levels were significantly higher in ColI-Wnt7b than the control at both one and two months of age ( Fig . 2C ) . Histomorphometry showed a higher osteoblast number normalized to bone surface in ColI-Wnt7b over control mice at two months of age ( Fig . 2D ) . The density of osteocytes however was not changed ( Fig . S5 ) . Dynamic histomorphometry in these animals revealed that mineral apposition rate ( MAR ) , the percentage of mineralizing surface ( MS/BS ) , and bone formation rate ( BFR/BS ) were all increased in the humerus of ColI-Wnt7b over the normal counterpart ( Fig . 2E–G ) . To examine whether WNT7B affected bone resorption , we measured serum CTX-I levels . Despite the high bone mass , ColI-Wnt7b mice exhibited a higher serum CTX-I level than normal at one month of age ( Fig . 2H , left ) . At two months , CTX-I levels were similar between ColI-Wnt7b and control mice ( Fig . 2H , right ) . Static histomorphometry showed that both osteoclast number per bone surface ( #Oc/mm ) and the percentage of bone resorption surface ( Oc S/BS ) were reduced in the ColI-Wnt7b mice at two months of age , whereas osteoclast spreading ( µm/Oc ) was not changed ( Fig . 2I ) . These results indicate that osteoclastogenesis was likely suppressed in the WNT7B-overexpressing mice , but the total activity of bone resorption was not reduced at any of the ages examined . Thus , WNT7B increases bone mass mainly through stimulation of osteoblast number and activity . As WNT7B induction by 2 . 3ColI-Cre or Osx-Cre began in the embryo , we next determined whether WNT7B affected embryonic bone formation . Whole-mount skeletal staining with alcian blue and alizarin red revealed that at E18 . 5 , both ColI-Wnt7b and Osx-Wnt7b embryos exhibited thicker bones with more intense red staining than normal , indicative of higher bone mass ( Fig . 3A , data not shown ) . Histological sections of the embryonic long bones confirmed excessive bone mass occluding the presumptive marrow cavity ( Fig . 3B , data not shown ) . Because both types of mutant embryos exhibited essentially the same phenotype , we have used either mutant for the embryonic analyses depending on their availability at the time of experiment . In situ hybridization of osteoblast markers in the bones of E18 . 5 ColI-Wnt7b embryos confirmed the presence of excessive osteoblasts within the presumptive marrow cavity ( Fig . S6 ) . Analyses of E14 . 5 Osx-Wnt7b embryos revealed a slight delay in chondrocyte maturation , as indicated by the shorter domains of Col10a1 ( general hypertrophy marker ) and MMP13 ( late hypertrophy marker ) ( Fig . 3C ) . However , osteoblast differentiation in these embryos appeared to be normal , even though the expression domains of AP , Runx2 , and Osx in the perichondrium were slightly reduced , as expected from the delay in chondrocyte maturation ( Fig . 3C ) . At E16 . 5 , the Osx-Wnt7b long bones possessed a much thicker bone collar than normal , but no bone marrow in stark contrast to the control ( Fig . 3D ) . In situ hybridization revealed that the presumptive marrow region was occupied by cells expressing Osx but not osteocalcin ( OC ) and therefore likely to be preosteoblasts ( Fig . 3D ) . Immunostaining for the endothelial marker CD31 indicated that the region was vascularized even though no marrow cavity was formed ( Fig . 3E ) . At E18 . 5 , the presumptive marrow region was populated with mature osteoblasts expressing OC ( Fig . 3F ) . In summary , WNT7B does not prematurely initiate bone formation in the perichondrium , but augments the process in both cortical and trabecular regions of the late-stage embryo . We next sought to determine whether temporal activation of WNT7B specifically in postnatal bones stimulates bone formation . To this end , we created a mouse line ( referred as Runx2-rtTA ) that expressed reverse tetracycline transactivator ( rtTA ) from the Runx2 regulatory elements through bacterial artificial chromosome ( BAC ) recombineering ( Fig . 4A ) . To characterize the Runx2-rtTA line , we produced mice with the genotype of Runx2-rtTA;TetO-Cre;R26-mT/mG ( termed Runx2-mTmG ) and assessed GFP expression with or without doxycycline ( Dox ) . Without Dox , no GFP was detected in these mice at either embryonic or postnatal stage ( data not shown ) . When Dox was administered to the embryos through the dams , the Runx2-mTmG neonates , but not the control littermates , displayed strong GFP throughout the skeleton when viewed whole-mount under a fluorescence microscope ( Fig . 4B , C ) . Confocal microscopy of long bone sections confirmed GFP expression only in the Runx2-mTmG neonates , but not in the control littermates ( Fig . 4D–G ) . Closer examination of the Runx2-mTmG samples revealed GFP expression by a small subset of chondrocytes within the growth plate ( Fig . 4G1 ) , but most prominently in osteoblast-lineage cells associated with the primary spongiosa and the cortical bone ( Fig . 4G2 , G3 ) . Additionally , GFP was detected in sporadic bone marrow stromal cells and perivascular cells located within the marrow cavity ( Fig . 4G3 ) . To characterize the Runx2-rtTA transgene postnatally , we raised the Runx2-mTmG mice until one month of age before treating them with Dox for 15 days . Whereas the control littermates exhibited no GFP ( Fig . 4H , I ) , the Runx2-mTmG mice displayed GFP in both primary and secondary ossification centers as well as the cortical bone ( Fig . 4J , K ) . Higher-magnification images revealed that GFP was expressed by cells associated with the trabecular bone within the primary and secondary ossification centers , the cortical bone , as well as by the marrow stromal cells , but not by growth plate chondrocytes ( Fig . 4K1–K3 ) ( Fig . S7 ) . Staining for alkaline phosphatase activity revealed that the GFP-positive cells on the bone surfaces expressed the enzyme and therefore were most likely osteoblast-lineage cells ( Fig . S8 ) . Overall , the Runx2-rtTA mouse line provides a useful tool for targeting the osteoblast-lineage cells in postnatal animals . We next employed the Runx2-rtTA allele to activate WNT7B expression in postnatal bones . Specifically , we generated mice with the genotype of Runx2-rtTA;TetO-Cre;R26-Wnt7b ( hereafter Runx2-Wnt7b ) and treated them with Dox from one month through two months of age . Untreated Runx2-Wnt7b mice did not have a phenotype compared to wild type littermates . Moreover , Dox itself did not affect bone mass in any of the control genotypes ( missing at least one of the three alleles present in the Runx2-Wnt7b mouse ) . However , Dox notably increased bone mineral density in the long bones of Runx2-Wnt7b mice , as indicated by X-ray radiography ( Fig . 5A ) . MicroCT analyses of the proximal tibial metaphysis revealed a 6 . 6-fold increase in trabecular BV/TV over the untreated littermates with the same genotype ( Fig . 5B ) ( Table 3 ) . Histology confirmed a marked increase in the trabecular bone mass in both primary and secondary ossification centers of the Dox-treated Runx2-Wnt7b mice ( Fig . 5C ) . The increased bone mass was not produced by suppression of bone resorption , as serum CTX-I levels were unaltered in the Dox-treated mice ( Fig . 5D ) , even though osteoclast number or surface normalized to bone surface was reduced ( Fig . 5E ) . On the other hand , osteoblast numbers normalized to bone surface were markedly increased in the Dox-treated over non-treated Runx2-Wnt7b mice ( Fig . 5F ) . Thus , temporal induction of WNT7B in postnatal mice greatly increases bone mass thorough stimulation of bone formation . We next investigated the signaling mechanism mediating WNT7B regulation of osteoblast differentiation . To explore the potential that WNT7B activated β-catenin signaling in bone , we used the TOPGAL transgene as a reporter in vivo [40] . By comparing the LacZ staining signal on sections of long bones from ColI-Wnt7b mice versus littermate controls , we did not detect any consistent upregulation of the signal in the perichondrium , trabecular or cortical bone , all tissues targeted by ColI-Cre ( Fig . S9 ) . We next utilized ST2 cells , a bone marrow stromal cell line undergoing osteoblast differentiation in response to virally expressed WNT7B [25] . Consistent with the in vivo finding above and our previous results , WNT7B did not activate the Lef-luciferase reporter , a readout for β-catenin signaling , in transient transfection assays [25] ( Fig . 6A ) . However , WNT7B activated mTORC1 signaling in ST2 cells , as indicated by increased phosphorylation of S6 and 4EBP1 ( Fig . 6B ) ( Fig . S10A ) . We further found that S6 and 4EBP1 phosphorylation was stimulated in the long bones of Osx-Wnt7b mice over the control ( Fig . 6C ) ( Fig . S10B ) . Thus , WNT7B activates mTORC1 both in vitro and in vivo . We then explored the molecular mechanism mediating mTORC1 activation by WNT . Inhibition of either PI3K or PI3K-mediated AKT activation markedly suppressed mTORC1 activity with or without WNT7B expression in ST2 cells ( Fig . 6B , D ) , but knockdown of β-catenin had no effect ( Fig . 6E ) . Similarly , purified recombinant WNT3A protein activated S6 and 4EBP1 phosphorylation in a PI3K- and AKT-dependent manner ( Fig . 6F ) ( Fig . S10C ) . The phosphorylation of S6 is specific to mTORC1 activation as we previously showed that knockdown of raptor abolished the induction by WNT3A , and here rapamycin eliminated the phosphorylation [29] ( Fig . 6F ) . Because the purified protein offers the advantage of studying signaling events after short-term treatments , we used WNT3A for subsequent experiments . Recombinant DKK1 protein dose-dependently suppressed WNT3A-induced mTORC1 activation ( Fig . 6G and data not shown ) . Knockdown of LRP5 increased basal mTORC1 due to an unknown mechanism , but did not suppress the induction by WNT3A ( Fig . 6H , I ) . In contrast , knockdown of LRP6 either alone , or together with LRP5 , abolished WNT3A-induced mTORC1 , indicating a predominant role of LRP6 in this regulation ( Fig . 6H , I ) . Inhibition of GSK3 by LiCl suppressed the basal mTORC1 level , but did not reduce the extent of induction by WNT3A ( Fig . 6J ) . Thus , WNT3A activates mTORC1 through LRP6-PI3K-AKT signaling , likely independent of GSK3 inhibition . We next examined the potential role of mTORC1 in WNT-induced osteoblast differentiation . Rapamycin , a potent mTORC1 inhibitor , suppressed WNT7B-induced osteoblast differentiation in ST2 cells , as determined by alkaline phosphatase activity assay and von Kossa staining ( Fig . S11 ) . To test the relevance of mTORC1 activation in WNT7B-induced bone formation in vivo , we took advantage of Osx-Cre that can be suppressed by Dox to activate R26-Wnt7b or delete Raptor alone or in combination , specifically after one month of age . When Osx-Cre was Dox-suppressed until one month of age and then released for one month via Dox removal , the Osx-Wnt7b mice exhibited a profound high-bone-mass phenotype as indicated by both X-ray radiography , histology and µCT analyses ( Fig . S12A , B ) ( Table S1 ) . Serum biochemistry and histomorphometry confirmed that the high bone mass was caused by increased bone formation ( Fig . S12C–G ) . In contrast , when Osx-Cre;Raptorf/f mice were Dox-treated till one month of age and then weaned off Dox for three weeks immediately before harvest , they did not exhibit any bone phenotype detectable by X-ray radiography , µCT or histology , when compared to either Osx-Cre;Raptorf/+ or wild-type littermates ( Fig . 7A , B , E , F , I , J and data not shown ) . Thus , inducible overexpression of WNT7B at one month of age caused high bone mass , but inducible deletion of Raptor at this age for three weeks did not affect bone mass . Next , we asked whether deletion of Raptor would affect the high-bone-mass phenotype caused by WNT7B expression . To increase the ratio of the desired genotype ( Osx-Cre;R26-Wnt7b;Raptorf/f ) among the progenies , we set up mating pairs between Osx-Cre; R26-Wnt7b; Raptorf/+ and Raptorf/f mice . Progenies with either Osx-Cre;R26-Wnt7b;Raptorf/+ , or Osx-Cre;R26-Wnt7b;Raptorf/f ( hereafter Osx-Wnt7b-RaptorCKO ) genotype were treated with Dox from conception until one month of age , and then weaned off Dox for three weeks before harvest . Mice with the genotype of Osx-Cre;R26-Wnt7b;Raptorf/+exhibited a very high bone mass according to X-ray radiography and µCT analyses ( Fig . 7C , G ) . In comparison , the bone mass in the Osx-Wnt7b-RaptorCKO mice was notably reduced ( Fig . 7D , H ) . Histology showed that the bone marrow cavity was expanded in the Osx-Wnt7b-RaptorCKO mice compared to Osx-Cre;R26-Wnt7b;Raptorf/+ littermates , although still smaller than that in the Osx-Cre;Raptorf/+or Osx-Cre;Raptorf/f mice ( Fig . 7I–L ) . Western analyses of bone protein extracts revealed that S6 phosphorylation was reduced by ∼50% in Osx-Wnt7b-RaptorCKO mice compared to Osx-Cre;R26-Wnt7b;Raptorf/+ littermates ( Fig . 7M , lanes 3 and 4 ) . Immunohistochemistry confirmed a marked reduction of S6 phosphorylation in the primary spongiosa of Osx-Wnt7b-RaptorCKO mice compared to the Osx-Cre;R26-Wnt7b;Raptorf/+ control ( Fig . 7N ) . Histomorphometric studies indicated that Raptor deletion reduced the WNT7B-induced osteoblast hyperactivity ( Fig . 7O , P ) , but did not suppress the increase in osteoblast number ( Fig . 7Q ) . Moreover , Raptor deletion had no effect on bone resorption , as neither the serum CTX-I level nor any of the osteoclast parameters changed ( Fig . 7R ) . Thus , mTORC1 signaling contributes to WNT7B-induced bone formation through stimulation of osteoblast function .
We have provided evidence that WNT7B is a potent bone anabolic protein both during embryogenesis and in the postnatal life of mice . Specifically , WNT7B markedly increases both the number and function of osteoblasts . We further identify mTORC1 as an important mediator for WNT-mediated bone anabolism . At the mechanistic level , WNT proteins activate mTORC1 through PI3K-AKT signaling . Of note , mTORC1 appears to mediate the increase in osteoblast activity but not number in response to WNT7B . In our genetic experiments , inducible deletion of Raptor did not completely abolish S6 phophorylation induced by WNT7B in bone protein extracts . Therefore , the observed degree of correction in osteoblast activity may be an underestiamte of the full contribution of mTORC1 to WNT7B-induced osteoblast function . Because of the same reason , we cannot rule out the possibiltiy that the remaining portion of WNT7B-induced mTORC1 activtiy contributed to the increase in osteoblast number in the compound mutants . Alternatively , mTORC2 hyperactivation may be a contributing factor as we observed heightened mTORC2 signaling in the bones of the Osx-Wnt7b mice ( data not shown ) . Moreover , since WNT7B also activates PKCδ through phosphoinositide signaling [25] , PKCδ activation may contribute to WNT7B-induced osteoblastogenesis . On the other hand , our data do not support β-catenin as a main effector for WNT7B function in the present setting . First , WNT7B did not activate β-catenin signaling in ST2 cells although it induced osteoblast differentiation . Second , in vivo studies with the TOPGAL allele failed to detect increased β-catenin signaling in the bones of either ColI-Wnt7b or Osx-Wnt7b embryos . Finally , the bone phenotype of the Osx-Wnt7b mouse was distinct from that of the mouse with a stabilized form of β-catenin expressed in Osx-lineage cells , which included premature mineralization and suppression of OC expression [21] . Overall , a comprehensive understanding of the mechanisms underlying the potent bone anabolic function of WNT7B may provide molecular targets for developing novel bone anabolic drugs . In addition to the strong bone anabolic effect , WNT7B also appeared to suppress osteoclast numbers when normalized to the bone surface area . This finding held true both in mice beginning to express WNT7B in the embryo ( ColI-Wnt7b ) and in those expressing it only postnatally ( Runx2-Wnt7b with Dox ) . In either model , total bone resorption activity as measured by serum CTX-1 was either increased or not changed depending on the age , when compared to control littermates . Thus , we conclude that the effect of WNT7B on osteoclasts did not add to the high-bone-mass phenotype . Nonetheless , it is of future interest to determine the mechanism for the suppression of osteoclast number by WNT7B . We show that GSK3 inhibition suppresses basal level phosphorylation of S6 but not its induction by WNT3A . This observation contradicts a previous report that GSK3 inhibition mediates mTORC1 activation by WNT3A [27] , but is in agreement with another study identifying GSK3 as an activator of S6K1 via direct phosphorylation [41] . The basis for the discrepancy between these studies is not known at present . Nonetheless , our results support an alternative model that WNT proteins activate mTORC1 through PI3K-AKT signaling . Previous studies have implicated other WNT proteins in controling bone mass . Wnt10b−/− mice showed an initial increase in bone mass at one-month of age , but subsequently exhibited age-dependent bone loss [35] , [37] . Transgenic mice overexpressing WNT10B from either FABP4 or OC promoter increased bone mass in postnal mice [35] , [36] . However , the WNT10B-induced bone phenotype was less severe than that of the WNT7B-expressing mice . In addition , haploinsufficiency of WNT5A was reported to reduce bone mass in postnatal mice , and WNT5A was shown to stimulate both osteoblast differentiation via the suppression of PPARG-mediated adipogenesis , and osteoclastogenesis through upregulation of RANK in the macrophage progenitors [26] , [39] . However , overexpression of WNT5A in our study did not have an obvious effect on bone mass . We acknowledge that our small sample size is not sufficiently powered to detect minor changes . Moreover , WNT5A may have effects on bone formation and resorption that offset each other in the overexpression model . Nonetheless , the present study identifies WNT7B as a potent anabolic WNT ligand in the mouse . It is of interest to note that despite its robust bone anabolic activity , WNT7B did not obviously increase the width of the long bones . This observation is somewhat surprising because SOST-deficient or LRP5 high-bone-mass mutant mice displayed a clear increase in periosteal growth [15] , [42] . It is possible that the SOST and LRP5 regulate endogenous WNT ligands that are of distinct signaling properties from WNT7B , or that the level of WNT7B expressed from the Rosa26 locus in our model does not reach the necessary threshold within the periosteal compartment . On the other hand , we cannot rule out the possibility that mutations in SOST or LRP5 may alter the activity of other non-WNT signals responsible for periosteal growth . Future studies are necessary to distinguish these possibilities .
The Animal Studies Committee at Washington University has reviewed and approved all mouse procedures used in this study . To generate the Runx2-rtTA transgene , we modified a Runx2 BAC ( bacterial artificial chromosome , clone# RP23-180J20 ) ( Children's Hospital of Oakland Research Institute ) to replace the first exon of Runx2 with the cDNA for rtTA2S-M2 [43] . Briefly , a ∼500 bp PCR amplicon immediately upstream of the Runx2 starting ATG ( forward primer: 5′ GGAAGCCACAGTGGTAGG 3′; reverse primer: 5′ TGTAAATACTGCTTGCAGCC 3′ ) , the cDNA for rtTA2S-M2 excised from pUHrT62-1 [43] , and a ∼600 bp PCR amplicon immediately downstream of the Runx2 starting ATG ( forward primer: 5′ CCGTGTCAGCAAAGCTTC 3′; reverse primer: 5′ CAGGCTAATAGAGATATCTG 3′ ) were inserted into pSV-Flp at the PmeI , XhoI , and SalI site , respectively . The resulted plasmid was digested with AscI/PmeI to release the targeting construct . Subsequent BAC recombineering was performed as described [44] , [45] , [46] . Pronuclear injection was performed at Washington University Pathology/Immunology Micro-Injection Core . The Rosa26-Wnt7b and -Wnt5a mouse strains were generated with a similar strategy as previously described for Rosa26-ΔNGli2 [47] . The 2 . 3ColI-Cre , Osx-Cre , TetO-Cre , Wnt1-Cre , R26-mT/mG , and Raptorf/f mice are as previously described [21] , [48] , [49] , [50] , [51] , [52] . Mice were exposed to doxycycline ( Sigma , St . Louis ) through drinking water containing 2% sucrose . Either 1 mg/ml or 50 µg/ml Dox in the drinking water was used for the Runx2-rtTA or the Osx-Cre mice , respectively . Whole-mount skeletal staining with alizarin red and alcian blue is as previously described [53] . For paraffin sections , dissected limbs were fixed with 10% formalin and sectioned at 6 µm thickness . For frozen sections , limbs were fixed with 4% paraformaldehyde , incubated in 30% sucrose and sectioned in OCT at 8 µm thickness . Limbs from E16 . 5 and older embryos were decalcified in 14% EDTA for 1–2 days after fixation . Histology and in situ hybridization with 35S-labeled probes were performed on paraffin sections as previously described [18] , [53] . X-ray radiography was performed with a Faxitron X-ray system set at 25 kv for 20 seconds . µCT analyses were performed with Scanco µCT 40 ( Scanco Medical AG ) according to ASBMR guidelines [54] . Quantification of the trabecular bone in the tibia was performed with 100 µCT slices ( 1 . 6 mm total ) immediately below the growth plate . In the Raptor deletion experiment , the combined trabecular and cortical bone mass was quantified with 550 µCT slices ( 8 . 8 mm total ) starting from 1 . 6 mm below the articular surface . For sections , bones were fixed in 10% buffered formalin overnight at room temperature , followed by decalcification in 14% EDTA with daily change of solution for 2 weeks . After decalcification , bones were processed for paraffin embedding and then sectioned at 6 µm thickness . H&E and TRAP staining were performed on paraffin sections following the standard protocols . For dynamic histomorphometry , mice were injected intraperitoneally with calcein ( 20 mg/kg , Sigma , St . Louis , MO ) at 7 and 2 days before sacrifice , and bones were fixed in 70% ethanol and embedded in methyl-methacrylate for plastic sections . Both static and dynamic histomorphometry were performed with the commercial software Bioquant II . For serum-based biochemical assays , serum was collected from mice after 6 hours of fasting . Serum osteocalcin levels were determined with the Mouse Osteocalcin EIA Kit ( Biomedical Technologies , Stoughton , MA ) . Serum CTX-I assay was performed using the RatLaps ELISA kit ( Immunodiagnostic Systems , Ltd . ) . Bone protein extracts were prepared from femurs and tibias of postnatal mice with RIPA buffer . The ends of the bones were surgically removed , and the bone marrow was discarded by centrifugation . The bones were then rinsed twice with cold PBS , flash-frozen in liquid nitrogen , and ground manually into a fine power with a mortar and a pestle . The bone power was incubated with 200 µl RIPA buffer on ice for 30 minutes before the supernatant was collected for Western analysis . GFP was examined either directly by fluorescence microscopy or by immunostaining on frozen sections using a chicken polyclonal GFP antibody ( Abcam , Cambridge , MA ) . CD31 immunostaining was performed on frozen sections using a rat CD31 antibody ( BD Biosciences , San Jose , CA ) . To detect P-S6 , paraffin sections were de-paraffinized , treated with trypsin for 10 minutes , and blocked with 10% sheep serum before being incubated with a rabbit polyclonal antibody against Phospho-S6 Ribosomal Protein ( Ser240/244 ) ( Cell Signaling Technology , Danvers , MA ) . ST2 cells were cultured in α-MEM ( Sigma ) with 10% fetal bovine serum ( referred as growth medium ) . Retrovirus expressing GFP or WNT7B was produced as previously described [25] , and diluted 1∶1 with growth medium before use . For viral infections , cells were incubated with the virus for 8 hours before switched to growth medium . For Western analyses of P-S6 in the virally infected cells , the cells were cultured in complete medium for 32 hours , and then in serum-free medium for 16 hours before harvest . AP staining was performed at 3 days after the viral infection . Von Kossa staining was performed with infected cells cultured for 6 days ( media changed every three days ) in growth medium supplemented with 50 µg/ml ascorbic acid and 10 mM β-glycerophosphate . Rapamycin ( LC Laboratories ) dissolved in DMSO was used at 20 nM . For transient transfection assays , ST2 cells seeded in 24-well plate at 3×104/well overnight were transfected for 8 hours with 200 ng Lef1-luc reporter and 20 ng pRL-Renilla ( Promega ) mixed with 1 µl Lipofectamine ( Invitrogen ) , and then cultured in fresh growth medium for 16 hours . The transfected cells were then infected with the GFP- or WNT7B-expressing virus for 8 hours , incubated with fresh growth medium containing either vehicle or 50 ng/ml WNT3A for 2 days before harvest . Luciferase assays were performed with Dual-Luciferase Reporter Assay System ( Promega ) . Antibodies for S6K1 , P-S6K1 ( T389 ) , S6 , P-S6 ( S240/244 ) , FoxO3a , pFoxO1 ( T24 ) /3a ( T32 ) , P-Lrp6 ( S1490 ) , Lrp5 , β-actin , and α-tubulin were purchased from Cell Signaling ( Beverly , MA ) . Antibodies for Lrp6 and β-catenin were from Santa Cruz Biotechnology ( Santa Cruz , CA ) . Recombinant mouse Wnt3a and Dkk1 were purchased from R&D Systems ( Minneapolis , MN ) , and used at 50 ng/ml and 500 ng/ml , respectively . AKT inhibitor IV was from EMD Millipore ( Billerica , MA ) , and used at 10 µM . PI3K inhibitor LY294002 was from XXXX and used at 50 µM . LiCl and NaCl were purchased from Sigma ( Saint Louis , MO ) and used at 20 mM . Rapamycin was purchased from LC Laboratories ( Woburn , MA ) , and used at 20 nM . To generate shRNA lentiviruses , shRNA vectors were co-transfected into HEK293T cells with the packaging plasmids pCMV-dR8 . 2 dvpr ( Addgene ) and pCMV-VSV-G ( Addgene ) using FuGENE 6 ( Roche ) . Supernatants were collected 48 hrs after transfection , and passed through 0 . 45 µm nitrocellulose filters . ST2 cells were infected with viral supernatants diluted 1∶1 with growth medium and supplemented with 5 µg/mL Polybrene . For the β-catenin knockdown experiment , ST2 cells were infected with shβ-catenin or shLacZ lentivirus for 8 hrs . After 16 hrs of recovery , the cells were further infected with retroviruses expressing GFP ( IE ) or Wnt7b ( 7B ) for 8 hrs . After 24 hrs of recovery , the cells were then cultured in serum-free growth medium for 16 hrs before cells were lysed for Western blot . For Lrp5/6 knockdown experiment , ST2 cells were infected with shLrp5 , shLrp6 or shLacZ virus for 8 hrs . Infected ST2 cells were incubated with fresh growth medium for 24 hrs , and then cultured in serum-free medium for 16 hrs . The serum-starved cells were treated with either vehicle or Wnt3a for 1 hr before being harvested for Western blot analysis . All quantitative data are presented as mean ± STDEV with a minimum of three independent samples . Statistical significance is determined by two-tailed Student's t-test . | The human bone tissue is of considerable regenerative capacity as reflected in bone remodeling and in fracture healing . However , bone tissue regeneration deteriorates with age , and tremendous unmet medical needs exist for safe and effective strategies to stimulate bone formation in older individuals commonly inflicted with osteoporosis or osteopenia . WNT signaling has emerged as a promising target pathway for developing novel bone anabolic therapeutics . Identifying bone-promoting WNT ligands and elucidating the underlying mechanisms may lead to useful therapeutic targets . The present study reports that WNT7B potently enhances bone formation through activation of mTORC1 in the mouse . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"genetics",
"developmental",
"biology",
"biology"
] | 2014 | WNT7B Promotes Bone Formation in part through mTORC1 |
The wing of the fruit fly , Drosophila melanogaster , with its simple , two-dimensional structure , is a model organ well suited for a systems biology approach . The wing arises from an epithelial sac referred to as the wing imaginal disc , which undergoes a phase of massive growth and concomitant patterning during larval stages . The Decapentaplegic ( Dpp ) morphogen plays a central role in wing formation with its ability to co-coordinately regulate patterning and growth . Here , we asked whether the Dpp signaling activity scales , i . e . expands proportionally , with the growing wing imaginal disc . Using new methods for spatial and temporal quantification of Dpp activity and its scaling properties , we found that the Dpp response scales with the size of the growing tissue . Notably , scaling is not perfect at all positions in the field and the scaling of target gene domains is ensured specifically where they define vein positions . We also found that the target gene domains are not defined at constant concentration thresholds of the downstream Dpp activity gradients P-Mad and Brinker . Most interestingly , Pentagone , an important secreted feedback regulator of the pathway , plays a central role in scaling and acts as an expander of the Dpp gradient during disc growth .
Matching of pattern to size , a phenomenon referred to as scaling , manifests itself in numerous examples around us . During development , individual body parts scale up with the overall body size , starved animals form smaller adults with proportionally smaller body parts [1] , [2] , and amphibian embryos can form normally proportioned adults after extreme surgical operations [3] . Also , retardation of growth in Drosophila wing imaginal discs , the larval precursors of the adult wings , slows down the growth of the rest of the body [4] . Similarly , experimental reduction of growth rates in part of the wing disc leads to proportional growth defects in the rest of the tissue , and the final organ , though smaller , conserves its proportions [5] . How scaling is achieved is an intriguing question that has long fascinated biologists [6] , [7] , [8] , [9] . Recent findings started shedding light onto this question [2] , [4] , [5] . Here , we define scaling as the preservation of proportions across different sizes during organ growth , identify an important factor in this process , and establish the Drosophila wing imaginal disc as a model to study scaling quantitatively and at the molecular level . The fruit fly Drosophila melanogaster represents an excellent model system for quantitative analyses as it can be manipulated at will using its exquisite genetic tool kit . The positions of the veins in the adult wing scale rather precisely with the final wing size , presumably ensuring that the wing is functional [10] , [11] . This observation indicates that there are active mechanisms that coordinate growth and patterning of the wing . The easiest imaginable way of coordinating growth and patterning is by having the same molecules control both processes . Drosophila Decapentaplegic ( Dpp ) , a TGF-β superfamily member , is essential for the formation of all imaginal discs [12] . Dpp signaling has been extensively studied in the wing imaginal disc . In this tissue , Dpp is produced in a stripe of cells anterior to and abutting the anterior/posterior ( A/P ) compartment boundary , spreads into both compartments to form a gradient , and patterns the growing tissue ( Figure S1 ) . Dpp is a morphogen with the capability to specify distinct target gene expression domains at different distances from its source . The boundaries of these domains are instrumental in setting the positions of veins during subsequent development of the wing imaginal disc ( Figure S1C ) [13] , [14] , [15] . This patterning function of Dpp coupled to its ability to promote growth [16] , [17] , [18] make Dpp an attractive candidate for being involved in scaling . The Dpp signal transduction pathway is highly conserved and relatively simple ( Figure S1B ) . Ligand-mediated receptor activation induces phosphorylation of Mothers-against-Dpp ( Mad , P-Mad in its phosphorylated and active form ) and nuclear translocation of heteromeric complexes of P-Mad and the co-Smad Medea . These complexes directly regulate the expression of a large number of target genes and have the ability to activate as well as suppress transcription [14] . One of the most critical functions of Dpp signaling is to suppress brinker ( brk ) transcription because Brk acts as a potent transcriptional repressor of many Dpp target genes ( Figure S1 ) [19] , [20] , [21] . Repression of brk is achieved via short “silencer elements” ( SEs ) in the brk enhancer; the Drosophila Smad proteins P-Mad and Medea bind as a trimer ( two Mad , one Medea ) to the SEs and recruit the co-repressor Schnurri ( Shn ) [22] , [23] , [24] . Consequently , the extracellular Dpp gradient and the resulting intracellular P-Mad gradient are translated into an inverse nuclear gradient of Brk [25] . In the lateral regions of the wing disc , where Dpp signaling is relatively low , the Brk gradient delimits the expression domains of the Dpp target genes daughters-against-dpp ( dad ) , spalt ( sal ) , and optomotor blind ( omb ) ( Figure S1 ) . In patches of marked cells where brk function is deleted ( i . e . brk loss of function clone ) , dad , sal , and omb are derepressed [14] , [19] , [21] . The P-Mad/Medea complex can also directly bind to and activate transcription of dad and sal [26] , [27] . Hence dad and sal enhancers read both P-Mad and Brk levels , and their sensitivity to these two factors as well as others appears to determine their expression domains . While the role of Dad is less well studied , Sal and Omb expression boundaries are crucial for the determination of the positioning of veins L2 and L5 of the adult wing , respectively ( Figure S1C ) [28] , [29] , [30] . How are the positions of these veins determined ? The pouch section of the wing imaginal disc , which will give rise to the adult wing , is subdivided into alternating vein and intervein territories during the larval stages ( Figure S1C ) . The combined activity of the Epidermal Growth Factor , Notch , Hedgehog , and Dpp pathways culminate in the restricted expression patterns of transcription factors that identify different veins . For example , the zinc-finger proteins Knirps and Abrupt are expressed and required specifically in L2 and L5 , respectively . Loss of function mutations of these genes result in the loss of the corresponding veins [28] , [31] . Knirps is expressed within the anterior edge of the Sal expression domain , while the L5 primordium forms within the posterior edge of the Omb domain adjacent to cells expressing high levels of Brk ( Figure S1C ) [28] , [29] . Hence , Sal , Omb , and Brk play instrumental roles in setting the positions of L2 and L5 under the control of the Dpp activity gradient . Recently , pentagone ( pent ) emerged as an important target gene of Dpp signaling , playing essential roles for both growth and patterning functions of the pathway . Pent is secreted and directly interacts with the heparan sulfate proteoglycan Dally to promote long-range distribution of the Dpp ligand . Absence of pent causes a severe contraction of the Dpp activity gradient resulting in growth and patterning defects of the adult organ . pent transcription , like brk , is directly repressed by Dpp signaling via SEs and acts as an inbuilt feedback loop with a crucial role in shaping and fine-tuning the Dpp morphogen gradient readout ( Figure S1 ) [32] , [33] . Here , we made use of this wealth of information available with regard to the molecular readout of the Dpp signaling activity in the wing imaginal disc and investigated whether the Dpp activity gradients , namely P-Mad and Brk , as well as the downstream domain boundaries ( Dad , Sal , and Omb ) scale and thus adapt to the size of the growing tissue . After establishing a protocol to reliably quantify the spatial and temporal changes in the Dpp activity gradients , we found that both P-Mad and Brk scale rather well with the tissue size . We then tried to uncover the molecular mechanisms that ensure proper scaling of these activity gradients . A recent mathematical model termed expansion-repression integral feedback control suggested that scaling emerges as a natural consequence of a feedback loop which is based on two diffusible components: a morphogen and a hypothetical molecule termed expander [34] . The expander facilitates the spread of the morphogen and in turn is repressed by it , and therefore only produced far away from the morphogen source . As a consequence , the gradient expands as long as the expander molecule is produced . The gradient stops expanding once the morphogen levels are high enough to completely inhibit expander production in the whole field . Because the expander molecule is assumed to be stable and diffusible , the morphogen gradient remains expanded , even when no more expander is produced . In the context of a slowly growing tissue , more expander could be produced in the lateral regions as the tissue grows . The morphogen gradient would thus expand , until expander production would again be inhibited in the entire field . Since Dpp signaling negatively controls the expression of Pent , which itself positively regulates the Dpp activity gradient , we tested whether Pent might act as an expander of the Dpp gradient during disc growth . Our results suggest that Pent indeed plays a role in scaling the Dpp activity gradient . The Dpp activity gradient is read out by several target genes , such as dad , sal , and omb , domains of which , we found , scale with tissue size . How is scaling transmitted from the activity gradients to the target gene domains ? Inspired by the French flag model for pattern formation [35] , [36] , we tested whether the target genes dad , sal , and omb respond to similar concentration thresholds of P-Mad and Brk activities during disc growth . In this case , provided that the activity gradients scale , the boundaries characterized by these constant thresholds would shift as the gradient expands , ensuring perfect scaling of the target gene domains with tissue size ( Figure 1E ) . Interestingly , our results do not support such a model , but rather suggest that P-Mad and Brk activity gradients are combined in a gene-specific fashion to ensure proper scaling of the targets . Finally , we compared our dataset to a similar dataset that was recently used to propose a uniform growth model in the wing imaginal disc [37] .
Before we could ask questions regarding the scaling behavior of Dpp signaling readout during growth of the wing imaginal disc , it was necessary to establish methods to acquire images that can be quantified . We concentrated our analysis on the pouch of the wing imaginal disc , which gives rise to the future wing . To extract the pouch and determine the A/P and D/V compartment boundaries , we co-stained all discs with Wingless ( Wg ) and Patched ( Ptc ) antibodies ( Figure 1A ) . Ptc is induced at very early stages and is restricted to the anterior side of the A/P boundary , hence marking the A/P boundary at all the examined stages . Wg expression gets refined later; it outlines the pouch and the D/V boundary starting from 65–70 h into development ( Figure S2A ) . Since we wanted to measure parameters exclusively in the pouch area , the discs from 65-h to 70-h-old larvae were the youngest we included in our analysis . To span the subsequent development of the disc , we subdivided the third instar larval stage into 10 h intervals . In this manner , we defined five time classes ( TC ) and color-coded them as follows: TC1 in purple: 65–75 h after egg laying ( AEL ) ; TC2 in green: 75–85 h AEL; TC3 in orange: 85–95 h AEL; TC4 in blue: 95–105 h AEL; TC5 in red: 105–120 h AEL ( Figure 2A ) . Note that timing refers to hours after egg laying and not to hours after hatching; a larva of 70 h AEL would be 46-h-old after hatching . To minimize variation within a single time class , we collected eggs for 1 h only , and dissected only male larvae to avoid variation due to sexual dimorphism . To minimize errors due to sample handling , we processed all samples in parallel in identical solutions , mounted discs from all TCs on the same slide , and imaged them under identical settings . Further details of sample collection and processing are provided in the Materials and Methods section . To account for the amplitudes of the protein gradients in our analysis and to test for the hypothesis that downstream target gene domains are defined at constant thresholds of P-Mad and Brk , we treated fluorescence intensities as a measure of protein concentrations . To ascertain that the changes in fluorescent intensities reflected changes in protein concentrations in a linear manner , we imaged fluorescent dyes of known concentrations at the same settings we used for our images and determined the linear range for our imaging conditions . We found that the intensities obtained in our experimental recordings indeed fell into the linear range of our imaging conditions ( Figure S2D–E ) . We define scaling as the preservation of proportions across growth—i . e . if an expression domain spans 30% of the tissue in a young disc , it should also span 30% in an older and larger disc to achieve perfect scaling . In order to assess scaling qualitatively , we compared the protein profiles in relative versus absolute positions . Profiles that scale to some degree look closer together ( in other words collapse ) when plotted in relative positions compared to when plotted in absolute positions ( Figure 1B ) . We also applied a quantitative approach that allows us to assess scaling of a morphogen gradient or a gene expression domain [38] . The degree of scaling is quantified in the form of a scaling coefficient S , which equals one when scaling is perfect . Importantly , this measure is position dependent , so that a gradient can scale to varying degrees at different positions in the patterning field ( Figure 1C–C″ ) . In contrast to the P-Mad and Brk gradients , the scaling of downstream gene expression domains ( Sal , Omb ) was measured only at a single position , namely their domain boundary . Fitting a Hill function to each profile returns the position of the domain boundary for that disc; note that in this case , the amplitudes are not informative ( Figures 1D , S2C ) . When the protein profile does not expand sufficiently to compensate for tissue growth , the scaling coefficient obtained is below one ( referred to as hypo-scaling ) . In contrast , when the protein profile expands more than would be needed to compensate growth , a scaling coefficient above one is obtained ( referred to as hyper-scaling ) . This measure of scaling is described in more detail in the Materials and Methods section and Figure 1 provides a schematic step-by-step representation of how we quantified scaling in our data . The first event downstream of Dpp receptor complex activation is the phosphorylation of the signal transducer Mad , which we visualized and quantified with P-Mad antibodies . We analyzed P-Mad gradients in wing imaginal discs from different TCs ( Figure 2A ) . We extracted the P-Mad profiles either exactly along the D/V boundary or with different offsets into the dorsal and ventral compartments within the wing pouch ( Figures 1A and S3A ) . This approach yields a global view of the dynamics of the P-Mad gradients during development ( Figure 2B , see Figures S3 and S4 for P-Mad profiles in relative and absolute distances , extracted at several offsets ) . We observed that the P-Mad levels are significantly suppressed at the D/V boundary in the last TC ( TC5; red ) . Along the D/V axis , the average amplitudes are 25%–30% lower than in the other TCs and the profiles become steeper at this last stage ( Figure S3B ) . However , this effect diminishes gradually away from the D/V boundary , suggesting that it is caused by a factor acting at the D/V boundary ( Figure S3 ) . In order to minimize the impact of this effect and of the influence of a secondary Dpp source expressed in the dorsal posterior compartment ( red arrows in Figure S1A ) [39] , we performed our scaling analysis for protein profiles in the ventral compartment with 15% offsets ( light purple demarcation in Figure 1A ) . For further analyses , we concentrated on the posterior half of the pouch to exclude Dpp secreting anterior cells from the analysis and to avoid complications arising from the modifications of Dpp receptor levels via Hh , which is active only in the anterior compartment [40] , [41] . Qualitatively , from TC2 to TC4 , the P-Mad gradient expands and adjusts to the disc size , displaying this trend regardless of where it was measured ( Figures 2C and S3 ) . Quantitatively , P-Mad shows close to perfect scaling with S = 0 . 83±0 . 27 at a threshold concentration corresponding approximately to the mid-posterior compartment ( x = 0 . 48 Lp , where Lp stands for the length of the posterior compartment ) . The individual discs are represented with color-coded circles according to their age ( Figure 2D ) . In order to obtain a position-dependent picture of scaling , we considered several other protein concentration thresholds and calculated a scaling coefficient at each threshold ( Figure 2E ) . Scaling coefficients ( blue circles ) and correlation coefficients ( green crosses ) —informative for the goodness of fit—were plotted as a function of average relative positions , with corresponding to the intercept with the A/P axis and to the end of the pouch for each disc . We found that the P-Mad gradient shows overall very good scaling for ( Figures 2E and S4E–F; closer to the A/P boundary , the error bars are too large for meaningful conclusions ) . Accordingly , the scaling of a target gene domain that strictly depends on P-Mad levels should follow the same trend . We therefore analyzed known P-Mad targets with this question in mind . brk is a direct target of Dpp signaling . Its transcription is completely repressed in cells with high levels of Dpp activity , such as the medial cells , and is derepressed to varying levels in response to the decreasing Dpp activity gradient in the lateral parts of the wing disc ( Figure S1 ) [19] , [20] , [21] . We generated two independent datasets for Brk that we present in two separate figures ( Figures 3 and S5 ) . Importantly , both datasets yielded very similar results , demonstrating the reproducibility of our protocol . We examined whether the P-Mad dynamics were mirrored in Brk protein levels . We found that , as the disc grows , the region of maximum Brk expression is found at a further absolute distance from the A/P boundary , consistent with the fact that P-Mad gradients now reach further out and suppress brk transcription ( Figure 3A–B ) . Cells expressing highest levels of Brk are found around 80% Lp throughout development , suggesting that behind this relative position , P-Mad levels are too low to suppress brk transcription ( Figures 3C , S5D ) . As a result of P-Mad gradients scaling while keeping their amplitudes roughly constant , cells at the same relative position in the disc have very similar P-Mad levels across development . Interestingly , the same cells are subject to increasing levels of Brk: while the magnitude of the increase tends to be smaller away from the D/V boundary , we detected a 10- to 20-fold increase in the average amplitudes in the 40-h interval we studied ( Figures 3C and S5A , B , D ) . We observed a similar trend in the expression levels of Brk with a brk-GFP reporter line in which GFP expression is driven by the wing enhancer of brk ( not shown ) [25] . Hence , the changes in Brk protein levels are unlikely to be due to post-transcriptional events . How are the constant P-Mad levels at a given relative position translated into increasing Brk levels ? Since discs were co-stained for Brk and P-Mad , we investigated the relation between Brk concentrations and P-Mad concentrations within each disc . Figure 3D shows the average measured response of P-Mad and Brk within each TC ( after normalizing their maximum concentration over all TCs to one ) . At P-Mad concentrations above 40% of maximum levels , Brk responds similarly to P-Mad at all TCs . However , below this threshold ( to the right of the arrow ) , it appears that Brk can accumulate ( Figure 3D ) . Finally , if we fit decaying exponentials to the Brk expression profiles , the resulting decay lengths correspond roughly to mid-posterior compartment throughout development , a position where P-Mad scales very precisely ( Figure 2E ) . Hence , the length scale of the Brk expression profile corresponds to very similar P-Mad levels across TCs ( Figures 3E and S5G ) . Therefore , the expression pattern of Brk depends on P-Mad while the increase in protein levels cannot be explained with the P-Mad dynamics alone . Finally , we studied the scaling properties of the Brk profiles . Apart from being non-quantitative , looking at the collapse of the profiles adjusted to compartment size is a good indicator of the level of scaling ( Figure 1B ) . We found that Brk profiles show good scaling only between TC3 and TC4 ( Figure 3C , orange and blue profiles ) . However , when Brk intensities are normalized to their maximum , all profiles collapse rather well ( Figures 3F and S5E ) , and we measured nearly perfect scaling in the lateral part of the pouch ( while they seem to hyper-scale more medially ) , which is in agreement with the measured scaling for P-Mad ( Figures 3G and S5F , compare to Figure 2E ) . Overall , we conclude that the range but not the levels of the Brk gradients scale with the tissue size . Since there are no antibodies available which recognize Dad , we visualized changes in dad expression over time with a dad-GFP transgene where the GFP expression is controlled by a 2 kb dad enhancer fragment ( Figure 4A ) . This enhancer fragment incorporates positive input from P-Mad as well as negative input from Brk [26] , [42] . Hence , dad-GFP represents a good tool to monitor combined activity of P-Mad and Brk . While dad-GFP forms a gradient reminiscent of P-Mad , it does not tail off as far as P-Mad , presumably because it is sharpened by Brk ( Figure 4B ) [26] . As a result , while the dad-GFP expression pattern can be treated as a gradient ( Figure S6H ) , a Hill function also yields a good fit . The boundary of the dad-GFP domain was obtained by fitting a Hill function to each profile and corresponds to x = Kdad ( Figure S2C for explanations on the Hill fit ) . We investigated scaling of the dad expression domain along the D/V boundary and with several offsets . Because the dad-GFP domain boundary is not straight but contracts at the D/V boundary , especially during the last TCs ( Figures 4A and S6A ) , scaling is quite different depending on where it is measured: the dad-GFP domain boundary shows good scaling along the D/V axis ( S = 0 . 94±0 . 15 ) , at 25% ( S = 1 . 07±0 . 31 ) and 15% ( S = 0 . 85±0 . 19 ) ventral offsets , while it hypo-scales when measured close to the D/V boundary ( S = 0 . 46±0 . 17 at 5% dorsal offset where the expression domain is narrowest ) ( Figures 4F and S6E–G ) . Therefore , while the dad expression domain definitely expands with the growing tissue , it scales to varying degrees at different distances from the D/V boundary . Similar to Brk levels , dad-GFP levels increase with time ( Figures 4A–B and S6C ) . Since dad transcription is controlled by both P-Mad and Brk , we asked whether the dad domain was specified at constant thresholds of these gradients . We found that levels of both P-Mad and Brk corresponding to the dad domain boundary position x = Kdad increase over time ( Figure 4D–E ) . Hence , the Dad domain is not defined at constant P-Mad and Brk concentration thresholds . Proper positioning of the veins in the developing wing requires Dpp signaling and is important to ensure adult wing functionality [10] , [11] , [15] . We asked whether the Sal and Omb domains , informative for positioning veins L2 and L5 , respectively , already scale with tissue size during larval stages ( Figure S1C ) . Sal starts to be expressed in the pouch only from the beginning of the third instar stage while Omb is already induced earlier ( Figures 5A and 6A ) . The Sal domain in the anterior compartment spans 40%–45% of the pouch , while it is much narrower in the posterior compartment reaching only up to 15% ( Figures 5A–B and S7B , G ) . Since the vein L2 is located in the anterior compartment , we investigated the scaling properties of Sal in both compartments . Sal profiles expand with the growing disc with increasing amplitudes of roughly 4-fold , and most of this amplitude increase takes place within the first 20 h of the third instar stage ( Figures 5A and S7B–C , E ) . It was previously reported that the Sal domain boundary position correlates with the disc size at the end of development [11] . Consistent with this result , we found that the Sal domain boundary correlates with the lengths of both anterior and posterior compartments throughout development . However , a good correlation is not sufficient to ensure scaling , and indeed we observed that the Sal domain boundary exhibits significant hyper-scaling in the posterior compartment ( S = 1 . 44±0 . 3 , Figure S7G ) , while it scales well in the anterior compartment ( S = 0 . 88±0 . 11 , Figure 5C ) . This finding suggests that there might be additional factors at work in the anterior compartment to ensure proper Sal scaling . The omb gene is expressed in a domain larger than Sal , spanning about half the pouch in the posterior compartment ( Figure 6A–B ) , and its domain boundary sharpens during development ( Figure S8D ) . We found that the position of the boundary is well correlated with the length of the posterior compartment and Omb exhibits close to perfect scaling ( S = 1 . 07±0 . 11 at 15% ventral offset , Figure 6C ) . We also investigated whether the boundaries of Sal and Omb expression domains are defined at constant P-Mad and Brk levels . We found that the anterior Sal domain boundary corresponds to decreasing P-Mad levels and increasing Brk levels over time ( Figures 5D–E and S7H–I ) . In the case of Omb , clonal analysis with brk loss of function alleles suggests that positioning of the Omb domain boundary strictly depends on Brk activity [19] , [43] . In fact , Omb has no direct input from Mad/Medea complexes and is only indirectly activated by Dpp via repression of Brk [14] . In light of these findings , it is surprising that the Omb domain boundary corresponds to similar levels of P-Mad and increasing amounts of Brk during growth ( Figure 6D–E ) . This result suggests that the dad , sal , and omb enhancers become desensitized to Brk as development proceeds . Overall , we have shown that the P-Mad gradient and the expression domains of its target genes scale rather well with the growing wing disc . Additionally , Teleman et al . found that when the posterior compartment is enlarged or reduced in size via modifications of Insulin signaling activity , the size of the Sal domain adjusts accordingly [44] . These observations suggest that there might be an active mechanism in place to ensure scaling of Dpp activity with tissue size , and raise the question of the identity of the involved players . We recently identified a Pent-dependent feedback loop as a major modifier of the Dpp activity gradient [33] . Here we repeated our analyses in a pent mutant background using a null allele , pent2–5 , in order to examine its potential involvement in scaling the Dpp activity gradient during disc growth . While Pent function is absolutely essential for proper Dpp gradient formation and maintenance , in its absence flies are semi-viable and overall smaller with wings that lack L5 , hence the name of the gene ( Figure 7H ) [33] . In the absence of pent , the defects in the Dpp activity gradient are visible very early on , with P-Mad and Brk forming very steep gradients that resemble sharp domains in all the TCs we examined ( Figure 7A–D ) . P-Mad amplitudes are similar to wild-type levels in pent deficient discs and stay rather constant across growth ( Figure S9 ) . We observed that the P-Mad domain expands from TC1 to TC2 , and after that does not expand any further and hence does not scale with tissue size ( Figure 7C , very poor collapse of profiles in relative positions can be seen in Figure S9 ) . Interestingly , none of the D/V related changes that took place in TC5 in the wild-type background were observed in the absence of pent; P-Mad levels were not suppressed significantly along the D/V axis and the profiles looked similar at different distances from the boundary , suggesting that Pent is a contributor to this effect ( Figure S9 ) . Indeed , Pent binds to Dally [33] , which has a role in shaping gradients of both Wg and Dpp [45] , [46] , [47] , [48] , and hence the D/V centered effects on the P-Mad profiles we describe here are likely related to this connection . Are these changes in P-Mad dynamics in pent mutants reflected in the expression patterns of its target genes ? Repression of Brk via P-Mad does not seem to be affected by the removal of pent , as Brk still closely follows P-Mad ( Figure 7B , D ) . In the absence of pent , the Brk domain moves more interiorly following the narrower P-Mad domain . Cells with the highest levels of Brk are roughly in the middle or even more proximal , which represents a significant shift compared to being at x = 0 . 8 Lp in wild-type discs ( Figures 7D and S10D ) . Expression of Brk more interiorly in the absence of pent is likely to be a major contributor to the growth defects of these mutants as Brk is a well-established growth inhibitor [49] , [50] . Consistent with this hypothesis , heterozygosity for brk is able to suppress these effects to a large extent ( Figure S11 ) . Like in the case of P-Mad , the graded expression pattern of Brk is lost in the absence of pent , and the Brk domain does not scale with the tissue size ( Figures 7D and S10D ) . Next , we asked whether this failure of Dpp activity gradients to adjust to tissue size in pent mutants led to narrower expression domains of downstream targets . We found that this was indeed the case , especially in the posterior compartment ( Figure S10A–C ) . In wild-type discs , we observed very good scaling of Sal in the anterior compartment ( S = 0 . 88±0 . 11 at 15% ventral offset ) where it helps to position L2 while it hyper-scales in the posterior compartment . Interestingly , in pent mutant discs , scaling of Sal is less affected in the anterior compartment ( S = 0 . 75±0 . 19 at 15% ventral offset ) and the adult flies still have a properly positioned L2 ( Figures 7E , H and S10E ) . Scaling of Omb in the posterior compartment , however , is reduced from almost perfect in wild-type ( S = 1 . 07±0 . 11 at 15% ventral offset ) to nearly lost ( S = 0 . 39±0 . 09 at 15% ventral offset ) , while correlations are still very good ( Figure 7F ) . Similarly , scaling of dad-GFP along the D/V boundary and with various offsets is greatly reduced in pent mutants ( Figures 7G and S10A ) . In the absence of pent , the Omb domain boundary defined by the Hill fit shrinks to a quarter of the posterior compartment ( x/Lp = 0 . 26 ) . Despite this shrinkage , the Omb domain overlaps significantly with the Brk domain in this background , especially at the end of the third instar stage , a phenomenon not observed to this extent in wild-type discs . A large stripe of cells express both Omb and Brk , raising the possibility that failure to define L5 might be due to this extensive overlap ( Figure S11 ) . We conclude that the adaptation of the Dpp activity gradient to tissue size described in the first part of this study strictly requires Pent function .
In this study , we carefully analyzed the dynamics and the scaling properties of the Dpp activity readouts in the growing wing imaginal discs . We discuss our findings with regard to models that were put forward to explain scaling ( the expansion-repression model ) [34] , pattern formation ( Figure 1E ) , and uniform growth [37] . We measured pathway activity using an antibody specific to the phosphorylated form of Mad , and compared the P-Mad levels in space and time with the activity levels of direct target genes , such as brk , which plays key roles in both growth and patterning [19] , [20] , [49] , [50] . Transcription of brk is directly repressed via P-Mad binding at defined SEs , resulting in inversely graded brk expression [23] , [25] , [51] . Brk is the only known regulator affecting the positioning of the expression boundary of omb , while sal and dad translate input from both P-Mad and Brk into their expression boundaries [14] . We analyzed the dynamics of all of these readouts using antibodies where possible , to avoid potential misinterpretations due to reporter stability . We found that P-Mad levels scale very well posterior to 0 . 4 Lp with the exception of TC5 profiles near the D/V boundary . Previous studies that examined P-Mad scaling reached contradictory conclusions: the P-Mad gradients in late stage discs were reported to correlate with tissue size in a previous study [44] and to have no correlation in another [11] . Similarly , examination of P-Mad gradients across discs of different sizes led to the conclusion that the gradient did not expand [52] , but more recently the P-Mad gradient was shown to scale with tissue size [37] . We believe that most of the confusion can be attributed to different profile extraction protocols as well as to the use of various definitions of scaling , as discussed below . Since P-Mad is an early signature of the activation of the Dpp signaling pathway , we wanted to find out how its scaling properties translate to its immediate key target , the brk gene . In addition to brk being directly repressed by P-Mad , the Brk protein itself is necessary for graded brk transcription [53] . We found that the range of Brk expression strictly follows P-Mad in both wild-type and pent mutant discs . Similar to what we observe for P-Mad , Brk also shows very good scaling for positions posterior to 0 . 4 Lp . By contrast , levels of Brk increase steadily as the discs grow and cannot be explained by P-Mad dynamics alone . This increase in Brk levels could be due to the build-up of the unknown activator of brk transcription or , alternatively , the SEs in brk could become desensitized to the repressive input of P-Mad . Regardless of the cause of the increase , cells at a given relative position experience increasing levels of the Brk repressor over time . Traditionally , Dpp and P-Mad gradients have been described by a decaying exponential with characteristic decay length λ [11] , [37] , [54] . This decay length is different for each profile and corresponds to the position at which the protein levels have decreased by a factor e . The correlation between the decay length and the tissue size has been used as a proxy for scaling , e . g . in the work of Bollenbach et al . , which found no significant scaling for the Dpp and P-Mad profiles at the end of 3rd instar stage ( note that they used the length of the pouch along the A/P boundary as a measure of tissue size , [11] ) . Similarly , the width of the P-Mad profile has been used to characterize the spread of the gradient and it was concluded that the width of the P-Mad profile is constant during growth [55] . In contrast , we detected that the P-Mad profile expands as the tissue grows . This discrepancy may be due to the fact that Hufnagel et al . lacked TC3 and TC4 in their sample collection , the period where the P-Mad gradient expands before contracting again when measured close to the D/V boundary ( Figure S4B , compare red and green ) . Thus , possibly the measurements of the P-Mad profiles were done in the vicinity of the D/V boundary , where at TC5 P-Mad has a sharp profile reminiscent of 30 h younger discs . Hence , the choice of position can significantly alter the final interpretations of the data . Consistent with our results , Wartlick et al . recently showed that the decay lengths of Dpp-GFP , P-Mad , brk-GFP , and dad-RFP do correlate with the length and the area of the posterior compartment during tissue growth [37] . Importantly , they also assessed scaling qualitatively in the whole field by looking at the collapse of the profiles in relative positions and normalized intensities ( Figure 1B ) . This method has two advantages: it does not require any fitting of the profiles , and it shows scaling at all positions and not just at the characteristic decay length position ( i . e . x = λ ) . In our work , we used the raw intensity measurements without fitting any function to the corresponding profiles , since the exponential is not the best fit at all time classes . Similarly , we wanted to assess scaling in the whole field and not just at one characteristic position . To this end , in addition to looking at the collapse of the profiles , we used our measure of scaling [38] , which gives a quantitative measure for each position in the tissue ( Figure 1C ) . Lastly , we would like to emphasize that we did not normalize the P-Mad intensities before measuring scaling coefficients as the absolute protein levels are crucial for signal interpretation in the simplest scenario . In the case of Brk , however , whose amplitudes increase steadily , scaling only emerged after normalizing the intensities . For comparison , we also show in Figure S12 the ratios of the decay lengths over the pouch sizes for all the genes we investigated . Since the more downstream target genes were better fitted with a Hill function , we also report the ratio of the corresponding transition points as a function of pouch size for those genes . In agreement with Wartlick et al , we find that P-Mad and Brk decay lengths correlate well with tissue size , with λP-Mad = 0 . 21 Lp and λBrk = 0 . 18 Lp . We note that our estimate of λP-Mad is smaller than the previously reported value ( 0 . 34 Lp ) , likely due to the fact that we measure tissue size along the D/V boundary , from the intersection of the A/P to the limits of the pouch , as opposed to the length of the posterior compartment at its widest position . Our measure of λBrk is very similar to that of Wartlick et al . Hence , considering that we have a larger value for the tissue size , Brk protein must form a gradient with a larger decay length than the brk-GFP reporter that was used by Wartlick et al . [37] . A recent mathematical model termed “expansion-repression integral feedback control” suggests that scaling can emerge as a natural consequence of a feedback loop [34] . The hypothetical “expander” molecule facilitates the spread of the morphogen and in turn is repressed by it; scaling is achieved given that the expander is stable and diffusible . The known properties of Pent fit the requirements of this hypothetical agent: Pent is secreted , required for Dpp spreading , and pent transcription is directly inhibited by Dpp signaling . However , we do not know how stable Pent is , and pent transcription is never abolished in the entire field in which the gradient acts during larval stages . To test whether Pent could be a key player involved in scaling of Dpp activity during disc growth , we repeated our analyses at all time points in the absence of Pent . We found that the P-Mad and Brk gradients indeed fail to scale with the tissue size in this mutant background . Scaling of dad-GFP and Omb are also strongly affected , while Sal still exhibits some degree of Pent-independent scaling in the anterior compartment . Importantly , while the function of Pent is essential for proper scaling of the Dpp activity gradient , we note that Pent alone cannot account for the observed selective scaling of Omb and Sal domain boundaries . Scaling of these target genes specifically in those regions in which they have a patterning function points to the involvement of additional players , which will be the subject of future research . Hence , our findings strongly suggest that Pent is a very good candidate to be the expander in the “expansion-repression integral feedback control” model and therefore provide the first mechanistic insights into the question of scaling in wing patterning . The exact biochemical functions of Pent have to be determined in order to get a more mechanical view of gradient scaling in the developing wing imaginal disc . More than 40 y ago , Lewis Wolpert proposed the French flag model to explain pattern formation by morphogens ( Figure 1E ) [35] . Here we tested whether the activity gradients downstream of Dpp , namely P-Mad and Brk , are read out by their target genes at constant concentration thresholds . Thus , we measured average P-Mad and Brk concentrations at Dad , Omb , and Sal expression boundaries across development . We found that the amount of P-Mad at these boundaries slightly increased ( Dad ) , slightly decreased ( Sal ) , or was constant throughout development ( Omb ) . Among these three targets , the Omb domain is the widest and it corresponds to a region where the P-Mad gradients scale perfectly; as a result , P-Mad levels fluctuate very little at the Omb domain boundary . Interestingly , the domain boundary of Omb is thought to solely depend on Brk and hence constant P-Mad levels might be a mere coincidence . Remarkably , all the target genes we considered respond to significantly increasing levels of Brk , suggesting that the target genes desensitize to Brk over time , so that more and more Brk can be tolerated at the domain boundary . Alternatively , if we consider that the domain boundaries of dad-GFP and Sal do not respond to constant P-Mad levels either ( Figures 4D and 5D ) , another explanation could be that Brk and P-Mad signals are combined in a non-additive fashion in order to define the boundary position of the target genes . Following this assumption , we looked for a simple combination of these signals that is constant at the target gene domain boundary for all TCs . For dad-GFP in the posterior compartment , the ratio P-Mad2/Brk is constant at the domain boundary ( t test p value = 0 . 13 under the null hypothesis that the slope is equal to zero; in pent2–5 , t test p value = 0 . 88 ) , while for Sal in the anterior compartment , the multiplicative combination P-Mad5*Brk4 is constant ( t test p value = 0 . 42; in pent2–5 , t test p value = 0 . 91 ) . We propose that Brk and the unknown activator of Omb could be similarly combined in order to determine the Omb domain boundary . We used our data to further test a model that was recently proposed to explain the uniform growth in the wing imaginal disc [37] . The model poses that the temporal changes in Dpp signaling levels drive tissue growth; cells divide when they experience a relative increase of 50% in the levels of Dpp signaling . Since it is the relative differences and not the absolute amount of Dpp signal that regulate cell divisions , the model can account for the uniform growth of the wing disc . Since the relative increase in Dpp activity slows down , the cell cycles lengthen as the disc grows . Growth stops when the cell division time exceeds 30 h . The model of Wartlick et al . is based on the finding that Dpp activity scales with tissue size and that cells at a given relative position experience increasing levels of Dpp signaling over time . In contrast , we do not observe a general temporal increase in the level of Dpp signaling at a given relative position in our study . P-Mad is the most upstream and the most dynamic readout available for the activity of the Dpp pathway and we find that the relative increase in P-Mad levels throughout development is not significantly different from zero at most relative positions ( in –A′ , almost all the error bars ( 95% confidence interval ) cross the value Δc/c = 0 ) . Why is the increase in Dpp-GFP levels not reflected in P-Mad levels ? A potential explanation for this might be that the observed accumulation of Dpp-GFP was due to the stability and accumulation of Gal4 since Dpp-GFP was under UAS control [37] . The authors showed that the half-life of the Dpp-GFP fusion protein is only 20 min , but the Gal4 stability was not considered . Alternatively , the system could get desensitized over time and more and more Dpp would be required to lead to similar P-Mad levels . Finally , increases in Dad levels could counteract the increase in Dpp levels , since Dad is an inhibitory Smad [14] , [56] . Wartlick et al . monitored Dpp signaling levels using a dad-RFP reporter and found a 5-fold increase in the course of 36 h [37] . In our analysis , we used a similar tool , dad-GFP , and failed to fully reproduce their results . Though we also find that dad-GFP scales with tissue size , its levels increase merely 2-fold over 40 h , and this increase takes place only in the medial 25% region of the disc , while cells in the lateral part experience a decrease in dad-GFP levels ( Figure S13C–C′ ) . This disparity in the fold increases is likely due to the higher stability of RFP [57] since the enhancer used , to our knowledge , is identical in both studies . Additionally , we found that the levels of Brk , another direct target of the pathway with a very well-established role in suppressing growth in lateral regions , increase in average 4-fold in the interval studied , an observation not reported by Wartlick et al . ( Figure S13B–B′ ) . In the lateral areas , increase in Dpp activity ( if present ) is below detection levels and would be opposed by increasing Brk levels . Importantly , increasing Brk levels , if they were to depend solely on Dpp , would suggest decreasing Dpp activity in lateral areas as Brk expression is directly suppressed by Dpp activity . Hence , our data raise serious questions about the validity of this uniform growth model , especially in the lateral regions of the pouch . We favor an alternative model that does not rely on Dpp activity alone to explain uniform growth in the wing disc [58] .
Flies were constantly kept in a 26°C incubator and the eggs were collected on grape juice plates . It is known that the females can keep the fertilized eggs for up to 8 h , so a freshly laid egg can be anywhere between minutes to 8 h old . We circumvented this problem by treating flies with CO2 prior to collection , which is thought to relax the muscles and facilitate the deposition of old eggs . This first collection was discarded and the flies were transferred to a clean collection chamber . Additionally , as sexual dimorphism exerts itself early on , only male larvae were included in our analysis where possible . Indeed , male flies are comparatively smaller than female flies and including both sexes could bias our scaling results during wing imaginal disc growth . Male larvae were positively selected for by the presence of a clear , oval genital disc , which is clearly visible starting from 80 h AEL . Therefore , our 70 h collections had both male and female larvae . We observed that 70 h AEL corresponds to the beginning of the third instar stage at 26 °C as hatching larvae were frequently encountered . Dissected larvae were fixed immediately , washed , and stored at 4°C . Once all time classes were obtained ( usually within 2 d ) , all samples were processed for antibody staining in parallel using identical solutions . Larvae of different time classes ( TC1: 65–75 h AEL , TC2: 75–85 h AEL , TC3: 85–95 h AEL , TC4: 95–105 h AEL , TC5: 105–120 h AEL ) were transferred into cold fixative ( 4% pfa in PBS , pH = 7 ) and fixed for 25 min at room temperature on a rotator . Following extensive washes in PBT ( PBS+0 . 03% TritonX ) , the discs were blocked in PBTN ( PBT+2% Normal Donkey Serum , Jackson Immuno Research Laboratories ) for 1 h at 4°C on a rotator , and incubated with primary antibodies overnight at 4°C . The discs were washed several times with cold PBT and incubated in secondary antibodies for 2 h at room temperature on a rotator . After another round of washes with PBT , the excess fluid was removed and replaced with Vectashield mounting media ( Vector Labs ) . All discs from a dataset ( i . e . all 5 TCs ) were mounted on the same slide to reduce potential variation in thickness between the slide and the coverslip across different samples . Brain discs were used as spacers . All discs from a dataset were imaged under identical microscopy settings using a Leica SP5 confocal microscope ( 1 µm thick sections ) . Rb-α-P-Mad ( 1∶1 , 500 , Ed Laufer , [41] , [59] ) ; rb-α-Sal ( 1∶40 , Reinhard Schuh , [60] ) ; rb-α-Omb ( 1∶1 , 200 , Gert O . Pflugfelder , [61] ) ; m-α-Wg ( a . k . a . 4D4 , 1∶120 , DSHB , University of Iowa ) ; m-α-Ptc ( a . k . a . Apa1 , 1∶600 , DSHB , University of Iowa ) ; gp-α-Brk ( 1∶1 , 000 , Gines Morata ) . All secondary antibodies were used in 1∶1 , 000 dilutions and were from the AlexaFluor series of Invitrogen . dad-GFP transgenic flies were described in [42] . After the image acquisition , we manually selected by visual inspection four consecutive slices above and below the brightest slice from each stack and performed a mean projection of these nine slices . Using a reduced number of slices and performing the mean projection allowed us to reduce the noise as well as avoid the signal from the peripodial membrane . Indeed , we made sure that these nine slices contained signal from the columnar cells of the pouch only . We then manually contoured the inner pouch boundary as well as the anterior-posterior ( A/P ) and dorsal-ventral ( D/V ) boundaries , as marked by the Wg and Ptc stainings . All discs were rotated to have anterior to the left and dorsal upwards orientation . The remaining analyses were applied solely to the pouch . We extracted the profiles along the D/V boundary , since it is a natural coordinate in the wing pouch , or parallel to it with a small offset of 5% of the height of the pouch into the dorsal compartment to avoid potential interference with Wg , which is expressed at the D/V boundary . We repeated our analysis also with 15% and 25% offsets into the ventral compartment . Note that since the D/V boundary is not a thin line but a stripe , we applied mean filtering with a rectangular sliding window of fixed size ( 20×3 ) pixels ( height×width ) to smoothen the images of ( 1024×1024 ) pixels before further analyses . Also , because we used nuclear markers , the 1 d extracted profiles looked very rugged and we therefore applied Gaussian filtering before quantifying scaling . We assumed that the changes in cell density are negligible . We aimed to quantify scaling of Dpp target genes . In a previous work [38] , we defined scaling as the relative response in gene expression domain position due to variations in tissue size L:Here perfect scaling corresponds to a scaling coefficient S that equals to one , while a scaling coefficient below one indicates hypo-scaling , and a scaling coefficient greater than one corresponds to hyper-scaling . We emphasize that perfect correlation is not equivalent to perfect scaling , since it only guarantees a strictly linear response of the domain position with tissue size , but not the preservation of proportions . Thus , correlations are not informative on whether the shift in domain boundary position during growth is adequate , or if a specific domain position tends to hyper- or hypo-scale [38] . In our analysis , we concentrated on the posterior compartment and used the length of the posterior compartment ( Lp ) as a measure of the tissue size . Lp is indeed a good measure of tissue size as it changes proportionally with the square-root of the area of the posterior compartment ( Figure S2B ) . Among the Dpp activity readouts we examined , there are proteins that form gradients ( P-Mad , Brk , dad-GFP ) as well as genes that are expressed in rather sharp domains where the boundary position is likely to matter more than the amplitude of the signal ( Omb , Sal , dad-GFP ) . We analyzed the scaling properties of dad both as a gradient and as a domain as it can be defined either way . In the case of sharp domains , we extracted the protein expression profiles ( as deduced from the fluorescent intensities ) for each gene of interest in discs of different ages and asked whether the position of the expression domain boundary in the posterior compartment scales with the compartment length ( Figure 1D–D′ ) . The boundary of the domain was obtained by fitting a Hill function ( with free exponent ) to each profile ( Figure S2C ) . We then estimated scaling by weighted linear regression ( the weights w on the domain boundaries come from the fitting procedure ) :Note that the linear regression assumes that and are correlated ( and are the average domain boundary position and the average posterior compartment length , respectively ) . Note that for small deviations and from the mean in boundary position and tissue size we can approximate and [38] . For assessing the scaling properties of P-mad , we took into account its amplitude , assuming that the absolute concentrations are important for the signal interpretation by the target genes . Moreover , we wanted to characterize scaling over the whole field where the protein is expressed and not just at one particular position , because the gradient can scale differently across positions . We therefore considered several thresholds of protein concentration readout . Note that while an exponential function can provide a reasonable fit to P-Mad and Brk profiles away from their source , we did our calculations without fitting any specific curve to the profiles . For each threshold , we plotted the corresponding positions against the lengths of the posterior compartments for our collection of discs ( Figure 1C–C′ ) . Thus , we got a scaling coefficient at each threshold , which we can then associate to the position . This way , we obtain a scaling coefficient for several relative positions in the patterning field ( Figure 1C″ ) . All the linear regressions that we perform are weighted if the data points on the plot have an error bar ( weight = 1/error2 ) . The gray area represents the 95% confidence interval on the linear regression , that we approximate using the estimated standard error ste on the parameter s: upper 95% confidence interval = s+1 . 96*ste , lower 95% confidence interval = s−1 . 96*ste . Note that in the scaling plots ( e . g . Figure 2D ) , the data points are centered around ( 0 , 0 ) , so that the regression line must pass through ( 0 , 0 ) . In that case , the gray area represents the 95% confidence interval on the slope only , which is the scaling coefficient , without including that of the intercept . This is why the gray area narrows at ( 0 , 0 ) . All the p values are meant to assess whether the slope of the linear regression is significantly different from zero . Thus , we perform a t test under the null hypothesis that the slope is equal to zero . In the case where the p value is below 0 . 05 , we reject this null hypothesis . | Scaling , the fitting of pattern to size , manifests itself in numerous examples around us . During development , individual body parts scale up to fit the overall body size . Starved animals form smaller adults with proportionally smaller parts , and amphibian embryos can form normally proportioned adults after extreme surgical operations . How scaling is achieved is not well understood . Here , we establish the Drosophila wing imaginal disc , the precursor tissue of the adult wing , as a model to study scaling quantitatively during growth . In this model , we define scaling as the preservation of proportions of gene expression domains with tissue size during disc growth . The Decapentaplegic ( Dpp ) morphogen is known to play a central role in Drosophila wing formation and co-coordinately regulates growth and patterning . We found that as the disc grows , the Dpp response expands and scales with the tissue size . Interestingly , scaling is not perfect at all positions in the field . The scaling of the target gene domains is best where they have a function; Spalt , for example , scales best at the position in the anterior compartment where it helps to form one of the anterior veins of the wing . Analysis of mutants for pentagone , a transcriptional target of Dpp that encodes a secreted feedback regulator of the pathway , indicates that Pentagone plays a key role in scaling the Dpp gradient activity . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"animal",
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] | 2011 | Dpp Signaling Activity Requires Pentagone to Scale with Tissue Size in the Growing Drosophila Wing Imaginal Disc |
Despite important advances from Genome Wide Association Studies ( GWAS ) , for most complex human traits and diseases , a sizable proportion of genetic variance remains unexplained and prediction accuracy ( PA ) is usually low . Evidence suggests that PA can be improved using Whole-Genome Regression ( WGR ) models where phenotypes are regressed on hundreds of thousands of variants simultaneously . The Genomic Best Linear Unbiased Prediction ( G-BLUP , a ridge-regression type method ) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations . However , breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP . Using theory , simulations , and real data analysis , we study the performance of G-BLUP when applied to data from related and unrelated human subjects . Under perfect linkage disequilibrium ( LD ) between markers and QTL , the prediction R-squared ( R2 ) of G-BLUP reaches trait-heritability , asymptotically . However , under imperfect LD between markers and QTL , prediction R2 based on G-BLUP has a much lower upper bound . We show that the minimum decrease in prediction accuracy caused by imperfect LD between markers and QTL is given by ( 1−b ) 2 , where b is the regression of marker-derived genomic relationships on those realized at causal loci . For pairs of related individuals , due to within-family disequilibrium , the patterns of realized genomic similarity are similar across the genome; therefore b is close to one inducing small decrease in R2 . However , with distantly related individuals b reaches very low values imposing a very low upper bound on prediction R2 . Our simulations suggest that for the analysis of data from unrelated individuals , the asymptotic upper bound on R2 may be of the order of 20% of the trait heritability . We show how PA can be enhanced with use of variable selection or differential shrinkage of estimates of marker effects .
Many important human traits and diseases are moderately to highly heritable . This , together with advances in genotyping and sequencing technologies , brought the promise of genomic medicine [1] . In the last decade genome-wide association studies ( GWAS ) have uncovered an unprecedented number of variants significantly associated with important complex human traits and diseases [2] . However in most cases , the combined effects of variants found to be significantly associated with various traits and diseases explain such a small proportion of inter individual differences in genetic risk that the usefulness of genomic information in clinical practice remains limited . In part , this reflects lack of power of standard GWAS to detect phenotype-marker associations for small effect variants [3] , [4] . A number of studies have shown that prediction accuracy can be increased by including in the model variants that may not show significant association at the marginal level ( e . g . , [5] . A few authors [6]–[8] went further and suggested that the analysis and prediction of complex traits may be improved with the use of Whole-Genome Regression methods ( WGR; [9] ) where phenotypes are regressed on hundreds of thousands of markers concurrently . For instance , using G-BLUP ( Genomic Best Linear Unbiased Predictor , one of the most commonly used WGR methods ) Yang et al . [7] found that roughly 50% of the genetic variance of human height can be explained by regression on common SNPs . Similar results were confirmed for other complex traits [10] . The ability of a model to predict yet-to-be observed phenotypes ( hereinafter referred to as PA , for prediction accuracy ) constitutes one of its most important properties from the perspective of its potential use for preventive and personalized medicine . The study by Makowsky et al . [8] assessed PA of G-BLUP and , using family data , reported a cross-validation R2 of 0 . 25 . However , the R2 ranged from 0 . 36 for individuals having 3 or more close relatives in the training data set to 0 . 11 for individuals with no close relatives in the training data set . The result confirms previous findings from the field of animal breeding [11] suggesting important influences of close familial relationships on the PA of G-BLUP methods . This raises an important question: what levels of PA could be expected when G-BLUP is used to predict complex human traits and diseases using data from unrelated individuals ? In this article , using theory , simulation and real data analysis we study the factors that affect the extent of missing heritability and the prediction accuracy of G-BLUP for the analysis of human data . The article is organized as follows . The methods section begins with an overview of G-BLUP . We describe the assumptions that define the model and derive analytical expressions that relate genomic relationships to prediction accuracy in two scenarios: ( a ) when the genotypes used for analysis are those at causal loci ( hereinafter referred to as analysis under perfect LD between markers and QTL ) and ( b ) under imperfect linkage disequilibrium ( LD ) between the markers used to compute genomic relationships and the genotypes at causal loci . The derivation of the R2 formula under perfect LD between markers and QTL follows from standard properties of the multivariate normal density and similar results have been presented before [12] , [13] . However , under imperfect LD the model does not hold ( because of misspecification of the covariance function ) and the standard formulas cannot be used . Based on a few assumptions we derive a closed-form upper bound on prediction R2 for the case of imperfect LD . Predictions from the formulas derived in the methods section are validated in simulated and real data analyses using data from related ( Framingham Heart Study [14] ) and nominally unrelated ( a sub-study of GENEVA [15] ) individuals . In the Discussion section the analytical and empirical findings of our research are discussed and put into context and various implications of our results are considered .
Genomic BLUP can be motivated in many different ways: as a Ridge Regression ( RR , [17] ) on marker genotypes , as a Bayesian Gaussian Regression on markers or as a random effects model . A detailed description of this model is given in Supplementary Methods . Here we briefly describe G-BLUP adopting the random effects perspective where phenotypes are viewed as the sum of a random effect representing genomic signal ( ) and a model residual ( ) , ( 1 ) both of which are assumed to follow multivariate normal ( MVN ) distributions . The vector of genomic values is assumed to follow a MVN distribution with mean equal to zero and variance-covariance matrix proportional to , a marker-derived matrix of realized genomic relationships between pairs of individuals ( ) . Model residuals , are regarded as independent of u and assumed to follow IID normal distributions , centered at zero and with variance . Therefore , ( 2 ) where I denotes an identity matrix of dimension n . Importantly , the ability of the model described by expressions ( 1 ) and ( 2 ) to separate signal ( u ) from noise ( ) depends completely on how well G describes realized genetic relationships at unobserved causal loci . In empirical analyses , genomic relationships are usually computed using crossproduct terms between genotypes . In such cases , estimates derived from G-BLUP methods are equivalent to those that can be derived by regressing phenotypes on marker genotypes using a linear model , , with marker effects treated as IID draws from a normal distribution , . See Supplementary Methods for further details about the equivalence of G-BLUP and some linear regressions on marker covariates . The predictive ability of a model is commonly assessed using the variance of prediction errors ( or prediction error variance ) , , where represents a prediction , for instance , . The proportional reduction in phenotypic variance accounted for by predictions ( referred to as R2 in this article ) can be quantified usingwhere , represents the phenotypic variance of individual n+1 . Below we look at two scenarios: ( i ) prediction accuracy when markers and QTL are in perfect LD and ( ii ) prediction accuracy when markers and QTL are in imperfect LD . To obtain further insight on the impacts of imperfect LD between markers and QTL on the proportion of missing heritability and on PA , a simulation study and real data analysis were performed using data sets from related and from unrelated individuals .
Table 1 shows the distribution of allele frequencies ( computed among the 5 , 800 individuals used for analysis in each of the data sets ) by set of markers and data set . The distribution of allele frequencies observed in the FHS and GEN was very similar , with a correlation of MAF between the two data sets of 0 . 997 . The distribution of minor allele frequencies of subsets of randomly chosen markers ( either those designated as non-causal loci or those designated as causal loci in the RAND scenario ) was very similar , with more than 65% of the markers having a MAF greater than 0 . 15 . On the other hand , as a consequence of the sampling scheme used , in the Low-MAF scenario , the distribution of allele frequencies at causal loci had an over representation of low MAF loci . We also computed the squared correlation of genotypes of adjacent markers at various lags ( in this case defined as the number of markers in the interval in the map ) , from lag 1 to lag 100 in FHS and GEN . For the set of SNP used in this study ( 400 K ) the average inter marker distance was 7 . 2 kb . Plots of the patterns of association between genotypes at adjacent markers are given in the Supplementary Data ( see figures S1 and S2 ) . Although , for some pair of markers , the squared correlations in FHS and GEN were different; however , the overall patterns ( e . g . , the average squared-correlation at lag 1 , 2 , … , 100 , or percentiles of the squared correlations at various lags ) were identical in both data sets . The eigenvalue decomposition of the marker-derived genomic relationship matrices revealed that the cumulative variance explained by the 1st 5 eigenvalues were 0 . 35 , 0 . 51 , 0 . 64 , 0 . 78 and 0 . 90% in FHM and 0 . 35 , 0 . 51 , 0 . 61 , 0 . 69 , and 0 . 77% in GEN , respectively . Ordinary least squares regression of adjusted height on the 1st PC explained a proportion of the variance ( in the training sample ) equal to 4% in FHM and to 2% in GEN . Therefore , although both data sets exhibit some extent of population stratification , the proportion of variance of genotypes explained by high order principal components was low . Estimates of and of prediction R2 , averaged across 30 MC replicates are displayed in Table 2 . Results by MC replicate are provided in Tables S1 , S2 , S3 , S4 , S5 of the Supplementary Data . Table 5 gives estimated posterior means of for the pedigree-model ( P-BLUP , applied to FHS only ) , and of for G-BLUP and wG-BLUP fitted to the full and combined data sets . The estimate of for the P-BLUP model in FHS was 0 . 857; this value is within the range , slightly higher , of what is generally considered the heritability of human stature ( i . e . , 0 . 8 ) . The estimate of in FHS with G-BLUP was slightly smaller ( 0 . 837 ) . Both results are in agreement with previous reports for this trait and data set ( e . g . , Makowsky et al . [8] ) as well as with the simulation study presented in this article , with one small difference: in the simulation study the estimate of from marker based G-BLUP was slightly higher than that of P-BLUP , while in the real data analysis the opposite happened . One possible explanation is that in the real data analysis P-BLUP captured some non-additive genetic effects and/or some components of permanent environment that are not captured by G-BLUP . Finally , in FHS , the estimated derived using wG-BLUP was similar , albeit slightly lower , than with G-BLUP ( 0 . 814 ) . In short , regardless of the method ( P-BLUP , G-BLUP or wG-BLUP ) no missing heritability is observed in the analysis of family data . On the other hand , the analysis of data from unrelated individuals ( GEN ) exhibited a great extent of missing heritability ( roughly 53% for G-BLUP , computed as 100×[1−0 . 374/0 . 80] ) both for G-BLUP and even greater for wG-BLUP . These results are also in agreement with previous reports for the trait ( e . g . , [7] ) and with the trend observed in the simulation study in scenario Low-MAF . However , the extent of missing heritability was higher than what was observed in the simulation , perhaps suggesting that the levels of imperfect LD between genotypes at markers and those at causal loci affecting human height are even more extreme that those present in the simulation .
The ability of G-BLUP to separate true signal ( g ) from noise ( ) depends entirely on how well marker derived genomic relationships ( ) describe genetic relationships realized at unobserved causal loci ( ) . Genomic relationships at subsets of loci in the genome ( e . g . , markers , causal loci ) can be viewed as the result of a random process with expected value given by the pedigree relationships ( ) and variation due to Mendelian sampling . Because of the random nature of this process , genomic relationships vary across regions of the genome and therefore , the patterns of genomic similarity at markers and at causal loci may be different . If the variance of the realized genomic relationships ( across regions of the genome ) is small relative to their expected value , the patterns of realized genomic relationships at markers will provide a good description of the patterns of realized genetic relationships at unobserved causal loci . Hill and Weir ( 2011 ) [20] have characterized various moments of the distribution of genomic relationships and concluded that the coefficient of variability decreases as the expected value , , increases . Therefore , for pairs of unrelated individuals , a large coefficient of variation of genomic relationships across regions of the genome is expected . The analyses reported here support this; indeed , the regression of realized genomic relationships computed at different subsets of markers is close to one ( 0 . 98 , see Table 4 ) for closely related individuals and very small ( of the order of 0 . 10 , see Table 4 ) for pairs of nominally unrelated individuals . Therefore , two contrasting situations are encountered: some of the elements of the marker derived genomic relationship matrix represent very well the true covariance function ( i . e . , the patterns of realized genetic relationships at observed causal loci ) but others ( all the off-diagonal elements corresponding to distant relatives and to pairs of unrelated individuals ) show patterns of realized genomic relationships that do not describe well the patterns of realized genetic relationships at causal loci . This has direct and different impacts on estimation of variance parameters and on PA , because variance parameters and PA are driven , in part , by different forces . To illustrate with an extreme scenario , suppose that G , the matrix of realized genomic relationships at causal loci , is diagonal ( i . e . , all off-diagonal terms of G equal zero ) . In this case , it would still be possible to estimate variance parameters and genomic heritability ( simply based on the fact that the diagonal elements of G are not constant ) . Yet , the prediction accuracy for phenotypes in the TST data set will be null because all the off-diagonals of G are equal to zero . In this study we have chosen to center and to standardize markers using estimates of allele frequency derived from the sample . As stated , centering does not have an effect on predictions or on estimates of variance parameters [25] , provided that the model contains an intercept . On the other hand , standardization can have an effect . When markers are standardized to unit variance , the relative contributions of markers to the genomic relationship matrix are the same . This is good practice if it enhances the ability of marker derived genomic relationships to describe the patterns of genetic similarity realized at causal loci . If the distribution of allele frequency at causal loci has a higher representation in the low minor allele frequency spectrum than the one observed at the markers , or if the size of effects is inversely related to minor allele frequency , then standardization may reduce the extent of missing heritability and may improve prediction accuracy . The results of the simulation study indicate that when markers and QTL are in perfect LD , no missing heritability is observed , as expected . This holds regardless of whether the training sample comprises data from related or unrelated individuals . When markers and QTL are in imperfect LD two contrasting situations were encountered: ( a ) with family data no missing heritability was observed , and ( b ) with unrelated individuals , we either observed a small extent of missing heritability ( when markers and QTL were sampled from the same distribution of loci , the RAND scenario ) or a greater extent of missing heritability ( this happened when the distribution of allele frequency at markers and causal loci was different , the Low-MAF scenario ) . The estimates of variance components and of genomic heritability for human height reported here are consistent with previous results for this trait . In other words , no missing heritability was observed in the analysis of family data [8] and a great extent of missing heritability ( roughly 50% ) was observed with unrelated individuals [7] . Predictions based on G-BLUP are weighted averages of phenotypes in the TRN data set ( see , eq . 4 ) . The weights are heavily determined by the realized TST-TRN genomic relationships ( i . e . , the off-diagonal entries of G ) . Therefore the PA that can be derived from G-BLUP is highly dependent on the magnitude of these coefficients and on the extent to which marker derived genomic relationships represent the underlying patterns of genetic similarity realized at causal loci . Using standard properties of the multivariate normal distribution one can derive closed-form expressions for prediction error variances and for prediction R2 ( see Supplementary Methods ) . These expressions are valid if the model holds . This requires , among other things , that the markers used to compute genomic relationships are in perfect LD with genotypes at causal loci . Under such conditions , prediction R2 has an upper bound given by an index that is the product of the heritability of the trait times a weighted sum of squares of the realized genomic relationships between the individuals used for TRN and those in the TST data set ( see eq . 5 ) . The expected value of realized genomic relationships is given by the pedigree derived additive relationships . For distantly related individuals the expected value of genomic relationships is small and , consequently , data from unrelated individuals are expected to contribute little to prediction accuracy . Nevertheless , if the model holds , PA is anticipated to increase monotonically with the size of the TRN data set ( each additional phenotype in the TRN data set brings additional information ) and , asymptotically prediction R2 converges to the heritability of the trait . However , this does not occur when markers and QTL are in imperfect LD . Indeed , under imperfect LD , prediction R2 can have an upper bound that is much lower than the heritability of the trait . Assuming a linear relationship between the realized genomic relationships at markers and at causal loci , an upper bound to prediction R2 under imperfect LD between markers and QTL ( ) was derived ( see expression 7 ) . This upper bound is given by the product of two terms : ( a ) the R2 that can be obtained ( using the same TRN sample ) if markers and QTL were in perfect LD and ( b ) a coefficient that depends on the coefficient of linear regression between TRN-TST realized genomic relationships at markers and those at causal loci . This result was derived assuming that realized genomic relationships at causal loci in the TRN data set are known , and therefore , represents an upper bound on prediction under imperfect LD . The regression coefficient drives the size of the reduction factor on prediction R2 . When the TRN and TST data set are related due to close familial relationships , the regression of genomic relationships at markers on those at causal loci is moderately high ( e . g . , of the order of 0 . 8–0 . 9 for pairs of related individuals , or of the order of 0 . 35 when we consider a mixture of both related and unrelated individuals as in the FHS , see Table 3 ) . Using a value of 0 . 35 ( average for the FHS ) the minimum expected reduction factor in prediction R2 due to imperfect LD , , is of the order of 40–50% . On the other hand , when TRN and TST data sets are composed of nominally unrelated individuals , the regression is much smaller ( of the order of 0 . 1 ) . A large reduction factor in prediction R2 is therefore predicted ( of the order of 80% computed as 100×[1–2×0 . 1+0 . 12] ) . Importantly , the minimum shrinkage in R2 predicted by our formula matched very closely the observed shrinkage due to imperfect LD estimated in the simulation ( roughly , the minimum shrinkage factor was 80–90% of the observed shrinkage in R2 , see Table 3 ) . The maximum R2 that can be attained under perfect LD ( assuming infinitely large samples and that the model holds ) is h2 , the heritability of the trait . Imperfect LD between markers and QTL induces shrinkage in R2; in case of data sets of nominally unrelated individuals similar to GEN a minimum shrinkage in R2 of 80% is anticipated; therefore , the expected asymptotic upper bound for R2 is 20% of h2 , or 16% in the case of height . This estimate applies to data sets of similar characteristics that the GEN data set . Prediction problems involving individuals that are less ( more ) distantly related than the average individual in GEN are expected to have a lower ( higher ) upper bound on R2 . Similarly , our estimates reflect the specifics of the SNP chip used and how genomic relationships were computed . In finite samples , as pointed out in previous studies [28]–[31] , estimation errors in marker effects will reduce the perfect LD R2 to values smaller than h2 . Some proposed formulas for the expected value of R2 under perfect LD take the forms [31] , or [30] , where m is the number of independent causal loci and N is the number of records in the training data set . These formulas could be used to obtain a reference for the expected R2 under perfect LD . However , the derivation of these formulas assumes that genotypes at causal loci are fully orthogonal . We applied these formulas using m = 5 , 000 , and , the setting of our simulation if we assume that causal loci are in linkage equilibrium , and obtained R2 values of 0 . 47 and 0 . 37 using the formulas suggested in [30] and [31] , respectively . These values are lower than those obtained in our simulation for GEN , where R2 under perfect LD ranged between 0 . 52–0 . 54 . GENEVA and the FHS contain samples drawn from relatively homogeneous populations . On the other hand , when allele frequencies vary across subpopulations , so does the relative contribution of each locus affecting the trait to genetic variance in each of the subpopulations . This raises the question of what estimates of allele frequencies should one use when analyzing data involving different subpopulations . In the present study this was not an issue because the correlation of estimates of allele frequencies derived from GEN and FHM was virtually 1 ( 0 . 99 ) . However , when this is not the case , if genomic relationships are scaled with estimates of allele frequencies derived from the entire sample , then marker derived genomic relationships will provide a poorer description of the realized genetic relationships in each of the sub-populations . This may result in a lower estimate of and a much higher R2-shrinkage factor . Both FHS and GEN , especially the former , show some degree of population stratification , as judged by the inspection of the loadings of the 1st two eigenvectors derived from G . However the cumulative proportion of variance explained by the first two eigenvectors was relatively small . In the presence of stratification , there may be reasons to remove between cluster variability , and to obtain within cluster estimates of variance components and of prediction accuracy . Following the approach used by Janss and coauthors [32] one could derive genomic relationships that do not include the contribution to genetic similarity of the 1st k principal components of G . The use of such genomic relationships would yield a within cluster estimate of . These estimates can be plugged into the equations presented here to derive an upper bound on prediction R2 that does not account for genetic similarity attributable to substructure . The effectiveness of G-BLUP depends critically on the extent to which marker derived genomic relationships reflect the patterns of realized genetic relationships at causal loci . The size of the coefficient of variation of realized genomic relationships across regions of the genome depends on the number of independently segregating segments among the pair of individuals whose realized genomic relationship we wish to assess . For pairs of unrelated individuals this is largely controlled by the span of LD in the population . For pairs of related individuals this is largely controlled by within family disequilibrium . In animal and plant breeding populations G-BLUP has exhibited very good predictive performance because the two conditions needed for G-BLUP to perform well are generally met: LD span over long regions and data include highly related individuals . Under these conditions variable selection is difficult to perform and may not be needed because the patterns of genetic similarity realized at markers and at causal loci are similar . However , the analysis of human data from unrelated individuals represents the exact opposite situation . Here LD spans over shorter regions [33] and within family disequilibrium cannot be exploited . Under these conditions the use of markers that are in imperfect LD with QTL results in very low prediction accuracy of G-BLUP . Variable selection constitutes a natural way of increasing the extent of LD between markers and QTL . However , for complex traits , stringent variable selection can induce poor coverage of regions with small , but not negligible , contribution to variance . Therefore , we are faced with the need for finding an appropriate balance: as variable selection becomes more stringent , LD between markers and QTL increases , but the some proportion of the variance contributed by QTL of small effects may be lost . The appropriate balance will likely depend on the genetic architecture of the trait but also , importantly , on features of the sample . With family data , the benefits of variable selection are relatively small . However with unrelated individuals , variable selection , including large numbers of markers ( e . g . , 5 K top SNPs ) , or perhaps better some form of smooth differential weighting of the contribution of individual markers to genomic relationships , seems to be an effective way of improving prediction accuracy . This could be done either combining information from a prior study , as implemented in this article , or using methods that perform variable selection and differential reduction of estimates of effects simultaneously . The literature on WGR offers several penalized and Bayesian methods that can achieve this goal . The application of these methods to plant and animal breeding data has not shown marked improved gains in PA relative to G-BLUP . However , for the reasons discussed in this paper , we anticipate that the situation may be different when these methods are applied to the analysis and prediction of complex traits using data from unrelated individuals . In conclusion , we have provided an analytical framework to quantify the maximum prediction R2 that can be attained using G-BLUP and have compared the properties of G-BLUP in samples of related and unrelated individuals . The analytical expressions derived are consistent with our simulation and empirical results and suggest that the analysis of nominally unrelated individuals presents a number of challenges that standard G-BLUP does not address . These can be partly met by incorporating prior knowledge of the relative importance of SNPs for a given trait . Further research will be required to optimize the modeling of such prior knowledge towards improved trait prediction . | Despite great advances in genotyping technologies , the ability to predict complex traits and diseases remains limited . Increasing evidence suggests that many of these traits may be affected by a large number of small-effect genes that are difficult to detect in single-variant association studies . Whole-Genome Regression ( WGR ) methods can be used to confront this challenge and have exhibited good predictive power when applied to animal and plant breeding populations . WGR is receiving increased attention in the field of human genetics . However , human and breeding populations differ greatly in factors that can affect the performance of WGRs . Using theory , simulation and real data analysis , we study the predictive performance of the Genomic Best Linear Unbiased Predictor ( G-BLUP ) , one of the most commonly used WGR methods . We derive upper bounds for the prediction accuracy of G-BLUP under perfect and imperfect LD between markers and genotypes at causal loci and validate such upper bounds using simulation and real data analysis . Imperfect LD between markers and causal loci can impose a very low upper bound on the prediction accuracy of G-BLUP , especially when data involve unrelated individuals . In this context , we propose and evaluate avenues for improving the predictive performance of G-BLUP . | [
"Abstract",
"Introduction",
"Materials",
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] | [
"mathematics",
"medicine",
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] | 2013 | Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor |
The Kato-Katz ( KK ) stool smear is the standard test for the diagnosis of Schistosoma mansoni infection , but suffers from low sensitivity when infections intensities are moderate to low . Thus , misdiagnosed individuals remain untreated and contribute to the disease transmission , thereby forestalling public health efforts to move from a modality of disease control to one of elimination . As an alternative , the urine-based diagnosis of schistosomiasis mansoni via the circulating cathodic antigen immuno-chromatographic test ( CCA-ICT ) has been extensively evaluated in Africa with the conclusion that it may replace the KK test in areas where prevalences are moderate or high . The objective was to measure the performance of the CCA-ICT in a sample study population composed of residents from non-endemic and endemic areas for schistosomiasis mansoni in two municipalities of Minas Gerais state , Brazil . Volunteers ( 130 ) were classified into three infection status groups based on duplicate Kato-Katz thick smears from one stool sample ( 2KK test ) : 41 negative individuals from non-endemic areas , 41 negative individuals from endemic areas and 48 infected individuals from endemic areas . Infection status was also determined by the CCA-ICT and infection exposure by antibody ELISA ( enzyme-linked immunosorbent assay ) to S . mansoni soluble egg antigen ( SEA ) and soluble ( adult ) worm antigen preparation ( SWAP ) . Sensitivity and specificity were influenced by whether the trace score visually adjudicated in the CCA-ICT was characterized as positive or negative for S . mansoni infection . An analysis of a two-graph receiver operating characteristic was performed to change the cutoff point . When the trace score was interpreted as a positive rather than as a negative result , the specificity decreased from 97 . 6% to 78 . 0% whereas sensitivity increased from 68 . 7% to 85 . 4% . A significantly positive correlation between the CCA-ICT scores and egg counts was identified ( r = 0 . 6252 , p = 0 . 0001 ) . However , the CCA-ICT misdiagnosed as negative 14 . 6% of 2KK positive individuals , predominantly those with light infections ( fewer than 100 eggs/g feces ) . Considering 2KK as reference test , the discriminating power of the CCA-ICT ( the area under the curve [AUC] = 0 . 817 ) was greater than the SEA-ELISA ( AUC = 0 . 744 ) and SWAP-ELISA ( AUC = 0 . 704 ) . Our data for the performance of the CCA-ICT in the Brazilian communities endemic for schistosomiasis mansoni support those from Africa , i . e . , in areas with greater infection prevalence and intensities , the CCA-ICT may be useful as a tool to indicate community-based preventative chemotherapy without individual diagnosis . However , because of the Brazilian Ministry of Health’s recommendation for individual diagnosis in areas where prevalence is less than 15% , i . e . , those areas in which infection intensities are likely to be lowest , the CCA-ICT lacks the sensitivity to be used as standalone diagnostic tool .
Schistosomiasis is a chronic and morbid global disease caused by the Schistosoma blood fluke that resides in the cardio-vascular system . Official estimates are that 200 million are infected [1 , 2] but it has been suggested that as many as 700 million people are afflicted , either due to active disease or as a consequence of irreversible organ damage even after drug treatment [3 , 4] The disease is transmitted by intermediate snail hosts that infest streams , irrigation systems , canals , reservoirs and ponds commonly used by communities as their source of water . Of the major species of schistosomes infecting humans , namely , Schistosoma japonicum , Schistosoma haematobium and Schistosoma mansoni , the latter is the solely responsible for disease in South America , including Brazil [5] . Treatment and control of schistosomiasis relies solely on the World Health Organization ( WHO ) recommended drug , praziquantel ( PZQ ) [1 , 6–10] . PZQ is safe , reasonably effective , cheap to produce and administer , and quickly ameliorates morbidity [10–15] . Efforts to make PZQ more widely available through increased production and expanded treatment programs e . g . [6 , 16 , 17] are increasing , perhaps most notably demonstrated by the 2012 ‘London declaration’ whereby major pharmaceutical company , non-governmental organizations and philanthropic foundations pledged to make PZQ and other anthelmintic drugs more widely available ( http://www . unitingtocombatntds . org/ ) . As PZQ administration increases , the incidence and prevalence of schistosomiasis are expected to decrease . Of concern , however , is that further progress to drive prevalence and incidence to zero is hindered by the insensitivity of the current ‘gold standard’ , diagnostic Kato-Katz ( KK ) test to detect and facilitate treatment of intestinal schistosomiasis ( caused by S . mansoni and S . japonicum ) [18–20] . The KK test employs thick smears of feces ( standardized to 41 . 7 mg on glass slide templates ) and has a theoretical sensitivity of 24 eggs per gram ( epg ) of feces for a single slide . The test has worked effectively for 40 years in reducing global prevalence rates [21] . However , the principal and well known drawback of the KK test , particularly when employed according to the standard WHO format of two smears per stool [22] for community-based diagnosis and treatment , is its increasing insensitivity to diagnose what the WHO [22] stratifies as ‘moderate’ ( 100–399 epg ) and ‘light’ ( <100 epg ) infection intensities [23–28] , which will become more widespread due to expanded and more intense de-worming programs . Factors contributing to poor sensitivity include fecal stool consistency , and intra-specimen and day-to-day variation in fecal egg counts [23 , 29–33] . Consequently , infection prevalence is often seriously under-estimated due to missed diagnoses of infection [34 , and references above] . Also , assessment of therapeutic efficacy post-treatment with PZQ can be confounded . Apart from the key issue of poor sensitivity , the KK test is a skill-intensive technique that requires time and trained personnel in the field , as well as microscopes and associated equipment [35] . Finally , and under-appreciated are the costs associated with the KK test , at least in the context of sub-Saharan Africa , which are between US$ 1 . 73 and US$ 6 . 89 [21 , 36–37] . Together , these detracting features of the KK test complicate any move from the modality of disease ( morbidity ) control to one of elimination . There is , therefore , a pressing need for a field-applicable , reliable and sensitive diagnostic tool . Immunodiagnosis is generally more sensitive than examination of stool , particularly in low transmission areas in which infection intensities are light [6] . Typical antibody-detection assays utilize crude antigen extracts such as schistosome egg antigen ( SEA ) or soluble adult worm antigen preparation ( SWAP ) . However , parasite-specific antibodies can remain for years after the infection has been cleared . As a result , such assays are unable to distinguish between current and previous infections . Also , antibody levels in serum do not necessarily correlate with infection intensity as determined by epg feces [38] . Over the last several years in sub-Saharan Africa , major efforts , not least the SCORE initiative funded via the Bill and Melinda Gates Foundation [39] and references therein , have focused on understanding the utility of a rapid immunochromatographic test ( ICT ) to diagnose schistosomiasis and perhaps replace the KK test . This ICT , which is commercially available ( Rapid Medical Diagnostics , Pretoria , RSA; http://www . rapid-diagnostics . com/ ) , measures a parasite-derived ‘circulating cathodic antigen ( CCA ) ’ in the urine of patients infected with S . mansoni . The recent data ( referenced below ) suggest that the CCA-ICT performance is good enough to warrant its application as a standalone tool in surveying disease prevalence and risk , and , thus , facilitate the deployment of PZQ therapy at the community level . Our report measures the performance of the CCA-ICT in a sample study population comprising residents from non-endemic and endemic areas for schistosomiasis mansoni in Minas Gerais state , Brazil . We compared the performance of the CCA-ICT with the SEA-ELISA and SWAP-ELISA . From the data arising , we interpret the potential utility of the CCA-ICT in the context of how chemotherapy of schistosomiasis is promoted in Brazil .
Ethical clearance for this study was obtained from the Ethical Research Committee of Universidade Vale do Rio Doce-UNIVALE ( PQ 019/08-11 ) . District health , participants and parents/legal guardians were informed about the purpose and procedures of the study . Parent/legal guardians provided written informed consent for their children to participate . Participation was voluntary and individuals could withdraw at any time and without further obligation . All parasitological results were coded and treated confidentially . Any individual who was determined to be positive for schistosomiasis , soil-transmitted helminthiases or intestinal protozoal infections was treated with the appropriate dose of PZQ , albendazole or metronidazole , respectively , produced and distributed by the Brazilian Ministry of Health ( Farmanguinhos , Oswaldo Cruz Foundation , Jacarepaguá , RJ , Brazil ) . The study was conducted from August 2012 to December 2013 in Governador Valadares and Manhuaçu , two municipalities in Minas Gerais state , Brazil ( Fig 1 ) . We enrolled 130 residents in non-endemic and endemic areas for schistosomiasis mansoni . The study design was based on a judgmental sampling using a non-probabilistic approach . Stool and urine samples were collected on the same day . As described below , parasitological stool examination by the KK [20] and Hoffmann-Pons-Janer ( HPJ ) tests [40] was performed immediately and the urine samples were frozen for subsequent analysis by CCA-ICT by a separate laboratory team that was blinded to the results of the KK/HPJ tests . Upon data mining , the KK results were categorized into three study groups; individuals resident in non-endemic areas who were negative on 2 KK slides ( “2KK-NEG non-endemic area”; n = 41 ) , individuals resident in endemic areas who were negative on 2 KK slides ( “2KK-NEG endemic area”; n = 41 ) and individuals resident in endemic areas who were positive for S . mansoni eggs on at least 1 of 2 slides ( “2KK-POS endemic area”; n = 48 ) . The 2KK-NEG endemic area individuals had a previous history of at least three negative stool tests for S . mansoni despite being exposed to sources of infection . The 2KK-NEG non-endemic subjects had no previous history of S . mansoni infection , and neither received treatment for schistosomiasis nor had contact with sources of infection . Detailed information for the study groups is presented in Table 1 . Diagnosis of S . mansoni was performed using the 2KK test ( 41 . 7 mg of stool per smear ) [20] . The intensity of infection was expressed as eggs per gram ( epg ) of feces using the arithmetic mean of the egg count obtained from the two slides multiplied by 24 . Infection intensity [22 , 41] was categorized as negative ( 0 epg ) , light ( 1–99 epg ) , moderate ( 100–399 epg ) or heavy ( ≥ 400 epg ) . Thick smears were also examined for eggs of soil-transmitted helminths ( hookworm , Ascaris lumbricoides and Trichuris trichiura ) and Enterobius vermicularis . Each stool sample was also assessed by the HPJ sedimentation method for helminth eggs and protozoa ( Entamoeba sp . and Giardia lamblia ) [40] . The examinations were performed by expert technicians working at the Research Laboratory of Immunology , Universidade Vale do Rio Doce ( UNIVALE ) . The data regarding intestinal protozoa infection was considered for sub-grouping the study population as Protozoa–and Protozoa + to evaluate the CCA-ICT performance ( Table 2 ) . Considering the very low rate of S . mansoni co-infection with other helminths ( only nine out of 130 subjects ) , the effect of other helminth infections on CCA-ICT performance was not studied further . A single urine sample from each individual was collected to test for the presence of S . mansoni CCA using the CCA-ICT . Urine samples were initially stored at -20°C then prepared as aliquots of 1 ml and frozen at -80°C before use . The CCA-ICT was performed according to the manufacturer’s instructions . Briefly , one drop of urine was added to the sample well of the ICT cassette and allowed to absorb . Then , one drop of buffer ( provided with the kit ) was added . The test score was read 20 min after adding the buffer . Positive color reactions were scored as trace ( very light band ) , weak ( + ) , medium ( ++ ) and strong ( +++ ) . Female Swiss Webster mice ( 4–6 weeks old ) were infected subcutaneously with 100 S . mansoni cercariae of the LE strain . After 45 days , the animals were sacrificed by cervical dislocation and perfused via the hepatic portal system using a 0 . 85% saline solution containing 50U/l heparin [42] . Adult worms were washed three times with 0 . 15 M phosphate-buffered saline , pH 7 . 2 , mechanically grinding ( Virtiz Precisa , Dietikon , Switzerland ) and centrifuged at 9 , 500 g for 1 h at 4°C ( Eppendorf AG , Hamburg , Germany ) . The supernatant was dialyzed using a cellulose membrane ( Sigma-Aldrich D9777 , St Louis , USA ) against 0 . 9% saline for 48 h at 4°C . The material was then centrifuged at 1 , 250 g for 15 min at 4°C and the supernatant stored at -20°C . An aliquot was measured for protein content ( Nanodrop , Thermo Scientific 2000 , USA ) in order to normalize protein concentration in the SWAP-ELISA used to detect human antibodies ( see below ) . After perfusion of infected mice , livers were removed to recover parasite eggs . The eggs were homogenized and mechanically ground ( Virtiz Precisa ) for 40 min in 0 . 85% saline . The homogenate was centrifuged at 9 , 500 g for 1 h at 4°C . After 48 h of dialysis with a cellulose membrane ( Sigma-Aldrich ) against a 0 . 9% saline solution , an aliquot of the supernatant was measured for protein content ( Nanodrop ) to normalize protein concentration in the SEA-ELISA used to detect human anti-egg antibodies ( see below ) . ELISA tests were performed according to [43] . Microtiter plates ( MaxiSorpTM Surface; NUNC , Denmark ) were sensitized with 100 μl/well of antigen solution diluted in 0 . 05 M carbonate-bicarbonate , pH 9 . 6 , for 16 h at 4°C . The plates were washed three times with washing buffer ( 0 . 15 M PBS , pH 7 . 2 , 0 . 05% Tween 20 ( LGC Biotecnologia , BR ) . Non-specific sites were blocked with 10% fetal bovine serum in washing buffer at 37°C for 1 h . After washing three times , 100 μl of serological sample diluted in PBS were added and the plates incubated at room temperature for 1 h . After washing three times , plates were incubated at room temperature for 1 h with conjugated anti-IgG human peroxidase ( Sigma-Aldrich A0170 Lot: 062m4819 , St . Louis , USA ) diluted in washing buffer . After washing three times , 100 μl of substrate 3’ , 3’ , 5 , 5-tetramethylbenzidine ( TMB/H2O2 Invitrogen T0440 , St Louis , USA ) were added to each well . The reaction was stopped after 20 min of incubation in the dark by addition of 50 μl/well of 2N sulfuric acid . Absorbance was measured at 450 nm in a microplate reader ( Model 3550 , Bio-Rad Laboratories , Tokyo , Japan ) . Each serum sample was used in duplicate in two separate assays . Average levels of optical density ( OD ) were determined from the quadruplicate measurements . For the SWAP-ELISA , 1 μg/ml of SWAP was employed . Serum samples were diluted 1:50 and the conjugated secondary antibody was diluted 1:60 , 000 . For the SEA-ELISA , 3 μg/ml of SEA was employed . Serum samples were diluted 1:150 and the conjugated secondary antibody was diluted 1:40 , 000 . We used a judgmental sampling and a non-probability selection approach . Data were analyzed using GraphPad Prism software 5:03 , STATA version 13 ( College Station , Texas , USA ) and SPSS , software version 11 . 5 . Differences were considered significant when α was 0 . 05 . The relationship between the intensity of infection determined by the 2KK test and the CCA-ICT results was examined by the Spearman correlation test . The chi-square test was used to analyze categorical variables . The power of association ( the degree of agreement ) between the 2KK and CCA-ICT was evaluated using Kappa ( k ) statistics where k <0 indicates an absence of agreement; k = 0 to 0 . 2 is poor concordance; k = 0 . 21 to 0 . 4 is weak agreement; k = 0 . 41 to 0 . 6 is moderate agreement , k = 0 . 61 to 0 . 8 is good agreement and k = 0 . 81 to 1 . 0 means excellent agreement [44 , 45] . The performance criteria of the CCA-ICT were sensitivity and specificity , positive and negative predictive values , positive and negative values of likelihood ( LR ) and the area under the curve ( AUC ) . Cutoff estimates using the technical TG-ROC ( Two-Graph Receiver Operating Characteristic ) [46] and the ROC [47] were developed using the MedCalc , version 7 . 7 . 0 . 0 ( Alexandria , USA ) . The AUC indicates the probability of accurately identifying true positives , where one could distinguish between non-informative ( AUC = 0 . 5 ) , less accurate ( 0 . 5<AUC≤0 . 7 ) , moderately accurate ( 0 . 7<AUC≤0 . 9 ) , highly accurate ( 0 . 9<AUC<1 ) and perfect tests ( AUC = 1 ) [48] . Data from the 2KK test were used as the primary reference standard for diagnostic comparisons , and data from a combination of the 2KK test , SWAP-ELISA and SEA-ELISA served as the alternative reference test .
The CCA-ICT results were first categorized based on the adjudicated visual scores as represented in Fig 2A , ranging from negative through trace , weak ( + ) and medium ( ++ ) to strong ( +++ ) . Of the “2KK-NEG non-endemic area” samples , 78% were negative by the CCA-ICT whereas 22% were either trace or weakly positive . For the “2KK-POS endemic area” samples , 14% were negative by the CCA-ICT , and 17% ( 8/48 ) , 25% ( 12/48 ) , 17% ( 8/48 ) and 27% ( 13/48 ) were adjudicated as trace , weak , medium and strong , respectively ( Fig 2B ) . ROC curve analysis indicated that the trace score should be considered negative in order to segregate the “2KK-NEG non-endemic area” from the “2KK-POS endemic area” ( Fig 2C ) . Sensitivity ( SS ) and specificity ( SP ) were 68 . 7% ( 95% confidence interval ( CI ) : 53 . 7%-81 . 3% ) and 97 . 6% ( CI: 87 . 1%-99 . 6% ) , respectively , and the positive ( PPV ) and negative predictive values ( NPV ) were 97 . 1% ( CI: 84 . 7%-99 . 9% ) and 72 . 7% ( CI: 59%-83 . 9% ) , respectively . The value for the area under the ROC curve ( AUC ) , indicating how likely the CCA-ICT will make a correct diagnosis , was 0 . 832 ( CI: 0 . 761–0 . 902; Fig 2C ) . Fig 3A shows the distributions of the CCA-ICT results when ‘trace’ was considered a negative result . The performance indices demonstrated that the CCA-ICT showed a similar positivity for the “2KK-NEG non-endemic area” and the “2KK-NEG endemic area” ( 2 . 4% and 4 . 8% , respectively ) with specificities of 97 . 6% and 95% , respectively . For the “2KK-POS endemic area” , the CCA-ICT falsely categorized 31% as uninfected ( 15/48: 7 negative and 8 trace responses ) . Fig 3B shows the association between the CCA-ICT scores for “2KK-POS endemic area” and the infection intensity classified by mean egg counts ( epg ) according to the WHO [21] . Interestingly amongst the “2KK-POS endemic area” persons displaying negative results in the CCA-ICT , 67% ( 10/15 ) and 33% ( 5/15 ) harbored light and moderate infections , respectively . For those “2KK-POS endemic area” with positive CCA-ICT , 33 . 3% ( 11/33 ) , 42 . 4% ( 14/33 ) and 24 . 2% ( 8/33 ) harbored light , moderate and heavy infections , respectively . A significantly positive correlation between the CCA-ICT data and intensity of infection was identified ( r = 0 . 6252 , p = 0 . 0001; Fig 3C ) . The strength of association between CCA-ICT ( positive and negative data ) and 2KK test was also evaluated by the Kappa coefficient . A value of 0 . 663 ( p<0 . 0001 ) was determined indicating a satisfactory agreement between the tests . As demonstrated above , 31% of “2KK-POS endemic area” individuals were misclassified as uninfected ( false negatives ) by the CCA-ICT when ‘trace’ was considered a negative result . Two-graph receiver operating characteristic ( TG-ROC ) analysis was , therefore , performed to recalculate the cutoff in order to enhance sensitivity . An advantage of the TG-ROC curve approach over the ROC curve is the more direct reading of the value of a cutoff point associated with a specific combination of sensitivity and specificity . The changes in the CCA-ICT performance when the TG-ROC curve cutoff was shifted from ‘trace’ considered as negative to 'trace' considered as positive are shown in Fig 4A and 4B , respectively . Sensitivity increased from 68 . 7% to 85 . 4% ( CI: 72 . 2%-93 . 9% ) but specificity dropped from 97 . 6% to 78% ( CI: 62 . 4%-89 . 4% ) . The AUC maintained a moderate power of discrimination ( 0 . 817; CI: 0 . 736–0 . 899 ) ( Fig 4C ) , whereas PPV and NPV were revised downwards and upwards , respectively ( compare Figs 4C and 2C ) . Shifting the TG-ROC curve cutoff from ‘trace’ to ‘negative’ resulted in fewer false negatives ( 7 or 14 . 6%; Fig 4D ) . It was also observed that among the seven misdiagnoses by the CCA-ICT in the “2KK-POS endemic area” , five had light infections and two had moderate infections ( See Fig 3B ) . Interestingly , the cutoff shift led to an increase in false positives among the “2KK-NEG non-endemic area” ( 9/41 ) and “2KK-NEG endemic area” ( 5/41 ) . This result prompted us to investigate a possible association with intestinal protozoan parasites . We compared the CCA-ICT scores from people infected with intestinal protozoa with individuals who were negative . The percentage of CCA-ICT positive results was not significantly different based on intestinal protozoa status , neither from “2KK-NEG endemic area” ( p = 0 . 3499 ) nor “2KK-NEG non-endemic areas” ( P = 0 . 1000 ) , suggesting that intestinal protozoa infections do not influence the urine CCA-ICT results ( Table 2 ) . The association between the CCA-ICT with serum antibody reactivity detected by SEA-ELISA and SWAP-ELISA is presented in Fig 5 . At first , the ROC curve analysis for SEA-ELISA ( Fig 5A ) and SWAP-ELISA ( Fig 5B ) was carried out to estimate the cutoff and performance indices ( sensitivity , specificity , PPV , NPV and AUC ) using the “2KK-NEG non-endemic area” and “2KK-POS endemic area” as reference groups . The cutoffs of OD = 0 . 592 for the SEA-ELISA and OD = 0 . 355 for the SWAP-ELISA along with the respective AUC values of 0 . 744 ( CI: 0 . 654–0 . 834 ) and 0 . 704 ( CI: 0 . 615–0 . 793 ) were obtained . These AUC values were lower that obtained with the CCA-ICT ( AUC = 0 . 817 ) , when ‘trace’ was considered positive ( Fig 4C ) . Upon analyses of the SEA-ELISA and SWAP-ELISA results , the study groups were redefined and referred to as “2KK SEA SWAP NEG” ( n = 18 ) and “2KK SEA SWAP POS” ( n = 38 ) . For these analyses , the ‘trace’ was considered positive . Analysis of the data ( Fig 5C ) demonstrated that in 2KK SEA SWAP NEG , 27 . 8% ( 5/18 ) of subjects were classified as CCA-ICT positive , a proportion similar to the 22% obtained for the “2KK-NEG non-endemic area” ( Fig 4D ) . Moreover , the CCA-ICT diagnosed as negative 13 . 2% ( 5/38 ) of subjects in the 2KK SEA SWAP POS , a proportion of false negatives similar to the 14 . 6% previously obtained for 2KK-POS endemic area ( Fig 4D ) .
The expansion and acceleration of schistosomiasis control programs that rely on PZQ have successfully decreased the prevalence and the intensity of infections [41 , 49] . However , further gains are threatened by the low sensitivity of the current ‘gold-standard’ KK method [50–52] , especially in light of findings that recurrent reinfection with low intensity infections can still lead to persistent morbidity [41 , 53–54] . Accurate and sensitive case detection is , therefore , paramount to controlling and ultimately eliminating schistosomiasis . Additional critical factors include time-to-result so that patients are not lost to the necessary treatment , and cost , which must be low enough to be incorporated into existing control programs [7 , 55] . A major challenge in schistosomiasis diagnosis is the lack of true gold-standard test , i . e . , a test with 100% specificity and 100% sensitivity . It is well known that the standard KK test may have a sensitivity as low as 25% in the low transmission setting [64] . An ideal situation would be to have different “gold standards” . For a more accurate sensitivity assessment , the “gold standard” would be repeated KK slides . In this context , considerable effort has been made to develop alternative diagnostic tools . As highlighted in the introduction , the CCA-ICT has been heavily scrutinized in Africa as an alternative to the KK test . In the present investigation , we have studied the performance of CCA-ICT in non-endemic and endemic areas for schistosomiasis mansoni located in two municipalities in Minas Gerais state , Brazil . The CCA-ICT requires visual interpretation and scoring , and when the color reaction is scored as ‘trace’ determining a true cutoff for a negative diagnosis is ambiguous , as noted in previous African studies [56–59] . The issue is important , as sensitivity and specificity are influenced by the interpretation of ‘trace’ as either a positive or negative diagnosis . In order to resolve the matter , we employed ROC curve analysis to understand how the adjudication of the cutoff impacts sensitivity and specificity . We found that when a trace score was considered as a positive diagnosis of infection , the CCA-ICT sensitivity increased resulting in fewer false negative cases . Although this interpretation increases the number of false positives , it is still preferable to missing the diagnosis of infected individuals , which can sustain both transmission of the disease and morbidity . Trace positive reactions may indicate infected individuals who are not ( yet ) passing eggs in stool , low intensity infections , re-infection after treatment [60] or incomplete clearance of the parasite by PZQ . Although the capture of false positives for treatment isn’t a concern given PZQ’s excellent safety profile , the use of an imperfect diagnostic criterion may incur higher costs as a result of administering unwarranted treatment [58–59 , 61–62] . In Africa , the latest data for the CCA-ICT suggest that it performs at least as well as the KK test , applied in the WHO standard format or variations thereof [39 , 57 , 63–65] . As the drug delivery strategy most often employed in sub-Saharan Africa is preventative chemotherapy ( PCT ) , whereby PZQ treatment is offered at the community level without recourse to individual diagnosis , the recommendations are that the CCA-ICT could replace the KK test . In Brazil , by contrast , individual diagnosis is recommended by the Brazilian Ministry of Health prior to administration of drug therapy [66–67] and here the concern for the CCA-ICT is the number of missed diagnoses ( false negatives ) , among those who are lightly or moderately infected , either when the trace CCA-ICT score is considered as negative ( 31% false negatives ) or positive ( 14 . 6% false negatives ) based on the particular ROC curve analysis implemented . Our data for false negative rates are consistent with those reported for the CCA-ICT among those with light and moderate infection intensities in Africa , e . g . , approximately 26% in pre-school-age children [64] , and 12–18% [63] , 23% [68] and 10–44% in school-age children [69] ( See S1 Table for a data synopsis of CCA-ICT performances in previous studies ) . Our data are also consistent with reports demonstrating that , like the KK test , the CCA-ICT is less reliable in detecting low intensities of infection [39 , 59 , 61 , 65 , 70] . We found that , among the seven “2KK POS endemic area” adjudicated as negative by the CCA-ICT , five had low and two had moderate intensities of infection . For those with light infection intensities , fluctuations between positive and negative status for both the 2KK and CCA-ICT were noted with the conclusion that more than one urine or stool sample should be collected on different days to increase the KK and CCA-ICT agreement [71] . Also , it is possible that genetic variability in S . mansoni and its impact on CCA may contribute to the differences in the performance of CCA-ICT [57 , 72] . Overall , the significant chance of missed diagnoses by the CCA-ICT would lessen its attractiveness as standalone tool for individual diagnosis: the KK test , in some variation , would still have to be employed or perhaps a more rigorous combination of different tests . This in turn adds effort and cost to any diagnostic intervention . In spite of the unreliability of the CCA-ICT to detect light infections , there was a significant positive correlation between the CCA-ICT scores and the 2KK test by both the Spearman rank test ( r = 0 . 625 ) and the Kappa test ( κ = 0 . 663 ) . Certainly , for heavy infections intensities at or over 400 epg , all of the CCA-ICT scores were either strong ( +++ ) or medium ( ++ ) . This suggests that the CCA-ICT has little difficulty in identifying those who are heavily infected . The association identified in the current Brazilian setting between the strength of the CCA-ICT scores and infection intensity as judged by the 2KK test has been noted in Africa [59 , 61 , 63–65 , 68–69 , 73–74] with the suggestion that the CCA-ICT is a viable alternative to the 2KK test in areas of moderate to high prevalence of S . mansoni infection . Of interest is our finding of positive CCA-ICT scores among nine of the 41 subjects in the “2KK-NEG non-endemic area” group . In spite of our best efforts to ensure that we were working with a non-endemic group , it is nonetheless possible that these individuals harbored light infections or perhaps single-sex or pre-patent infections . Absenting these considerations , several hypotheses have been put forward to explain the false positivity of the CCA-ICT . According to Van Dam et al . [75–76] , there is the possibility of non-specific cross-reactivity with Lewis-X tri-saccharide epitopes in inflammatory biomarkers present in circulating granulocytes . Lewis-X determinants have also been identified in nematode parasites [77] . On this particular point , our data could not offer an explanation as only nine of the 130 subjects were infected with other helminths . We did ascertain , however , that the accuracy of the CCA-ICT was not influenced by the presence or absence of intestinal protozoa . Using the 2KK test as the reference , the sensitivity ( 85 . 4% ) , NPV ( 82 . 1% ) , specificity ( 78% ) and PPV ( 82% ) of the CCA-ICT ( Fig 4C ) were comparable to the results reported in the literature , e . g . , a range of 81 . 4 to 91% for sensitivity , 84 . 0 to 95 . 5% for NPV , 47 to 81 . 0% for specificity and 36 to 84 . 0% for PPV [58–59 , 61 , 70] ( S1 Table ) . Similar data for sensitivity and specificity were also recorded when comparing data from multiple-CCA-ICT and stool samples or a combination of several diagnostic methods were used in order to determine the “real” status of infection [39 , 62–63 , 65 , 74 , 80] ( S1 Table ) . The efficacy of the CCA-ICT was also compared with the performance of the SEA-ELISA and SWAP-ELISA which measure antibody and are considered the most sensitive techniques to diagnose exposure to schistosomiasis [43 , 78–79] . However , the major drawback of antibody-based assays is their inability to differentiate between past and current infections which make these tests inadequate to assess disease prevalence in populations that have received treatment [38] . Our ROC curve analyses showed that the discriminative power of the CCA-ICT ( AUC = 0 . 817 ) was greater than either the SEA-ELISA ( AUC = 0 . 744 ) or the SWAP-ELISA ( AUC = 0 . 704 ) . Even when combining the results of the 2KK , SEA and SWAP assays ( AUC = 0 . 795 ) , the CCA-ICT maintained a better performance . In this regard , the advantages of the quick and easy-to-use CCA-ICT for POC diagnosis over expertise-driven and lab-based tests are abundantly clear . The exceptions to the Brazilian Ministry of Health’s recommendation of individual diagnosis prior to treatment are in areas where prevalence rates are between 15 and 25% ( household co-inhabitants may receive treatment without diagnosis ) and above 25% whereby community therapy may be administered to school children and preschoolers without prior diagnosis [66] . In these settings , the CCA-ICT could provide added value given its ease of use , robustness and assuming that the additional cost can be accommodated into the health system infrastructure . However , the long-standing Brazilian Ministry of Health’s strategy to drive prevalences below 25% [66 , 67 , 81] is proving successful [66 , 82–84] such that individual diagnoses will be increasingly required . In these settings the value of the CCA-ICT as the sole diagnostic test is likely to be less . | Detecting parasite eggs in stool by the Kato-Katz ( KK ) stool smear is the standard diagnostic test for infection with the flatworm parasite , Schistosoma mansoni . However , the test can miss those who have low burdens of infection , i . e . , with few eggs in their feces . These misdiagnosed individuals , therefore , do not receive drug treatment and can continue to transmit the parasite into the environment putting the community at risk of infection . As an alternative diagnostic approach , the circulating cathodic antigen immuno-chromatographic test ( CCA-ICT ) is a simple-to-use handheld device ( similar to a pregnancy test ) that only needs urine to provide a quick and visual indication of whether one is infected or not . The consensus from studies in Africa is that the CCA-ICT could replace the KK test in those areas where people are more likely to harbor moderate to high worm burdens ( i . e . , more eggs in stool ) , but , like the KK test , it can miss those harboring light infection intensities . We evaluated the CCA-ICT performance in urine samples from 130 individuals living in areas non-endemic and endemic for schistosomiasis mansoni within the municipalities of Governador Valadares and Manhuaçu , Minas Gerais state , Brazil . The CCA-ICT performance characteristics , chiefly , sensitivity and specificity , depended on whether a ‘trace’ visual reading of the test was considered as a positive or negative diagnosis . We noted a positive correlation between the CCA-ICT scores and egg counts . However , the CCA-ICT misdiagnosed as negative about 15% of KK positive individuals , predominantly those with light infections . The CCA-ICT , nonetheless , had better discriminating power than commonly used antibody-based tests . We conclude that the CCA-ICT offers reasonable performance to diagnosis S . mansoni infection . However , in areas where infections intensities are light , the test lacks the sensitivity to be used as standalone diagnostic tool . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2016 | Evaluation of the CCA Immuno-Chromatographic Test to Diagnose Schistosoma mansoni in Minas Gerais State, Brazil |
T cells adopt a polarized morphology in lymphoid organs , where cell-to-cell transmission of HIV-1 is likely frequent . However , despite the importance of understanding virus spread in vivo , little is known about the HIV-1 life cycle , particularly its late phase , in polarized T cells . Polarized T cells form two ends , the leading edge at the front and a protrusion called a uropod at the rear . Using multiple uropod markers , we observed that HIV-1 Gag localizes to the uropod in polarized T cells . Infected T cells formed contacts with uninfected target T cells preferentially via HIV-1 Gag-containing uropods compared to leading edges that lack plasma-membrane-associated Gag . Cell contacts enriched in Gag and CD4 , which define the virological synapse ( VS ) , are also enriched in uropod markers . These results indicate that Gag-laden uropods participate in the formation and/or structure of the VS , which likely plays a key role in cell-to-cell transmission of HIV-1 . Consistent with this notion , a myosin light chain kinase inhibitor , which disrupts uropods , reduced virus particle transfer from infected T cells to target T cells . Mechanistically , we observed that Gag copatches with antibody-crosslinked uropod markers even in non-polarized cells , suggesting an association of Gag with uropod-specific microdomains that carry Gag to uropods . Finally , we determined that localization of Gag to the uropod depends on higher-order clustering driven by its NC domain . Taken together , these results support a model in which NC-dependent Gag accumulation to uropods establishes a preformed platform that later constitutes T-cell-T-cell contacts at which HIV-1 virus transfer occurs .
One of the primary natural targets of HIV-1 is the T cell . HIV-1 spread between infected and uninfected T cells likely occurs frequently in densely packed environments such as lymph nodes in vivo . Two-photon imaging studies have shown that a majority of T cells in lymph nodes are highly motile and have a polarized morphology [1] , [2] , [3] , [4] , [5] , [6] . Therefore , it is likely that , in lymphoid organs , HIV-1 replicates within and is transmitted by polarized T cells . However , the life cycle of HIV-1 in polarized T cells has not been examined in detail . HIV-1 assembly occurs at the plasma membrane and is driven by the HIV-1 polyprotein Gag . Gag is the primary structural protein of retroviruses , including HIV-1 , and is both necessary and sufficient for formation of virus-like particles [7] . HIV-1 Gag is composed of four structural domains: matrix ( MA ) , capsid ( CA ) , nucleocapsid ( NC ) and p6 . MA mediates Gag targeting and binding to the plasma membrane , primarily through the myristoyl group on the N terminus of MA , which inserts into the plasma membrane , as well as MA basic amino acids that interact with phosphatidylinositol-4 , 5-bisphosphate [PI ( 4 , 5 ) P2] , a plasma-membrane-specific phospholipid [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] . CA mediates Gag dimerization through an interface in its C terminal domain ( CTD ) , in which amino acids W184 and M185 play key roles [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] . NC binds specifically to the viral genomic RNA , which is essential for packaging viral genomes into virions [34] . In addition , NC contributes to multimerization of Gag , whereby RNA is thought to serve as a scaffold [21] , [25] , [28] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] . p6 contains peptide sequences that recruit cellular endosomal sorting complex required for transport ( ESCRT ) proteins , which facilitate the release of virus particles [44] , [45] , [46] . A polarized T cell forms a leading edge at the front and a protrusion called a uropod at the rear [47] , [48] , [49] . There are several proteins known to be enriched in the uropod , including intercellular adhesion molecule ( ICAM ) -1 , -2 , and -3 , P-selectin glycoprotein ligand ( PSGL ) -1 , CD43 , and CD44 [50] , [51] . The microtubule organizing center ( MTOC ) is also known to localize to the base of the uropod [49] , [52] . Previous studies have observed that in T cells and monocytes , HIV-1 proteins localize to a cell protrusion , which resembles a uropod [53] , [54] , [55] , [56] , [57] . Furthermore , virus particles are enriched in several uropod-associated proteins , such as ICAM-1 , ICAM-2 , CD43 and CD44 [58] , [59] . A raft-associated lipid known to localize to uropods , GM1 [60] , also associates with virus particles [54] , [61] , [62] . Altogether , these observations suggest that uropods potentially serve as sites of virus assembly in polarized T cells . However , the nature of Gag localization in polarized T cells and its significance to virus spread have yet to be fully determined . T cell uropods have been shown to mediate contact between T cells and other cells , which is consistent with the observation of adhesion molecule enrichment in uropods [63] , [64] , [65] . Thus , it is possible that HIV-1 accumulation at the uropod may play a role in cell-to-cell transmission . Cell-to-cell transmission is ten to several thousand times more efficient than cell-free transmission [53] , [57] , [66] , [67] , [68] , [69] , [70] , [71] . Recent studies have described specific cell contact structures that facilitate cell-to-cell transmission [67] , [72] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] , [81] , [82] , [83] , [84] , [85] , [86] . Live cell imaging studies have revealed that particles of HIV-1 and murine leukemia virus ( MLV ) are transferred from infected cells to uninfected target cells along the surface of filamentous extensions called membrane nanotubes and cytonemes [80] , [81] , [82] , [87] , [88] . Virological synapses ( VS ) , which appear to structurally resemble immunological synapses [77] , [78] , [89] , [90] , [91] , [92] , [93] , [94] , [95] , [96] , [97] , are also thought to facilitate the direct transfer of budding virus particles from one cell to another [53] , [67] , [71] , [78] , [80] , [95] . However , the mechanisms leading to the establishment of these transmission routes , especially the VS , remain to be elucidated . In this study , we determined unambiguously that the uropod is the cell structure to which membrane-associated Gag accumulates in polarized T cells . Gag-containing uropods mediated frequent contact with uninfected target cells . Virtually all observed VS , defined by accumulation of CD4 and Gag to cell contacts , showed enrichment of the uropod marker CD43 , suggesting a major role for HIV-1 localization to the uropod in virus spread . Consistent with this possibility , upon disruption of uropod formation , cell-to-cell transfer of HIV-1 was significantly reduced . Gag on the cell surface copatched strongly with uropod markers not only in polarized T cells but also in non-polarized T cells . Gag-containing patches dispersed on the membrane of non-polarized cells appeared to laterally move and concentrate at the uropod when cells became polarized . These patches maintained colocalization with uropod markers , suggesting that uropod-directed microdomains play a role in polarized Gag localization . Uropod localization of Gag required higher-order multimerization or clustering mediated by NC . These findings strongly support that multimerization-dependent Gag localization to uropods represents one mechanism by which the VS is formed .
To examine Gag localization in polarized T cells , we expressed a YFP-tagged Gag ( Gag-YFP ) in either primary T cells or in a polarized T cell line , P2 . To express Gag-YFP , T cells were infected with VSV-G-pseudotyped HIV-1 that encodes Gag-YFP . Two days post-infection , cells were immunostained for uropod markers PSGL-1 or CD43 ( Figure 1A and B ) . Alternatively , the MTOC , which localizes to the base of the uropod , was detected using anti-α-tubulin ( Figure 1C ) . In both primary CD4+ T cells and P2 cells , approximately 50-60% of cells showed polarized morphology , and infection with VSV-G-pseudotyped HIV-1 did not substantially alter the percentage of polarized cells ( Table 1 ) . Primary CD4+ T cells expressing Gag-YFP showed strong colocalization of Gag on the plasma membrane with both uropod markers PSGL-1 and CD43 , as well as co-polarization with the MTOC in virtually all Gag-positive cells with uropods ( Figure 1A–C and Table 2 ) . In contrast , Gag showed segregation from LFA-1 , a non-uropod-associated protein [98] ( Figure 1D ) . Similar to primary T cells , P2 cells also showed strong colocalization of PSGL-1 and Gag-YFP , as well as co-polarization of the MTOC and Gag-YFP ( Figure 1E and F and Table 2 ) . In these cells , plasma-membrane-associated Gag was highly polarized and detected only in the uropod region ( Gag polarization was quantitatively analyzed as shown below ) . Similarly , untagged Gag detected at the plasma membrane using anti-Gag antibodies also showed strong colocalization with uropod markers ( Figure S1 ) . These results indicate that Gag localizes to uropods in polarized T cells . To determine whether uropod localization of Gag-YFP is stable , we performed live cell analysis of primary T cells expressing Gag-YFP . We observed that Gag-YFP maintains localization in the uropod during T cell migration for a minimum of almost 30 min ( Figure 1G and Movie S1 ) . To determine whether uropod-associated Gag is able to form mature particles , P2 and primary CD4+ T cells were infected with VSV-G-pseudotyped HIV-1 encoding Gag-iYFP . This Gag derivative contains YFP inserted between MA and CA and forms mature Gag proteins and free YFP upon cleavage by viral protease [99] . When cells expressing Gag-iYFP were immunostained with an anti-p17MA antibody , which only recognizes the mature , cleaved matrix domain of Gag [100] , [101] , the YFP signal was observed to colocalize substantially with p17MA signal at the uropod ( Figure 2A and Table 3 ) . We also observed that both Gag-iYFP and Gag-YFP colocalize well with HIV-1 Env in the uropod ( Figure 2B and Table 3 ) . These results suggest that at least a subset of Gag localized at uropods is capable of forming Env-containing virus particles that undergo Gag processing essential for virion maturation . It should be noted that , similar to previous studies [61] , [96] , we performed immunostaining of Env prior to fixation . Thus , the possibility of antibody crosslinking playing a role in Env localization should be considered . Uropods in uninfected T cells have been shown to mediate contact between T cells and other cells [63] , [65] . Therefore , accumulation of Gag to , and particle formation at , the uropod may facilitate cell-to-cell transmission of HIV-1 . To examine whether contact of HIV-1-infected T cells with target T cells is preferentially mediated by uropods , we performed live cell imaging experiments . Fresh target primary T cells were stained with a blue fluorescent dye , CMAC , and cocultured with Gag-YFP-expressing primary T cells . This coculture was then immunostained with anti-PSGL-1 , which had been prelabeled with Zenon AlexaFluor594 . We observed that the uropod containing Gag-YFP maintained contact with CMAC-stained T cells for over 20 min as the cells moved through the field ( Figure 3A and Movie S2 ) . These observations suggest that HIV-1-infected T cells are able to mediate stable contacts with target cells through their uropods . We next quantified the newly formed contacts between Gag-YFP-expressing primary T cells and CMAC-stained target primary T cells formed during a 3-h coculture period . An example of T cell contacts is shown in Figure 3B . We found that the majority of newly formed contacts occurs at the uropod ( Figure 3C ) , despite the average uropod constituting only approximately 25% of the total cell surface ( data not shown ) . These results indicate that Gag-containing uropods stably and preferentially form new contacts with uninfected T cells . In these experiments , when target cells are also polarized , infected cell uropods formed a similar number of contacts with both ends of target cells ( data not shown ) . It is possible that cell contacts formed by the infected cell uropods observed above actively participate in VS formation . To address this possibility , we examined localization of CD4 , which is known to accumulate to the VS on the cell surface of target cells . P2 cells infected with VSV-G-pseudotyped HIV-1 expressing Gag-CFP and Env were immunostained for CD43 and mixed with target cells prelabeled with non-blocking , FITC-conjugated anti-CD4 . After 3 h of coculture , cells were analyzed by live cell microscopy . When infected P2 cells were in contact with target SupT1 cells , CD4 on the surface of SupT1 cells accumulated to junctions formed between Gag-CFP-positive , CD43-positive uropods and target cells ( Figure 4A ) . We found during quantitative analyses that CD4 accumulation to the cell-cell junctions predominantly takes place when Gag-CFP-positive uropods , but not non-uropod regions , of infected P2 cells are in contact with target SupT1 cells ( Figure 4C ) . Such CD4 accumulation was rarely observed at junctions formed between Gag-CFP-negative or uninfected P2 cells and SupT1 cells ( Figure 4A–C ) . These results suggest that infected T cell uropods are actively involved in recruitment of CD4 to cell junctions , perhaps through accumulation of Env ( Figure 2 ) . As cell junctions enriched in viral antigens such as Gag and the HIV receptor CD4 are defined as the VS in previous studies [61] , these results support a model in which uropods or uropod-derived membrane components specifically participate in formation of the VS . In order to explore whether Gag accumulation to uropods facilitates transmission of HIV-1 , we performed a cell-to-cell virus transfer assay . In this flow-cytometry-based assay , we measured transfer of YFP fluorescence , representing virions , from infected P2 cells expressing Gag-YFP to CMTMR-stained SupT1 target cells . Similar assays have been used in previous studies for analyzing cell-to-cell virus spread [53] , [71] , [102] , [103] , [104] . Representative flow cytometry plots for control cocultures are shown in Figure 5A . In these assays , we and others have observed that binding of cell-free virions to target cells is undetectable [53] ( data not shown ) . Therefore , transfer of fluorescence represents cell-to-cell virus transfer . Consistent with previous reports [53] , we observed a significant decrease in virus transfer when cells were cocultured in the presence of an anti-CD4 antibody ( Leu3A ) that prevents CD4-Env interaction , but not an isotype control IgG ( Figure 5A and C ) . These data confirm the importance of Env in cell-to-cell transfer of HIV-1 . Using this assay , we examined effects of cell depolarization on cell-to-cell HIV-1 transfer using a myosin light chain kinase inhibitor , ML7 . As expected , treatment of Gag-YFP-expressing P2 cells with this inhibitor disrupted uropod formation and dispersed Gag-YFP on the plasma membrane ( Figure 5B ) . ML7 did not have a major impact on efficiency of VLP release by Gag-YFP calculated as the amount of virion-associated Gag as a fraction of total Gag ( Figure S2 and Text S1 ) . However , because ML7 treatment reduces protein synthesis ( data not shown ) , it was possible that any decrease in cell-to-cell virus transfer by treatment with ML7 may have arisen from reduced Gag expression instead of disruption of cell polarity . To rule out the indirect effect of protein synthesis inhibition on virus transfer , we included 10 µg/ml cycloheximide in all coculture conditions , including those treated with Leu3A and control IgG described above . As shown in Figure 5A , substantial virus transfer occurred even in the presence of cycloheximide . Finally , we observed that in the presence of cycloheximide , ML7 treatment significantly decreased cell-to-cell virus transfer ( Figures 5C and S3 ) . Together with the data showing that the uropod participates in formation of the VS ( Figure 4 ) , these results suggest that polarized localization of Gag and/or assembling particles at the uropod contributes to cell-to-cell transfer of virus particles to target cells . Because the results presented thus far suggest that Gag-laden uropods play a major role in cell-to-cell virus transmission , we next sought to elucidate the mechanism by which Gag accumulates to uropods . Gag has been shown previously to associate with microdomains , such as lipid rafts and tetraspanin-enriched microdomains ( TEMs ) [10] , [54] , [61] , [105] , [106] , [107] , [108] , [109] , [110] , [111] , [112] , and these microdomains are observed at the VS [61] , [80] , [113] , [114] . Since subsets of these microdomains are implicated in polarized localization of proteins in leukocytes [49] , [60] , [115] , [116] , [117] , [118] , it is conceivable that Gag utilizes uropod-specific microdomains for transport to the uropod . In this case , one would expect to observe copartitioning of Gag and uropod markers to the same microdomain even in unpolarized cells . A common method to test whether two proteins share a propensity for associating with the same microdomain is to test for colocalization , or “copatching” , after crosslinking with antibodies specific to each of the two proteins [119] , [120] , [121] , [122] , [123] , [124] . We used this assay to examine whether Gag-YFP associates with uropod-directed microdomains in unpolarized P2 cells . As Gag forms multimers on its own , antibody-mediated crosslinking was needed only for cell surface marker proteins that include the uropod markers PSGL-1 and CD43 and the non-uropod marker LFA-1 . Because Gag has been previously shown to colocalize with TEMs using similar methods [109] , we also included the tetraspanin CD81 in the analysis . As observed in previous reports [109] , we found that Gag copatches with CD81 ( Figure 6A ) ( correlation coefficient or CC = 0 . 46; Figure 6E ) . Relative to CD81 , however , the uropod markers PSGL-1 and CD43 copatched more extensively with Gag-YFP ( CC = 0 . 69 and 0 . 70 , respectively; Figure 6E ) . On the other hand , even though LFA-1 showed punctate localization as well , we observed a segregation of LFA-1 and Gag-YFP ( Figure 6D ) as indicated by the negative correlation coefficient ( CC = −0 . 14; Figure 6E ) . Because copatching between Gag and uropod markers was observed even in non-polarized cells ( Figure 6B and C ) , these results suggest that Gag localizes to uropod-specific microdomains prior to , and perhaps during , T cell polarization . To examine whether Gag localized at uropods had originated from the Gag-positive patches observed in morphologically unpolarized cells , we conducted live-cell microscopy of Gag-YFP-expressing P2 cells that were first depolarized by low temperature treatment prior to image recording at 37°C . In these experiments , we observed that Gag-containing patches maintained colocalization with PSGL-1 and laterally moved on the plasma membrane to the forming uropod as cells re-polarized . ( Figure 6F and Movie S3 ) . Lateral movement of Gag-YFP was also observed in cells that were not immunostained for any marker , indicating that the observed movement was not caused by antibody-mediated crosslinking ( Movie S4 ) . These observations support a model in which Gag associates with uropod-specific microdomains while establishing localization at the rear end of polarized T cells . It has been shown that Gag and Env interact with each other [125] , [126] , [127] , [128] , [129] , [130] , [131] . Furthermore , it has been shown that Env is required for the formation of virological synapses between infected and uninfected T cells [53] , [61] , [67] , [68] , [78] , [80] , [89] , unlike those formed between uninfected T cells and infected macrophages [75] . Therefore , Env may play an active role in Gag localization to uropods . To address this possibility , we examined T cells expressing an HIV-1 molecular clone that encodes Gag-YFP but not Env ( KFS/Gag-YFP ) . In these cells , Gag-YFP co-localized strongly with PSGL-1 and copolarized with the MTOC at the uropod ( Figure 7A ) , just as observed in cells expressing both Gag-YFP and Env ( Figure 1 ) . To examine microdomain partitioning , we also performed copatching assays for KFS/Gag-YFP and uropod markers in unpolarized cells . We observed that KFS/Gag-YFP copatches with the uropod markers PSGL-1 and CD43 ( Figure 7B ) at comparable levels to wild type ( data not shown ) . We also compared Gag polarization indices between cells expressing Gag-YFP in the presence ( Gag-YFP ) or absence ( KFS/Gag-YFP ) of Env . The Gag polarization index describes the extent of Gag distribution along the plasma membrane from one cell pole to the other ( see Materials and Methods ) . A lower index represents stronger polarization . We observed that the polarization index for KFS/Gag-YFP is nearly identical to that for Gag-YFP ( Figure 7C , p = 0 . 28 ) . We also found that the absence of Env had no impact on the preference for uropod-mediated contact between Gag-YFP-expressing primary T cells and CMAC-stained target primary T cells ( Fig 7D , p = 0 . 23 ) . This finding suggests that Env may not be required for initial contact formation , even while it is required for transfer of virus particles ( Figure 5 ) [53] , [132] and maintenance of cell-cell conjugates [53] , [68] , [78] , [80] . Taken together , these results indicate that HIV-1 Env is dispensable for localization of Gag to the uropod . To identify the molecular determinants of Gag that facilitate its localization to the uropod , we examined a panel of Gag mutants ( Figure 8 ) . Because MA is essential for specific targeting of Gag to the plasma membrane [11] , [12] , [100] , [133] , [134] , [135] , [136] , [137] , [138] , [139] , it is conceivable that MA also regulates specific localization of Gag to uropods . To test this possibility , we examined the effect of MA deletion on Gag localization to uropods . As MA is also essential for general membrane binding , to restore Gag membrane binding of the MA deletion mutant , we added to the N terminus of Gag a heterologous membrane binding sequence , an N-terminal 10-amino-acid sequence of Fyn kinase [Fyn ( 10 ) ] . This sequence contains acylation signals for one myristoyl and two palimitoyl groups , and fully restores Gag membrane binding in the absence of the entire MA sequence [11] . Notably , the Fyn ( 10 ) sequence by itself is not capable of targeting proteins to uropods . As shown in Figure 9A , CFP attached to the Fyn ( 10 ) sequence [Fyn ( 10 ) -CFP] localized around the entire plasma membrane . In contrast , Fyn ( 10 ) /Gag-YFP localized to the uropod in the same cell ( Figure 9A ) . These results indicate that the addition of Fyn ( 10 ) did not alter uropod localization of full-length Gag [Fyn ( 10 ) /Gag-YFP] in T cells , and that some region in Gag is required for its uropod localization . Notably , we observed that Fyn ( 10 ) /ΔMA/Gag-YFP , in which the entire MA sequence is deleted , still localized to the uropod efficiently in T cells ( Figure 9B ) . Taken together , these results indicate that Gag localization to the uropod requires sequences downstream of MA and not the MA sequence itself . The downstream sequence of MA includes CA and NC domains . During virus particle formation , these domains are known to promote the dimerization and multimerization of Gag . To examine the roles played by Gag-Gag interactions in uropod localization , we analyzed Gag derivatives with changes in either CA or NC . Because Gag multimerization defects also reduce steady-state membrane binding [28] , [42] , [140] , the Fyn ( 10 ) sequence was added to the CA and NC mutants . We first examined the plasma membrane localization of two YFP- and CFP-tagged CA mutants: an amino acid substitution mutant WM184 , 185AA ( Fyn ( 10 ) /WMAA/Gag-YFP/-CFP ) and a deletion mutant lacking the C-terminal domain ( Fyn ( 10 ) /delCA-CTD/Gag-YFP/-CFP ) . We observed previously by FRET microscopy that these CA mutants are deficient in Gag-Gag interactions in HeLa cells [28] . P2 cells were coinfected with VSV-G-pseudotyped viruses encoding derivatives of Gag-YFP or Gag-CFP , and their localization and multimerization were examined by fluorescence and FRET microscopy , respectively . WT Gag-YFP/-CFP and Fyn ( 10 ) /Gag-YFP/-CFP showed high FRET in the uropod , indicating that Gag multimers localize to uropods ( Figure 10A and B ) . Notably , both Fyn ( 10 ) /delCA-CTD/Gag-YFP/-CFP and Fyn ( 10 ) /WMAA/Gag-YFP/-CFP also showed clear localization to the uropod ( Figure 10C and D ) although , as expected , these Gag mutants displayed low FRET ( Figure 10C and D ) . The polarization index for Fyn ( 10 ) /WMAA/Gag-YFP was also nearly identical to that of Fyn ( 10 ) /Gag-YFP ( Figure 10E ) . Taken together , these results demonstrate that CA-mediated dimerization is not required for localization of Gag to the uropod . To examine the role of NC in Gag localization to uropods , we next analyzed a mutant Gag that lacks most of the NC sequence ( Fyn ( 10 ) /delNC/Gag-YFP/-CFP ) . In contrast to the MA and CA mutants that localized to the uropod , Fyn ( 10 ) /delNC/Gag-YFP/-CFP localized over the entire plasma membrane ( Figure 11A ) . An NC mutant in which 15 NC basic residues essential for RNA binding were substituted with alanine or glycine ( Fyn ( 10 ) /14A1G/Gag-YFP/-CFP ) also showed non-polarized localization ( Figure 11B ) . These results indicate that NC is required for Gag localization to the uropod . Pleitropic impacts of NC mutations on Gag assembly precluded us from obtaining interpretable results regarding the effects of these mutations on cell-to-cell transfer ( Figure S2 and Text S1 ) . As confirmed by FRET microscopy ( Figure 11A and B ) , both Fyn ( 10 ) /delNC/Gag-YFP/-CFP and Fyn ( 10 ) /14A1G/Gag-YFP/-CFP that are defective in polarized localization are also defective in Gag-Gag interaction . Therefore , it is possible that NC-mediated Gag multimerization or clustering plays a key role in Gag localization to the uropod . Alternatively , other functions of NC may facilitate Gag localization to the uropod . To distinguish between these possibilities , we examined a Gag derivative in which NC was replaced by a leucine zipper sequence ( LZ ) derived from GCN4 ( LZ/Gag-YFP/-CFP ) . Gag derivatives in which NC is replaced with this LZ sequence , which has no homology to NC , have been shown previously to multimerize efficiently [141] , [142] , [143] . We observed that LZ/Gag-YFP/-CFP localized to the uropod in a majority of cells expressing this Gag derivative and yielded a WT level of FRET ( compare Figure 11C with Figure 10A ) . Quantitative analysis of polarization indicated that LZ/Gag-YFP was not as efficiently polarized as WT , but nonetheless significantly more polarized than the NC point mutant Fyn ( 10 ) /14A1G/Gag-YFP ( Figure 11D ) . These results suggest that NC promotes Gag localization to the uropod through its ability to facilitate higher-order Gag multimerization . As the LZ sequence used above is a dimerization sequence , it would drive higher-order multimerization only in the presence of an additional dimerization motif such as CA-CTD . Thus , we hypothesized that although Fyn ( 10 ) /delCA-CTD/Gag-YFP/-CFP and Fyn ( 10 ) /WMAA/Gag-YFP/-CFP localize to uropods ( Figure 10C and D ) , in these contexts , LZ in the place of NC would be unable to promote Gag localization to uropods ( Figure 11E and F ) . Indeed , cells expressing these constructs , Fyn ( 10 ) /WMAA/LZ/Gag-YFP and Fyn ( 10 ) /delCA-CTD/LZ/Gag-YFP , showed localization of Gag over the entire plasma membrane , a pattern identical to that of the NC mutants ( compare Figure 11E and F with A and B ) . Taken together , these results demonstrate that dimerization mediated by either CA-CTD or LZ alone is insufficient for localization to the uropod . However , NC-mediated higher-order multimerization or clustering of Gag , which likely occurs even in the absence of the CA-CTD dimerization motif , is essential for localization to the uropod .
In lymphoid organs , where HIV-1 likely spreads efficiently from infected to uninfected T cells , T cells adopt a polarized morphology and are highly motile . Thus , studying HIV-1 replication in polarized T cells may help us to better understand how the virus spreads in vivo . In this study , we found that HIV-1 Gag accumulates to , and forms mature virions at , the uropod in polarized T cells ( Figures 1 and 2 ) . These observations led us to ask whether uropod localization of HIV-1 Gag plays a role in the spread of the virus . In uninfected T cells , uropods are enriched in adhesion molecules and known to mediate contact with other cells [49] . Therefore , polarized virus assembly at uropods could facilitate cell-to-cell transmission of HIV-1 . Consistent with this possibility , live cell microscopy and quantitative cell-cell contact analyses showed that HIV-1-infected cells contact target cells preferentially through their uropods ( Figure 3 ) . Furthermore , a substantial majority of Gag- and CD4-positive cell-cell contacts , which define the VS [61] , were observed where uropod-derived ( CD43-positive ) , but not non-uropod ( CD43-negative ) , regions of infected cells mediated contact with target cells ( Figure 4 ) . Consistent with these microscopy data , we observed that ML7 , a myosin light chain kinase inhibitor that blocks the polarization of T cells and formation of uropods , both dispersed Gag over the cell surface and reduced cell-to-cell transfer of virus particles significantly ( Figure 5 ) . We note that ML7-sensitive , actin-myosin-based processes besides cell polarization may affect cell-to-cell virus transfer . However , taken together , these results indicate that uropods of polarized T cells play an important role in cell-to-cell transfer of HIV-1 . Notably , bone marrow hematopoietic stem cells have been shown to mediate not only contacts with osteoblasts , but also cell-to-cell transfer of plasma-membrane-associated molecules via their uropods [144] . This process , postulated to mediate intercellular signal transfer , may be a common cell-cell communication mechanism shared among cells of the hematopoietic cell lineage , including T cells . Thus , localization of HIV-1 components to , and subsequent virus assembly at , the uropod may represent yet another example in which viruses hijack cellular processes to facilitate its own replication . It has been reported by several groups that cell-to-cell HIV-1 transmission occurs via the VS . However , the steps leading to formation of the VS are not well defined . Observations described in this study suggest that at least one path toward establishment of the VS is the accumulation of viral components and assembling virions to the uropod . Uropods could then serve as a pre-formed platform that constitutes a VS upon cell-cell contact [Figure 12B ( a ) ] . Consistent with this possibility , previous studies showed that disruption of the cytoskeleton , which also impairs cell polarity , reduces Gag accumulation to contact sites between infected and uninfected T cells [77] , [78] , [80] , [145] . It is conceivable that suppression of uropod formation or inhibition of Gag localization to uropods may account for the observed reduction of VS formation upon cytoskeleton disruption . It is important to note that our data do not preclude other modes of VS formation . For example , if morphologically unpolarized cells with dispersed Gag make contact with a target cell , Gag could re-localize laterally to the contact site . Consistent with such Gag movement , a recent imaging study showed that most cell conjugate formation precedes Gag redistribution when apparently unpolarized Jurkat cells were used as donor cells [67] . Such lateral movement could also occur in polarized cells that initially contact target cells through a non-uropod region of the cell [Figure 12B ( b ) ] . Movement of Gag-containing patches to contact sites has been observed in recent VS studies [67] , [80] . It is possible that these patches may have originated at the uropod , although this point remains to be determined by long-term live cell monitoring of polarized T cells . Thus , in either mode of VS formation , prior formation of a platform enriched in Gag and other viral components , which takes place at the uropod , may be an important first step in cell-to-cell virus transfer . Our data support that polarized localization of Gag to the uropod plays an important role in HIV-1 spread . If so , what drives localization of Gag and virus assembly to uropods ? Previous studies have shown that some cell-surface proteins localize to the uropod upon antibody crosslinking through an undefined mechanism [146] , [147] , [148] . As dimerization and multimerization can be considered to be a form of crosslinking , we examined whether Gag localization to uropods similarly depends on Gag multimerization . While CA dimerization mutations did not alter localization of Gag to the uropod ( Figure 10 ) , mutations that disrupt higher-order multimerization mediated by NC-RNA interactions did ( Figure 11 ) . Mutations in NC caused Gag to localize over the entire plasma membrane despite the presence of the CA dimerization interface . Furthermore , a heterologous dimerization sequence , LZ , restored the uropod localization of NC-deleted Gag . Finally , this LZ-dependent localization required the intact CA dimerization interface , supporting the importance of higher-order Gag multimerization . Therefore , although both CA dimerization and NC-RNA interaction are important for Gag assembly , it is the NC-dependent higher-order multimerization that is essential for Gag localization to the uropod . In this regard , uropod localization of Gag may be driven by a mechanism similar to the one targeting multimerizing proteins to endosome-like domains reported recently to exist on the plasma membrane[149] . The nature of the NC-dependent higher-order multimer directed to uropods remains to be elucidated; however , as CA dimerization mutants that did not yield substantial FRET signals still localized to uropods ( Figure 10 ) , it is likely that the uropod targeting process does not require the NC-dependent multimer to be in a highly aligned and packed form . As NC by itself can bind RNA in the absence of CA [150] , we speculate that Gag clustering through binding to the same RNA molecule is sufficient for localization to uropods . Protein-protein interactions , which include clustering or multimerization of membrane proteins , are known to stabilize the microdomains with which those proteins associate [151] . In this study , we showed that Gag copatches moderately with CD81 and strongly with uropod markers PSGL-1 and CD43 even in non-polarized cells ( Figure 6 ) . We also observed , using live cell analysis , that Gag patches move laterally on the cell surface of unpolarized cells and accumulate at the forming uropod as cells polarize . These results support a model in which Gag , a multimerizing protein , associates with uropod-specific microdomains that carry Gag to the uropod . However , the mechanism by which these microdomains localize to the uropod remains unclear . It is important to note that not all types of microdomains are destined for the uropod . GM3-containing lipid rafts have been shown to localize to the leading edge [60] , [118] , [152] . Therefore , it is likely that there are complex sorting mechanisms by which specific subsets of microdomains are moved to the uropod . In this regard , it is of note that although LFA-1 behaves as a leading edge/non-uropod marker in T cells in suspension [153] ( this study ) , this adhesion molecule redistributes to mid-cell and uropod regions upon contact with ICAM-1-containing surfaces [154] , [155] . Therefore , LFA-1 in infected cells may still modulate uropod-mediated T-cell-T-cell contacts upon encountering ICAM-1-bearing target cells , which would be in agreement with previous studies [96] , [103] . While our data showed that patches of Gag laterally move to the uropod as cells re-polarize , they do not rule out the possibility that in an already polarized cell , de novo assembly of viruses preferentially occurs at the uropod or the cell contact without the lateral movement of Gag clusters . A recent study showed that MLV , another retrovirus , preferentially forms particles at contact sites in HEK293 cells [88] . This observation indicates that the site of retrovirus assembly can be polarized upon cell-cell contact formation in otherwise unpolarized cells . Notably , the polarized budding of MLV in HEK293 cells was found to be dependent on the MLV Env cytoplasmic tail . Similarly , the cytoplasmic tail of HIV-1 Env was reported to be important for polarized HIV-1 Gag localization in Jurkat T cells that appeared morphologically unpolarized [156] . In contrast , in our study , we found that in the absence of Env or cell-cell contact , Gag-YFP remained efficiently localized to the uropod in polarized T cells , including P2 and primary CD4+ T cells ( Figures 1G and 7; data not shown ) . Therefore , it is possible that in T cells with a high propensity to establish front-rear polarity , Gag may not require Env or cell-cell contact to achieve polarized assembly . Further studies will determine the molecular mechanisms by which assembly sites for retroviruses are polarized in different cell types . Although Env was dispensable for Gag localization to the uropod , formation of stable cell conjugates as well as virus transfer have been shown to require Env-receptor interaction [53] , [67] , [68] , [78] , [80] , [132] . Consistent with these findings , we observed that anti-CD4 blocking antibody ( Leu3A ) diminished cell-to-cell virus transfer ( Fig . 5 ) and that prelabeling of infected P2 cells with anti-Env antibody ( b12 ) reduced formation of cell conjugates with SupT1 cells ( data not shown ) . Therefore , while uropods are enriched in adhesion molecules and form contacts with other cells frequently [49] regardless of the presence of Env , the Env-CD4 interaction is likely to stabilize such contacts during formation of the VS . In summary , this study elucidates a series of molecular events leading to the formation of a VS . The observations made in this study has led us to form a working model ( Figure 12 ) in which higher-order multimerization , or clustering , mediated by NC is required for Gag association with uropod-specific microdomains . This microdomain association then facilitates localization of the assembling virus to the uropod . According to this model , the uropod , laden with HIV-1 components and particles , then serves as a pre-formed platform that mediates contact with target cells [Figure 12B ( a ) ] or redistributes to contacts formed elsewhere [Figure 12B ( b ) ] . Such contacts could then constitute a VS , which likely facilitates cell-to-cell virus transfer of HIV-1 .
The HIV-1 molecular clone pNL4-3 [157] and its derivatives encoding Gag-YFP and Gag-CFP fusion proteins ( pNL4-3/Gag-YFP/-CFP ) [11] , [28] were described previously . The latter two constructs contain an extensive deletion of pol and silent mutations to reduce ribosomal frameshift to the pol reading frame and does not express Vif or Vpr . For YFP and CFP , monomeric Venus [158] , [159] and monomeric Cerulean [160] variants were used , respectively . pNL4-3/WM184 , 185AA/Gag-YFP/-CFP ( renamed as pNL4-3/WMAA/Gag-YFP/-CFP ) , pNL4-3/delCA-CTD/Gag-YFP/-CFP , pNL4-3/14A1G/Gag-YFP/-CFP , pNL4-3/delNC/Gag-YFP/-CFP and the Fyn ( 10 ) -modified versions of those plasmids were previously described [28] . In this study , pNL4-3/Fyn ( 10 ) fullMA/GagVenus described previously [11] , [28] was renamed as pNL4-3/Fyn ( 10 ) /Gag-YFP for simplicity . pNL4-3/Fyn ( 10 ) /ΔMA/Gag-YFP was previously described [11] . pNL4-3/KFS/Gag-YFP was generated by cloning the XhoI-SalI fragment of pNL4-3/KFS ( a kind gift from Dr . Eric Freed [161] ) into pNL4-3/Gag-YFP . To construct pNL4-3/LZ/Gag-YFP/-CFP , the sequence encoding GCN4 leucine zipper in the ZWT plasmid , a kind gift from Dr . Heinrich Gottlinger [142] , was cloned into pNL4-3/Gag-YFP/-CFP using standard molecular cloning techniques . The double mutants pNL4-3/Fyn ( 10 ) /WMAA/LZ/Gag-YFP/-CFP and pNL4-3/Fyn ( 10 ) /delCA-CTD/LZ/Gag-YFP/-CFP were generated by cloning a fragment containing the leucine zipper sequence from pNL4-3/LZ/Gag-YFP into pNL4-3/Fyn ( 10 ) /WMAA/Gag-YFP/-CFP and pNL4-3/Fyn ( 10 ) /delCA-CTD/Gag-YFP/-CFP , respectively . pNL4-3/Gag-iGFP ( a kind gift from Dr . Benjamin Chen [99] ) was used to construct pNL4-3/Gag-iYFP . Stocks of HIV-1 mutants , pseudotyped with vesicular stomatitis virus G protein ( VSV-G ) , were prepared by transfecting 5 . 6×105 293T or HeLa cells with 1 . 5 µg pNL4-3 derivative encoding a Gag-YFP/-CFP fusion protein , 1 . 5 µg pCMVNLGagPol-RRE [105] , and 0 . 5 µg pHCMV-G ( a kind gift from Dr . J . Burns [162] ) . Two days post transfection , virus-containing supernatants were filtered through a 0 . 45 µm filter and used for inoculation of T cells . To prepare a polarized T cell line , T cell clones were obtained by limiting dilution of A3 . 01 T cells ( AIDS Research and Reference Reagent Program ) . Typical A3 . 01 cell cultures naturally contain 10–20% of cells with a polarized morphology . After limiting dilution , T cell clones were examined for cell morphology and polarized PSGL-1 localization . A cell clone , in which approximately 50–60% of cells were polarized , was designated “P2” and used for experiments in this study . These cell lines , as well as the SupT1 cell line ( AIDS Research and Reference Reagent Program ) , were cultured in RPMI containing 10% fetal bovine serum ( FBS ) ( RPMI-10%FBS ) . Primary T cells were isolated from buffy coats obtained from the New York Blood Center . The buffy coats were diluted in a 1∶1 ratio with phosphate buffered saline ( PBS ) containing 2% FBS ( PBS-2%FBS ) , and peripheral blood mononuclear cells ( PBMCs ) were isolated using centrifugation through ficoll ( GE Healthcare ) according to the manufacturer's instructions . Isolated PBMCs were then plated on polystyrene petri dishes for 2 h to separate the adherent monocytes and non-adherent lymphocytes . Lymphocytes were activated in RPMI-10%FBS containing phytohemagglutinin ( PHA ) ( Sigma . St . Louis , MO ) ( 6 µg/ml ) and IL-2 ( 20 units/ml ) ( Roche . Basel , Switzerland ) for 2–3 days . Primary CD4+ T cells were isolated with the MACS magnetic antibody bead kit ( Miltenyi Biotec . Bergisch Gladbach , Germany ) using anti-CD4 beads and MS columns . Cells were then cultured overnight in RPMI-10%FBS and IL-2 ( 20 units/ml ) and used for experiments . IL-2 has been shown to induce a comparable level of T cell polarization and locomotion to those induced by chemokines such as RANTES and MIP-1α [163] , [164] . Cells were infected with virus stocks by spin infection; 3×105 P2 cells or 5×105 primary T cells were resuspended in 200 µl virus stock with 4 µg/ml polybrene and centrifuged at 2500 rpm for 2 h at 15°C . Cells were cultured at 37°C in RPMI-10% FBS for 2–3 days ( in the presence of 20 units/ml IL-2 for primary T cells ) . Mouse anti-PSGL-1 ( NP_002997 . 1 ) , CD43 ( AH003828 . 1 ) , CD81 , or LFA-1 ( all from BD Biosciences Pharmingen . San Diego , CA ) were prelabeled with the secondary antibody ( AlexaFluor-594-conjugated goat anti-mouse IgG ( Invitrogen . Carlsbad , California ) ) for 30 min . Infected cells were cultured in 200 µl of RPMI-10%FBS containing this antibody mixture for 1 h at 37°C , after which they were washed with RPMI-10%FBS and fixed in 1 ml 4% paraformaldehyde in PBS ( PFA ) . After washing with PBS-2%FBS , cells resuspended in a small volume ( ∼10 µl ) of the same buffer were mixed with equal volume of Fluoromount-G ( SouthernBiotech . Birmingham , Alabama ) , and 3 µl of this mixture was mounted on glass slides . Images were acquired with a Nikon TE-2000U inverted epifluorescence microscope . Z-series of images were acquired with 0 . 2 µm intervals between focal planes . Maximum intensity projection images of the z-series images composed of 56 focal planes were obtained with ImageJ software ( NIH; downloaded from http://rsbweb . nih . gov/ij/ ) . Copatching quantification was performed using the correlation plot function of the Metamorph 6 software ( Molecular Devices . Sunnydale , California ) . To identify punctate signals objectively and to remove background signals from copatching analyses , the background , calculated as the median intensity of a 32×32-pixel region surrounding each pixel , was subtracted from the original image[165] , point noise was removed using a 3×3 median filter[166] , and the minimum threshold was set to twice the average fluorescence intensity of the cell of interest and applied to the images . These images were then used for calculation of Pearson's correlation coefficients ( CC ) , representing copatching . To avoid altering cell morphology , cell suspensions were placed in round-bottom tubes and left still at 37°C in the presence of 5% CO2 for at least 1 h prior to fixation . Subsequently , most of the culture supernatant was removed carefully , and cells were fixed in 1 ml 4% PFA for 20 minutes . Fixed cells were washed with PBS-2%FBS and then incubated for 1 h in PBS-2%FBS containing primary antibodies against cell surface molecules ( PSGL-1 and LFA-1 ) followed by washing with PBS-2%FBS . For experiments in which CD43 was used as a uropod marker , cells were first incubated with anti-CD43 for 30 min as done in previous studies [64] . Subsequently , cells were rinsed with RPMI-10%FBS twice , incubated with AlexaFluor 594-conjugated anti-mouse IgG for 30 min , rinsed with RPMI-10%FBS twice , and cultured for an additional 30 min at 37°C prior to fixation . Detection of Env on the cell surface was performed similarly , except that primary and secondary antibodies used were anti-gp120 ( IgG1 b12; AIDS Research and Reference Reagent Program ) and AlexaFluor-594-conjugated anti-human IgG ( Invitrogen ) , respectively . For detection of α-tubulin ( to identify the MTOC ) and mature p17MA , fixed cells were permeabilized by a 10-min incubation in PBS containing 0 . 2% saponin ( Fluka Biohemica . Buchs , Switzerland ) and 5% FBS prior to incubation with primary antibodies , anti-α-tubulin ( Sigma; clone B-5-1-2 ) and anti-p17MA ( Applied Biotechnologies . Columbia , Maryland ) , respectively . Primary antibodies were detected by treating cells with AlexaFluor 594-conjugated goat anti-mouse IgG for 30 minutes . Cells were then washed again with PBS-2%FBS and mounted on glass slides , as described above , for microscopy . Cells were infected with VSV-G-pseudotyped HIV-1 encoding Gag-YFP . Two days post-infection , cells were immunostained with anti-PSGL1 prelabeled with Zenon AlexaFluor 594 reagent ( Invitrogen ) according to manufacturer's instruction or AlexaFluor 594-conjugated anti-mouse IgG as described for the copatching assay . To morphologically depolarize cells , 4-well chamber coverslips ( Nunc . Rochester , NY ) , containing Gag-YFP-expressing cells , were placed at 4°C for 30 min . To repolarize cells , the chamber coverslips were then transferred to a pre-warmed ( 37°C ) microscope stage . Time-lapse images were acquired with an interval of 30 s for up to 1 h . The images were then converted to AVI files by ImageJ . Cells were co-infected with VSV-G-pseudotyped HIV-1 encoding YFP- and CFP-tagged versions of each Gag mutant , cultured and fixed as described above . Cells were subsequently permeabilized , immunostained for α-tubulin , and mounted as described above . Images were collected using four filter combinations: AlexaFluor 594 excitation/AlexaFluor 594 emission , YFP excitation/YFP emission , CFP excitation/CFP emission , and CFP excitation/YFP emission . FRET was calculated using FRET stoichiometry as previously described [28] , [167] . 5×105 primary CD4+ T cells were infected with VSV-G pseudotyped HIV-1 encoding Gag-YFP or KFS/Gag-YFP . Two days post infection , 5×105 fresh primary CD4+ T cells were stained with 1 µM CMAC ( Invitrogen ) for 30 min . Infected T cells were then co-cultured with CMAC-stained target T cells for 3 h in a chamber coverslip at 37°C . Images of 50–60 polarized and YFP-expressing cells were then acquired , and the number of contacts these cells formed with CMAC-labeled cells , which represent newly formed contacts , were quantified and categorized as uropod- or non-uropod-mediated contacts . For analysis of the VS , 2×105 P2 cells were infected with VSV-G-pseudotyped HIV-1 encoding Gag-CFP . Two days post-infection , cells were immunostained with anti-CD43 and a minimal amount of AlexaFluor-594-conjugated anti-mouse IgG . After extensive washing , 1×105 of these cells or the same number of uninfected P2 cells were mixed with 1×105 target cells ( SupT1 cells ) that were prelabeled with non-blocking FITC-conjugated anti-CD4 ( Clone L120 , BD Biosciences . San Jose , California ) and cocultured for 3 h in chamber coverslips at 37°C . These cocultures in the chamber coverslips were placed on a microscope stage set at 37°C , and images were acquired using appropriate excitation and emission filters . 2×105 P2 cells were infected with VSV-G-pseudotyped HIV-1 encoding Gag-YFP . Two days post-infection , 5×105 target SupT1 cells were stained with 1 µM CellTracker CMTMR ( Invitrogen . Carlsbad , California ) for 15 min , washed with RPMI-10%FBS , incubated for 2 h in RPMI-10%FBS , and washed again in RPMI-10%FBS . Infected donor and CMTMR-stained target cells were cocultured in 0 . 5 ml RPMI-10%FBS for 3 h in the presence or absence of the myosin light chain kinase ( MLCK ) inhibitor , ML7 ( 40 µM ) ( EMD Biosciences . San Diego , California ) , or the solvent negative control DMSO . The CD4 blocking antibody Leu3A ( 0 . 25 µg/ml ) ( BD Biosciences ) and isotype anti-mouse IgG control antibody ( 0 . 25 µg/ml ) ( Santa Cruz Biotechnology . Santa Cruz , California ) were utilized to validate the assay , as it was shown previously using a similar assay that virus transfer was dependent on Env-CD4 interaction [53] ( Figure 4A ) . To rule out the possibility that inhibitors affect viral protein synthesis and thereby indirectly alter virus transfer , 10 µg/ml cycloheximide , which abolishes protein synthesis , was added at the beginning of coculture . After 3 h of coculture , cells were fixed in 4% PFA . Double-positive cells , which represent CMTMR-positive target cells that received YFP-containing virus particles , were identified by flow cytometry ( see Figure 4A for examples ) . Results were presented as a percentage of double-positive cells compared to total CMTMR-stained target cells . To measure morphological polarization of T cells , outlines of Gag-YFP-expressing P2 cells were determined by manually tracing the cell perimeter using the ImageJ program . Circularity values were then calculated based on this outline using the Measure function of ImageJ . The output values range between 0 and 1 , with 1 representing a perfect circle . This method has been described previously [168] . Morphologically polarized cells with circularity values below 0 . 8 were further examined for polarization of Gag localization . To quantify polarity of Gag localization , a 10-segmented grid was placed over each cell along the cell's longest axis . The number of segments that contained plasma-membrane-associated Gag was then used as the polarization index . Lower values correspond to more polarity of Gag on the cell surface . Examples of these quantifications are shown in Figure S4 . | CD4+ T cells are natural targets of HIV-1 . Efficient spread of HIV-1 from infected T cells to uninfected T cells is thought to occur via cell-cell contact structures . One of these structures is a virological synapse where both viral and cellular proteins have been shown to localize specifically . However , the steps leading to the formation of a virological synapse remain unknown . It has been observed that T cells adopt a polarized morphology in lymph nodes where cell-to-cell virus transmission is likely to occur frequently . In this study , we show that in polarized T cells , the primary viral structural protein Gag accumulates to the plasma membrane of a rear end structure called a uropod . We found that Gag multimerization , driven by its nucleocapsid domain , is essential for Gag localization to uropods and that HIV-1-laden uropods mediate contact with target cells and can become part of the virological synapse . Our findings elucidated a series of molecular events leading to formation of HIV-1-transferring cell contacts and support a model in which the uropod acts as a preformed platform that constitutes a virological synapse after cell-cell contact . | [
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... | 2010 | Nucleocapsid Promotes Localization of HIV-1 Gag to Uropods That Participate in Virological Synapses between T Cells |
Triatoma infestans —the principal vector of the infection that causes Chagas disease— defies elimination efforts in the Gran Chaco region . This study identifies the types of human-made or -used structures that are key sources of these bugs in the initial stages of house reinfestation after an insecticide spraying campaign . We measured demographic and blood-feeding parameters at two geographic scales in 11 rural communities in Figueroa , northwest Argentina . Of 1 , 297 sites searched in spring , 279 ( 21 . 5% ) were infested . Bug abundance per site and female fecundity differed significantly among habitat types ( ecotopes ) and were highly aggregated . Domiciles ( human sleeping quarters ) had maximum infestation prevalence ( 38 . 7% ) , human-feeding bugs and total egg production , with submaximal values for other demographic and blood-feeding attributes . Taken collectively peridomestic sites were three times more often infested than domiciles . Chicken coops had greater bug abundance , blood-feeding rates , engorgement status , and female fecundity than pig and goat corrals . The host-feeding patterns were spatially structured yet there was strong evidence of active dispersal of late-stage bugs between ecotopes . Two flight indices predicted that female fliers were more likely to originate from kitchens and domiciles , rejecting our initial hypothesis that goat and pig corrals would dominate . Chicken coops and domiciles were key source habitats fueling rapid house reinfestation . Focusing control efforts on ecotopes with human-fed bugs ( domiciles , storerooms , goat corrals ) would neither eliminate the substantial contributions to bug population growth from kitchens , chicken coops , and pig corrals nor stop dispersal of adult female bugs from kitchens . Rather , comprehensive control of the linked network of ecotopes is required to prevent feeding on humans , bug population growth , and bug dispersal simultaneously . Our study illustrates a demographic approach that may be applied to other regions and triatomine species for the design of innovative , improved vector control strategies .
Of the approximately 140 species of Triatominae ( Heteroptera: Reduviidae ) currently recognized , Triatoma infestans ( Klug ) expresses the extreme of an evolutionary trend toward domesticity [1] , [2] . This adaptation and epidemiological significance as the most important vector of human Chagas disease justified targeting T . infestans for elimination in the southern cone countries of South America since 1991 [3] . Insecticide control campaigns reduced infestations but did not interrupt transmission of human Trypanosoma cruzi infection in the Gran Chaco region of Argentina , Bolivia and Paraguay for various reasons [4] , [5] . Inadequate housing and subsistence rural economies facilitate the persistence of T . infestans in the Gran Chaco . Rural house compounds there typically include a dwelling house for people and all its associated special-purpose outbuildings [6] . Henceforth ‘ ( peri ) domestic ecotopes’ will refer to human-made or -used ecotopes , either peridomestic or domestic ( domestic ecotopes are sometimes called human sleeping quarters or domiciles ) . ( Peri ) domestic ecotopes are heterogeneous physically ( size , materials ) and demographically , with substantial variations in refuge availability , microclimatic conditions , host species composition , host density , and bug abundance per site [6]–[12] . The concept of relative habitat suitability is an important research topic that has been neglected in Triatominae . The ranked metrics of infestation prevalence and bug abundance suggested that chicken coops and goat and pig corrals were the most important ecotopes in the dry Chaco [8] , [10] , [11] , [13]–[15] , but most of these surveys did not include domiciles and the one that did include them had a different goal [13] . The current study aims to fill this knowledge gap and identify the types of ( peri ) domestic structures that function as key sources of bugs at the initial stages of house reinfestation after an insecticide spraying campaign . The nutritional status of triatomine bugs ( indexed by body weight-to-body length ratios , W∶L ) affects all vital rates , the bugs' propensity to fly substantial distances , and the regulation of bug population size [16] , [17] . The very few estimates of the blood-feeding frequency of domestic [16] , [18]–[20] and peridomestic [8] , [10] populations of Triatominae were restricted to T . infestans and Rhodnius prolixus Ståhl . Similarly , the only two studies that assessed variations in the nutritional status or body weight of domestic bug populations included only 3 selected houses infested by T . infestans and 1 house highly infested by R . prolixus [16] , [18] . T . infestans from chicken coops were in better nutritional status than bug populations from pig and goat corrals [8] , [10] , [21] , and no comparisons with domestic bug populations were ever made . With one exception [22] , there is a striking lack of published information on the distributions of W∶L and female fecundity of other species of Triatominae collected in human dwellings . Estimates of female fecundity and body weight of bug populations developing in closed , small experimental huts housing 1–4 chickens [e . g . ] , [ 23]–[25] are less informative because variations in conditions and resources differ substantially from those in field bug populations . Whether the combined effects of habitat type and host species composition impinge on the blood-feeding rates , nutritional status and fecundity of ( peri ) domestic bug populations has not been investigated simultaneously in a well-defined area and is another objective of the current study . The probability that T . infestans bugs initiate flight depends at least on temperature , W∶L , and season [17] , [26]–[28] . Previous studies based on bug nutritional status and light-trap catches of T . infestans versus analysis of spatio-temporal patterns of reinfestation suggested conflicting results regarding the duration and detailed time structure of the dispersal season and subsequent establishment [10] , [27] , [29] . The heterogeneity of rural house compounds in the dry Chaco of Santiago del Estero Province in northern Argentina offers unique opportunities to investigate how habitat and host species may affect various population attributes of T . infestans . A separate article focused on the human-feeding rates of domestic T . infestans in the study area [20] . Here we adopted a demographic approach and measured several fitness components as putative indices of relative habitat suitability for ( peri ) domestic bug populations in early spring . We considered each site of occurrence of T . infestans as the unit of analysis to model variations in response variables . At a geographic scale including 270 houses , we measured infestation and bug abundance per site , stage structure , and host abundance . At a detailed scale including 64 infested sites , we also assessed daily blood-feeding rates , host choices , engorgement and nutritional status , and female fecundity ( defined as numbers of chorionated eggs per female , including females with no chorionated eggs ) . We also investigated whether early spring ( September–October ) may represent a pulsed dispersal period of T . infestans [29] that previous light-trapping experiments did not detect [27] . Partial background evidence suggested that T . infestans would be most abundant and productive in chicken coops and other ecotopes associated with chickens ( storerooms and open sheds ) ; chicken-associated bugs would feed more often and reach a higher engorgement status , W∶L , and female fecundity than bugs from other habitats , and out-migrate much less frequently than those in pig and goat corrals [8] , [10] , [21] , [27] . Although this ranking served as the initial hypothesis of our study , how domestic bugs and other peridomestic populations fare is uncertain given the lack of prior information . We also hypothesized that the host-feeding choices of ( peri ) domestic bugs should be structured by type of habitat . Testing these hypotheses is important because the current understanding of the population dynamics of Triatominae at meaningful spatial scales is still fragmentary and limits the development of improved vector control tactics . We summarize quantitatively how the seven main ecotopes herein identified ( domicile , storeroom , kitchen , chicken coop , pig corral , goat corral , granary ) contribute to quantities that people may wish to control in the interest of public health: ( a ) the number of bugs that feed on humans ( as a surrogate for the risk of human infection in different ecotopes ) ; ( b ) the egg production of female bugs ( as a surrogate for the contribution of different ecotopes to bug population growth ) ; and ( c ) the number of dispersing females ( as a surrogate for the contribution of different ecotopes to bug dispersal and reinfestation of uninfested sites ) .
Field work was carried out in the austral spring months of October–November 2003 in 11 neighboring rural communities with 270 houses in Figueroa Department ( 27° 23′S , 63° 29′W ) , Santiago del Estero Province , Argentina ( Figure S1 ) . The study area had been sprayed with pyrethroid insecticides by vector control personnel approximately three years before our fieldwork , and no further interventions were made [14] . Most houses were made of adobe walls and thatched roofs , with one or two adjacent bedrooms and a front veranda 5–10 m wide ( i . e . , domestic areas ) , and had multiple peridomestic structures as described ( Text S1 ) . Ecotope is a type of bug habitat with similar physical characteristics , function and resident hosts ( e . g . , chicken coop ) . For any house compound , a given ecotope may have more than one bug collection site ( e . g . , two separate chicken coops at the same house ) . A cross-sectional survey of house infestation was conducted in October–November 2003 before new control interventions [14] . Each house was visited and georeferenced , and the location and type of building material of each ( peri ) domestic structure were recorded . Four teams , each composed of one supervisor and three skilled bug collectors , searched for triatomine bugs in all ( peri ) domestic sites of 233 inhabited houses using timed manual collections with a dislodging spray ( 0 . 2% tetramethrin , Espacial 0 . 2 , Argentina ) . Two persons searched for bugs in the peridomestic ecotopes usually found infested by using 0 . 25 person-h on each site . Another person searched in human sleeping quarters during 30 min ( 0 . 5 person-h per domicile ) . On average , one person-h was used for each house compound . The ecotope where each bug was collected was classified as one of 16 types based on its function and main local host ( see Results ) . Searches were usually conducted between 0800 and 1400 hours on non-rainy days , but sometimes searches started later because householders were not available . Weather conditions during the period from 2000 to 0600 hours that preceded every bug collection day were suitable for blood-feeding [20] and mostly so for flight dispersal ( ≥22°C and wind speed <5 km/h [27] . All ( peri ) domestic sites positive for T . infestans among houses inspected from 20 to 27 October 2003 were considered eligible for detailed blood-feeding studies with emphasis on domestic sites . Time constraints for processing the bugs within 8 h of capture dictated that insects from 64 ( 22 . 9% ) of the candidate sites in 57 house compounds were processed for body measurements , urine color , nutritional status and bloodmeal sources . These 64 sites were all of the sites which satisfied the time constraints , and were not a sample of a larger number of possible sites . In view of host-feeding and fecundity results , we took a supplementary sample ( from frozen specimens ) including 57 peridomestic sites from 29 other house compounds to increase the sample size of peridomestic bugs and females to approximately 60 and 30 per ecotope , respectively; this supplementary sample lacked data on urine color , engorgement status and W∶L . All triatomine bugs collected were kept in a cooler at 10–12°C until arrival to the field laboratory , and then were identified to species and counted by stage as described [14] . Sex identification in fifth-instar nymphs was based on the presence of an immature female reproductive system on the eighth tergite [30] , confirmed via morphological differences in the eighth and ninth tergites [31] . Late-stage bugs were defined as fourth instars , fifth instars , adult females , and adult males . All late-stage bugs were weighed individually in an electronic balance ( precision , 0 . 1 mg , Ohaus , Pine Brooks , NJ ) and measured from clypeus to abdominal tip with a hand-held vernier caliber accurate to 0 . 02 mm . Late stages were individually examined for the presence of colorless urine within 8 h of capture using the method developed by Catalá [23] . The site-specific proportion of T . infestans that fed during the preceding night ( i . e . , daily blood-feeding rate ) was estimated as a weighted average of the observed proportion of fourth- and fifth-instar nymphs with colorless urine multiplied by a temperature-dependent correction factor and the ( uncorrected ) proportion of adult bugs with colorless urine , relative to the number of bugs examined for urine color [23] . The underlying physiological rationale of the method , comparison with other data , estimation details and the exact formula are given elsewhere [20] . The engorgement status ( formerly called qualitative nutritional status in [10] , [32] ) of late-stage T . infestans was determined by direct observation of a cross-sectional view of the abdomen perpendicular to the long axis of the body to assess the degree of cuticle distension ( nymphs ) [10] and the volume and shape of the anterior midgut against a flashlight ( adults ) [33] . Bugs were classified as unfed , little fed , medium fed , and fully fed ( i . e . , starved , or with scarce , good , or large blood contents ) . All bugs were kept frozen at −20°C upon arrival to the laboratory in Buenos Aires . Bugs were dissected and the midgut with the blood meal was extracted into a previously labeled , weighed vial [20] , [32] . The number of chorionated eggs present in the oviducts was counted . Bloodmeal contents were tested with a direct ELISA assay against human , dog , cat , chicken , pig , goat and murid rodent ( rat or mouse ) antisera with high sensitivity and specificity values as described [20] , [32] . We report the proportion of reactive bugs ( i . e . , those positive against any of the tested antisera ) that contained each type of host blood . The data for each house visited were entered in two databases: one for site infestation ( Table S3 , data sheet Infestation_data ) that included a unique identifier code for each of the 1 , 297 collection sites at 233 inhabited houses , and one for each bug examined for urine color , body weight ( W , mg ) , total body length ( L , mm ) , W∶L ratio ( mg/mm ) and other attributes in a sample of sites ( Table S3 , data sheet Feeding_fecund_flight_data ) . The latter database included a total of 769 late-stage insects examined for at least one of these attributes: 544 were examined for transparent urine; 551 for W; 550 for L and engorgement status; 729 for ELISA; 214 fifth instars for sex identification , and 216 females for fecundity . All proportions herein reported have attached standard errors clustered by bug collection site as estimated by Stata 12 [34] . The relative abundance of fifth-instar nymphs ( the stage with submaximal reproductive value ) was taken an an index of successful development over several months and future recruitment of adult stages ( i . e . , productivity ) in infested sites . Total bug catch per infested site , productivity , and female fecundity were highly overdispersed and no transformation normalized the data . Therefore we used negative binomial regression with robust standard errors to test for ecotope effects on the response variables using Stata 12 [34] , [35]; relative abundance ( RA ) , relative productivity ( RP ) and relative fecundity ( RF ) ( labeled in Stata output as ‘incidence-rate ratios’ ) and their 95% CI were calculated . Logistic regression with robust standard errors was used to test for ecotope effects on site-specific infestation prevalence . Daily blood-feeding rate and engorgement status ( with unfed and little-fed bugs pooled in one class , and medium- or fully-fed bugs in another ) were used as response variables in random-intercept logistic regression models clustered by collection site using Stata 12 [34] , [35] . Different bug collection sites were assumed independent ( i . e . , having a domestic site within the same house compound would not affect the blood-feeding rate ( or any other demographic parameter ) of bugs in the chicken coop of the same compound ) . The predictor variables were ecotope ( a categorical variable with seven levels ) , bug stage ( a categorical variable with three levels , with fourth- and fifth-instar nymphs pooled , males and females ) , total bug abundance per unit of catch effort per site , and mean maximum temperature during the night preceding bug catch . Interaction terms were added one by one to the main-effects model and retained in the final model if the coefficients of the interaction terms had P<0 . 05 for a test of the null hypothesis that the coefficient was zero . We used the allometric equation in log-transformed form ( log W = log ( a ) +b*log L ) to estimate the parameters a and b for males , females , fourth- and fifth-instar nymphs using random-intercept linear regression analysis [35] . Throughout log = loge . Fourth- and fifth-instar log W distributions were bimodal in every ecotope and could be separated visually into an upper ( heavier ) and lower ( lighter ) distribution with separation points at 4 . 1 and 5 . 2 mg , respectively . The distributions of log L in fourth- and fifth-instar nymphs were also bimodal and could be separated visually into an upper and lower distribution at 2 . 55 and 2 . 85 mm , respectively . We ran separate regressions for the lower and upper distributions for fourth- and fifth-instar nymphs , using the log W separation points for each stage to break each stage's distribution into two parts . In a second step we added ecotope and its interaction with log L as independent variables . Individual adult W∶L and the maximum temperature between 2000 and 0600 hours of the night preceding capture were used to estimate an individual probability of flight initiation of adult bugs from a given site using the model described in [17] . In practice , such maxima were always at 2000 hours . At this time in October in the same region , the differences between internal and external temperatures within various ( peri ) domestic ecotopes were nil [9]; therefore we did not adjust records of external temperature for ecotope-specific dampening effects . Adult T . infestans bugs with a probability of flight initiation greater than 0 . 05 were taken as potential fliers . Mark-recapture experiments with adult T . infestans conducted in a salt flat in Cordoba recorded that a small fraction of the bugs that flew did so for just a few meters from the release point [26] . Therefore , we assumed that a small fraction ( 0 . 05 ) of the adult bug population that might fly would travel such short distances that they would not count as dispersers . We also tried trivial flight thresholds of 0 . 01 , 0 . 05 , and 0 . 10 to see how sensitive the results were to the choice of the threshold .
Of 1 , 297 identified sites searched for triatomine bugs , 279 ( 21 . 5% ) were positive for T . infestans and 2 , 145 bugs were caught ( Table 1 ) . The ecotopes most frequently infested were domiciles ( 38 . 7% ) , granaries ( 33 . 3% ) , chicken coops ( 30 . 2% ) , storerooms ( 29 . 5% ) , goat corrals ( 26 . 6% ) and pig corrals ( 23 . 9% ) . In absolute numbers , however , domiciles led the ranking ( 94 ) followed by storerooms , goat and pig corrals ( 41–45 ) , and chicken coops ( 26 ) . These five ecotopes plus kitchens and granaries ( the seven main ecotopes ) included 274 of the 279 infested sites detected . Using logistic regression analysis , we rejected the null hypothesis of no differences among main ecotopes in infestation prevalence ( Wald χ2 = 27 . 80; df = 6; P<0 . 001 ) . Domiciles had greater odds of being infested than goat corrals –the reference category ( OR = 1 . 74 , CI = 1 . 13–2 . 67 ) – and kitchens were less likely to be infested ( OR = 0 . 43 , CI = 0 . 23–0 . 79 ) . Bug abundance was highly overdispersed in every main ecotope ( range of variance-to-mean ratios , 5–15 ) ( Figure 1 ) . Using negative binomial regression analysis , the relative abundance of T . infestans per infested site differed significantly among the seven main ecotopes ( Wald χ2 = 23 . 58; df = 6; P<0 . 001 ) . Compared to infested goat corrals , infested granaries ( RA = 3 . 31 , CI = 1 . 52–7 . 22 ) , kitchens ( RA = 2 . 50 , CI = 1 . 42–4 . 38 ) , chicken coops ( RA = 1 . 86 , CI = 1 . 18–2 . 92 ) and storerooms ( RA = 1 . 73 , CI = 1 . 04–2 . 87 ) had significantly larger relative bug abundance . Similarly , in infested sites , productivity was significantly larger in granaries ( RP = 4 . 84 , 1 . 74–13 . 43 ) , chicken coops ( RP = 3 . 07 , 1 . 71–5 . 51 ) , kitchens ( RP = 2 . 93 , 1 . 42–6 . 01 ) and storerooms ( RP = 2 . 16 , 1 . 17–3 . 97 ) than in goat corrals ( Wald χ2 = 32 . 0; 6 df; P<0 . 0001 ) . The stage structure of T . infestans populations ( after pooling first to third instars ) differed significantly among the seven main ecotopes ( χ2 = 92 . 0; df = 24; P<0 . 001 ) ( Figure 2A ) . Fifth-instar nymphs comprised the largest fraction of the population in chicken coops ( 38 . 8% ) , granaries ( 36 . 0% ) and storerooms ( 30 . 8% ) –all ecotopes where chickens were the main or only bloodmeal source– compared with goat corrals ( 24 . 7% ) or pig corrals ( 21 . 2% ) . The mean percentage of adult females ( 41 . 7% ) differed marginally significantly among the main ecotopes ( χ2 = 11 . 1; df = 6; P = 0 . 085 ) , with more females in domiciles ( 48 . 9% ) and chicken coops ( 46 . 2% ) than in storerooms ( 33 . 6% ) , pig corrals ( 35 . 8% ) and goat corrals ( 41 . 5% ) ( Figure 2B ) . However , the sex ratio of fifth-instar nymphs ( n = 214 ) was 50% and did not differ significantly between ecotopes ( χ2 = 4 . 84; df = 4; P = 0 . 30 , excluding granaries and pig corrals ) ( Figure 2B ) . Recent apparent colonization attempts were more likely in domestic than peridomestic sites ( Text S1 ) . Daily blood-feeding rates averaged 29 . 6% ( Table 1 ) . Median feeding interval was 4 . 0 d across ecotopes and varied widely from 2 . 8 d in chicken coops to 10 . 2 d in kitchens; it was the least variable in domiciles . These estimates are derived from data in this paper , except the estimate for domiciles from [20] . Blood-feeding rates were not significantly associated with ecotope and bug stage ( Figure S2 ) , bug abundance per site and mean maximum temperatures during the night preceding bug capture using random-intercept logistic multiple regression analysis ( Wald χ2 = 8 . 9; df = 6; 64 sites with 544 observations; P = 0 . 354 ) . Of 729 T . infestans tested , 695 ( 95 . 1% ) had at least one host identification and were considered reactive ( Table 2 ) . Of 107 bugs classified as unfed 13 ( 12 . 2% ) were non-reactive , whereas of 416 bugs classified as fed 7 ( 1 . 7% ) were non-reactive ( Fisher's exact test , P<0 . 0001 , rejecting the null hypothesis that unfed and fed bugs had the same proportion of non-reactive bugs ) . The reactive bugs fed mainly on chickens ( 44 . 9% ) and humans ( 28 . 9% ) , and much less frequently on goats ( 11 . 9% ) , pigs ( 10 . 9% ) , dogs ( 4 . 7% ) , and cats ( 2 . 0% ) ( Table 2 ) . Only two rodent bloodmeals were detected and they occurred in a domicile and a granary . The host-feeding patterns were spatially structured according to habitat type and correlated closely with the main resident host ( s ) . In domiciles , the main bloodmeal sources were humans ( 68 . 2% ) followed by chickens ( 21 . 8% ) and dogs ( 9 . 0% ) [20] . Chicken blood meals prevailed in chicken coops , storerooms and kitchens ( range , 82 . 0–100% ) , and occurred in all ecotopes except pig corrals . Bugs from pig corrals and from goat corrals were mainly fed on pigs and goats , respectively . Domiciles , storerooms and goat corrals had the largest number of different bloodmeal sources identified , and mixed blood meals were most frequent in storerooms ( 9 . 5% ) . Most bugs with identified sources had unmixed blood meals ( 96 . 7% ) and very few had fed on two ( 2 . 9% ) or on three ( 0 . 4% ) host species . No significant association between the percentage of bugs that fed on a given host and bug stage was detected by separate χ2 tests . The occurrence of blood meals from hosts that characteristically did not use certain ecotopes provided clues to bug dispersal events between domestic and peridomestic ecotopes ( Text S1 ) . Fully-fed T . infestans declined from 29 . 6% in chicken coops and 17 . 7% in domiciles to 8 . 7% in kitchens and 5 . 9% in pig corrals , with marginally significant differences among ecotopes ( χ2 = 19 . 3; df = 12; P = 0 . 081 ) ( Figure 3A ) . Conversely , unfed bugs peaked in kitchens ( 34 . 8% ) and pig corrals ( 26 . 5% ) ; they were the fewest in domestic sites ( 20 . 2% ) and chicken coops ( 15 . 5% ) . Females were more frequently fully or medium engorged than males ( Figure 3B ) . Random-intercept logistic regression analysis of engorgement status showed significant interaction effects ( P<0 . 05 ) between ecotope and bug stage ( Wald χ2 = 51 . 9; df = 14; P<0 . 001 ) , with female bugs having a six times greater odds of being medium or fully engorged than nymphs ( reference category ) . Engorgement status and W∶L ratios were positively and highly significantly ( P<0 . 001 ) related among fourth instars ( Spearman's correlation coefficient , ρS = 0 . 779 ) , fifth instars ( ρS = 0 . 821 ) , females ( ρS = 0 . 660 ) , and males ( ρS = 0 . 526 ) . The proportion of bugs that fed on the preceding night increased steadily from 4 . 2% in unfed bugs to 20 . 7% , 38 . 0% and 68 . 8% in successive categories of increasing engorgement ( χ2 = 119 . 6; df = 3; P<0 . 001 ) . We investigated the relationship between log W and log L and whether it varied with bug stage and ecotope using random-effects multiple linear regression ( Table S1 ) . Log W increased highly significantly with log L among the lower and upper distributions of fourth and fifth instars and in females , but not in males ( Figure 4 ) . When ecotope effects were added to these models , significant interaction effects ( P<0 . 05 ) were detected for lighter fourth instars ( with greater increase in log W per unit of log L in domiciles relative to kitchens –the reference category ) ; lighter fifth instars ( with lower increase in pig corrals ) , and heavier fifth instars ( with lower increase in storerooms ) . Most females ( 81% ) were gravid ( Table 3 ) . Females with no eggs occurred much more frequently in pig corrals and goat corrals ( 31–32% ) than in chicken coops and kitchens ( <8% ) . The fraction of gravid females differed significantly among ecotopes ( χ2 = 11 . 8; df = 5; P = 0 . 038 , excluding granaries ) . The frequency distribution of chorionated eggs per female ( including females who were not gravid ) was substantially and significantly overdispersed overall and at every ecotope . The mean number of chorionated eggs per female increased from 7 . 7–8 . 6 in goat corrals and storerooms to 14 . 8 in chicken coops ( Table 3 ) . Bugs from goat corrals ( RF = 0 . 62 , CI , 0 . 40–0 . 97 ) and storerooms ( RF = 0 . 69 , CI , 0 . 51–0 . 94 ) had a significantly lower fecundity than those from kitchens ( reference category ) using negative binomial regression clustered by site ( Wald χ2 = 14 . 9; df = 5; P<0 . 011; n = 214 females ) . The number of chorionated eggs per female correlated positively and strongly ( r = 0 . 516; P<0 . 001 ) with W∶L ( Figure S3 ) . The percentage of all female adults that were potential fliers predicted by the model [17] peaked in kitchens ( 25 . 0% ) and domestic sites ( 6 . 4% ) and was very low in storerooms , chicken coops and pig corrals in early spring ( Figure 5 ) . Potential male fliers outnumbered females at every ecotope and declined from 60 . 0% in chicken coops and 58 . 3% in pig corrals to 30 . 8% in kitchens , 25 . 0% in domiciles , and 20% in storerooms . Ecotopes ranked differently by sex . The use of a trivial flight threshold of 0 . 01 ( instead of 0 . 05 ) yielded a similar qualitative ranking of ecotopes favorable for flight among females ( 25 . 0% , 6 . 4% , 0% , 0% and 0% , respectively ) and males ( 50 . 0% , 58 . 3% , 30 . 8% , 16 . 7% , and 6 . 7% , respectively ) . Nor did higher thresholds ( 0 . 1 and 0 . 15 ) modify the patterns observed with a threshold of 0 . 05 . Using the W∶L ranges of observed fliers ( females , 5 . 9–11 . 1 mg/mm; males , 3 . 6–10 . 3 mg/mm ) in light-trapping surveys of T . infestans [27] , we estimated that 17 . 0% ( 19 of 112 ) of all females and 38 . 8% ( 52 of 134 ) of all males measured for W∶L qualified as potential fliers under adequate weather conditions . Table 1 column ( a ) shows that domiciles had , in total , 351 bugs that were reactive and fed on humans . Storerooms had 8 reactive , human-fed bugs and goat corrals 6 . None of the other ecotopes had any reactive , human-fed bugs . The per-site risk index also shows that the overwhelming majority of human-vector feeding contacts occurred in domiciles ( Table S2 ) . Table 1 column ( b ) shows that the six principal ecotopes other than granaries all produced substantial numbers of eggs . Domiciles had almost three times as many eggs as the next-ranked ecotope , kitchens , followed by storerooms and pig corrals . Goat corrals , though ranked last among the six ecotopes , had about half as many eggs as chicken coops . All six of these ecotopes contributed substantially to bug population growth . Per site , however , chicken coops contributed nearly 50% more eggs than domiciles and 2–4 times more than any other ecotope ( Table S2 , column b ) . Table 1 column ( c ) shows that the total number of flight-dispersing adult females ( per unit effort ) expected from kitchens was 10 and from domiciles 7 , with no contributions from other main ecotopes . However , flight-dispersing females per kitchen site were expected to be 2 . 6 times more frequent than flight-dispersing females per domicile site ( Table S2 ) .
Our study illustrates a demographic approach to identifying key source habitats which may be applied to other seasons , regions , and species of triatomine bugs . For nearly all of them there is a very limited quantity and quality of demographic data that may assist in understanding their population dynamics and response to vector control actions . This information is vital for the design of innovative , improved vector control actions in key ecotopes where bugs achieve maximum fitness . We anticipate that our framework is relevant for the control of Triatoma brasiliensis in Brazil , T . pallidipennis in Mexico , and T . dimidiata in parts of Central America , all of which have extensive peridomestic bug populations that tend to invade human sleeping quarters [69]–[71] . Domestic sites and chicken coops provide higher-quality habitats than other ecotopes , and are therefore involved in the rapid recovery of T . infestans populations after control interventions . Human sleeping quarters would serve as prime targets of dispersant bugs after insecticide spraying campaigns that do not achieve community-wide vector elimination , and then would become productive habitats and sources for bug propagation . This dual functioning of human sleeping quarters ( as targets and as sources ) and its early role in reinfestation has been overlooked in the past . Domiciles outnumbered each type of peridomestic ecotope in the absolute and relative frequencies of infested sites , but taken collectively peridomestic structures outnumbered infested domestic sites by a factor of three . Various lines of evidence consistently indicate that peridomestic foci bolster vector persistence after insecticide spraying and increase the risk of domestic invasion and recolonization in the Argentinean Chaco [7] , [11]–[13] , [15] , [38] . Some structures such as goat corrals may serve as productive habitats at certain times depending on local host supply , refuge availability and weather , and then become sources when conditions become harsh . We propose that the mosaic of suitable patches in peridomestic areas would shift over seasons , whereas human sleeping quarters provide more stable habitats and near-optimum conditions with a more stable supply of suitable hosts . This article demonstrates that focusing control efforts on the three ecotopes ( domiciles , storerooms , and goat corrals ) that housed reactive , human-fed bugs would neither eliminate the substantial contributions to bug population growth from kitchens , chicken coops , and pig corrals nor stop dispersal of adult female bugs from kitchens . Rather , comprehensive control of the linked network of ecotopes in a typical house compound and community is required to prevent feeding on humans , bug population growth , and bug dispersal simultaneously . For the elimination of T . infestans and sustained interruption of parasite transmission , one of the worst things vector control programs can do during the attack phase is to restrict the residual application of insecticides to human sleeping quarters and exclude peridomestic structures; or to replace full-coverage insecticide spraying with selective treatments that have little or no residuality ( fumigant canisters , pour-on insecticides ) and whose effectiveness at a public health scale has weak or no support , as some of them did to reduce costs and increase apparent coverage albeit with much lower quality and impact [5] , [72] . For example , vector control programs that did not spray insecticide on peridomestic structures but used pour-on insecticides on dogs , goats and chickens did not suppress peridomestic infestations [49 , p . 233] . There is often a large difference between standard recommendations to vector control programs [73] and the realities of vector control during recent decades , in conjunction with the increasing decentralization of health services and reduced operational capacity in the field . Good practices in vector control need to be further promoted [74] . For elimination purposes in high-risk areas such as the Gran Chaco , improved vector control at present should include full-coverage , professional application of a double dose of suspension concentrate pyrethroid insecticides to main peridomestic ecotopes and single ( standard ) doses to domiciles and other lower-quality habitats [13] , with additional re-treatment of key ecotopes at defined time intervals . For more sustainable vector and disease control , however , improved vector control needs to be combined with long-term investment in improved housing and appropriate animal husbandry [4] , [6] , [73] , [75] in the frame of broad social participation and education . | The major vectors of Chagas disease are species of triatomine bugs adapted to human sleeping quarters and peridomestic annexes where they feed on humans and domestic or synanthropic mammals or birds . Knowledge of the demography and nutritional status of Triatominae in real-life settings is still fragmentary , and this affects our ability to prevent or reduce house reinfestation after insecticide spraying . In addition to showing where the bugs are likely to live ( occupancy and density information ) , our observations and analysis of flight dispersal provide insights into where bugs are likely to originate . Data on nymphal and adult sex ratios , nutritional status , and female fecundity point to the key ecotopes and sites driving the population growth of the bugs and fueling house reinfestation . Focusing control efforts on the three ecotopes ( human sleeping quarters , storerooms , and goat corrals ) that housed reactive , human-fed bugs would neither eliminate the substantial contributions to bug population growth from kitchens , chicken coops , and pig corrals nor stop dispersal of adult female bugs from kitchens . Rather , comprehensive control of the linked network of ecotopes in a typical house compound and community is required to prevent feeding on humans , bug population growth , and bug dispersal simultaneously . | [
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] | 2014 | Key Source Habitats and Potential Dispersal of Triatoma infestans Populations in Northwestern Argentina: Implications for Vector Control |
T . cruzi strains have been divided into six discrete typing units ( DTUs ) according to their genetic background . These groups are designated T . cruzi I to VI . In this context , amastigotes from G strain ( T . cruzi I ) are highly infective in vitro and show no parasitemia in vivo . Here we aimed to understand why amastigotes from G strain are highly infective in vitro and do not contribute for a patent in vivo infection . Our in vitro studies demonstrated the first evidence that IFN-γ would be associated to the low virulence of G strain in vivo . After intraperitoneal amastigotes inoculation in wild-type and knockout mice for TNF-α , Nod2 , Myd88 , iNOS , IL-12p40 , IL-18 , CD4 , CD8 and IFN-γ we found that the latter is crucial for controlling infection by G strain amastigotes . Our results showed that amastigotes from G strain are highly infective in vitro but did not contribute for a patent infection in vivo due to its susceptibility to IFN-γ production by host immune cells . These data are useful to understand the mechanisms underlying the contrasting behavior of different T . cruzi groups for in vitro and in vivo infection .
Chagas disease is a chronic , systemic , parasitic infection caused by the protozoan Trypanosoma cruzi . The disease affects about 8 million people in Latin America , of whom 30–40% either have or will develop cardiomyopathy , digestive megasyndromes , or both [1] . Knowledge of the pathology and immune response to T . cruzi infection has been largely obtained from murine models . These models have shown that the innate and adaptive immune responses play an important role in parasite control , depending on the combined action of various cellular types including NK , CD4+ and CD8+ as well as on the production of antibodies by B cells [2]–[5] . Resistance to T . cruzi infection has been associated with the production of the pro-inflammatory cytokines IL-12 and IFN-γ and with the local production of RANTES , MIP-1α , MIP-1β and MCP-1 . These cytokines activate the production of nitric oxide by macrophages , which is responsible for elimination of the parasite [6]–[9] . TNF-α has also been associated with macrophage activation as a secondary signal for nitric oxide production [10] . In contrast , cytokines such as IL-4 and TGF-β are associated with parasite susceptibility [11] , [12] . T . cruzi strains have been divided into six discrete typing units ( DTUs ) according to their genetic background . These groups are designed T . cruzi I to VI [13] . The geographical distribution of these groups indicate that T . cruzi II to VI are the main causal agent of Chagas' disease in the southern parts of South America , with T . cruzi I only present in the sylvatic cycle [13]–[15] . In contrast , in Colombia , Venezuela , and Central America T . cruzi I have been reported as the primary parasite present in human cases [16]–[18] . T . cruzi G strain ( obtained from Nobuko Yoshida and originally from Mena Barreto ) , isolated from an opossum in the Amazon region , belongs to genotype I and shows a particular behavior; Metacyclic trypomastigotes from G strain show low infectivity in vitro and no in vivo parasitemia [19] . This phenotype was attributed to the expression of a glycoprotein GP90 , a stage specific negative modulator of cell invasion [20] . Conversely amastigotes from G strain are highly infective in vitro [21] , [22] but do not sustain a patent infection in vivo ( data not published ) , regardless that amastigotes and blood stream trypomastigotes are the main forms encountered during the disease progression . Indeed , the presence of blood stream typomastigotes reflects the full completion of the developmental parasite life cycle program otherwise known to proceed in the most frequent conditions . These results raised questions that remained to be addressed along the past years . How come amastigotes from G strain are highly infective in vitro and do not contribute for a patent infection in vivo ? How would the host respond to the infection that complete abolishes parasitemia ? Thus , to gain insight concerning this issue we performed this study that provided us a unique result; IFN-γ production per se is sufficient to control infection by G strain amastigotes . It is extensively known the role of IFN-γ in controlling intracellular parasite infection [23]–[26] . However it is completely new for us that this cytokine is unique in controlling infection by T . cruzi G strain . It is generally observed that T . cruzi strains are sensible to most of immune response mechanisms and also that are able to overcome these responses establishing an acute phase characterized by parasitemia and animal death [6]–[9] . Indeed , this is the first report which showed that the traditionally well known immune response mechanism based on IFN-γ production is sufficient to control infection by low virulent T . cruzi G strain .
Female wild type BALB/c and C57BL/6 mice and also , iNOS , Nod2 , Myd88 , IL-12p40 , TNF-α , IFN-γ , CD4 , CD8 , IL-18 , gp91 phox subunit of NADPH oxidase knockout ( KO ) were provided and maintained at the animal facilities of the Department of Biochemistry and Immunology , School of Medicine of Ribeirão Preto , USP ( Ribeirão Preto , Brazil ) . Male or female mice were six to eight weeks old and were kept under standard conditions on a 12-h light , 12-h dark cycle in a temperature-controlled room ( 25±2°C ) with food and water ad libitum Maintenance and care of these animals complied with the guidelines of the Laboratory Animal Ethics Committee from the Institution . Animal euthanasia was performed in accordance with international welfare grounds , according to the American Veterinary Medical Association Guidelines on Euthanasia . T . cruzi from G was maintained in Vero cells culture . To obtain the amastigotes forms , trypomastigotes were incubated in LIT medium ( liver infusion tryptose ) , pH 5 . 8 overnight . Vero , HeLa and MEF ( murine embryonic cells ) cells were maintained in Dulbecco's modified Eagle's medium ( DMEM ) ( Gibco BRL , Gaithersburg , MD ) with L-glutamine and Dglucose ( 4500 mg/L ) , sodium bicarbonate ( 2000 mg/L ) , HEPES ( 2380 mg/L ) , sodium pyruvate ( 1100 mg/L ) , supplemented with Fetal bovine serum ( 10% ) and Penicillin ( 60 mg/L ) , gentamicin ( 40 mg/L ) and streptomycin ( 10 mg/L ) . Cells were grown at 37°C with 5% CO2 in cell plates . HeLa and MEF cells were plated into 24 wells plate ( 105 cells/well ) . Each well contained a 13 mm round coverslips and were left overnight . After , amastigotes from G ( 20 parasites/cell ) strain were put in contact with cells for 3 hours . After , wells were washed three times with PBS to remove non-internalized parasites . 3 and 48 hours post-infection cells were fixed with Bouin solution and Giemsa stained . Then , coverslips were glued onto glass slides . Number of internalized parasites and multiplication were counted in a total of 100 infected cells . The experiment was performed in triplicate and three times . Inflammatory peritoneal macrophages , from C57BL/6 , were recruited with the injection of thioglicollate 3% ( 3 g/L ) . Two days after , animals were intraperitoneally injected with 105 amastigotes from G strain and macrophages extracted only after 3 hours . The cells were plated into 24 well plates ( 5×105 cells/well ) . Finally , cells were Bouin fixed and Giemsa stained , 48 and 72 hours post-inoculation and number of internalized parasites was counted in a total of 100 infected macrophages . The experiment was performed in triplicate and three times . Undifferentiated cells were extracted from C57BL/6 bone marrow . Primarily , the femur of mice was withdrawn and cells were extracted with a PBS squirt into the marrow . Afterwards , these cells were placed on a Petri dish with a medium containing 20% of fetal bovine serum and 30% of the supernatant of L929 cell line which secretes M-CSF ( macrophage colony-stimulating factor ) a macrophage differentiated factor . Once differentiated , cells were plated in a 96 well plate , some of them stimulated with 10 or 100 ng/mL of IFN-γ and others not . Subsequently , cells were infected with amastigotes from G strain ( 20 parasites/cell ) and the release of trypomastigotes was observed over ten days . BALB/c , C57BL/6 and iNOS , TNF-α , IL-12p40 , IL-18 , CD4 , CD8 , IFN-γ and gp91 phox subunit of NADPH oxidase KO animals were intraperitoneally inoculated with 105 amastigotes from G strain . Each group was composed of five animals . Blood was collected from animal orbital plexus and 5 µL was placed on a slide to parasitemia analyses . Parasitemia and animals mortality was observed over thirty days post-inoculation . C57BL/6 mice were intraperitoneally inoculated with 105 amastigotes from G strain . Each group was composed of five animals . Ten days after inoculation , the animals were immunosuppressed with Decadron ( dexamethasone ) 10 µg/mL . The medication was added to the water bottle of the immunossuppressed groups . Control groups were given just water . Blood was collected from animal tail and 5 µL was placed on a slide to parasitemia analyses . Parasitemia and animals mortality was observed over forty days post-inoculation . C57BL/6 mice were intraperitoneally inoculated with 105 amastigotes from G strain . Each group was composed of four female animals . Control group was not infected . Blood was collected through orbital plexus at 8 and 25 days post-inoculation . Lymphocytes were separated from other blood cells using Ficoll-PaqueTM gradient ( Amersham Biosciences ) . Cells were washed with FACS buffer , counted , and 5×105cells were labeled with CD16/32-APC and CD69-PE or NK1 . 1-PE ( BD ) . NK1 . 1 is a surface molecule expressed in NK cells in selected strains of mice , including C57BL/6 ( an specific marker ) ; CD16 and/or CD32 are expressed on NK , monocytes , macrophages , dendritic cells , kupffer cells , granulocytes , mast cells , B lymphocytes , immature thymocytes and some activated mature T lymphocytes ( here an unspecific marker ) ; CD69 is expressed upon activation of lymphocytes ( T , B , NK , and NK-T cells ) , neutrophils and macrophages , also on IL-2 activated NK cells ( an activation marker ) . The samples were acquired by FACSCantoII ( BD ) , and the results were analyzed by FlowJo software ( version 7 . 6 . 3 ) . The significance of experiments was determined by one way ANOVA performed according to VassarStats program ( Richard Lowry 1998–2006 ) , available http://faculty . vassar . edu/lowry/VassarStats . html or by GraphPad Prism program , version 5 . 01 for Student-t analysis . The results were considered significant when p<0 . 05 . The mortality analysis was performed by a survival curve according to GraphPad Prism program .
Invasion assays using amastigotes from G strain were performed during 3 hours and the multiplication verified 48 h post HeLa and MEF cells invasion . The results showed that G strain amastigotes showed high invasion and multiplication indexes in both mammalian cell lines ( Figure 1 A and B ) . Also , 80 to 90% of cells were infected by the parasite . However , when BALB/c and C57BL/6 mice were intraperitoneally inoculated with amastigotes from G strain , no parasitemia was observed in both animal models ( Figure 1 C ) . One could argue that this T . cruzi strain would be non-virulent . Nonetheless , in immunossupressed C57BL/6 animals , parasitemia reached high peak after 24 days post-inoculation ( Figure 1 D ) . In order to verify the impact of different host immune components in protection against infection by amastigotes from T . cruzi G strain we performed a screening using different knockout mice model . First , we inoculated Myd88 , Nod2 , CD4 and CD8 KO animals . The results showed no change in the course of infection and mortality comparing to WT mice ( Figure 2 a , b , c , d , e , f , g , and h ) . After , we verified if cytokines would play any role in animal protection against amastigotes from T . cruzi G strain infection . In this sense , we used TNF-α , IL-18 , IL-12 and IFN-γ KO mice . We observed that deficiency on TNF-α and IL-18 secretion did not have impact on parasitemia and animal survival ( Figure 3 b , c , f and g ) . On the other hand , IL12p40 KO mice showed parasitemia on the 12 day post-inoculation ( p<0 . 01 ) and 40% of mortality by the 30 post-inoculation ( p<0 . 01 ) ( Figure 3 a and e ) . Moreover , IFN-γ KO mice presented a high parasitemia peak by the 16 day post-inoculation ( p<0 . 001 ) and all animals died by the 24 day ( p<0 . 01 ) ( Figure 3 d and h ) . These results turned our attention to the role of macrophages during T . cruzi G strain clearance . To gain insight about this issue , we performed an ex vivo assay . For that purpose , C57BL/6 mice were intraperitoneally inoculated with amastigotes from G strain for 3 h . Inflammatory peritoneal macrophages were collected and seeded into coverslips . After 48 and 72 h of culture the number of intracellular amastigotes was counted . It was observed that G strain amastigotes did not multiply intracellularly in the macrophage cultures ( Figure 4 A ) . Moreover , naive macrophages obtained from C57BL/6 mice bone marrow undifferentiated cells were in vitro infected with amastigotes from G strain and the number of trypomastigotes in the supernatant was counted after three , five and seven days of infection and treatment or not with different concentrations of IFN-γ . The number of trypomastigotes from G strain in the supernatant was higher in non-treated cells ( p<0 . 001 ) , showing that naive macrophages could not impair parasite multiplication and differentiation . However , the number of released parasites was dramatically reduced in treated cells in an IFN-γ dose dependent manner ( Figure 4 B ) . The next step was to identify the mechanism activated by macrophages during parasite clearance . For that purpose we inoculated amastigotes from G strain in iNOS and gp91 KO mice . However , the results showed no difference in the course of infection , neither on the mortality rates ( Figure 5 a , b , c and d ) . One important question raised by our results is the source of IFN-γ , since CD4 and CD8 cells seemed not to play an important role . Also , it is worth mentioning that the control occurred during the first days after inoculation . Thus , we performed flow cytometry in order to verify if NK cells were recruited during infection . The results showed that in non inoculated animals the lymphocytes population in peripheral blood were stained neither for CD16/32 nor for NK1 . 1 and CD69 , indicating a phenotype of inactivated T cells ( Figure 6 b and c ) . However , when we observed the animals by 8 days post-inoculation , we were capable of identifying another distinct cell population , which was denominated of “large granular lymphocytes” ( LGL ) . It is know that the NK cells are largest than other lymphocytes and they have granular contents . Thereby , this LGL population had the same NK cells phenotype . We observed that this population was mostly double positive to CD16/32 and NK1 . 1 ( Figure 6 d′ ) or CD69 ( Figure 6 e′ ) , confirming NK phenotype , and that they were activated . Afterward , our results demonstrated a dramatic increase in activated NK cell by the 8 day post-inoculation ( p<0 . 001 ) ( Figure 6 a′ , d′ , and e′ ) , however this behavior was not maintained in 25 day post-inoculation , returning to basal levels ( Figure 6 a″ d″ and e″ ) .
T . cruzi is a very heterogeneous flagellate parasite; and its populations are characterized by a diverse morphology , a heterogeneous biological behavior , a high genetic variability , and distinctly different clinical courses . The clonal-histotropic model of Chagas' disease [27] describes a correlation between the clonal-population structure of T . cruzi and its tissue tropism; and it gives a possible explanation for the variety shown by this parasite . It is now accepted that T . cruzi strains can be divided into six DTUs , T . cruzi I to VI [13] . To our awareness this is the first study that evaluated the immune response against T . cruzi amastigotes from strain belonging to group I . In this context , understanding the way host responds to amastigotes is quite important , once amastigotes are the main forms encountered during the chronic phase of the disease . Our first observation is that infection by amastigotes from G strain did not activate signal pathway dependent on Myd88 nor reliant on Nod2 receptor . Probably other innate immune response related receptors are triggered during amastigotes infection . This issue will be addressed in additional studies . Infection in other knockout animals showed that G strain amastigotes were only susceptible to IL-12 and IFN-γ production . The major cytokine responsible for IFN-γ secretion is IL-12 . However we observed just a low peak of parasitemia in IL-12 KO mice . This result may be explained by the fact that IL-18 also induces IFN-γ secretion . Moreover , IL-18 KO mice showed no parasitemia . Thus , these results showed the redundant role of these cytokines in inducing IFN-γ production and infection control . A double knockout mice model for both cytokines would be helpful to sustain this hypothesis . IFN-γ is an important mediator of resistance to T . cruzi . Besides iNOS , IFN-γ regulates the expression of a large number of genes , including chemokines and chemokine receptors , which were shown to play a role in IFN-γ mediated protection in T . cruzi infection . Early during infection , IFN-γ is secreted by NK cells and other cell types , as part of the innate response , and later on the infection course by activated CD4+ and CD8+ T cells [23]–[25] . Recently , authors demonstrated for the first time in vivo , the specific importance of direct IFN-γ mediated activation of macrophages for controlling infection with multiple protozoan parasites [26] . Here we observed that IFN-γ plays crucial and unique role in controlling infection by amastigotes from T . cruzi G strain . Nitric oxide ( NO ) and reactive oxygen species ( ROS ) are two key inflammatory mediators involved not only in pathogen clearance but also in tissue injury . Nitric oxide is produced by different isoforms of NO synthase , among them the inducible isoform ( iNOS ) that is activated by IFN-γ and TNF-α [28] . During T . cruzi infection , NO can directly or indirectly modulate the effector leukocyte machinery through diverse mechanisms . This process involves microbicidal effects derived from toxic-free radicals ( peroxinitrite and superoxide ) generated after NO production , as well as regulation/enhancement of the inflammatory response induced during this type of infection , a dual role in the immunity that is usually observed for NO . This well-known immune duality is usually dependent on concentration and , once dysregulated , may lead to host cell toxicity , autoimmunity or parasite persistence due to immune evasion , all of which can lead to pathology . NO is involved in the control of T . cruzi-induced parasitemia and directly kills the parasite in vitro . NO affects T . cruzi by chemically modifying cysteine-containing proteins and/or by binding to metallo-proteins that mediate crucial metabolic processes . The strength of NO toxicity is dependent on the sensitivity of the parasite , which differs among parasite strains and according to the physiological microenvironment [29] . Moreover , Oxidative burst of activated phagocytes results in the release of ROS , e . g . , superoxide ( O2•− ) , hydrogen peroxide ( H2O2 ) , and hydroxyl radical , via activation of NADPH oxidase ( NOX ) and/or myeloperoxidase ( MPO ) enzymes . The inflammatory cytokines and ROS are important for the control of T . cruzi and may be cytotoxic to the host cellular components . Many of the ROS are highly reactive and diffusible and may be released into the extracellular milieu . Whereas intracellular ROS serve mainly for host defense against infectious agents , the extracellular release of ROS , when present in abundance , directly damages the surrounding tissues or promotes inflammatory processes ( revised in [30] ) . Our results obtained from iNOS and gp91 KO mice showed no parasitemia during the 30 days post-inoculation . Thus , is conceivable to believe that NO and ROS may play redundant role during parasite clearance . Another hypothesis that does not completely exclude the first is that other mechanisms of parasite clearance would be activated by G strain parasites , such as a group of IFN-γ induced genes that plays a major role in host control of intracellular pathogens . These genes belong to a family encoding a series of 47- to 48-kDa GTPases for instance LRG-47 that can influence T . cruzi Y strain control by simultaneously regulating macrophage-microbicidal activity and hemopoietic function [31] . Our results showed the impact of innate immune response in controlling infection by amastigotes from G strain . In addition , CD4 and CD8 KO mice showed no difference in the infection course . This information may represent important finding to design novel immune strategies focused on enhancing the innate immune response to control pathology that may be caused by different strains of the parasite in the same host . To gain insight into the source of IFN-γ production , we performed flow cytometry and observed that the lymphocyte population in the peripheral blood samples showed an inactivated phenotype in infected and non infected animals . While in infected animals , we observed a significant increase in NK population with an activated phenotype . This result suggested that the main source of IFN-γ produced to protect animal against amastigotes from T . cruzi G strain is NK cells . However , depletion of NK cell in WT and IFN-γ KO mice would be interesting to confirm the hypothesis . In conclusion , our research showed that although amastigotes from G strain were highly infective in vitro they did not induce a patent infection in vivo due to the high susceptibility to IFN-γ production early in infection . This study highlighted the need to consider strain biases when investigating host immune response against T . cruzi . | Trypanosoma cruzi , an obligate intracellular protozoan , is the etiological agent of Chagas disease that represents an important public health burden in Latin America . The infection with this parasite can lead to severe complications in cardiac and gastrointestinal tissue depending on the strain of parasite and host genetics . Currently , six genetic groups ( T . cruzi I to VI ) have been identified in this highly genetic and diverse parasite . The majority of published data concerning host immune response has been obtained from studying T . cruzi II to VI-infected mice , and the genetic differences between T . cruzi II to VI and T . cruzi I strains are large . Here we aimed to understand how amastigotes from T . cruzi I G strain are highly infective in vitro and do not contribute for a patent parasitemia in vivo . Our results showed that amastigotes from G strain are highly susceptible to IFN-γ treatment in vitro and secretion by immune cells in vivo . This information may represent important findings to design novel immune strategies to control pathology that may be caused by different strains in the same host . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"model",
"organisms",
"immunology",
"biology"
] | 2012 | IFN-γ Plays a Unique Role in Protection against Low Virulent Trypanosoma cruzi Strain |
Human schistosomiasis , mainly due to Schistosoma mansoni species , is one of the most prevalent parasitic diseases worldwide . To overcome the drawbacks of classical parasitological and serological methods in detecting S . mansoni infections , especially in acute stage of the disease , development of cost-effective , simple and rapid molecular methods is still needed for the diagnosis of schistosomiasis . A promising approach is the loop-mediated isothermal amplification ( LAMP ) technology . Compared to PCR-based assays , LAMP has the advantages of reaction simplicity , rapidity , specificity , cost-effectiveness and higher amplification efficiency . Additionally , as results can be inspected by the naked eye , the technique has great potential for use in low-income countries . A sequence corresponding to a mitochondrial S . mansoni minisatellite DNA region was selected as a target for designing a LAMP-based method to detect S . mansoni DNA in stool samples . We used a S . mansoni murine model to obtain well defined stool and sera samples from infected mice with S . mansoni cercariae . Samples were taken weekly from week 0 to 8 post-infection and the Kato-Katz and ELISA techniques were used for monitoring the infection . Primer set designed were tested using a commercial reaction mixture for LAMP assay and an in house mixture to compare results . Specificity of LAMP was tested using 16 DNA samples from different parasites , including several Schistosoma species , and no cross-reactions were found . The detection limit of our LAMP assay ( SmMIT-LAMP ) was 1 fg of S . mansoni DNA . When testing stool samples from infected mice the SmMIT-LAMP detected S . mansoni DNA as soon as 1 week post-infection . We have developed , for the first time , a cost-effective , easy to perform , specific and sensitive LAMP assay for early detection of S . mansoni in stool samples . The method is potentially and readily adaptable for field diagnosis and disease surveillance in schistosomiasis-endemic areas .
Schistosomiasis , a disease caused by parasitic worms of several species of genus Schistosoma , is one of the 17 neglected tropical diseases ( NTDs ) recognized by World Health Organization ( WHO ) [1] . Presently , human schistosomiasis , mainly caused by Schistosoma mansoni species , is one of the most widespread of all human parasitic diseases , ranking second only to malaria in terms of its socioeconomic and public health importance in developing countries in tropical and subtropical areas , especially in Sub-Saharan Africa . The disease is endemic in 74 countries infecting more than 200 million people worldwide , with 732 million people at risk of infection in known transmission areas [2] , [3] , [4] . On a global scale , one of thirty individuals has schistosomiasis [5] . It is also noted that the prevalence of imported schistosomiasis is increasingly a problem in non-endemic areas due to the growing number of international travelers to endemic areas , expatriates and immigrants from endemic countries [6] , [7] , [8] . Over time , several diagnostic techniques including parasitological and immunological methods have been tested for diagnosis of schistosome infection . As is well known , traditional parasitological methods , such as Kato-Katz assay for counting eggs in feces , are relatively inexpensive and easy to perform providing basic information on prevalence and infection intensity . However , a major limitation of these methods is their lack in sensitivity , especially in low-grade infections , as occurs in areas of low prevalence or in individuals with recent infections [9] . In addition , they are cannot be carried out in the acute phase of schistosomiasis since parasite has not started yet to lay eggs . When parasites cannot be directly detected , the immunological methods are usually applied to patients with schistosomiasis clinical signs . However , serology-based analyses currently continue to present problems , such us obtaining schistosome antigens , inability to discriminate between current or previous infection , high level of cross reactivity as well as persistence of antigens and antibodies after chemotherapy usually causing false positive results [10] . To overcome the shortcomings of both parasitological and immunological diagnostic methods , the development of new , more sensitive and specific molecular diagnostic tools for the diagnosis of schistosomiasis are desirable . In recent years , several studies have reported the application of polymerase chain reaction ( PCR ) -based assays for high sensible and specific detection of Schistosoma spp . DNA in human clinical samples , such as feces [11] , [12] , [13] , [14] , [15] , sera [11] , plasma [16] and urine [17] . Even though these studies have demonstrated that PCR-based technologies provided reliable , specific and sensitive tools , they are not still widely used in low-income countries because highly skilled personnel and expensive cyclers are needed . Therefore , the development of cost-effective , simple and rapid detection methods is still needed for the diagnosis of schistosomiasis . An interesting alternative to PCR-based technologies is a molecular technique named loop-mediated isothermal amplification ( LAMP ) . This assay is a one-step amplification reaction that amplifies a target DNA with high specificity , efficiency and rapidly under isothermal conditions [18] . LAMP employs a DNA polymerase ( Bst polymerase ) with strand-displacement activity , along with two inner primers ( FIP , BIP ) an outer primers ( F3 , B3 ) which recognize six separate regions within a target DNA . The auto-cycling reactions lead to accumulation of a large amount of the target DNA and other reaction by-products , such as magnesium pyrophosphate , that allow rapid visual detection and real-time measurement of turbidity [19] or visual fluorescence in the presence of fluorescent intercalating dyes [20] . Moreover , as LAMP assay is an isothermal amplification method it does not require an expensive cycler and can be performed in economical heating blocks or water baths . On this basis , simple heating methods , such as chemical heaters or thermal bottles , have been recently designed for removing the dependence upon stable electricity and allowing for LAMP to be conducted at any time in any setting [21] , [22] , [23] , [24] . Thus , LAMP assay has all the characteristics required of realtime assays along with simple operation for easy adaptability to field conditions . Since the LAMP assay was first reported [18] , many LAMP reactions have been developed for molecular detection and diagnostics of bacterial , viral , fungal and parasitic diseases in both animals and plants [25] and their performance observed by comparing to molecular techniques , such as PCR , in order to evaluate the feasibility of LAMP technology [26] , [27] . As recently reviewed by Mori et al [28] , of the 17 NTDs recognized by WHO , 14 have been studied using LAMP assay , including schistosomiasis caused by S . japonicum in experimentally infected rabbits to evaluate the technique for early diagnosis and efficacy of chemotherapy [29] , [30] . Other successfully approaches for LAMP assay to be used for Schistosoma spp . detection have been mainly focused in field settings for monitoring infected snails with S . mansoni , S . haematobium [31] , [32] and S . japonicum [33] , [34] . Thus , with the aim to improve in developing new , applicable and cost-effective molecular tools for the diagnosis of schistosomiasis , in our work we have developed a LAMP assay for early specific detection of S . mansoni in mice stool samples . In the present study , specific LAMP primer set designed was tested using different reaction mixtures each containing different Bst polymerases to compare results and cost-effectiveness . We also evaluated the sensitivity of the LAMP assay in comparison with classical diagnostic techniques , such as Kato-Katz and ELISA . To the best of our knowledge , this is the first report using LAMP assay for early diagnosis of active schistosomiasis in mice stool samples .
The study protocol was approved by the institutional research commission of the University of Salamanca . Ethical approval was obtained from the Ethics Committee of the University of Salamanca ( protocol approval number 48531 ) , which approved the animal protocol . Animal procedures in this study complied with the Spanish ( Real Decreto RD53/2013 ) and the European Union ( European Directive 2010/63/EU ) guidelines on animal experimentation for the protection and humane use of laboratory animals and were conducted at the accredited Animal Experimentation Facility ( Servicio de Experimentación Animal ) of the University of Salamanca ( Register number: PAE/SA/001 ) . Six to seven-week old female CD1 mice weighing 16–24 g ( Charles River Laboratories , Barcelona , Spain ) were used in the study as the source for blood and stool samples . Animals were housed at the accredited Animal Experimentation Facility of the University of Salamanca in standard polycarbonate cages and placed in humidity and temperature controlled environment with a 12 hour photoperiod and received sterilized food and water ad libitum . Mice were each infected with 200 S . mansoni cercariae which were obtained from Biomphalaria glabrata snails previously infected with S . mansoni miracidia as described elsewhere [35] . The infection was carried out following the methodology previously described by Smithers et al [36] . Uninfected mice ( control group ) were used as source for negative samples . All mice blood and stool samples were taken weekly from week 0 to week 8 post-infection ( p . i . ) . Animals were monitored regularly by qualified members in animal welfare at the Animal Experimentation Facility of the University of Salamanca . Infected mice were humanely euthanised by intraperitoneal injection with pentobarbital at a 60 mg/Kg dose using 30 g needles at week 8 p . i . A total of 90 stool samples and 90 blood samples were taken from all mice throughout infection . Five stool samples as well as five blood samples were taken weekly and processed from both infected and uninfected mice groups from week 0 to week 8 p . i . Feces weekly obtained from each infected mouse was divided into two portions: one was immediately processed and examined by triplicate for counting eggs using the Kato-Katz technique [37] in a conventional microscope and another was stored at −20°C to be used afterward for DNA extraction for molecular assays . The Kato-Katz technique was used as the gold standard assay to pre-determine the existence of S . mansoni infection in stool samples . Results obtained were expressed as mean±SE . Feces weekly obtained from each non-infected mouse were kept frozen individually until DNA extraction as mentioned below . After collection of the whole blood from each mouse in a defined time-point p . i . , the sera samples were obtained by allowing the blood to clot for 15–30 minutes at room temperature and removing the clot by centrifuging at 1 , 000–2 , 000× g for 10 minutes in a refrigerated centrifuge . The resulting supernatants were immediately transferred into a clean tube and stored at −20°C until use for the evaluation of specific humoral immune response by ELISA for IgG detection . Firstly , soluble somatic extracts from adult S . mansoni worms ( SmAg ) were obtained and determined protein concentration as previously described [38] . Briefly , polystyrene microtiter plates ( Costar , USA ) were coated with 100 µL/well of SmAg at a protein concentration of 5 µg/mL diluted in carbonate buffer ( pH 9 . 6 ) . Diluted serum at 1∶100 was added to the wells and incubated for 1 h at 37°C . Horseradish peroxidase rabbit anti-mouse IgG ( Sigma , USA ) 1∶2000 was added . Washes were carried out three times with 200 µL of PBS-Tween 20/well . After incubation for 1 h at 37°C , substrate solution ( ortho phenylene diamine+H2O ) was added and the reaction was stopped after 10 min with 3 N H2SO4 . Sera , tested by duplicate , were considered positive when the OD value exceeded the mean ± 2 SE absorbance of sera from non-infected animals . The first step in primers design was based on literature searches to identify potential sequences of DNA which were suspected to be used in specific detection of S . mansoni . Genbank sequences initially considered were tested in silico through BLAST searches [39] and alignment analysis . Finally , a 620 base pair ( bp ) sequence corresponding to a mitochondrial S . mansoni minisatellite DNA region was preferred and retrieved from GenBank ( Accession No . L27240 ) [40] for the design of specific primers . Forward and backward outer primers ( F3 and B3 ) and forward and backward inner primers ( FIP: F1c-F2 and BIP: B1c-B2 , respectively ) were designed using the Primer Explorer V4 software ( Eiken Chemical Co . , Ltd . , Japan; http://primerexplorer . jp/e/ ) . Several LAMP primer sets were suggested by the software and further refinement in primer design was developed manually based on the criteria described in “A Guide to LAMP primer designing” ( http://primerexplorer . jp/e/v4_manual/index . html ) . Specific LAMP primers sequences finally selected as well as their positions relative to the 620 bp target sequence are shown in Figure 1 . All the primers were synthesized by Eurofins MWG Operon . The outer LAMP primer pair , designated F3 and B3 , was initially tested for the amplification of S . mansoni DNA by a touchdown-PCR to verify whether the correct target was amplified . The PCR assay was conducted in 25 µL reaction mixture containing 2 . 5 µL of 10× buffer , 1 . 5 µL of 25 mmol/L MgCl2 , 2 . 5 µL of 2 . 5 mmol/L dNTPs , 0 . 5 µL of 100 pmol/L F3 and B3 , 2 U Taq-polymerase and 2 µL ( 1 ng ) of DNA template . Initial denaturation was conducted at 94°C for 1 min , followed by a touchdown program for 15 cycles with successive annealing temperature decrements of 1 . 0°C every 2 cycles . For these 2 cycles , the reaction was denatured at 94°C for 20 s followed by annealing at 58°C–56°C for 20 s and polymerization at 72°C for 30 s . The following 15 cycles of amplification were similar , except that the annealing temperature was 55°C . The final extension was performed at 72°C for 10 min . The specificity of PCR using outer primers F3 and B3 was also tested with heterogeneous DNA samples from other parasites included in the study . Moreover , the sensitivity of the PCR was also assayed to establish the detection limit of S . mansoni DNA with 10-fold serial dilutions prepared as mentioned above . The assay was performed with 2 µL of the diluted template in each case . Negative controls ( ultrapure water instead DNA template ) were included . The PCR products ( 3–5 µL ) were subjected to 2% agarose gel electrophoresis stained with ethidium bromide and visualized under UV light . Two different reaction mixtures containing the primers designed were used to assess the LAMP assay to compare results . On one hand , the LAMP assay was performed using the Loopamp DNA amplification Kit ( Eiken Chemicals Co . , Tokyo , Japan ) following manufacturers' instructions . Briefly , the reaction was carried out with a total of 25 µL reaction mixture containing 12 . 5 µL of 2× Reaction Mix , 40 pmol of each FIP and BIP primers , 5 pmol of each F3 and B3 primers , 1 µL of Bst DNA polymerase ( Large fragment; LF ) , along with 2 µL of DNA template . On the other hand , we tried to set up our own LAMP reaction mixture testing another Bst polymerase , namely Bst 2 . 0 WarmStart DNA polymerase , as well as different betaine and MgSO4 concentrations instead of those supplied by the commercial kit . Thus , LAMP reactions mixtures ( 25 µL ) contained 40 pmol of each FIP and BIP primers , 5 pmol of each F3 and B3 primers , 1 . 4 mM of each dNTP ( Bioron ) , 1× Isothermal Amplification Buffer −20 mM Tris-HCl ( pH 8 . 8 ) , 50 mM KCl , 10 mM ( NH4 ) 2SO4 , 2 mM MgSO4 , 0 . 1% Tween20- ( New England Biolabs , UK ) , betaine ( ranging 0 . 8 , 1 , 1 . 2 , 1 . 4 or 1 . 6 M ) ( Sigma , USA ) , supplementary MgSO4 ( ranging 2 , 4 , 6 or 8 mM ) ( New England Biolabs , UK ) and 8 U of Bst 2 . 0 WarmStart DNA polymerase ( New England Biolabs , UK ) with 2 µL of template DNA . To establish the standard protocol for the two LAMP reaction mixtures assayed , different temperatures were tested using a heating block ( K Dry-Bath ) set at 61 , 63 and 65°C for 60 min and then heated at 80°C for 5 min to terminate the reaction . In each case , the optimal temperature was determined and used in the following tests . Because of the highly sensitivity of LAMP reaction , DNA contamination and carry-over of amplified products were prevented by using sterile tools at all times , performing each step of the analysis in separate work areas and minimizing manipulation of the reaction tubes . Negative controls ( ultrapure water or DNA from non-infected stool samples ) were included in each LAMP reaction . These controls never amplified .
The results obtained using Kato-Katz and indirect ELISA techniques on weekly stool and sera samples , respectively , from infected mice with 200 S . mansoni cercariae are showed in Figure S1 . After infection , using the Kato-Katz technique we could only detect eggs in feces from week 6 to week 8 p . i . Specific detectable antibody levels could be measurable by ELISA from week 4 p . i . to week 8 p . i . To make sure that the expected target was amplified , a conventional PCR reaction was performed using outer primers F3 and B3 to amplify S . mansoni DNA . Then , a 206 bp amplicon was successful obtained ( Figure 2A ) . In order to determine the lower detection limit of the PCR , a 10-fold serial dilution ranging from 10−1 to 10−9 of S . mansoni DNA was amplified . The minimum amount of DNA detectable by PCR was 0 . 1 ng ( Figure 2B ) . Moreover , when DNA samples from other parasites included in the study were subjected to this PCR assay , amplicons were never obtained ( Figure 2C ) . Additionally , when in silico comparisons of the expected 206 bp sequence were carried out using BLASTn searches with the currently available genomes of S . mansoni , S . haematobium and S . intercalatum at Wellcome Trust Sanger Institute web site ( http://www . sanger . ac . uk ) and S . japonicum at GenDB web site ( http://www . genedb . org ) , respectively , the higher homology in alignment length , percentage of identities and E-value were obtained for S . mansoni . For S . intercalatum , S . haematobium and S . japonicum , much lower values were found ( Table S1 ) . The optimal incubation temperature for LAMP assay using the Loopamp DNA amplification Kit tested with the S . mansoni primer set was established in a conventional heating block using a range of temperatures ( 61 , 63 and 65°C ) for 60 min to optimize the reaction conditions and then heated at 80°C for 5 min to inactivate the enzyme . The LAMP reaction could successfully take place at temperatures of 61°C , 63°C and 65°C -within the temperature range ( 60–65°C ) recommended by the manufacturers'- although better results on agarose gels were obtained when using 63°C for amplification . Thus , the optimal temperature for LAMP using the commercial kit was established at 63°C and used for all the following applications . To establish the standard protocol for our in house LAMP assay using Bst 2 . 0 WarmStart DNA polymerase we also applied a range of temperatures ( 61 , 63 and 65°C ) for testing different mixtures containing variable concentrations of betaine ( ranging 0 . 8 , 1 , 1 . 2 , 1 . 4 or 1 . 6 M ) combined with supplementary variable concentrations of MgSO4 ( ranging 2 , 4 , 6 or 8 mM ) . The best amplification results were obtained when the reaction mixture contained 1 M of betaine combined with supplementary 6 mM of MgSO4 ( resulting a final concentration of 8 mM MgSO4 in 1× Isothermal Amplification Buffer ) and was incubated for 60 min at 63°C in a heating block . Thereby , the reaction mixture , in addition to the specific primer set designed -hereafter SmMIT-LAMP- , was set up as the most suitable and used in all successive LAMP reactions . Once the most favorable conditions and molecular components were established for the two different LAMP reactions , all positive results could be visually observed by the naked eye by inspecting white turbidity as well as the color change after adding SYBR Green I . Additionally , after electrophoresis on agarose gels a ladder of multiple bands of different sizes could be also observed in positive samples ( Figure 3 ) . To determine the specificity of LAMP assay for S . mansoni , 16 additional DNA samples from other parasites were tested for amplification . We obtained identical results using both the Loopamp DNA amplification Kit and SmMIT-LAMP reaction mixtures . Thus , a positive result was only obtained using DNA from S . mansoni whereas DNA samples from other specimens were not amplified . These results indicate that no cross-amplification was observed with these heterogeneous species in the LAMP assay , demonstrating its high specificity ( Figure 4A ) . Nevertheless , when sensitivity was evaluated using S . mansoni DNA 10-fold serially diluted , the limit of detection of LAMP using the Loopamp DNA amplification Kit was 10 fg ( Figure 4B ) , whereas the limit of detection using SmMIT-LAMP was established in 1 fg ( Figure 4C ) . These results showed that sensitivity of the SmMIT-LAMP assay is tenfold higher than that of the LAMP assay by using a standard reaction mixture supplied by the commercial kit . Furthermore , the detection limit of SmMIT-LAMP was 105 times more than that previously obtained by PCR ( see Figure 2B ) . When testing stool samples from mice infected with S . mansoni by the Loopamp DNA amplification Kit , we detected positive results continuously from week 2 p . i . to week 8 p . i . in all samples analyzed ( Figure 5A ) . By contrast , when using SmMIT-LAMP for amplification , positive results were continuously obtained in all stool samples from week 1 p . i . to week 8 p . i . ( Figure 5B ) . Therefore , the SmMIT-LAMP assay developed was able to detect S . mansoni DNA in stool samples one week earlier in comparison to the LAMP assay accomplished with a standard commercial reaction mixture .
Human schistosomiasis , caused by several species of the trematode Schistosoma , is a major endemic parasitic disease in many tropical regions of Asia , Africa and South America , with S . mansoni being the most important species in terms of prevalence , morbidity , mortality and socioeconomic impact [2] . This morbidity and mortality is mainly associated with the chronic stage of infection , when egg deposition followed by granuloma formation in different organs , especially in liver and intestine , occurs . Although the use of praziquantel as chemotherapeutic treatment and control for the disease has a clear effect on morbidity [41] , [42] , resistance has been already described after repeated mass drug administration [43] . Thus , methods that allow early diagnosis , both in acute and chronic stages , are a prerequisite for effective disease control . Moreover , diagnostic tools able to detect S . mansoni infections mainly in acute stage would be of great value permitting early treatment that could prevent the pathology associated with chronic infections . Currently , the gold standard method for diagnosis of S . mansoni infections is the Kato-Katz technique to count of parasite eggs excreted in feces because of its low operational costs , practicality and ability to be quantitative . However , this method requires sequential samples and is unable to detect prepatent infection , low levels of infection particularly found in children [44] or infections in individuals with a low worm burden and those in low disease transmission areas [45] . On the other hand , although many of the serology-based analyses developed present greater sensitivity than Kato-Katz technique , they currently continue to present problems in acute infections detection , lack of sensitivity , cross-reactions and false positives usually corresponding to patients who have already eliminated the parasite after efficient chemotherapy [46] , [47] . All this together is currently a drawback and a considerable number of schistosomiasis patients can be incorrectly diagnosed . In this scenario , there is a need in developing new specific and more sensitive molecular diagnostic tools easy to perform in field conditions for diagnosis of schistosomiasis due to S . mansoni . An interesting and promising approach is the LAMP technology . Compared to PCR-based assays , LAMP has the advantages of reaction simplicity , rapidity , specificity , cost-effective and higher amplification efficiency . Furthermore , since DNA amplification and reading of results require minimum equipment , the technique has great potential for use in low-income countries [26] , [27] , [28] . These advantages of the technique make it appealing for use in schistosomiasis-endemic regions . In our study , we used a S . mansoni murine model in order to test a new LAMP assay for early diagnosis of schistosomiasis in stool samples . Classical diagnostic techniques , such as Kato-Katz and ELISA were used for monitoring infection . Mice have been shown to be permissive to S . mansoni [48] and also widely used in studying dynamics of schistosome infections , including diagnosis [49] . The use of a S . mansoni murine model allowed us to collect well-defined stool samples that would otherwise have been difficult to obtain from human patients , such as stool samples from recently acquired infections . Thus , using the Kato-Katz technique we could detect eggs in feces collected from infected mice from week 6 to 8 p . i . When measuring specific detectable antibody levels by ELISA we could detect IgG in infected mice from week 4 p . i . until the end of the experiment . Very similar results were previously obtained by our group in detecting eggs in stool and specific antibodies in sera from mice infected with 200 S . mansoni cercariae [50] . As it would be logical to expect , classical diagnostic techniques were not effective to detect the acute stage of infection . In our work , a 620 bp sequence corresponding to a mitochondrial S . mansoni minisatellite DNA region [40] was selected as a target for designing a LAMP-based method to detect S . mansoni DNA . The minisatellite region in the mitochondrial genome of S . mansoni seems to be unique to that species and has been already used as a target for PCR-based identification of infected snails [51] . In addition , mitochondrial sequences have some advantages over the more usual nuclear targets for amplification approaches . As each cell contains many mitochondria , multiple copies occur in every cell providing many copies of any mitochondrial DNA target region . Thus , greater sensitivity will be possible if the target sequence is present in high copy number and is highly specific and widely conserved within a particular pathogen species or group [52] . The mito-LAMP strategy has already been successfully developed for detection of several parasites , including Opisthorchis viverrini [53] , Trichinella spiralis [54] , Echinococcus granulosus [55] and Plasmodium spp . or P . falciparum specifically [56] . Once the primer set was designed , we attempted to verify the specificity for a 206 bp expected fragment amplification using a conventional PCR performed with the two outers primers to amplify S . mansoni DNA . As a result , a correctly sized amplicon was obtained . Additionally , no cross-reactions were found when using DNA as a target from other parasites tested in the study , including several Schistosoma species , such as S . haematobium , S . japonicum or S . intercalatum , thereby ensuring high specificity for target amplification . Furthermore , in silico comparisons of the expected 206 bp sequence with the on line available genomes of Schistosoma spp . showed the higher homology in alignment length with S . mansoni . After verifying the specificity of the outer primers by PCR in only S . mansoni DNA amplification , we attempted to establish the most suitable reaction mixture for the four specific primers operation in the LAMP assay . To do this , we used a standard reaction mixture supplied by the Loop Amplification kit containing Bst DNA polymerase LF and an in house reaction mixture containing Bst 2 . 0 WarmStart polymerase . The latter , is an in silico designed homologue of Bst DNA polymerase LF with a reversibly-bound aptamer , which inhibits polymerase activity at temperatures below 45°C . This feature prevent about the possible undesired activity from DNA polymerases during preparation of reaction mixtures at room temperature [57] , [58] , [59] , allowing the preparation as well as storage of the LAMP reactions for hours without changing in the final readout , as recently reported in diagnosis of brugian filariasis by LAMP using Bst 2 . 0 WarmStart polymerase [60] . It should be noted that this feature is a very important advantage in order to perform a LAMP assay in field settings where usually limited resources are found . Regarding specificity , both LAMP reaction mixtures performed equally well at established optimal incubation temperature and exclusively S . mansoni DNA was amplified . However , when sensitivity of the LAMP reaction mixtures were evaluated using S . mansoni DNA 10-fold serially diluted , the limit of detection using SmMIT-LAMP resulted tenfold higher than that obtained using the standard reaction mixture supplied by the commercial kit ( 1 fg vs . 10 fg , respectively ) , thus indicating that SmMIT-LAMP is sensitive enough to detect S . mansoni DNA at a very low level . We underline the importance of setting up the best conditions and molecular components for primers set operation in a LAMP assay . Besides , developing an in house LAMP assay is much more cost-effective than using more expensive commercial kits when a large number of samples must be tested . That increased sensitivity achieved using SmMIT-LAMP was subsequently corroborated when weekly stool samples from infected mice were tested using both LAMP reaction mixtures . In this sense , SmMIT-LAMP allowed us to detect S . mansoni DNA in all infected mice samples one week earlier than using the LAMP commercial reaction mixture ( 1 week p . i . vs . 2 week p . i . , respectively ) . Therefore , an early diagnosis of active S . mansoni infection was possible in stool samples using SmMIT-LAMP as soon as one week p . i . It is also noteworthy that green fluorescence by adding SYBR Green I was clearly observed in all successful LAMP reactions , whereas it remained original orange in the negative reactions . This color inspection by the naked eye is a great advantage of the LAMP technique and may be preferentially used under field conditions in endemic areas without requiring electrophoresis to visualize the amplification results . In conclusion , the results of our study demonstrated that the established SmMIT-LAMP assay is cost-effective , easy to perform , specific and sensitive enough for early detection of S . mansoni DNA in stool samples . Although further research for evaluation of the method for the application in patients' samples is required , the method is potentially and readily adaptable for field diagnosis and disease surveillance in schistosomiasis-endemic areas . | Schistosomiasis is one of the most widespread of all human parasitic diseases , Schistosoma mansoni being the most important species causing human intestinal schistosomiasis . The diagnosis of the disease is mainly based on parasitological and serological methods , but they are not effective in detecting S . mansoni infections in the acute stage of the disease . New diagnostic tools to detect the disease during the first weeks would be desirable , permitting early treatment and preventing the pathology associated with chronic infections . An approach is the loop-mediated isothermal amplification ( LAMP ) technique , which can amplify DNA with high specificity and sensitivity under isothermal conditions . DNA amplification and reading of results require minimum equipment , thus the technique has great potential for use in diagnosis of neglected tropical diseases . In our study , we developed and evaluated a LAMP assay for the early detection of S . mansoni DNA in stool samples from mice experimentally infected with the parasite . The results indicated that our LAMP assay is specific , sensitive and cost-effective in detecting S . mansoni DNA in stool samples as soon as one week post-infection , when parasitological and serological methods are not effective . The assay has the potential to be developed further as a field diagnostic tool for use in schistosomiasis-endemic areas . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
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] | 2014 | A Loop-Mediated Isothermal Amplification (LAMP) Assay for Early Detection of Schistosoma mansoni in Stool Samples: A Diagnostic Approach in a Murine Model |
Variation in synonymous codon usage is abundant across multiple levels of organization: between codons of an amino acid , between genes in a genome , and between genomes of different species . It is now well understood that variation in synonymous codon usage is influenced by mutational bias coupled with both natural selection for translational efficiency and genetic drift , but how these processes shape patterns of codon usage bias across entire lineages remains unexplored . To address this question , we used a rich genomic data set of 327 species that covers nearly one third of the known biodiversity of the budding yeast subphylum Saccharomycotina . We found that , while genome-wide relative synonymous codon usage ( RSCU ) for all codons was highly correlated with the GC content of the third codon position ( GC3 ) , the usage of codons for the amino acids proline , arginine , and glycine was inconsistent with the neutral expectation where mutational bias coupled with genetic drift drive codon usage . Examination between genes’ effective numbers of codons and their GC3 contents in individual genomes revealed that nearly a quarter of genes ( 381 , 174/1 , 683 , 203; 23% ) , as well as most genomes ( 308/327; 94% ) , significantly deviate from the neutral expectation . Finally , by evaluating the imprint of translational selection on codon usage , measured as the degree to which genes’ adaptiveness to the tRNA pool were correlated with selective pressure , we show that translational selection is widespread in budding yeast genomes ( 264/327; 81% ) . These results suggest that the contribution of translational selection and drift to patterns of synonymous codon usage across budding yeasts varies across codons , genes , and genomes; whereas drift is the primary driver of global codon usage across the subphylum , the codon bias of large numbers of genes in the majority of genomes is influenced by translational selection .
One of the first insights drawn from DNA sequence analyses was that synonymous codons are used both non-randomly and in taxon-specific patterns [1–3] . These results were surprising given that synonymous codon changes do not alter primary protein structure ( i . e . , they are silent ) and were therefore previously assumed to be selectively neutral . Two major explanations have been put forth to account for the non-random variation in codon usage seen within and across species , namely natural selection and neutral processes , such as mutational bias coupled with genetic drift . The discovery that codon usage is correlated with both the abundance of transfer RNA molecules in the genome and with gene expression levels raised the hypothesis that optimization of codons to match the available tRNA pool ( or tRNAome ) promotes or regulates translation and suggested a key role for codon usage in translational dynamics [4–10] . It is now well established that codon usage influences multiple cellular processes , especially translation . For example , usage of codons corresponding to the tRNA pool , known as codon optimization , has been linked to increased translation speed [11–14] , accurate tRNA pairing [15 , 16] , suppressed premature cleavage and polyadenylation of transcripts [17] , and mRNA stability [11 , 18] . Conversely , non-optimal codon usage has been associated with translation initiation [19] , accurate protein folding [20–22] , and signal recognition particle detection [23] . These molecular discoveries are complemented by a plethora of examples where specific synonymous substitutions have substantial fitness [24–27] and phenotypic effects in organisms across the tree of life , including Escherichia coli [28] , Saccharomyces cerevisiae [29 , 30] , Drosophila melanogaster [31] , and humans [32–34] . In summary , there is now substantial evidence to suggest that codon usage bias of certain codons in certain species is under strong selection—often through translational mechanisms . In the absence of selection or in populations where genetic drift is more powerful than selection , patterns of codon usage bias will reflect the effects of genome-wide mutational pressures , such as mutational bias or GC-biased gene conversion [35–39] . This was first suspected for species with extreme GC composition biases , such as the Gram positive bacterium Mycoplasma capricolum , which has a genomic GC composition of 25% , and only 2% of its codons end with G or C [40] . For species like M . capricolum , it was hypothesized that biased genome-wide mutational processes , such as mutational bias towards A/T bases and GC-biased gene conversion , would drive patterns of codon usage bias . GC-biased gene conversion has been shown to influence the GC content of third codon positions in an evolutionarily neutral manner in mammals , as well as at recombination hotspots in yeasts [41 , 42] . Mutational bias has been proposed as the major driver of codon usage bias in diverse studies in a variety of lineages , including bacteria , archaea , plants , and animals [37 , 38 , 43 , 44] . Even in the presence of selection on synonymous codon sites , it has been proposed that background substitution drives codon preference in organisms with widely different GC compositions [45] . Thus , major differences in codon usage patterns between species are often considered to be primarily driven by neutral mutational changes in GC content [36 , 37] . Selective and neutral explanations of codon usage bias are not mutually exclusive , and pioneers in this field were quick to suggest that codon bias is due to a balance between neutral and selective processes [40 , 46 , 47] . It is unclear , however , what that balance is , how it varies across levels of biological organization ( e . g . , codons , genes , genomes ) and across lineages , and what factors influence the balance [12 , 36 , 38 , 40 , 48 , 49] . Budding yeasts ( subphylum Saccharomycotina , phylum Ascomycota ) present a unique opportunity to examine the impact of neutral and selective processes on codon usage bias for several reasons . First , genomes and genome annotations of 332 species across the subphylum recently became available [50] , providing a state-of-the-art data set for the study of codon usage bias . Second , the genomic diversity across budding yeasts is comparable to the divergence between different animal phyla or between Arabidopsis and green algae , offering us the opportunity to examine variation in patterns of codon usage bias across a highly diverse lineage . Third , budding yeasts exhibit genetic code diversity and are the only known lineage with nuclear codon reassignments . Specifically , three different clades of buddying yeasts have undergone a reassignment of the CUG codon from leucine to serine ( two clades ) or alanine ( one clade ) [51–55] . Codon reassignments in the Saccharomycotina provide both a challenge and an opportunity in comparing codon usage bias across the subphylum . Finally , for the majority of budding yeast species in our data set we also have metabolic trait ( 285 species ) and isolation environment ( 174 species ) information , which not only illustrates the ecological diversity of this group but allows us to test for other contributors to codon usage bias [56 , 57] . To examine codon usage bias at the codon , gene , and genome levels , we examined the genomes of 327 budding yeast species in the subphylum Saccharomycotina . Analysis of codon usage bias , measured by relative synonymous codon usage ( RSCU ) revealed diversity in usage at all three levels ( codon , gene , genome ) examined . This variation in RSCU was highly correlated with GC composition when assessed broadly across the subphylum . Furthermore , the relationship between the relative frequency of each codon and the GC composition of the 3rd codon position showed very small deviations from the neutral expectation , except for codons for three amino acids ( proline , arginine , and glycine ) . However , at the gene level , nearly a quarter of all genes surveyed ( 381 , 174/1 , 683 , 203; 23% ) did not fit the neutral expectation of the relationship between the effective number of codons and synonymous GC composition . In 94% ( 308/327 ) of the budding yeast genomes , the overall fit of genes to the neutral expectation was very low . Investigation of possible causes of this deviation revealed that 81% ( 264/ 327 ) of budding yeast genomes exhibited moderate-to-high levels translational selection on codon usage bias . While there was no significant correlation between the total number of metabolic traits or isolation environments and selection , the strength of selection was significantly correlated with genomic tRNA gene content ( tRNAome ) . These results suggest that translational selection on codon bias is widespread , but not ubiquitous , in the budding yeast subphylum . Our inference of strong translational selection on codon usage bias suggests that translational regulation has played a major role in the evolution of this group .
Genomic sequence and annotation data were obtained from a recent comparative genomic study of 332 budding yeast genomes [50] ( S1 Table ) . Genomes of five species from the CUG-Alanine clade were removed from this analysis as their codon reassignment was discovered recently [53 , 54] and could not be accounted for by any existing software . To remove mitochondrial genome sequences from the remaining 327 budding yeast genomes , we employed blastn , version 2 . 6 . 0+ [58 , 59] with 56 partial or complete Saccharomycotina mitochondrial genomes ( S2 Table ) as our input queries . Hits that had 30 percent or more sequence identity to mitochondrial sequences were removed from our analyses . Similarly , protein-coding gene sequence data from the 327 genomes were filtered for mitochondrial genes by blasting ( blastx ) against mitochondrial protein-coding sequence data from 37 Saccharomycotina species ( S3 Table ) . The coding sequences were further filtered to conform to the required input for the species-specific tRNA adaptation calculations by stAIcalc , version 1 . 0 [60] . This filtering step removed all coding sequences that did not begin with the start codon ATG , did not have a whole number of codons , or were shorter than 100 codons ( S1 Table ) . Codons containing ambiguous bases were also removed . To examine the variation in codon usage across the yeast subphylum , we calculated the relative synonymous codon usage ( RSCU ) for each codon in the 1 , 683 , 203 protein-coding genes of the 327 budding yeast genomes that remained after filtering . RSCU is the observed frequency of a synonymous codon divided by the frequency expected if all the synonymous codons were used equally [9] . We computed RSCU values using DAMBE7 , version 7 . 0 . 28 [61] , because it allowed us to accommodate the known nuclear codon reassignment in the CUG-Ser1 and CUG-Ser2 clades [51–55] . To examine broad patterns of codon usage , hierarchical clustering of all RSCU values for each species was calculated and visualized in the R programming environment . To investigate which codons drive between-species differences in codon usage , we performed correspondence analysis of RSCU values [3] . This technique is highly suitable and informative because it reduces the high number of dimensions present in codon usage statistics into a very small number of axes [62 , 63] . To examine the influence of phylogeny on the observed variation in codon bias , we computed two measures of phylogenetic signal in R , Pagel’s λ [64] and Blomberg’s K [65] . The phylogeny used for this analysis was obtained through maximum likelihood-based inference from a data matrix comprised of 2 , 408 genes obtained from Shen et al . [50] . To assess the role of mutational bias in determining the observed patterns of codon bias in the yeast subphylum , we tested the observed patterns against neutral expectations , both across species and across codons . Between-species patterns in codon usage bias were measured by calculating the Pearson’s correlation of the RSCU of each codon against the GC composition of the 3rd codon position ( GC3 ) across all genes in each genome , for each of the 327 species . To account for the observed phylogenetic dependence within both variables , we also assessed the relationship between RSCU and GC3 using the phylogenetic generalized least squares ( PGLS ) . The influence of mutational bias within each set of codons encoding an amino acid was assessed by comparing the equilibrium solutions for relative codon frequencies based on GC3 content generated by Palidwor et al . [38] to the empirical values . Observed relative codon frequencies were calculated as the total number of observations of a codon divided by the total number of observations of the corresponding amino acid . Total codon counts within the genomes were calculated in DAMBE version 7 . 0 . 28 [61] . For each codon , predicted values of relative frequency were generated from the corresponding equilibrium solution . R2 values were then calculated based on the predicted and empirical relative frequency values . Data from the 98 genomes present in the CUG-Ser1 and CUG-Ser2 clades were removed from the analyses of the amino acids leucine and serine . To assess the influence of mutational bias within every genome , we compared the effective number of codons ( ENC ) [66] of each gene to the synonymous GC3 proportion of that gene . The ENC for each gene within the 327 genomes was computed in codonW ( v1 . 4 . 2; http://codonw . sourceforge . net/ ) which does not allow for CUG codon reassignment . This distribution was compared against the predicted neutral distribution proposed by dos Reis et al . [67] using the suggested parameters . This neutral distribution is a modified version of Wright’s proposed function [66] for calculating ENC [67] . We computed an R2 value between the observed and empirical ENC values based on the GC3 of each gene . To ensure that R2 values were not driven by phylogenetic signal , we calculated Blomberg’s K for the R2 values . Additionally we investigated the role of gene length in the deviation from the neutral expectation by comparing the distribution of lengths between neutral genes and those that deviate by 10% or 20% from the neutral expectation using a Wilcoxon Rank Sum test [68 , 69] . To determine if selection on translational processes has optimized the codon usage within each species , we tested if there is a significant correlation between the selective pressure on a gene and its level of optimization to the tRNAome for every genome . First , the species-specific value for each codon’s relative adaptiveness ( wi ) was calculated in stAIcalc , version 1 . 0 [60] . Calculation of wi values requires genomic tRNA counts , which we calculated in tRNAscan-SE 2 . 0 for all species [70] . The results from tRNAscan-SE 2 . 0 correctly identified the CUG-Ser1 and CUG-Ser2 tRNAs that have a CAG anticodon but the recognition elements for serine ( S4 Table ) . The species-specific tRNA adaptation index of each gene was then calculated by taking the geometric mean of all wi values for the codons ( except the start codon ) . One drawback of stAIcalc is that it does not account for the nuclear codon reassignment in the CUG-Ser1 and CUG-Ser2 clades . Therefore , we also tested all genomes after removing all CUG codons from all sequences . To test whether selection has influenced codon usage bias , we calculated the S-value proposed by dos Reis et al . [67] . This metric is the correlation between the tRNA adaptation index ( stAI ) and the confounded effects of the selection effect of the codon usage of a gene and uncontrollable random factors . Ultimately , the S-value measures the proportion of codon bias variance that cannot be explained by mutational bias or random factors alone . S-values were calculated with the R package tAI . R , version 0 . 2 ( https://github . com/mariodosreis/tai ) for each genome using the previously calculated stAI values . We calculated the S-value twice for each genome: once with CUG codons included and once without CUG codons . We also investigated the impact of gene length on the S-value by testing for a correlation between stAI value and gene length within a genome as well as comparing the S-value for a subset of genes whose protein products are over 1000 amino acids with the whole-genome value . To test whether the S-value for a given genome significantly deviated from what would be expected under neutrality , we ran a permutation test . Specifically , we ran 10 , 000 permutations where each genome’s wi values were randomly assigned to codons , the tAI values were then recalculated for each gene , and the S-test was run on that permutation . A genome’s observed S-value was considered statistically significant if it fell in the top 5% of the distribution formed by the 10 , 000 values obtained by the permutation analysis . To investigate which features may influence the level of translational selection occurring within a genome , we tested the contributions of tRNAome size ( calculated from tRNA-scan-SE ) , genome size , number of predicted coding sequences , total number of reported metabolic traits , and total number of reported isolation environments [50] on S-value variation . We performed linear regression analysis on individual and combinations of variables in R . In addition to the linear models , we tested a Gaussian distribution on a subset of features based on visual inspection . We also tested a PGLS analysis on S-value distribution to examine correlations that may be corrected by phylogenetic consideration . Finally , to check that genome completeness did not significantly influence our results , we measured the correlation between genome assembly N50 value and i ) total number of tRNA genes , ii ) the fit of genes to the neutral expectation of GC and ENC , and iii ) the genome wide S-value .
To measure variation in codon usage bias across budding yeast genomes , we measured the RSCU of each codon in each Saccharomycotina species . Hierarchical clustering of the codons revealed three major groups of codons ( Fig 1 ) . One group contained codons that were generally overrepresented ( RSCU > 1 ) in budding yeast genomes , which included A/U-ending codons and one G/C-ending codon ( UUG ) . The next group contained mostly G/C-ending codons and two A/U-ending codons ( AUA and GUA ) that were generally underrepresented ( RSCU < 1 ) across budding yeast genomes . Finally , the smallest group contained A/U-ending codons ( CUA , UUA , CGA , GGA , AUA , CCU , and GUA ) that were relatively underrepresented across some budding yeast genomes as compared to the first set of A/U-ending codons . Interestingly , the underrepresentation of the CUA codon , which encodes leucine , was driven most strongly by the CUG-Ser1 and CUG-Ser2 clades where the CAG leucine codon has been recoded as serine ( Fig 1 ) . To summarize the overall variation in codon usage between species , we conducted a correspondence analysis on RSCU across all 327 species . The majority of the variation in codon usage between species was described by the first dimension of the correspondence analysis ( 66 . 891%; Fig 2 ) , which was driven by differential usage of codons that vary at the third codon position , with the codons UUA , CGU , GGC and GUG making the largest contributions ( S1A Fig ) . The second axis , which explained 7 . 093% of the variation in codon usage , showed some clustering by clade , with the CUG-Ser clade , the CUG-Ser2 clade and the only member of the Alloascoidea clade ( Alloascoidea hylecoeti ) clustering separately from the rest of the clades . This clustering was driven primarily by the codons CUA , CUG , UUG , and UUA ( S1B Fig ) , with species in the CUG-Ser , CUG-Ser2 and A . hylecoeti being underrepresented in CUA and CUG and overrepresented in UUA and UUG . These four codons are all canonically decoded as leucine , suggesting that the reassignment of the CUG codon in the CUG-Ser1 and CUG-Ser2 clades is largely responsible for the separation of CUG-Ser1 and CUG-Ser2 clades from the rest . This result , however , does not explain the clustering of A . hylecoeti , which had the second highest overrepresentation of the UUA codon among the sampled Saccharomycotina , including the CUG-Ser1 and CUG-Ser2 clades . A . hylecoeti is the only representative genome of the major clade Alloascoideaceae in the dataset , and its genome contains tRNAs that decode all of the leucine codons , except for CUC . Moreover , there is no evidence of alternative codon usage in this species [71] . Additional species in this major clade will need to be sequenced to further understand why A . hylecoeti is an outlier in the relative usage of the UUA codon . We next tested whether values of the RSCU metric across species had phylogenetic signal by measuring Pagel’s λ [64] and Blomberg’s K [65 , 72 , 73] ( S5 Table ) . Pagel’s λ tests for the presence of phylogenetic signal in a given trait using tree transformation—making the tree more or less star-like . Values for Pagel’s λ vary from 0 , which denotes that the trait absence of any phylogenetic signal , to 1 , which denotes that the trait varies according to a Brownian model of random genetic drift . Codons’ values for Pagel’s λ ranged from 0 . 953 ( for CUU ) to 1 ( for multiple codons ) with p-values of <<0 . 001 . These data suggest that codon usage between closely related species is more similar than expected under a Brownian motion model . Blomberg’s K measures the ratio of trait variation among species to the contrasts variance . If the trait varies according to a Brownian model of random genetic drift Blomberg’s K will equal 1 . Blomberg’s K however can be greater than 1 which indicates that variance in the trait occurs between clades ( versus within ) . Interestingly , examination of Blomberg’s K identified between-clade variance ( K>1 ) for only the codons CGA , CCA , UUG , and CUA , with the majority of the variance of the remaining codons present within major clades ( K<1 ) . Taken together , Pagel’s λ and Blomberg’s K suggest that the phylogenetic signal for most codons resides towards the tips of the phylogeny and explains variation in RSCU between closely related species . Two of the four codons that have phylogenetic signal deeper in the phylogeny ( UUG and CUA ) canonically encode leucine and were identified as drivers of the second explanatory axis in the correspondence analysis . This result suggests that the phylogenetic correlation between CGA , CCA , UUG and CUA is not restricted to closely related species and represents phylogenetically-driven differences between major clades , whereas the phylogenetic correlation of most other codons is only between closely related species and not between major clades . The correspondence analysis of RSCU revealed that major differences in codon usage are largely explained by differences in the usage of G/C- and A/U-ending codons ( Fig 2 ) . To determine the influence of neutral mutational bias on the usage of individual codons , we used Pearson’s correlation and phylogenetic generalized least squares ( PGLS ) to examine the relationship between codon usage and mutational bias . Across all species , the Pearson’s correlation of GC3 and RSCU revealed that all G/C-ending codons and two A/U-ending codons were positively correlated with GC3 ( p-value < 0 . 001 in all cases ) ( S6 Table ) . The two A/U-ending codons that were positively correlated with GC composition bias were CUU and CGA . Interestingly , CGA was one of the codons identified by Blomberg’s K as being phylogenetically differentiated between clades . It is , therefore , not surprising that CGA and CUU are negatively correlated with GC3 in the phylogenetically corrected PGLS analysis ( Fig 3 , S7 Table ) . In the PGLS analysis all A/U-ending codons are negatively correlated with GC3 and all G/C-ending codons are positively correlated with GC3 . These results reveal that there is a strong correlation between mutational bias and codon usage at the genome level . While the Pearson’s correlation and PGLS analyses suggest that codon bias and GC composition due to mutational bias are correlated , these metrics do not account for the non-linear relationship between GC composition and codon usage . Therefore , we compared observed relative codon frequencies with equilibrium solutions generated by Palidwor et al . [38] . We compared the observed relative codon frequencies for every codon with the equilibrium solutions and measured fit using R2 ( Fig 4; S8 Table ) . All but one of the 2-fold degenerate codons had an R2 value > 0 . 5 when compared to the neutral expectation ( Fig 4C ) . For example , the codon GCC fit the neutral expectation very well ( R2 = 0 . 671; Fig 4a ) . The only 2-fold degenerate amino acid encoded by a codon that had an R2 < 0 . 5 was phenylalanine ( R2 = 0 . 236 ) . For the 3-fold and 4-fold degenerate codons , the R2 values for the individual codons varied but , as previously noted [38] , the summed predictions for G/C-ending codons and A/T-ending codons better fit the neutral expectation ( Fig 4C: second column ) . The exceptions to this were proline , arginine , and glycine , which showed deviations from the neutral expectation even with the summed statistics ( Fig 4B ) . To ensure that phylogenetic signal was not driving the deviations from the neutral expectation , we assessed Blomberg’s K of the individual species’ residuals used to compute the R2 value . A total of 7 codons had Blomberg’s K variances over 1 ( Fig 4C: S8 Table ) , suggesting that deviations from the neutral expectation were driven by differences between major clades . Even after accounting for phylogenetic signal and the improved fit of the summed predictions , codons for proline , glycine , and arginine still showed deviations from the neutral expectation , suggesting that their usages are at least partially driven by selection . Finally , there was no correlation between genome completeness and S-value ( 0 . 14 ) , the fit of genes to the neutral expectation of GC and ENC ( 0 . 11 ) , or tRNA count ( 0 . 00 ) . To assess the role of mutational bias across all genes within each genome , we next examined the relationship between the ENC of each gene and its GC3s vis-a-vis the neutral expectation ( i . e . , the relationship between ENC and GC3s if neutral mutational bias were the only force acting on codon usage ) . For each genome , we computed the number of genes that fell 10% and 20% of the maximum value outside of the neutral expectation between NC and GC3s [67] . Out of a total of 1 , 683 , 203 genes , 381 , 174 ( 23% ) genes fell outside the 10% threshold and 205 , 558 ( 12% ) fell outside of the 20% threshold ( Fig 5A; S9 Table ) . We tested the role of gene length in this analysis by comparing the length distribution of genes that deviated either 10% or 20% from the neutral expectation and those that fell within the neutral expectation . In 309 of the 327 species analyzed ( ~95% ) , genes that were outside either the 10% or 20% threshold were significantly longer than neutral genes ( S9 Table ) . In 44 species , only those genes that fell outside the 20% threshold were significantly longer than the neutral genes . Interestingly , which species exhibit the pattern of longer non-neutral genes is not associated with major clade , average gene length or the level of translational selection ( measured using the S-value; see below ) . We also tested each species’ overall fit to the neutral expectation by calculating an R2 fit to the neutral expectation ( Fig 5B & 5C ) . This analysis revealed that 7 genomes had R2 values greater than 0 . 5 , suggesting that codon usage in these species can largely be explained by neutral mutational bias . Twelve species had an intermediate R2 value between 0 . 25 and 0 . 5 ( or [0 . 25–0 . 50] ) , suggesting that neutral mutational bias is partially responsible for codon usage in most genes in these species . Finally , 72 species had low R2 values between 0 . 00 and 0 . 25 , while the remaining 277 species had values below 0 . The species with low and negative R2values deviate from the neutral expectation , suggesting that mutational bias is not the sole driving factor of codon bias within these genomes . The previous analysis suggested that most Saccharomycotina species deviate from the strictly neutral expectation between GC3s and NC within their genomes ( Fig 5 ) . To test whether translational selection influenced codon usage in budding yeast genomes , we calculated the S-value or the amount of selection on codon usage due to tRNA adaptation . To determine the effect of not accounting for CUG codon reassignment in our analysis , we calculated S-values for genomes with CUG and with all CUG codons removed ( S10 Table ) . The R2 value when comparing the S-value for the CUG and CUG-removed datasets was 0 . 99 . This suggests that our results are valid despite not accounting for the codon reassignment . S-values could not be produced for the species Martiniozyma abiesophila , Nadsonia fulvescens var . fulvescens , and Botryozyma nematodophila , because they did not produce viable wi values from stAI-calc due to software issues ( S11 Table ) . S-values were computed for the remaining 324 species , and significance was assessed using a permutation test ( Fig 6A ) . Thirty-four species from 6 of the 9 clades did not have S-values that were significant at the 0 . 05 or 0 . 95 level in the permutation test ( S10 Table ) . These non-significant results ranged in S-value between -0 . 252 and 0 . 577 , with a median value of 0 . 273 . This result suggests that , in these species , gene-level codon usage could not be distinguished from neutral mutational bias; therefore , it is unlikely that translational selection is broadly acting in these species . In contrast , 27 species exhibit moderate S-values between 0 . 28 and 0 . 5 ( Fig 6B ) , on par with levels of translational selection observed in C . elegans [S-value of 0 . 45; 67] . A moderately high S-value between 0 . 5 and 0 . 75 was observed in 157 species . Finally , a very high S-value above 0 . 75 was observed for 107 species , including S . cerevisiae ( Fig 6C ) , as previously reported [67] . Overall , 291 / 324 ( 94% ) of genomes examined showed moderate to very high S-values , suggesting that translational selection is widespread across budding yeast genomes . We also investigated the role of gene length on our measures of translational selection . We found that there was no correlation between our gene level measurement of codon adaptation to the tRNA pool ( stAI ) and gene length ( largest correlation was 0 . 097 for Candida tammaniensis ) . We did , however , find that when we examined only genes whose protein products are over 1000 amino acids , the S-value increased by an average of 0 . 14 in 288 of the 324 genomes analyzed . The largest increase in S-value was observed in the genome of Eremothecium gossypii , whose S-value increased from -0 . 08 to 0 . 64 . Of the 30 species for which the S-value of the longest genes was 0 . 25 or more greater than the whole genome value , 18 did not have a significant p-value in the permutation test of the genome S-value calculation ( i . e . , their genome-wide patterns of codon usage bias were consistent with neutrality ) . This analysis further illustrates that translational selection varies within the genome—even species for which codon usage patterns at the level of the whole genome are consistent with neutrality , translational selection may still act strongly on some of their genes . To determine which features are associated with S-values , we examined the relationship between S-values with the combinations of two or more of the following features: genome size , tRNAome size , gene number , number of metabolic traits , and number of isolation environments ( S12 Table ) . The linear model with the highest explanatory power , which accounted for 17 . 47% of the variation in S-value , includes genome size , tRNAome size , gene number , and total metabolic traits ( S13 Table ) . Among the four features in the model , tRNAome size had the biggest contribution , followed by genome size , gene number , and reported metabolic traits ( 0 . 612 versus 0 . 229 , 0 . 119 , and 0 . 039 , respectively ) . To gain further insight into the contribution of the tRNAome size , we tested a Gaussian model ( Fig 7 ) based on previously reported analyses [67] . The R2 value of the Gaussian model was higher than that of the linear model ( 0 . 11 vs 0 . 04 ) , although neither model had a very good fit . The Gaussian model suggests that the maximum selection occurs at an intermediate tRNAome size . Interestingly , the estimated maximum for S-value occurs at a tRNAome size of 336 tRNA genes , a value similar to the tRNAome size that corresponds with the maximum modeled S-value from previous models ( tRNAome of about 300 ) [67] . The phylogenetically corrected PGLS analysis revealed no correlation between S-value and either genome size or tRNAome ( S2 Fig ) . Overall , none of the features we tested had strong associations , individually or additively , with S-value , even when phylogenetically corrected .
In this study , we surveyed the patterns and forces underlying codon bias across 327 budding yeasts from the subphylum Saccharomycotina . Cluster , correspondence , and correlation analyses of the relative synonymous codon usage across the subphylum is consistent with mutational bias as a significant driver of codon bias—A/U ending codons are generally overrepresented and G/C ending codons are generally underrepresented . This finding is consistent with the low GC content ( average silent GC context of 42% ) found across the subphylum . Several previous studies have suggested that genome-wide mutational processes are the primary drivers of genome-wide codon usage [36 , 37 , 44] , and we clearly observed the influence of these neutral processes at the genome level . Notably , we also found evidence of selection in both specific codons and genes , which we discuss below . At the level of individual codon usage , two codons in particular—CGA and CUA—had multiple lines of evidence for violating assumptions of neutral GC-mutational bias and we present biological hypotheses for why these particular codons may be subject to increased selective pressure . For CGA , our results are consistent with previous reports that decoding of the CGA codon in S . cerevisiae is inhibitory to translation due to codon-anticodon interactions [74 , 75] . This effect , however , may not be universal across the Saccharomycotina: CGA was underrepresented ( RSCU < 1 ) in 222 species but overrepresented ( RSCU > 1 ) in 105 species . RSCU of CGA also varies between major clades of the Saccharomycotina with the Dipodascaceae/Trichomonascaceae clade having the highest average RSCU ( 1 . 47 ) and the Phaffomycetaceae clade having the lowest average RSCU ( 0 . 66 ) . Given that Dipodascaceae/Trichomonascaceae clade is distantly related to Saccharomycetaceae , the major clade that S . cerevisiae belongs to , it is likely that the two independent defects in translation that result in the inhibitory nature of CGA in S . cerevisiae [75] evolved within Saccharomycetaceae , after the divergence of the two clades . The codon CGA is not the only arginine encoding codon to violate the neutral assumptions ( Fig 4C ) . Deviations in the remaining arginine codons may be a result of strong directional selection due to the large number of degenerate codons encoding arginine , which may result in more opportunities for poor codon-tRNA pairing [76 , 77] . For CUA , departure from assumptions of neutral GC-mutational bias are likely driven by the reassignment of CUG in the CUG-Ser1 and CUG-Ser2 clades , which had profound effects on the remaining leucine codons since the majority of CUG codons that remained leucine were reassigned to UUG or UUA [52 , 78] . This conclusion is supported by the observation that the CUA codon is underrepresented in the CUG-Ser1 and CUG-Ser2 clades ( Fig 1; S14 Table ) compared to other major clades in the subphylum ( Fig 1: S14 Table ) . Underrepresentation of CUA is not exclusive to the CUG-Ser2 and CUG-Ser1 clades—the Dipodascaceae/Trichomonascaceae major clade had an average RSCU of 0 . 60 and includes 12 species ( of 37 ) with a very low RSCU less than 0 . 5 . This may suggest that the Dipodascaceae/Trichomonascaceae major clade experienced similar evolutionary pressures to those that may have contributed to codon reassignment , such as the hypothesized presence of a Virus-Like Element with killer activity in the CUG-Ser1 and CUG-Ser2 clades [55] . The most studied member of the Dipodascaceae/Trichomonascaceae major clade , Yarrowia lipolytica , possesses virus-like particles , but these particles do not appear to be associated with a killer phenotype [79 , 80] . This finding highlights the strong impact of codon reassignment on codon usage . We also observed deviations from the neutral expectation in all codons that encode proline that may be associated with the chemical structure of the proline peptide-bond . Biases in proline codon usage may be related to proline-induced stalling in translation [81] . This stalling was observed in S . cerevisiae riboprofiling data [81] and may be related to the slow incorporation of proline into the growing amino acid chain due to its imino side-chain [82 , 83] . Additionally , in S . cerevisiae , codons for proline and glycine ( which also deviate from the neutral expectation ) are involved in frameshift suppression via suppressor tRNAs that contain four-base anticodon sequences that allow for frameshift read-through [84 , 85] . As a whole , the results of the codon-specific analysis suggest that while many codons are highly correlated with mutational bias , specific codons may be under a variety of selective forces—especially translational selection—that alter codon usage . Almost a quarter of the 1 , 683 , 203 genes found in the 327 budding yeast genomes deviate from the neutral expectation by at least 10% . These results are consistent with the observation that codon bias varies between transcripts within a species [37 , 86] and is associated with increased expression . In fact , for the species Saccharomyces mikatae , the degree to which a transcript differs from the neutral expectation ( greater residual ) is moderately associated with greater expression at steady state [87] . For the majority of the species examined ( 320 ) , mutational bias is not the only force influencing codon bias among transcripts . We also determined that gene length is likely associated with levels of translational selection for many of the species we investigated . This is not surprising given previous work suggesting that gene length and translational selection are not independent [16 , 76 , 88 , 89] . For example , in S . cerevisiae and Escherichia coli , increased selective pressure on longer genes may be required to reduce missense errors during the translation of energetically expensive large products [16 , 88 , 89] . In contrast , the opposite pattern has been observed in Drosophila melanogaster , Caenorhabditis elegans , and Arabidopsis thaliana , where shorter genes exhibit higher levels of optimal codons [76] . While our results are generally consistent with an increased deviation from neutral expectation for longer genes , this is not the case for all budding yeast genomes—for 62 of the 327 species , the genes that deviate from neutrality by 10% were not longer than neutral genes . Interestingly , we could not associate this pattern with average gene length , total number of genes or whole genome S-value . Furthermore , 36 of the 324 measurements of translational selection did not increase by including only genes over 1 , 000 amino acids . Overall , we find support for increased translational selection in longer genes but caution that this is not a universal feature of the subphylum . Assessing how translational selection may influence codon usage bias within species , we found that the majority of species exhibited moderate or high contribution of selection to the variation in codon bias ( Fig 6A ) . Previous work suggested a model in which the highest amount of selection on synonymous codon usage occurs at intermediate genome size . At the lower end of genome size , low selection is hypothesized to be due to the correlation between small genomes and small tRNAomes with low tRNA gene redundancy . In turn , low tRNA gene redundancy restricts the ability of selection to act on codon bias [67 , 90] . At the larger end of genome size , low selection is hypothesized to be due to drift in species with small effective population sizes: this drift would increase the genome size and decrease the ability of selection to shape codon usage [12] . Within Saccharomycotina , the role of tRNAome size is consistent with these predictions , except for genome size . This exception is likely due to a low correlation between genome size and tRNAome size in this group . While tRNAome size and genome size are positively correlated when analyzed using a phylogenetically independent contrast ( PIC ) [91] , this correlation is not very strong ( adjusted R2 of 0 . 1629 ) . It is likely that other biological and ecological features play a significant role in the amount of translational selection occurring within these genomes . For example , generalist and specialist parasitic fungi have been shown to have significantly different amounts of translational selection occurring on codon usage [92] . In summary , we find that the balance between neutral and selective forces on codon usage varies between genomes , between codons , and between genes within a genome . Some Saccharomycotina species exhibit nearly neutral codon usage in line with those observed in humans or bacteria , such as Helicobacter pylori , while other budding yeast species show extremely high adaptation to the tRNA pool through translational selection [67] . This range in the magnitude of forces acting on codon usage in the Saccharomycotina and the low explanatory power of the factors examined suggest that it is difficult to predict a priori selection on codon bias based on lineage , cellularity , genome size , tRNAome , or GC composition . There is moderate to strong evidence for translational selection in most budding yeast genomes examined . This trend may be due to the rapid growth that characterizes most budding yeasts: growth efficiency has been linked to translational selection in codon usage [93 , 94] . One interesting implication of this abundance of translational selection is that codon optimization may be a useful proxy for highly expressed genes . It has long been known that ribosomal genes are among both the most highly expressed and highly codon usage-optimized genes across species [49 , 95] , leading to their use as the basis for the codon adaptation index [35 , 96] . In our dataset , there are 11 , 047 genes ( average of 35 per species ) that are as highly or more highly optimized than the ribosomal genes , suggesting there is a wealth of information about which genes may be highly expressed or differentially highly expressed across this lineage . | Synonymous mutations in genes have no effect on the encoded proteins and were once thought to be evolutionarily neutral . By examining codon usage bias across codons , genes , and genomes of 327 species in the budding yeast subphylum , we show that synonymous codon usage is shaped by both neutral processes and selection for translational efficiency . Specifically , whereas codon usage bias for most codons appears to be strongly associated with mutational bias and largely driven by genetic drift across the entire subphylum , patterns of codon usage bias in a few codons , as well as in many genes in nearly all genomes of budding yeasts , deviate from neutral expectations . Rather , the synonymous codons used within genes in most budding yeast genomes are adapted to the tRNAs present within each genome , a result most likely due to translational selection that optimizes codons to match the tRNAs . Our results suggest that patterns of codon usage bias in budding yeasts , and perhaps more broadly in fungi and other microbial eukaryotes , are shaped by both neutral and selective processes . | [
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... | 2019 | Variation and selection on codon usage bias across an entire subphylum |
Neutrophils are the host's first line of defense against infections , and their extracellular traps ( NET ) were recently shown to kill Leishmania parasites . Here we report a NET-destroying molecule ( Lundep ) from the salivary glands of Lutzomyia longipalpis . Previous analysis of the sialotranscriptome of Lu . longipalpis showed the potential presence of an endonuclease . Indeed , not only was the cloned cDNA ( Lundep ) shown to encode a highly active ss- and dsDNAse , but also the same activity was demonstrated to be secreted by salivary glands of female Lu . longipalpis . Lundep hydrolyzes both ss- and dsDNA with little sequence specificity with a calculated DNase activity of 300000 Kunitz units per mg of protein . Disruption of PMA ( phorbol 12 myristate 13 acetate ) - or parasite-induced NETs by treatment with recombinant Lundep or salivary gland homogenates increases parasite survival in neutrophils . Furthermore , co-injection of recombinant Lundep with metacyclic promastigotes significantly exacerbates Leishmania infection in mice when compared with PBS alone or inactive ( mutagenized ) Lundep . We hypothesize that Lundep helps the parasite to establish an infection by allowing it to escape from the leishmanicidal activity of NETs early after inoculation . Lundep may also assist blood meal intake by lowering the local viscosity caused by the release of host DNA and as an anticoagulant by inhibiting the intrinsic pathway of coagulation .
Leishmaniasis comprises human and animal diseases caused by parasites of the genus Leishmania that are transmitted by the bite of infected sand flies [1] . Leishmania transmission occurs when an infected sand fly probes the host's skin in search of a blood meal . During probing and feeding , sand flies salivate into the host's skin . Saliva contains powerful pharmacologic components that mediate blood-feeding success and facilitate Leishmania infection , first shown when Lutzomyia longipalpis salivary glands ( SGs ) were reported to enhance Leishmania major infection in mice [2] , [3] . In the last two decades , SG and recombinant salivary proteins were investigated for their effect in enhancing pathogen transmission in different model systems ( reviewed in [4] ) . The powerful Lu . longipalpis vasodilator maxadilan along with hyaluronidase were shown to facilitate transmission and establishment of L . major parasites [5] , [6]; however , as we show here , these salivary compounds are not the only active components of sand fly saliva that exacerbate parasite infection . Neutrophils are considered the host's first line of defense against infections and have been implicated in the immunopathogenesis of leishmaniasis [7]–[10] . Leishmania parasites evade killing by neutrophils by blocking oxidative burst and entering a nonlytic compartment unable to fuse with lysosomes or by resisting the microbicidal activity of neutrophil extracellular traps ( NETs ) [11] . The mechanism of NET formation ( NETosis ) in response to Leishmania sp . is still under investigation [11] , [12]; however , recent studies have shown the direct effect of Lu . longipalpis SG extract ( SGE ) in L . major parasite survival inside host neutrophils [13] . This effect was abrogated by pretreatment of SGE with proteases as well as preincubation with antisaliva antibodies , supporting the hypothesis that Lu . longipalpis salivary protein ( s ) help Leishmania survival inside neutrophils . The negative effect of NETosis to Leishmania was recently documented [12] . In this report , we present direct experimental evidence that Lundep ( Lutzomyia NET destroying protein ) , a secreted salivary endonuclease , is responsible for the NET-destroying activity of Lu . longipalpis and that this activity enhances parasite infectivity both in vitro and in vivo . Furthermore , Lundep may assist blood-meal intake by lowering the local viscosity caused by the release of host DNA and as an anticoagulant and anti-inflammatory by inhibiting the intrinsic pathway of coagulation .
Bioinformatic survey of the Lu . longipalpis sialotranscriptome [14] identified a transcript ( AY455916; Lundep ) containing the NUC-motif ( prokaryotic and eukaryotic double ( ds ) and single ( ss ) stranded DNA and RNA endonucleases also present in phosphodiesterases ) indicative of nonspecific DNA/RNA endonuclease . Alignment of the Lundep putative active center with other proteins of the same family shows the presence of the conserved RGH triad found in most DNases characterized so far ( Figure S1A ) . The importance of these residues for catalysis has been previously studied in detail [15] , [16] . Putative endonucleases retrieved using Lundep as query in the NCBI database , grouped into well supported clade , indicating that they are orthologs ( Figure S1B ) . Visual inspection of sand fly sequences revealed the presence of signal peptide and the putative active site triade RGH necessary for DNA hydrolysis . These putative secreted salivary endonucleases may have the same biological role as Lundep in other sand fly species . The expressed sequence tag of Lundep has a predicted signal peptide of 24 aa , indicative of secretion . Accordingly , endonuclease activity was confirmed in SGEs of female Lu . longipalpis ( Figure 1A ) . No endonuclease activity was detected in the SGs of males , which are not blood feeders ( Figure 1B ) . Moreover , this activity is present in secretions of probing Lu . longipalpis ( Figure 1C ) . Rabbit polyclonal antibodies against rLundep blocked the DNase activity of rLundep and SGE , indicating that Lundep is the major endonuclease in Lu . longipalpis SGs ( Figure S2 ) . Recombinant Lundep ( rLundep ) was cloned from a SG cDNA library using standard PCR procedures and subcloned into VR2001 expression vector . rLundep was expressed in HEK293 cells and purified by affinity and size exclusion chromatography ( Figure S3A ) . rLundep has a strict requirement of divalent metal ions for endonuclease activity ( Figure S3B , C ) with broad pH optimum ( 5 . 0–8 . 0 ) . Purified rLundep hydrolyzes both single-stranded ( ss ) - and double-stranded ( ds ) DNA with little sequence specificity ( Figure S4 ) . No significant RNase activity was detected ( Figure S5 ) . Lundep has a specific activity of 300 , 000 Kunitz U/mg as determined by a hyperchromicity assay on salmon sperm genomic DNA , where one Kunitz unit causes 0 . 001 change of absorbance at 260 nm per minute . Because the scaffold of NETs is DNA , we hypothesized that SGE or rLundep could help L . major parasites escape from the microbicidal activity of NETs . To test this , we analyzed the ability of Lu . longipalpis SGE and rLundep to destroy human NETs induced by phorbol 12-myristate 13-acetate ( PMA ) and L . major metacyclic promastigotes . Human neutrophils from healthy subjects were activated by PMA or L . major parasites for 4 h at 37°C . DNA—the major structural component of NETs—and neutrophil elastase ( HNE ) were detected by immunofluorescence ( Figure 2A–F ) . Released human neutrophil elastase ( HNE ) was quantitated using a fluorogenic substrate ( Figure 3A ) . First , we analyzed the ability of Lu . longipalpis SGE and rLundep to affect the integrity of PMA-induced NETs . PMA-activated neutrophils were incubated with culture medium ( negative control ) , rLundep , Lu . longipalpis SG , and commercial bovine DNase-I ( positive control; Figure 4 ) . After 30 minutes of incubation , supernatants were collected for HNE quantification and neutrophils fixed and stained for DNA ( blue ) and HNE ( green ) . NETs remained intact in cells treated with culture medium but were disintegrated by SG or rLundep ( Figure 2B , C ) . Mutagenesis of Lundep active site ( mLundep , RGH197AAA ) abrogated its DNase activity and did not affect NETs' integrity ( Figure 4 ) . We also looked at the HNE released from NETs as an indicator of NET destruction . HNE is normally bound to NETs and found at low concentrations in culture supernatants [17] . PMA-activated neutrophils showed that treatment with SGE or rLundep significantly increases the concentration of HNE compared with control samples ( Figure 3A ) . The effect of rLundep and Lu . longipalpis SGE on Leishmania-neutrophil interaction was analyzed in vitro . Neutrophils were activated with 106 Leishmania major expressing red-fluorescent protein ( Lm-RFP ) promastigotes for 4 h before treatment with Lu . longipalpis SG or rLundep . The NET-destroying activity of Lu . longipalpis SGE and rLundep hydrolyzed the parasite-induced NETs ( Figure 2E , F ) and significantly increased the concentration of HNE in the supernatants when compared with medium alone ( Figure 3B ) . Furthermore , Lu . longipalpis SGE and rLundep significantly increased L . major survival , indicating that parasites can escape from the leishmanicidal activity of NETs ( Figure 3C ) . Our results show that PMA- or L . major -induced NET are disrupted by treatment with commercial bovine DNase-I , Lu . longipalpis SGE , or rLundep ( Figure 2A–F; Figure 3 ) . These results indicate that the effect of Lu . longipalpis SG and rLundep in helping L . major parasites escape from NETs is exclusively due the catalytic activity of the salivary endonuclease . The infection model we used in this work is Lu . longipalpis-L . major . Although Lu . longipalpis is not the natural vector of L . major this specie of sand fly is permissive to L . major infections in laboratory conditions . Furthermore , our laboratory has a well-established murine model of infection for this pair . Sand-fly bites and needle injection have been previously shown to induce neutrophil recruitment to the parasite inoculation site [18] . These neutrophils capture L . major parasites early after inoculation and efficiently initiate L . major infection . To investigate whether rLundep had any effect in exacerbating parasite infectivity in vivo , a model of L . major infection in C57BL/6 mice was utilized . Four- to five-week-old mice ( five animals per group , three independent experiments ) were intradermally infected with 103 L . major promastigotes ( control ) or parasites admixed with rLundep . Co-inoculation of rLundep with L . major parasites resulted in a significantly increased cutaneous lesion , averaging 2-fold larger than those observed in the control group ( Figure 5A ) . By week 9 , control mice had their lesions significantly reduced , whereas cutaneous lesions in mice inoculated with the parasite-rLundep mixture had not healed . Furthermore , the presence of rLundep in L . major inoculum resulted in a markedly higher parasite burden in their lesions ( 15-fold ) when compared with parasite alone or in the presence of mLundep ( Figure 5B and Figure 6 ) . The plasma contact system consists of five plasma proteins that assemble when blood comes into contact with negatively charged surfaces . It has been previously shown that soluble DNA and NETs allow the assembly and activation of the contact system [19] . The effect of Lundep on the intrinsic coagulation pathway activation was based on the generation of human factor XIIa by soluble DNA or aPTT reagent . One hundred nM of Lundep or TBS was preincubated at 37°C with 100 µg of salmon sperm DNA or 10 µl of aPTT reagent in the presence of the chromogenic substrate S2302 . After 20 minutes , the reaction was initiated by adding 50 µl of human normal reference plasma , and the amidolytic activity of FXIIa was measured at 405 nm . Figure 7A shows that pretreatment of soluble DNA with Lundep markedly inhibited activation of FXIIa in human normal reference plasma while no effect was observed when aPTT reagent was utilized . Oehmcke et al . [19] demonstrated that NETs and activated PMN cells can initiate contact activation and promote thrombus formation in the arterial and venous systems [20] . Consequently , our results indicate that the DNase activity of Lundep may contribute to the antithrombotic and anti-inflammatory functions of Lu . longipalpis saliva . The feeding success of Lu . longipalpis on mice passively immunized with anti-Lundep or pre-immune IgG ( control ) was carried out on anesthetized mice . Starved female flies , 2 to 4 days old ( never previously fed on blood ) , were placed in meshed vials , and groups of 10 flies were applied to the surface of both ears of mice passively immunized with either anti-Lundep or pre-immune ( naïve ) IgG . Flies were allowed to feed for 10 minutes and scored by visual inspection as fully fed , partially fed , or unfed . Figure 7B shows that flies fed on mice passively immunized with anti-Lundep antibodies were significantly less successful in obtaining a blood meal , while flies feeding on passively immunize mice with naïve IgG fed significantly better in the 10-minute period ( p = 0 . 0001 , χ2 test ) . Tripet et al . [21] highlighted the benefits of feeding aggregations in Lu . longipalpis in particular when feeding on hosts pre-exposed to sand flies bites , suggesting that group feeding maximizes the effect of the salivary component injected at the biting site . Accordingly , abrogating or reducing the salivary nuclease activity of the flies may result in a more viscous blood pool affecting the dispersion of other salivary components involved in blood feeding .
Although the killing mechanism ( s ) of pathogens trapped by NETs is poorly understood , the relevance of secreted endonuclease as a mechanism of evading the microorganism-killing activity of NETs has been highlighted by the presence of endonuclease activity in bacteria-evading , NET-dependent killing [17] , [22] . The mechanism by which Leishmania promastigotes evade killing by neutrophils may be related to their ability to block oxidative burst and to enter a nonlytic compartment unable to fuse with lysosomes [23] or by resisting the microbicidal activity of NETs [11] . Munafo et al . [24] demonstrated that disrupting NETs with DNase-I attenuates extracellular production of reactive oxygen species ( ROS ) by neutrophils stimulated with bacteria . Moreover , this reduction in ROS production is independent of actin depolymerization and phagocytosis . Our results demonstrate that Lundep , a female-specific secreted endonuclease , is an important factor contributing to establishment of Leishmania infection . Our observations are also in agreement with previous reports showing that parasite-induced NETs have leishmanicidal activity , thought to be mediated by histones , one of the NETs' structural components [12] , [25] . NETs may also play a role in entrapment of parasites , hence interfering with their ability to enter host cells . Accordingly , by disrupting NETs , Lundep can effectively facilitate the survival of L . major parasites in neutrophils and , ultimately , in infecting macrophages and dendritic cells . Together , these in vitro and in vivo experiments demonstrate that rLundep and Lu . longipalpis SG degrade the DNA scaffold of NETs , destroying their functional integrity . Furthermore , Lundep protected L . major parasites from the leishmanicidal activity of NETs , increasing promastigote survival and exacerbating L . major infection . However , we cannot exclude the possibility of an induced pathology arise from anti-NET DNAse activity or some other untested mechanism . Because Lundep exacerbated infection with L . major in vivo and anti-Lundep antibodies abrogate the enzyme's function , Lundep may be considered a potential vaccine target in an anti-Leishmania vaccine cocktail . With regards to the role of salivary endonucleases in blood feeding arthropods , Calvo and Ribeiro [26] proposed that a salivary endonuclease from the mosquito Culex quinquefasciatus could act as a spreading factor for other salivary activities by reducing the local viscosity at the biting site and hence decreasing the time taken to obtain a blood meal . We also found that anti-Lundep antibodies significantly decreased the feeding success of female Lu . longipalpis flies in passively immunized mice . These results may have epidemiologic relevance in the potential use of Lundep in vaccine , as longer probing and feeding times may trigger defensive behavior of the host , resulting in a disruption of blood feeding or even killing of the sand fly . Evidence of cross-talking between inflammation and coagulation is mounting in the literature . Fuchs et al . [27] demonstrated that NETs are a unique link between inflammation and thrombosis-promoting thrombus organization and stability . Moreover , NETs provide a negatively charged surface that allows the binding and activation of a contact activation system . Activation of the plasma contact system triggers several cascade systems such as the kallikrein-kinin system , the intrinsic pathway of coagulation , the classical complement cascade , and the fibrinolytic system [28] rendering an unfavorable environment for blood feeding arthropods . Accordingly , hydrolyzing the DNA scaffold of NETs at the biting site may also reduce local inflammation and prevent propagation of blood clotting facilitating the intake of a blood meal . In conclusion , we provide experimental evidence that a secreted salivary endonuclease in Lu . longipalpis is capable of destroying NETs produced by activated human neutrophils and that this enzymatic activity exacerbates L . major infection in vivo . Furthermore , Lundep can assist the flies in blood feeding by reducing local inflammation elicited by the vertebrate host . We believe that our findings are of broad interest to the scientific community . Besides providing new insight into the basic biology of sand-fly blood feeding , the discovery of an endonuclease in SGs of Lu . longipalpis may also have broad implications for understanding the biologic function of secreted endonucleases in other arthropods and the pathogens they transmit .
Public Health Service Animal Welfare Assurance #A4149-01 guidelines were followed according to the National Institute of Allergy and Infectious Diseases ( NIAID ) , National Institutes of Health ( NIH ) Animal Office of Animal Care and Use ( OACU ) . These studies were carried out according to the NIAID-NIH animal study protocol ( ASP ) approved by the NIH Office of Animal Care and User Committee ( OACUC ) , with approval IDs ASP-LMVR3 and ASP-LMVR4E . Blast-p analysis was performed with Lundep ( AY455916 ) against the non-redundant database . Sequences were cleaned up to obtain a non-redundant set ( proteins with >95% identity in the core domain were treated as identical ) and aligned with ClustalX [30] . Alignments were manually checked , adjusted , and trimmed to include the conserved active site core . Phylogenetic analysis was performed using neighbor-joining analysis [31] . Gapped positions were treated by pairwise deletion . Poisson correction was used as a substitution model to determine pairwise distances . Confidence was determined using bootstrap analysis ( 10000 replicates ) with 346 informative sites . Endonuclease reactions contained 50 mM Tris , 150 mM NaCl , 5 mM MgCl2 , pH 8 . 0 ( TBS-M ) and 200 ng double stranded ( ds ) circular plasmid DNA ( VR2001; Vical Inc . , San Diego , CA ) in a final volume of 15 µl . Reaction mixtures were incubated with different dilutions of SG and recombinant Lundep . After 10 minutes at 37°C , samples were electrophoresed in 1 . 2% precast agarose gel ( egel; Invitrogen , San Diego , CA ) and visualized under ultraviolet ( UV ) light . RNase activity was carried out as described by Calvo and Ribeiro [29] . To demonstrate salivary secretion of endonuclease ( Lundep ) by Lu . longipalpis adult females , an ex vivo assay was designed . Ten female sand flies ( 4 days old , non-blood fed ) were starved of sugar for 24 h before the test . Starved sand flies were allowed to probe for 30 minutes in a 1% agarose gel containing 50 mM Tris , 150 mM NaCl , 10 mM NaH2CO3 , 1 mM MgCl2 , pH 8 . 0 , and 200 ng/ml ds circular plasmid DNA . The probing assay was carried out at room temperature and the probing gel kept in a slide warmer at 30°C ( Precision Scientific , Chicago , IL ) . The combination of CO2 released from the bicarbonate buffer plus the temperature stimulated the sand flies to probe the slide [29] . After probing , the agarose gel was further incubated at 37°C for 30 minutes and stained with ethidium bromide . DNA hydrolysis in the gel was visualized under UV light . PCR fragments coding for Lundep ( AY455916 ) were amplified ( Platinum Supermix; Invitrogen ) from Lu . longipalpis SG cDNA using gene-specific primers designed to amplify the mature peptide and added a 6x-His tag before the stop codon . PCR-amplified product was cloned into a VR2001-TOPO vector ( modified version of the VR1020 vector; Vical Incorporated ) and the sequence and orientation verified by DNA sequencing . Plasmid DNA ( 5 mg; VR2001-Lundep construct ) was obtained using EndoFree plasmid MEGA prep kit ( Qiagen , Valencia , CA ) and filter sterilized through a 0 . 22-µm filter . Recombinant Lundep was produced by SAIC Advanced Research Facility ( Frederick , MD ) transfecting FreeStyle 293-F cells . Transfected cell cultures were harvested after 72 h and the supernatant shipped frozen to our laboratory until further processing . Recombinant protein expression was carried out by affinity and size-exclusion chromatography as described elsewhere [32] . Protein identity and purity was determined by Edman degradation and mass spectrometry . rLundep ( 10 ng ) was separated by 4–20% NuPAGE in MES buffer ( Invitrogen ) . After electrophoresis , samples were electrotransferred onto nitrocellulose membrane using an iBlot gel transfer system ( Invitrogen ) . The membrane was incubated overnight at 4°C with TBS ( 25 mM Tris , 150 mM NaCl , pH 7 . 4 ) containing 5% ( w/v ) powdered nonfat milk ( blocking buffer ) , followed by incubation for 90 min at room temperature with anti-rLundep rabbit sera diluted 1∶1000 in blocking buffer . The membrane was washed 4x with TBS-T ( TBS , 0 . 05% Tween 20 ) and incubated with goat anti-rabbit alkaline phosphatase conjugated ( Sigma , St . Louis , MO ) diluted 1∶10000 in blocking buffer . The immunoblot was developed by addition of 1 ml of Western Blue-stabilized substrate for alkaline phosphatase ( Promega , Madison , WI ) . To determine the cation dependency of rLundep activity , reaction buffers ( 25 mM Tris , 150 mM NaCl ) at pH 8 . 0 containing different divalent cations ( MgCl2 , CaCl2 , ZnCl2 , NiCl2 or CoCl2 ) , 5 mM final concentration , were assayed . Reaction mixtures containing 10 nM rLundep in the appropriate buffer were incubated for 10 minutes at 37°C with 200 ng ds plasmid DNA . A negative control using 10 nM of rLundep without any ions was carried out . This negative control was also supplemented with 5 mM of EDTA to ensure no free divalent cations were present in this reaction . rLundep products were resolved in a 1 . 2% egel ( Invitrogen ) and visualized under UV light . For optimum pH , the following buffers were utilized: 50 mM sodium citrate ( pH 3 . 0 ) , 50 mM sodium acetate ( pH 4 . 0 and 5 . 0 ) , 50 mM MES ( pH 6 . 0 ) , 50 mM HEPES ( pH 7 and 7 . 4 ) , 50 mM Tris ( pH 8 . 0 and 9 . 0 ) , and 50 mM CAPS ( pH 10 . 0 ) . All reaction buffers contained 250 ng of plasmid DNA , 150 mM NaCl , and 5 mM MgCl2 . Enzymatic reactions and DNA visualization were carried out as described above . To determine substrate ( ds or ss ) specificity of rLundep , 1 nM of enzyme was incubated for 20 minutes at 37°C with different combinations of ds circular plasmid DNA or polynucleotides ( ss and ds ) as described [29] . Reactions were performed in TBS-M ( 20 µl final volume ) and 400 ng of plasmid DNA or 2 µg of polynucleotides . Synthetic ds polynucleotides ( 1∶1 molar ratio ) were produced by incubating ss-poly nucleotides for one cycle of 20 minutes at 95°C; 20 minutes at 85°C; 10 minutes at 72°C; 10 minutes at 60°C; and 10 minutes at 50°C . The pattern of nuclease activity of rLundep was determined by ion-exchange high-pressure liquid chromatography as described by Calvo and Ribeiro [29] . The DNA-hydrolytic activity assay was based on DNA hyperchromicity . A 1-ml quartz cuvette was loaded with 50 µg/ml DNA sodium salt from salmon testes ( Sigma ) dissolved in TBS-M buffer . Absorbance of the non-hydrolyzed DNA was measured before adding rLundep at three different concentrations ( 10 , 20 , and 50 nM ) to the reaction cuvette . The increase of absorption of the sample at 260 nm was monitored by UV-Vis spectroscopy every 10 sec for 10 minutes . Initial velocity was calculated from slope of the linear phase of the progress curve . One Kunitz unit causes 0 . 001 change of absorbance at 260 nm per minute . Spectra were measured in quadruplicate in a spectrophotometer at 37°C . Identical solutions without rLundep were utilized for the blank in all cases . Spectroscopic hydrolysis analysis was performed at 37°C on a Varian Cary 100 Bio dual-beam spectrophotometer equipped with a Cary Cell Peltier temperature controller ( Varian , Inc . , Palo Alto , CA ) . Mutation for Lundep was carried out using the QuikChange I site-directed mutagenesis kit ( Agilent Technologies , Santa Clara , CA ) following the manufacturer's recommendations . Briefly , a high-pressure liquid chromatography-purified 40-mer complementary primer set ( Lundep-RGH194AAA Forward: 5′-CTCAATTTTCTATCAGCCGCAGCTTTAAGCCCC GAAGTGG-3′ and Lundep-RGH194AAA Reverse: 3′-GGAGTTAAAAGATAGTCGGCG TCGAAATTCGGGGCTTCAC-5′ ) was designed to mutate R194 , G195 , and H196 in the catalytic center of Lundep for AAA ( RGH194AAA , mLundep ) . The primers were designed to carry the triple amino-acid mutation in the central region of the oligonucleotide , flanked by 13–15 nucleotides ( 3′ and 5′ ends ) . Lundep-VR2001 plasmid ( 10 ng ) was utilized as a template in the PCR reaction . Amplification cycles were 95°C for 1 minute followed by 18 cycles of 95°C for 50 sec , 63°C for 50 s , and 68°C for 7 minutes . After a final extension step of 10 minutes at 68°C , the PCR product was digested with 1 U of DnpI to digest the parental supercoiled dsDNA . Two µl of the DpnI-treated PCR product was used to transform XL10 Gold ultracompetent cells . mLundep-VR2001 plasmid DNA was isolated from transformed cells and the sequences verified by DNA sequencing . A positive plasmid construct containing the mutation was selected for expression as described above . Protein purification and DNase activity were carried out as described above . Polymorphonuclear cells were isolated from heparinized whole venous blood using Mono-Poly-Resolving Medium ( MP Biomedicals , Solon , OH ) according to the manufacturer's recommendations . Fresh heparinized blood was obtained from the NIH Clinical Center Department of Transfusion Medicine . The isolated fraction contained approximately 90–95% neutrophils as estimated by Trypan blue stain . The cells were counted and used immediately for NET production and visualization . NET production and visualization was carried out as described elsewhere [17] . Freshly purified human neutrophils ( 106; 200-µl volume ) were seeded in a Lab-Tek chamber slide ( Thermo Scientific , San José , CA ) for 1 h at 37°C . Seeded neutrophils were activated with 100 nM of PMA or 5×106 L . major expressing red-fluorescent protein ( Lm-RFP ) metacyclic parasites for 4 h at 37°C . Activated neutrophils were individually treated with 3 nM of rLundep , 1 Lu . longipalpis SG pair , 1 U of bovine DNase-I ( positive control ) , and 20 nM of mLundep or medium alone ( negative control ) . After 1 h at 37°C , supernatants were carefully removed for HNE quantification and the cells fixed and stained for DNA and HNE as described in [17] . Images were obtained using a DMIRE2 SP2 confocal microscope ( Leica , Solms , Germany ) . All experiments were carried out in triplicate . HNE concentration was measured in 50 µl of supernatant using the fluorogenic substrate N-methoxysuccinyl-Ala-Ala-Pro-Val-7-amido-4-methylcoumarin ( Sigma ) at a final concentration of 0 . 25 mM ( 100 µl final reaction volume ) . The assay buffer was 50 mM HEPES buffer , pH 7 . 4 , 100 mM NaCl , 0 . 01% Triton X-100 . After 1 h incubation at 37°C , the substrate hydrolysis was measured in a Spectramax Gemini XPS fluorescence microplate reader ( Molecular Devices , Menlo Park , CA ) with 365/450 nm excitation/emission wavelengths . HNE concentration was determined by using a standard curve of serial dilutions of purified HNE ( Elastin Products Company , Inc . , Owensville , MO ) . L . major promastigotes ( WR 2885 strain ) were cultured in Schneider's medium supplemented with 10% heat-inactivated fetal bovine serum , 2 mM l-glutamine , 100 U/ml penicillin , and 100 µl/ml streptomycin . WR 2885 strain was typed at the Walter Reed Army Institute of Research [33] . Infective-stage metacyclic promastigotes of L . major were isolated from stationary cultures ( 4–5 days old ) by negative selection using peanut agglutinin ( Vector Laboratories , Inc . , Burlingame , CT ) . Metacyclic promastigotes ( 1000 ) with or without 10 ng of rLundep in 10 µl of PBS buffer ( supplemented with 5 mM of MgCl2 ) were inoculated intradermally into both ears' dermis using a 29-gauge needle . Evolution of the lesion was monitored weekly by measuring ear thickness using a vernier caliper ( Mitutoya America Corporation , Aurora , IL ) . Human neutrophil from five healthy donors ( 2×106 ) were infected with 107 Lm-RFP metacyclic parasites . After 4 h of incubation at 37°C , samples were treated with rLundep ( 100 nM ) , 1 SG pair of Lu . longipalpis , 1 U of bovine DNase-I ( positive control ) or medium alone ( negative control ) . After 3 days of incubation at 23°C , cells were harvested and the supernatants spun down to evaluate the viable parasites . Live parasites were stained with Giemsa . All experiments were carried out in triplicates . A region containing the DsRed gene flanked by the 5′ and 3′ untranslated regions of the Leishmania donovani A2 gene was amplified by PCR using the following forward A2F and reverse A2R primers and the pKSNEO-DsRed plasmid as template [34] . The A2F primer 5′-TGGCATATGCGTCGACCGCTGCTTGCGTTC-3′ contains a SalI restriction site . The A2R primer 5′-ACGCGTGGATCCTGAATTCGAGCTCTGGAGAGA-3′ contains a BamHI restriction site . The resulting ∼3 . 7-kb PCR fragment was cloned and sequenced . It contained an internal BamHI site that was mutated using standard PCR techniques . The approximately 3 . 7-kb SalI/BamHI fragment with the mutated internal BamHI site was subsequently cloned into the SalI and BamHI sites of the pF4X1 . 4hyg plasmid ( Jena Biosciences , GmbH , Jena , Germany ) , resulting in the pA2RFPhyg plasmid . This plasmid was digested with SwaI to generate a linear fragment containing the RFP/Hyg expression cassette flanked by the 5′ and 3′ ssu sequences used for homologous recombination into the parasite 18S rRNA gene locus ( ssu locus ) as described in the original pFX1 . 4hyg plasmid ( Jena Biosciences ) . Promastigotes of the L . major Friedlin strain , NIH/FV1 ( MHOM/IL/80/FN ) were transfected by electroporation with 20 µg of SwaI-digested pA2RFPhyg plasmid as described previously [35] and plated onto 1% noble agar plates prepared in M199 Leishmania culture medium [36] supplemented with 4 µg/ml 6-biopterin ( Calbiochem ) and 100 µg/ml hygromycin B ( Roche ) . Integration of the RFP/Hyg cassette into the ssu locus of hygromycin B-resistant clones was confirmed by PCR . Parasite load was determined using a limiting dilution assay as described elsewhere [37] . Briefly , ear tissue was excised and homogenized in RPMI medium . The homogenate was serially diluted on Schneider medium 10% heat-inactivated fetal bovine serum , 2 mM l-glutamine , 100 U/ml penicillin , 100 µl/ml streptomycin , and 40 mM HEPES and seeded in 96-well plates containing biphasic blood agar ( Novy-Nicolle-McNeal ) . The number of viable parasites was determined from the highest dilution at which promastigotes could be found after 21 days of culture at 23°C . Polyclonal antibodies against rLundep were raised in rabbits by Spring Valley Laboratories , Inc . ( Woodline , MD ) using a standard protocol . Briefly , rabbits were immunized three times with 125 µg of rLundep every 21 days and the serum collected at day 72 . A 10-ml aliquot of rabbit serum ( immunized or naïve ) was diluted to 50 ml in phosphate buffer , pH 6 . 5 , and loaded onto a 5-ml HiTrap protein A HP column ( GE Healthcare , Piscataway , NJ ) and the IgG eluted using a linear gradient of citric acid ( 100 mM , pH 3 . 4 ) on an Akta purifier system ( GE Healthcare ) . Fractions containing purified IgG were pooled and dialyzed against 1× PBS for 16 h at 4°C . IgG quantification was based on 1 absorbance unit at 280 nm equals 0 . 7 mg/ml . To investigate the neutralizing activity of anti-rLundep antibodies on its enzymatic activity , an in vitro assay was developed . Purified anti-rLundep or naïve antibodies ( 0 and 5 µg/ml ) were mixed with rLundep ( 10 nM ) or SGEs ( 1 pair ) and incubated for 30 minutes at 37°C . Plasmid DNA hydrolysis and visualization was carried out as described above . The effect of rLundep on the intrinsic coagulation pathway was based on the generation of human factor XIIa by DNA or aPTT reagent . Ten µl of rLundep ( 100 nM ) or TBS was preincubated at 37°C with 100 µg of salmon sperm DNA ( Sigma ) or 10 µl of aPTT reagent ( Helena Laboratories , Beaumont , TX ) in the presence of 5 µl of the chromogenic substrate S2302 ( Diapharma , West Chester , OH ) . TBS alone was utilized as the reaction blank . After 20 minutes , the reaction was initiated by adding 50 µl of citrated-human normal reference plasma ( Diagnostica Stago , Inc . Parsippany , NJ ) and the amidolytic activity of fXIIa measured at 405 nm in a plate reader ( Molecular Devices ) . All reactions were supplemented with 5 mM MgCl2 . Final concentrations of rLundep and S2302 in the assay reaction were 13 nM and 300 µM , respectively . Purified rabbit anti-Lundep or naïve antibodies were given to the recipient mice via intraperitoneal inoculation of 100 µg ( 100 µl volume ) 15 minutes before exposing five to six CL57B/6 mice to sand flies ( 10 flies in each ear ) . Feeding success of sand flies in passively immunized mice was measured on anesthetized animals ( four per group ) as described Belkaid et al . [38] . Briefly , groups of 10 female flies ( 3 to 4 day old ) were caged in polystyrene tube the day before the experiments was carried out and deprived of sugar . Ready-to-use vials containing starved flies were applied to the surface of anesthetized mouse ear that was previously given either anti-Lundep antibodies or purified naïve IgG . Flies were allowed to feed for 10 min , removed , and scored as either fed or unfed . Flies with their entire abdomen fully engorged or with visible blood were considered as fed . Lu . longipalpis flies used in this experiment were not blood fed previously and had no eggs in their abdomen , enabling assessment of a blood meal by visual inspection . Data were analyzed using GraphPad Prism v 5 software ( GraphPad Software , Inc . , San Diego , CA ) and plotted as bar graphs or scatter plots . Comparisons were made with the 2-tailed t test with 95% confidence interval and 2-way analysis of variance . P<0 . 05 was considered significant ( * , p<0 . 05; ** , p<0 . 01; *** , p<0 . 001 ) . ) . For feeding success , a χ2 test with 95% confidence interval was used . | Salivary components from disease vectors help the arthropod to acquire blood . Here we show that an arthropod vector salivary enzyme affects the innate immune system of the host—mainly the destruction of neutrophil traps—allowing the Leishmania parasite to evade the host immune response and to cause an infection . This work highlights the relevance of vector salivary components in parasite transmission and further suggests the inclusion of these proteins as components for an anti-Leishmania vaccine . Importantly , because salivary proteins are always present at the site of natural transmission , this work further encourages the testing of vaccine candidates using the natural route of transmission—the bites of an arthropod vector—instead of current practices based solely on needle injection of parasites . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"biochemistry",
"infectious",
"diseases",
"immunology",
"biology"
] | 2014 | Lundep, a Sand Fly Salivary Endonuclease Increases Leishmania Parasite Survival in Neutrophils and Inhibits XIIa Contact Activation in Human Plasma |
Candida albicans in the immunocompetent host is a benign member of the human microbiota . Though , when host physiology is disrupted , this commensal-host interaction can degenerate and lead to an opportunistic infection . Relatively little is known regarding the dynamics of C . albicans colonization and pathogenesis . We developed a C . albicans cell surface protein microarray to profile the immunoglobulin G response during commensal colonization and candidemia . The antibody response from the sera of patients with candidemia and our negative control groups indicate that the immunocompetent host exists in permanent host-pathogen interplay with commensal C . albicans . This report also identifies cell surface antigens that are specific to different phases ( i . e . acute , early and mid convalescence ) of candidemia . We identified a set of thirteen cell surface antigens capable of distinguishing acute candidemia from healthy individuals and uninfected hospital patients with commensal colonization . Interestingly , a large proportion of these cell surface antigens are involved in either oxidative stress or drug resistance . In addition , we identified 33 antigenic proteins that are enriched in convalescent sera of the candidemia patients . Intriguingly , we found within this subset an increase in antigens associated with heme-associated iron acquisition . These findings have important implications for the mechanisms of C . albicans colonization as well as the development of systemic infection .
The yeast Candida albicans exists in a dichotomist relationship with the human host . C . albicans is frequently found as a commensal organism on the human skin , gastrointestinal ( GI ) tract and the vulvovaginal tract [1] . Close to 60% of healthy individuals carry C . albicans as a commensal in the oral cavity . Colonic and rectal colonization is even higher , ranging from 45% to 75% among patient groups . Alterations in the host immunity , physiology , or normal microflora rather than the acquisition of novel or hypervirulent factors associated with C . albicans , are suggested to lead to the development of candidiasis [2] . Both neutrophils and mucosal integrity of the GI tract , are critical in preventing hematogenously disseminated candidiasis [3] . The development of candidemia can begin with the translocation of C . albicans into the bloodstream from initial commensal GI colonization or the shedding from developing biofilms on indwelling catheters [4] , [5] . Fungal cells that evade the host immune system can spread to deep organ systems leading to hematogenously disseminated candidiasis , which has an estimated mortality rate of 40% , even with the use of antifungal drugs [2] . Information on in vivo gene expression would provide insight into how C . albicans interacts with host cells during the transition from commensal colonization to an opportunistic pathogen in the immunocompromised host . However , in vivo transcription profiling of C . albicans during commensal colonization or candidemia is technically challenging [6] . Instead , several genome-wide transcriptional analyses of C . albicans responses to host cells have been performed using ex vivo and in vivo infection models . These include phagocytosis of C . albicans cells by neutrophils [7] and macrophages [8] , exposure to human blood , plasma , and blood cells [9] , [10] , as well as invasion of perfused pig liver and reconstituted human epithelium [11] , [12] . Genes that are associated with morphological changes , metabolic adaptation , and oxidative stress are the major responses of C . albicans to host cells identified in these studies . The changes in gene expression identified in these in vitro model systems possibly reflect tissue- or stage-specific expression during an infection in patients . Profiling of antibody responses during infection in patients offers an alternative approach that can overcome technical challenges of in vivo transcription profiling . An antibody-based approach has been used to identify C . albicans gene expression during thrush in individuals with HIV [13] . Currently the isolation of C . albicans from blood cultures is the standard method for the diagnosis of candidemia . Nevertheless , blood cultures may only become positive late in infection , and in one study up to 50% of all autopsy-proven cases of candidemia were reported as negative in blood cultures [14] . Thus , the ability to rapidly and easily diagnose candidiasis is urgently needed . An alternative approach to microbiological confirmation of C . albicans infection is serological diagnosis . An immunoproteomic approach using two-dimensional electrophoresis followed by quantitative Western blotting and mass spectrometry has been used to profile serologic response to peptides from cell surface extracts in candidemia [15]–[17] . A significant proportion of antigens identified were glycolytic enzymes and heat shock proteins . An antigenic multiplex consisting of the peptides Bgl2 , Eno1 , Pgk1 , Met6 , Gap1 , and Fba1 provides 87% sensitivity and 74% specificity when distinguishing patients with candidemia from uninfected hospital patients [17] . However , this approach has several limitations; only the most abundant and soluble proteins can be resolved on the immunoblot , there is a lack of reproducibility of cell wall preparations , and most importantly , there is the inability to account for various stage- and tissue-specific gene expressions from the cultured cells . These limitations can be addressed by using a protein microarray to profile antibody responses [18]–[21] . To investigate the establishment of the humoral immunity during commensal sensitization , as well as the adaptive immune response to candidemia , we have developed a C . albicans cell surface protein microarray . Our rationale in developing a cell surface protein microarray is that the cell surface of C . albicans is the immediate target of the human immune system when C . albicans cells enter the bloodstream . Cell surface proteins play important roles in host interaction , and many of them are known virulence factors . In addition , a recent study showed that there is a significant expansion of cell wall , secreted and transporter gene families in pathogenic Candida species in comparison to non-pathogenic yeasts [22] . In this study , profiling of serological response on the protein microarray with sera from candidemia patients , blood-culture negative hospital patients and healthy individuals lead to the identification of serological signature specific for acute and convalescent stages of candidemia . Intriguingly , large proportions of the identified antigens are involved in oxidative stress , drug resistance and iron acquisition . Furthermore , strong IgG response to many proteins known to be induced and/or required for C . albicans invasion of epithelial and endothelial cells is observed in both candidemia patients and non-candidemia controls , including all healthy individuals . Our findings provide new insights into commensal colonization and pathogenesis of C . albicans , as well as the characterization of potential serodiagnostic antigens and vaccine candidates .
Hospital patient sera were collected from Shands Hospital at the University of Florida ( UF ) ( SH-UF ) from January 2004 to December 2006 . We collected sera from 21 patients with candidemia where the etiological agent was C . albicans . The median time from the date of positive culture to serum collection was two days . The study population was classified by age , gender , underlying disease , portal of entry , antifungal received , and outcome of stay ( Table S1 ) . A subset of the candidemia patients was followed through acute infection ( days 0–14 ) to early convalescent ( week 4 ) and mid convalescent ( week 12 ) infection . We also used sera from 12 hospital patients and 50 healthy individuals who had no evidence of candidiasis as our negative control groups . C . albicans cell surface proteins were chosen for the protein microarray because they interact directly with the host and thus are likely important for colonization and infection , as well as likely targets for the host immune system . Furthermore , many of their protein expression levels are regulated in response to extracelluar signals , such as stress , nutrients , host factors , or changes in environment . Known antigenic proteins are also included as controls ( Bgl2 , Eno1 , Pgk1 , Gap1 , Cdc19 , Tkl1 , Hsp90 , and members of the Hsp70 family ) [15] , [17] . The collection contains 451 His- and HA-tagged peptides ( Table S2 ) that represent 363 different proteins , since ORFs >3 , 000 bps were cloned into two or more segments . All tagged proteins were confirmed individually by western blot and again on the protein microarray . We have used the C . albicans cell surface microarray to evaluate the antibody profile of patients with candidemia against healthy individuals and uninfected hospital patients to determine relevant cell surface antigens that correlate with infection . Arrays were probed with a collection of sera consisting of different stages of candidemia: acute , early convalescent ( approximately 4 weeks after onset of infection ) and mid convalescent ( approximately 12 weeks after onset of infection ) , as well as uninfected hospital patients and healthy individuals . Figure S1 shows a representative image of the microarray hybridized with the serum of an acute candidemia patient . All hybridizations in this study were done under the same conditions and dilutions with protein microarrays printed from the same batch . Their serological reactivity is shown as a heatmap where the antigens are sorted by increasing normalized global mean intensity , with bright green having the weakest intensity , red being the strongest , and black in between ( Figure S2 ) . An examination of the IgG response to the entire C . albicans cell surface protein microarray showed that the mean global signal intensity was similar among different groups ( data not shown ) , although antigenic profiles are not identical between individuals . We were interested in determining the most seroprevalent antibodies in the acute candidemia patients and how their humoral response compared against the negative control groups . Antigens to the most seroprevalent antibodies were defined as serodominant antigens and characterized as having mean antigen reactivity 2-fold greater than the in vitro transcription/translation reaction mixture containing no vector . The top-forty serodominant antigens in the candidemia patients consisted of many previously characterized antigenic peptides such as Bgl2 [17] , Tkl1 [15] , Hwp1 [13] , [23] , Eft2 [15] , and Cdc24 [13] ( Table 1 ) . Also among the top-forty serodominant antigens were many previously identified virulence-associated and/or hyphal-regulated proteins ( eg . Int1 , Hwp1 , Als1 , Als3 , Als5 , Ece1 , Hyr1 , Cdc24 , and Utr2 ) ( Table 1 ) [24]–[32] . Interestingly , this serological response of acute candidemia patients was shared with both uninfected hospital patients and healthy individuals . The mean signal intensity to the top-forty serodominant antigens was 8 , 825 in acute candidemia patients , 8 , 837 in uninfected hospital patients , and 10 , 790 in healthy individuals . A two-way hierarchical cluster analysis of the top-forty serodominant antigens shows that the serum specimens of both the positive and negative candidemia groups were randomly dispersed throughout the hierarchical tree ( Figure 1A ) . To further confirm that the top-forty serodominant antigenic signatures are shared among acute candidemia patients , the uninfected hospital patients and healthy individuals , principal component analysis ( PCA ) was used to generate a three-dimensional projection of the data ( Figure 1B , 1C and 1D ) . The PCA shows that a large proportion of both the positive and negative acute candidemia sera are clustered together . These analyses suggest that IgG levels to the top-forty serodominant antigens are similar in both the negative control groups and acute candidemia sera . Since many of the top-forty antigens are either important for or induced during the invasion of epithelial or endothelial cells [11] , [33] , their expression in healthy people , inferred from the presence of their antibodies , indicates the existence of a permanent host-pathogen interplay in immunocompetent individuals . To determine stage-specific biomarkers of acute candidemia , the normalized serological expression of acute candidemia patients were compared against the humoral reactivity of the uninfected hospital patients and healthy individuals . Serodiagnostic antigens were defined as having an IgG response significantly greater in acute candidemia patients ( days 0–14 ) as compared to the negative control groups with Benjamini and Hochberg ( BH ) adjusted Cyber-T p-values <0 . 05 . Thirteen antigens met this requirement ( Table 2 ) . Moreover , among the proteins identified as serodiagnostic markers , proteins involved in oxidative stress response appeared to be enriched over other functional categories . Sln1 and Nik1 are two out of three histidine kinases on the cell surface protein microarray and they are both identified as serodiagnostic antigens . Sln1 and Nik1 are sensors for the high-osmolarity glycerol ( HOG ) pathway , a mitogen-activated protein kinase cascade responsible for osmotic and oxidative stress adaptation in C . albicans [34] , [35] . In addition , the expression levels of CDR4 , RAS2 , and ALS9 are up-regulated during oxidative stress [35] . Another functional group over-represented among the serodiagnostic antigens are transporters associated with drug resistance ( Cdr1 , Cdr4 , and Yor1 ) [36] . The 13-serodiagnostic antigens were also evaluated with a two-way hierarchical cluster analysis on candidemia positive and negative sera . Interestingly , the sera clustered into two distinct groups based on their responses to the 13 antigens ( Figure 2A ) . Cluster I contained 10 candidemia sera and only one uninfected hospital patient . Cluster II contained all 50 healthy individuals , 11 of the 12 hospital patients , and 8 acute candidemia sera ( Figure 2A ) . To further confirm that the antigenic signatures identified during the acute phase of candidemia differed from the negative control groups , PCA was used to create a three-dimensional projection of the data ( Figure 2B , 2C , and 2D ) . In agreement with the two-way hierarchical cluster analysis , two distinct groups were observed ( Figure 2B and 2C ) . Also , the PCA of the negative control groups showed individuals are clustered together with the exception of one outlying uninfected hospital patient found clustered with the acute candidemia patients ( Figure 2C and 2D ) . These data provide further support of the antigenic signature of patients during the acute phase of candidemia . Multiple linear regression models determined that the antigenic profiles of acute candidemia patients were not related to various risk factors ( i . e . age , gender , course of treatment , coexisting disease , and recovery/fatality ) ( data not shown ) . However , this determination is limited by the small sample size of our study . Multiple independent serodiagnostic antigens can dramatically improve the sensitivity and accuracy of serodiagnostic tests [37] . To establish a collection of antigens that could be used as a multiplex set to accurately distinguish candidemia cases from controls , we studied the discriminatory power of different sets of proteins using receiver operating characteristic ( ROC ) curves . First , ROC curves were generated for individual serodiagnostic antigens and the area under the ROC curves ( AUC ) for each antigen is listed in Table 2 . The top-five cell surface proteins all have an AUC greater than 0 . 76 , with CDR1 ( 3 ) ( AUC 0 . 87 , BH adjusted Cyber-T p-value <1 . 04e-7 ) giving the best single antigen discrimination ( Table 2 ) . The 13th antigen has an AUC of 0 . 630 , which still exceeds the upper 95% confidence interval for random expectations for the AUC . To extend the analysis to combinations of antigens , we used kernel methods and support vector machines to build linear and nonlinear classifiers . As inputs to the classifier , we used the highest-ranking AUC antigens in combinations of 2 , 5 , 10 , 11 , 12 and 13 proteins and the results were validated with 10 runs of three-fold cross-validation ( Figure 2E ) . Increasing the antigen number from 2 to 5 , and 5 to 10 produced improvements in the classifier . But as the antigens increased to 13 , a reduction in accuracy was observed . Using the ten most significant diagnostic antigens ( in rank order: Cdr1 ( 3 ) , Cfl91 , Cdr4 ( 3 ) , Als9 ( 2 ) , Cdc19 , Nik1 ( 2 ) , Chs8 ( 2 ) , Rta4 , Sln1 ( 2 ) , and Trk1 ( 2 ) ) , the classifier predicts 83% ( 95% CI , 76–89% ) sensitivity , 72% ( 95% CI , 68–76% ) specificity , and 74% ( 95% CI , 72–76% ) accuracy in diagnosis of acute phase candidemia from the negative controls ( healthy individuals and uninfected hospital patients ) ( Table 3 ) . We were next interested in identifying antigens that are significantly different between the early/mid convalescent candidemia patients ( weeks 4 and 12 of the infection , respectively ) and the negative control groups . The convalescent patient sera consisted of three patients whose serum was drawn under all three disease phases ( acute phase , early and mid convalescent phases ) , 4 patients who had blood drawn at the acute and early convalescent phases , and 3 patients whose blood was drawn only at the early convalescent phase . Using BH adjusted Cyber-T p-values <0 . 05 , we identified 33 antigens , 11 of which are from the 13 diagnostic antigens for the acute phase of infection ( Table 4 ) . Among the identified convalescent biomarkers were marked expansions in proteins involved in iron acquisition ( Rbt5 , Csa1 , Flc1 , and Cfl91 ) ( Table 4 ) . Cfl91 is a putative ferric reductase similar to Fre10 , which is required for the release of iron from transferrin and the reduction to ferrous iron [38] . The protein Flc1 has been identified as having heme uptake activity [39] whereas , both Rbt5 and Csa1 have been implicated as receptors of hemoglobin whose function is to deliver the hemoglobin by endocystosis to the vacuole where iron is released by acidification [40] , [41] . The remainders of the identified proteins have roles in cell wall biogenesis , membrane lipid organization , and drug resistance . We next evaluated antibody response to the 33 antigens in the acute , convalescent candidemia patients and the negative control groups by two-way hierarchical cluster analysis . The individuals in Cluster II were the same as those identified previously with 13 serodiagnostic antigens ( Figure 2A and 3A ) with the addition of one convalescent candidemia patient whose only sera was drawn during week 4 of the infection . Individuals in Cluster I consisted of candidemia patients with the exception of the one uninfected hospital patient from Figure 2A . Three of the candidemia patients' acute and convalescent profiles were all found in Cluster I , whereas four candidemia patients' profiles converted from Cluster II to I during the convalescence phase of the disease . In addition , the remaining two-candidemia patients whose only blood draws were during week 4 also grouped in Cluster I ( Figure 3A ) . This conversion of the antigenic profile from the negative control groups ( Cluster II ) to the antigenic profile consistent with candidemia ( Cluster I ) , indicates an adaptive immune response to C . albicans that is different from commensal sensitization . Again , PCA was used to further confirm that the antigenic signatures identified during the convalescent phase of candidemia differed from the negative control groups ( Figure 3B , 3C and 3D ) . ROC curves were generated to assess the ability to separate the control and convalescent candidemia . AUC was determined for each of the 33-serodiagnostic antigens and listed in Table 4 in decreasing order . The top-five ORFs all have an AUC greater than 0 . 94 . We then used SVMs to build multiplex classifiers with 2 , 5 , and 10 antigens with the highest-ranking AUC from Table 4 . The results were validated with 10 runs of three-fold cross-validation ( Figure 3E ) . Increasing the antigen number from 2 to 5 maintained the diagnostic accuracy in the classifier and a reduction in accuracy occurred as the antigens increased to 10 due to over-fitting . The top-five serodiagnostic antigens are associated with xenobiotic-transporting activity ( Cdr4 and Yor1 ) [36] , phospholipid-transporting activity ( Drs23 ) , a putative ferric reductase ( Cfl91 ) , and a mucin-like cell wall protein ( Ipf25023 ) ( Table 4 ) . Using the top-five antigens , the classifier predicts 93% ( 95% CI , 89–96% ) sensitivity , 96% ( 95% CI , 95–96% ) specificity , and 95% ( 95% CI , 94–96% ) accuracy in the differentiation of early/mid convalescent phase candidemia from the negative controls ( healthy individuals and uninfected hospital patients ) ( Table 3 ) . Having identified 33 antigens that are correlative with convalescent candidemia in comparison to the negative control groups , we next wanted to determine the temporal change in IgG response to these 33 antigens during the transition from acute infection ( AI ) , to early convalescent ( EC ) , and mid convalescent ( MC ) . A two-way hierarchical cluster analyses was performed on differential IgG responses to the 33 antigens in 3 patients with AI , EC and MC sera , and 4 patients with only AI and EC sera ( Figure S3 ) . A one tailed t-test was carried out to look for differences where the EC antigen intensity is significantly greater than the AI antigen intensity , possibly indicating the selection of a protective antibody response . We observed a significant increase in the IgG response from AI to EC in the following antigens , which are ranked according to their p-values: Apc5 ( 2 ) ( 1 . 12E-03 ) , Drs23 ( 3 ) ( 1 . 23E-03 ) , Vps62 ( 1 . 57E-03 ) , Rad50 ( 1 . 83E-03 ) , Ssu1 ( 3 . 17E-03 ) , Yor1 ( 3 ) ( 5 . 33E-03 ) , Ipf885 ( 5 . 33E-03 ) , Pga4 ( 5 . 88E-03 ) , Cdr4 ( 3 ) ( 7 . 22E-03 ) , Cfl91 ( 2 ) ( 0 . 0231 ) , Cyr1 ( 2 ) ( 0 . 0274 ) , Ipf25023 ( 2 ) ( 0 . 0330 ) , Gsl2 ( 2 ) ( 0 . 0374 ) , Chs1 ( 2 ) ( 0 . 0393 ) , and Snq2 ( 3 ) ( 0 . 0486 ) . The identified antigens could potentially be efficacious vaccine candidates due to the fact that the IgG response is being positively selected over the course of infection .
In this study , we have developed a C . albicans cell surface protein microarray and profiled host humoral responses during conmmensal colonization and during the progression of candidemia . Thirteen novel serodiagnostic antigens were identified for differentiating acute candidemia from commensal sensitization and 33 antigens were found to discriminate convalescent candidemia from non-candidemia controls . The sensitivity and specificity for the identification of acute candidemia determined by the top 10 antigens from the set of 13 serodiagnostic markers are comparable to that obtained using the method of 2D-PAGE and immunoblots [17] . When using the top 5 antigens from the set of 33 , both sensitivity and specificity are dramatically improved for convalescent candidemia . Pitarch et al . reported that the anti-Bgl2p IgG antibody levels mainly define the proteomic signature for candidemia patients [17] . In this study , Bgl2 is on the list of 33 diagnostic antigens from convalescent sera . Although it is classified as a serodominant antigen by acute candidemia sera , the BH-adjusted p-value of Bgl2 ( 0 . 116 ) is just above cutoff ( 0 . 05 ) to be considered as diagnostic by our definition , and the mean anti-Bgl2 antibodies in acute candidemia is higher than the mean in non-candidemia controls . Bgl2 is a glycoprotein and the glycan moieties on other b-1 , 3-glucanosyltransferases seem to contribute to antigenicity . Since our Bgl2 is expressed in vitro without any glycosylation , its antigenicity is likely different from the Bgl2 produced by C . albicans used in the 2D-PAGE immunoblots . The previously identified immunogenic heat shock protein 90 ( Hsp90 ) is also one of 33 biomarkers for convalescent candidemia identified from this study . Hsp90 has been shown to elicit a protective humoral response [42] , [43] and its antibodies are known to associate with patients that recover from candidiasis . The use of protein microarray technology allowed us to identify new diagnostic antigens that were missed by previous studies . The use of 2-D PAGE to accurately identify and separate clinical markers of candidemia from commensal sensitization is limited by the range in protein abundance and various properties associated with peptides such as their mass , isoelectric point , hydrophobicity , and post-translational modification , as well as the semi-quantitative nature of a Western [18] . Using a C . albicans cell surface protein microarray helped us overcome many of the technical difficulties found with traditional proteomics , since the expression level of recombinant-derived proteins vary by only a single log and the use of fluorescent-labeled antibodies allows for greater linearity , precision , and sensitivity in the quantitative measurement of the humoral response to C . albicans . One of the most beneficial aspects in the use of the protein microarray assay is its ability to detect significant differences in the IgG response that under traditional immunoblot conditions would be below the detectable threshold . However , a potential limitation to our study is that the microarray is based on recombinant peptides . Because of the cell free nature of our in vitro translated peptides , potential epitopes may have been lost due to miss folding and a lack of glycosylation , both of which may affect the conformational structure of the native protein . On the other hand , the removal of posttranslational modifications , such as glycosylation , from the peptides may have revealed hidden peptide epitopes only seen during a strong host immune response . A large collection of peptide epitopes may increase the specificity in diagnosis of infection . In support of this , our study has identified many new clinical biomarkers that are associated with differing states of interactions with the host as well as the characterization of potential new targets for therapeutics and vaccine candidates . To our knowledge , this is the first study using a protein microarray to analyze the serological response to an organism that is capable of existing as both commensal flora and an opportunistic pathogen in the human population . Commensal colonization of C . albicans is common in humans and attenuated host immunity is a perquisite for the transition from commensal colonization to infection . Historically , it was believed that C . albicans switched from a commensal to a pathogen using distinct pathogen-associated genetic programs when the host immune status was altered . An intriguing review challenges this notion , Hube postulates that C . albicans exists in a permanent host-pathogen interplay where overgrowth and invasion is only observed under immunocompromising conditions[44] . The review puts forth two-models of a permanent infection strategy: ( 1 ) constitutive gene expression where attenuated immunity induces little or no change in the pathogenic profile of C . albicans or ( 2 ) a variable transcriptional profile where C . albicans expression is dependent on the stage- and tissue-specific interactions with the host . Our study indicates the existence of permanent host-pathogen interplay with variable gene expression over the course of infection . The serological response to the entire C . albicans cell surface protein microarray detected considerable homogeneity as well as differences in the patterns of antigens recognized among patients and healthy individuals . The majority of healthy individuals and uninfected hospital patients have moderate to strong IgG responses to many C . albicans cell surface proteins that have long been associated with virulence or hyphal-regulation ( a hallmark of virulence in itself ) . In agreement with our protein microarray data , Naglik et al . observed similar levels of IgG titers to the hyphal wall protein Hwp1 in patients with oral candidiasis and asymptomatic mucosal infections as well as healthy culture-negative controls [23] . These serodominant cross-reactive antigens include adhesins such as Als1 , Als3 , Als5 , Hwp1 and Int1 and hyphal-regulated genes such as Als3 , Hwp1 , Ece1 , Hyr1 , and Cdc24 . Both functional groups are known to be important for invasion and virulence [45] . Among the identified serodominant antigens are many previously characterized immunogenic peptides such as Bgl2 [17] , Tkl1 [15] , Hwp1 [13] , [23] , Eft2 [15] , and Cdc24 [13] . Intriguingly , the average signal intensities to the top-forty serodominant antigens are higher in the healthy individuals than the uninfected hospital patients and acute candidiasis patients ( 10 , 380 vs . 8 , 837 and 8 , 825 , respectively ) . It is interesting to speculate whether the healthy individuals' IgG response limits colonization and overgrowth since many of the serodominant antigens are against adhesins . In particular is the strong humoral response to the integrin-like protein , Int1 , which may play dual roles in limiting both intestinal colonization of the cecum and systemic invasion of deep tissue organs [46] , [47] . Another interesting serodominant antibody response is to the protein Ece1 , which has been shown to promote adhesion and is important for GI colonization[48] . ECE1 transcription is highly expressed during GI colonization and invasion of host tissue [33] , [48] . However , one can not discount that the high IgG titer of colonized individuals may be due to a previous superficial infections such candidal vaginitis [49] , [50] . The microenvironmental conditions during commensal colonization of the host may also play a role in the induction of the IgG response to certain cell surface proteins . Previous studies have evaluated characteristics common to the GI and/or vulvovaginal tract such as blood , hypoxia , iron restriction and weak acid as modifiers of gene expression [9] , [51]–[53] . Intriguingly , the expressions of these genes share common features to the identified serodominant antibodies . Interestingly , genes transcriptionally up-regulated in blood ( Als1 , Als3 , Hwp1 , Ece1 , Hyr1 , and Bgl2 ) were serodominant and cross-reactive with both positive and negative candidiasis individuals , as were genes up-regulated under hypoxic conditions ( Als1 , Als3 , Hwp1 , Rbt5 , Utr2 , and Tos1 ) , iron restriction ( Int1 , Rbt5 , and Fet35 ) , and weak acid ( Crp1 , Fet35 , and Ipf9655 ) ( Table 1 ) . Furthermore , some of the serodominant antigens ( i . e . Als3 , Ece1 , Hwp1 , and Rbt5 ) have been shown to be induced during the invasion of epithelial or endothelial cells [11] , [33] . Therefore , the expression of the serodominant antigens in healthy individuals indicates the existence of permanent host-pathogen interplay during commensal colonization . In addition , the presence of serodominant IgGs in all 50 healthy individuals suggests that commensal colonization is much more prevalent than previously reported . One of the most challenging tasks in characterizing serodiagnostic antigens from C . albicans is the identification of discriminating peptides that can differentiate between commensal colonization and candidemia with high sensitivity and specificity . By profiling antibody response from patients with varying stages of candidemia against healthy individuals and candidemia-negative hospital patients , we have identified 13 diagnostic antigens for acute phase of candidemia and 33 for the early/mid convalescent candidemia . The serologic signature in candidemia patients likely reflects an alteration in the level of those proteins due to a change either in transcription and/or protein stability . Stage- and tissue-specific gene expression during the course of systemic infection is expected as C . albicans cells transition through differing microenvironments of the host . Among the 13 diagnostic antigens for acute candidemia , three are associated with drug resistance ( Cdr1 , Cdr4 , and Yor1 ) [36] . The exposure to antifungal drugs in patients undergoing acute candidemia may have acted as an additional environmental stress that stimulates the expression of these antifungal drug transporters [54] . Intriguingly , two out of the 13 biomarkers are the osmosensors Sln1 and Nik1 for the HOG pathway that is responsible for osmotic and oxidative stress adaptation in C . albicans [34] , [35] . The host-pathogen interaction commonly associated with oxidative stress is typically seen during phagocytosis by neutrophils , the initiating immune response to C . albicans overgrowth and infection . Furthermore , a study of global transcriptional responses to oxidative stress observed an increase in the transcriptional expression of CDR4 ( 4 . 1-fold ) , RAS2 ( 2 . 5-fold ) and ALS9 ( 1 . 5-fold ) [35] . Taken together , our data indicates a strong correlation between the IgG response to oxidative stress-related cell surface proteins and the initial cell-mediated immune response during acute candidemia . In further agreement , previous studies have shown that oxidative stress functions are primarily induced when C . albicans is initially exposed to human blood or following phagocytosis by neutrophils and granulocytes [7] , [9] , [10] , [55] . The 33 convalescent diagnostic antigens include proteins involved in iron acquisition , cell wall biogenesis , membrane lipid organization , and drug resistance . Of particular interest is the dramatic increase in antibodies to proteins for iron acquisition ( Cfl91 , ferric reductase; Rbt5 and Csa1 , hemoglobin receptors; and Flc1 , heme uptake ) . Iron is an essential nutrient for C . albicans . Circulating iron in serum is bound to transferrin and ferric reductases are required in the acquisition of iron from transferrin . Interestingly , Cfl91 is found as a biomarker for both acute and convalescent candidemia patients . Of particular interest is the increase antibody response to hemoglobin and heme-related proteins as these molecules are normally sequestered in erythrocytes [56] . The proteins Rbt5 , Csa1 and Flc1 are required for iron acquisition from hemoglobin or heme [39] , [40] and are diagnostic antigens only for convalescent candidemia . Thus , it is interesting to speculate whether free hemoglobin becomes a by-product of lysed erythrocytes after post-operative surgery or other invasive clinical procedures . Nevertheless , the data from this study should provide critical information for the development of diagnostic antigenic profiles for patients at risk for candidemia and for the assessment of progression of hematogenously disseminated candidiasis . Future studies will need to be done to determine whether serological differences exist between superficial and systemic infections , as well as commensal sensitization . The development of the antigenic profiles over the course of candidiasis ( acute infection , early convalescence , and mid convalescence ) may also provide insight into a protective humoral response against C . albicans . Even though previous sensitization to commensal colonization does not limit mortality or even morbidity in patients , experimental studies have identified protective antibodies against hematogenously disseminated candidiasis , such as heat shock protein 90 ( Hsp90 ) or β-mannan [57]–[60] . Future studies will need to address whether the serodiagnostic antigens identified in this study could provide protection from hematogenously disseminated candidiasis . Of particular interest are the convalescent serodiagnostic antigens where the EC antigen intensity is significantly greater than the AI antigen intensity , which may possibly indicate the selection of a protective antibody response .
Human sera from candidemia patients and hospitalized patients were collected from SH-UF under protocols approved and created by the UF Institutional Review Board . Sera from healthy individuals were obtained from volunteers at the General Clinical Research Center at the University of California , Irvine . Written , informed consent was obtained from participants . Candidemia was defined as the recovery of C . albicans from blood cultures . Sera from candidemia patients and hospitalized patients ( no clinical or microbiological evidence of candidemia ) were collected from SH-UF as previously published [61] . Briefly , patients at SH-UF were identified on the day blood cultures were positive for C . albicans . The Infectious Diseases Consultation Service at SH-UF identified controls . Sera were collected and stored at −70°C in the repository at the UF Mycology Research Unit . For patients with candidemia , sera were obtained from the earliest possible date on or after the date that the first positive cultures were drawn . In all cases , this was within 7 days of the first positive culture ( acute-phase sera ) . For ten patients with candidemia , sera were also recovered 4 to 12 weeks after the date on which the first positive cultures were drawn ( convalescent-phase sera ) . Cell surface proteins were selected from the Candida Genome Database ( CGD ) using keywords such as “cell surface” , “plasma membrane” , and “cell wall” . The CGD annotation of cell surface proteins is based on published experiments [32] , [62]–[66] , function-based prediction of cellular localization , and sequence prediction . Known antigenic proteins are also included as controls ( Bgl2 , Eno1 , Pgk1 , Gap1 , Cdc19 , Tkl1 , Hsp90 , and members of the Hsp70 family ) [15] , [17] . Coding regions of the genes were PCR amplified from the clinical isolate SC5314 of C . albicans with primers listed in Table S2 , and cloned into a pXT7 expression vector with a HA-tag at the N-terminus and His-tag at the C-terminus by homologous recombination in E . coli as described [67] . Protein expression was carried out using an E . coli based cell-free in vitro transcription/translation system ( RTS 100 E . coli HY kit , Roche ) . The protein microarray was made by printing the peptides onto nitrocellulose-coated FAST glass slides ( Schleicher & Schuell ) using the OmniGrid 100 ( GeneMachines ) in the UCI Microarray Facility . Each peptide was printed in duplicate and showed homogenous spot morphology as well as low background . Internal controls consisting of buffer alone and a reaction mixture with no DNA were also printed onto the FAST slides . After the addition of the plasma samples the microarray was incubated with a biotin-conjugated donkey anti-human IgG Fcγ fragment specific secondary antibody ( Jackson Immunoresearch ) . The secondary antibody was then removed and the microarray was incubated with Streptavidin: SureLight ® P-3 ( Columbia Biosciences ) . Details concerning microarray construction and controls , antibody profiling , data normalization , as well as the reproducibility and validity of the microarray are given in the Text S1 . All analysis was performed using the R statistical environment ( http://www . r-project . org ) . It has been noted in the literature that data derived from microarray platforms is heteroskedatic [68]–[70] . This mean-variance dependence has been observed in the arrays presented in this manuscript [71] , [72] . In order to stabilize the variance , the vsn method [73] implemented as part of the Bioconductor suite ( www . bioconductor . org ) was applied to the quantified array intensities . In addition to removing heteroskedacity , this procedure corrects for non-specific noise effects by finding maximum likelihood shifting and scaling parameters for each array such that the variances of a large number ( default setting used: 85% ) of the spots on the array are minimized . In other words , the method assumes that variance in binding for the vast majority of the proteins on the array are due to noise rather than true differential immunological response . In essence , 85% of the spots on the array are used as controls for sample-by-sample normalization . This calibration method has been shown to be effective on a number of platforms [74]–[76] . A simple ranking normalization where all of the proteins are ordered for each sample by binding intensity and assigning the integer rank was performed as well with similar results ( results not shown ) . Finally , VSN normalized data is retransformed with the ‘sinh’ function to allow visualization and discussion at an approximate raw scale . Diagnostic biomarkers between groups were determined using a Bayes regularized t-test adapted from Cyber-T for protein arrays [69] , [77] . To account for multiple testing conditions , the Benjamini and Hochberg ( BH ) method was used to control the false discovery rate [78] . Statistical analyses were performed with R 2 . 0 ( www . r-project . org ) and STATA ( version 10 . 0 , StataCorp ) . Multiple antigen classifiers were constructed using linear and non-linear Support Vector Machines ( SVMs ) using the “e1071” R package . To prevent overfitting and show the generalization of the classification method , 10 repeats of three-fold cross-validation were performed . In this methodology , the data is split into 3 class-stratified subsets . For each subset , a classifier is trained using the remaining two-thirds of the data . The classifier is then evaluated on the one-third of the data not used for training . This process is repeated for each split and for 10 different splits , yielding 30 evaluation measures . The ROCR package was used to construct receiver-operating-characteristic curves and perform sensitivity and specificity analyses . Blast2Go ( www . blast2go . org ) was used for gene ontology annotation and enrichment analysis . To confirm that the identified antigens were accurate , their vectors were resequenced . The Tables S3 and S4 list the statistical data of acute and convalescent candidemia patients , respectively . Detailed information for the genes/proteins from this study can be found at the Candida Genome Database http://www . candidagenome . org . The gene names and ORF numbers are listed here: INT1 ( 19 . 4257 ) , CWH41 ( 19 . 4421 ) , PGA13 ( 19 . 6420 ) , RBT5 ( 19 . 5636 ) , HWP1 ( 19 . 1321 ) , SLK19 ( 19 . 6763 ) , YPS7 ( 19 . 6481 ) , ALS3 ( 19 . 1816 ) , CHS2 ( 19 . 7298 ) , EFT2 ( 19 . 5788 ) , IPF9655 ( 19 . 3988 ) , GNP3 ( 19 . 7565 ) , PHR3 ( 19 . 5632 ) , ECE1 ( 19 . 3374 ) , BGL2 ( 19 . 4565 ) , PAN1 ( 19 . 19 . 886 ) , OSH2 ( 19 . 5095 ) , CRP1 ( 19 . 4784 ) , PRY1 ( 19 . 2787 ) , PGA60 ( 19 . 5588 ) , UTR2 ( 19 . 1671 ) , HNM1 ( 19 . 2003 ) , HYR1 ( 19 . 4975 ) , WSC4 ( 19 . 7251 ) , CDC24 ( 19 . 3174 ) , HYR3 ( 19 . 575 ) , DNF2 ( 19 . 932 ) , MEP2 ( 19 . 5672 ) , GCA1 ( 19 . 4899 ) , CWH43 ( 19 . 3225 ) , FRE10 ( 19 . 1415 ) , ALS5 ( 19 . 5736 ) , ALS1 ( 19 . 5741 ) , SLN1 ( 19 . 3256 ) , FCY21 ( 19 . 1357 ) , TOS1 ( 19 . 1690 ) , FET34 ( 19 . 4215 ) , TKL1 ( 19 . 5112 ) , CDR1 ( 19 . 6000 ) , CFL91 ( 19 . 1844 ) , CDR4 ( 19 . 5079 ) , ALS9 ( 19 . 5742 ) , CDC19 ( 19 . 3575 ) , NIK1 ( 19 . 5181 ) , CHS8 ( 19 . 5384 ) , RTA4 ( 19 . 6595 ) , TRK1 ( 19 . 600 ) , YOR1 ( 19 . 1783 ) , CSC25 ( 19 . 6926 ) , RAS2 ( 19 . 5902 ) , DRS23 ( 19 . 323 ) , IPF25023 ( 19 . 2296 ) , ALS6 ( 19 . 7414 ) , VPS62 ( 19 . 1800 ) , SNQ2 ( 19 . 5759 ) , IPF885 ( 19 . 7214 ) , CAG1 ( 19 . 4015 ) , HNM4 ( 19 . 2946 ) , APC5 ( 19 . 6861 ) , HSP90 ( 19 . 6515 ) , CSA1 ( 19 . 7114 ) , GSL2 ( 19 . 3269 ) , PGA4 ( 19 . 4035 ) , FLC1 ( 19 . 2501 ) , CHS1 ( 19 . 7298 ) , IPF22247 ( 19 . 4940 ) , YCK22 ( 19 . 2222 ) , SSU1 ( 19 . 7313 ) , RAD50 ( 19 . 1648 ) , and CYR1 ( 19 . 5148 ) . | Candida albicans has both a benign and pathogenic association with the human host . Previous to this study , little was known in regard to how the host humoral system responds to the commensal colonization of C . albicans , as well as the development of hematogenously disseminated candidiasis . We show using a C . albicans cell surface protein microarray that the immunocompetent host exists in permanent host-pathogen interplay with commensal C . albicans , and undergoes stage-specific antibody responses as the yeast transitions from a benign microbe to an opportunistic fungal pathogen . Also identified were serological signatures specific for acute and convalescent stages of candidemia . Our findings provide new insight in the characterization of potential serodiagnostic antigens and vaccine candidates to the opportunistic pathogen C . albicans . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"infectious",
"diseases/fungal",
"infections",
"microbiology/immunity",
"to",
"infections"
] | 2010 | Serological Profiling of a Candida albicans Protein Microarray Reveals Permanent Host-Pathogen Interplay and Stage-Specific Responses during Candidemia |
Bacillus anthracis , the causative agent of anthrax , secretes lethal toxin that down-regulates immune functions . Translocation of B . anthracis across mucosal epithelia is key for its dissemination and pathogenesis . Group 3 innate lymphocytes ( ILC3s ) are important in mucosal barrier maintenance due to their expression of the cytokine IL-22 , a critical regulator of tissue responses and repair during homeostasis and inflammation . We found that B . anthracis lethal toxin perturbed ILC3 function in vitro and in vivo , revealing an unknown IL-23-mediated MAPK signaling pathway . Lethal toxin had no effects on the canonical STAT3-mediated IL-23 signaling pathway . Rather lethal toxin triggered the loss of several MAP2K kinases , which correlated with reduced activation of downstream ERK1/2 and p38 , respectively . Inhibition studies showed the importance of MAPK signaling in IL-23-mediated production of IL-22 . Our finding that MAPK signaling is required for optimal IL-22 production in ILC3s may lead to new approaches for targeting IL-22 biology .
Bacillus anthracis is a Gram-positive bacterium that is the causative agent of anthrax , a rare and deadly disease that can infect the host by pulmonary , gastrointestinal ( GI ) or cutaneous routes [1 , 2] . In each route of infection , spores or vegetative bacteria must translocate through an epithelial barrier in order to disseminate and cause disease . Pathogenic bacteria achieve epithelial translocation by secretion of virulence factors that allow the bacteria to physically disrupt barriers , enter host cells , displace commensal bacteria and/or suppress immune responses , allowing the pathogens to disseminate . The mechanisms of bacterial epithelial translocation often involve modulation of host signaling pathways [3 , 4] . B . anthracis secretes lethal toxin , which contributes to barrier disruption via interference of epithelial , endothelial and immune cell function [5] . At the interface between the host and the environment , mucosal barriers have several lines of defense against invasive pathogens such as B . anthracis [6] . Mucin-rich surfaces and antimicrobial peptides prevent commensal and/or pathogenic bacteria from interacting with the epithelium . Recent studies have revealed a critical role for innate lymphocytes ( ILCs ) in barrier maintenance [7 , 8] . Examining the roles of ILCs in response to commensal and pathogenic bacteria is an area of active investigation . Understanding how pathogenic bacteria may modulate ILC function during infection is a new area of exploration . ILCs are a broad class of lymphocytes that lack diverse , rearranged antigen-specific receptors [9 , 10] . As such , ILCs respond to environmental signals , including cytokines , Toll-like receptor ( TLR ) ligands and other pathogen-associated molecular patterns ( PAMPs ) . Group 3 innate lymphocytes ( ILC3s ) are rare immune cells found primarily found in mucosal tissues [9] . ILC3s function in concert with T cells in maintaining tissue homeostasis and protecting the host during bacterial infection [11] . Environmental signals are central for ILC3 activation . The most potent activator of ILC3s is the cytokine IL-23 [12] , which is primarily secreted by activated macrophages and DCs . Other signals important for ILC3 activation include IL-1β , TLR ligands and neurotropic factors [13–15] . Activated ILC3s may produce the cytokines IL-22 , GM-CSF and to a certain extent IL-17 [10] . IL-22 is one of the most biologically important effector cytokines produced by ILC3s . ILC3s and CD4 T cells are major sources of IL-22 [16] . IL-22 functions as a pro-inflammatory or protective cytokine depending on the context of inflammation [17] . IL-22 is upregulated in many Gram-positive and Gram-negative bacterial infections , including pulmonary and GI infections such as Klebsiella pneumoniae , Streptococcus pneumoniae , Salmonella enterica ser . Typhimurium and Clostridium difficile [18–21] . In animal infection models using these pathogens , IL-22 primarily plays a protective role by maintaining barrier integrity and thereby limiting bacterial dissemination . This is achieved via an IL-22-mediated increase in proliferation and inhibition of apoptosis of epithelial cells and stem cells [22–24] . IL-22 also increases production of protective mucins and antimicrobial peptides [25 , 26] . Although the role of IL-22 in B . anthracis infection has not been reported , the cytokine could play a similar role in both pulmonary and GI anthrax infection . Regulation of IL-22 production by ILC3s is controlled through several well-described signaling pathways . IL-23-mediated activation of the STAT3 signaling pathway is essential for IL-22 production [27] . The other major transcription factor involved in IL-22 production is the aryl hydrocarbon receptor ( AHR ) , which is important not only for Il22 expression in ILC3s [28] , but also for the development of ILC3s from immature precursors [29] . STAT3 and AHR are important in the analogous adaptive immune system T helper 17 ( Th17 ) subset [30] . Because of the relatedness of ILC3s and Th17 cells , there are likely unidentified signaling pathways that contribute to IL-22 regulation in ILC3s . Lethal toxin is a well-characterized two-subunit bacterial toxin from B . anthracis comprised of protective antigen and lethal factor [31 , 32] . Together the two proteins form a complex that binds to one of two known cell surface receptors , mediating entry into the host cell [31] . In the cytoplasm , lethal toxin is a zinc metalloprotease that cleaves a number of identified proteins , including members of mitogen-activated protein kinase ( MAPK ) signaling pathways [33] and the inflammasome-activating sensor NLRP1B [34] . Lethal toxin effects are best described for macrophages and DCs , where its interference in MAPK signaling leads to reduced antigen presenting cell function , resulting in reduced T cell responses [35] . Lethal toxin also directly affects the function of T cells , B cells and NK T cells [36–39] . Whether lethal toxin impacts the function of ILCs has not been investigated . Here we show that lethal toxin impairs IL-22 production by ILC3s using in vitro and in vivo studies . Mechanistically , we identified previously unknown molecular circuits involving MAPK signaling that regulate IL-22 production in ILC3s . These data have implications on strategies to modulate either the function of ILC3 or the biological activity of IL-22 during infectious disease or inflammation .
We used Rag1-/- mouse splenocytes , a source of ILC3s , because the absence of T cells in this model makes ILC3s the dominant IL-22-expressing cell type [40] . Cells were treated with or without lethal toxin ( lethal factor and protective antigen ) for 3 hrs and then stimulated with or without IL-23 to induce IL-22 expression . As expected , IL-23 stimulation resulted in 2-fold greater amounts of secreted IL-22 ( Fig 1A ) . Lethal toxin treatment reduced IL-23-mediated IL-22 levels by approximately 40% compared to non-toxin treated controls ( Fig 1A ) . This decrease occurred in a lethal toxin dose-dependent manner ( Fig 1B ) and was dependent on enzymatically active lethal factor ( Fig 1C ) , which was capable of gaining cell entry via protective antigen-mediated translocation ( Fig 1A ) . The term lethal toxin is a misnomer and the toxin has limited ability to kill most cells [41] . Nevertheless , we examined whether the reduction of IL-22 production by ILC3s correlated with a decrease in cell viability . Using two different viability exclusion dyes via flow cytometry analysis , we observed no difference in the viability of ILCs treated with or without lethal toxin and/or IL-23 ( Fig 1D , 1E and 1F and S1A and S1B Fig ) . Thus , lethal toxin-mediated down-regulation of IL-22 was not due to reduced cell viability and instead suggested interference with a signaling pathway that regulates IL-22 production . Reduced levels of secreted IL-22 in heterogeneous lymphocyte mixtures of Rag1-/- splenocytes could be due to another cell affecting ILC3 production of IL-22 and/or modulation in IL-22 from other innate immune cells that have been reported to produce low levels of IL-22 , such as DCs or neutrophils [22 , 42 , 43] . The ILCs present in Rag1-/- splenocytes bound protective antigen in a dose-dependent manner ( S1C Fig ) , suggesting the cells were direct targets for lethal toxin . To examine IL-22 production specifically in ILCs , we isolated ILCs through cell sorting from Rag1-/- mice . Purified mouse ILCs ( Lin- CD127+ , which may include ILC1s , ILC2s and ILC3s ) were treated with or without lethal toxin and/or IL-23 and then Il22 expression and IL-22 production were examined 6 hr later . Lethal toxin down-regulated IL-23-mediated Il22 expression at the transcriptional level ( Fig 1G ) . As we found in the heterogeneous splenocyte cultures , lethal toxin down-regulated secreted IL-22 ( Fig 1H ) . These data indicate that lethal toxin modulated Il22 and IL-22 at both the mRNA and protein levels , respectively , in ILCs and this was caused by a direct intoxication of ILC3s . We next examined whether lethal toxin down-regulated IL-22 production in human ILCs . Using a mixed lymphocyte population obtained from a mucosal tissue ( tonsils ) we treated cells with or without lethal toxin for 3 hr and then stimulated the cells with or without IL-23 . As for mouse ILC3s , low levels of secreted IL-22 were detected in human lymphocyte cultures , which significantly increased by approximately 2-fold after IL-23 stimulation ( Fig 2A ) . Lethal toxin treatment prevented an induction of IL-22 levels by IL-23 , which depended on toxin translocation since lethal factor ( LF ) alone failed to inhibit IL-22 production ( Fig 2A ) . The inhibition by lethal toxin was dose-dependent and required enzymatically active toxin ( S2A and S2B Fig ) . We also examined the effects of lethal toxin on purified human ILCs ( Lin- CD127+ ) and found that IL-23-mediated IL-22 secretion was generally decreased in the presence of the toxin ( Fig 2B ) . In summary , lethal toxin down-modulated IL-23-mediated IL-22 production in both mouse and human ILC3s . These findings are consistent with the notion that lethal toxin inhibits IL-23 signal transduction leading to IL-22 production in ILC3s . Recognition of IL-23 by its cell surface receptor on cells primarily activates JAK2 and the downstream transcription factor STAT3 [44] . To determine if lethal toxin had any effect on STAT3 signaling , we tested the effect of the toxin on JAK2 and STAT3 phosphorylation . As primary ILC3s are rare and difficult to purify in sufficient quantities , we made use of a recently generated ILC3 cell line , MNK-3 , that was isolated from mouse fetal thymic immune precursors [45] . These cells produce high levels of IL-22 upon stimulation with IL-23 and their use permitted both semi-quantification of transcript expression and visualization of cellular protein and phospho-protein levels by western blot . We found that lethal toxin inhibited both constitutive and IL-23-mediated Il22 expression and IL-22 production in this cell line ( Fig 3A , 3B and 3C ) without affecting cell viability ( S3A–S3C Fig ) , recapitulating our experiments with primary mouse and human cells . To examine whether lethal toxin modulated JAK2 and/or STAT3 signaling , we next treated MNK-3 cells with lethal toxin for 2 hr , stimulated with IL-23 for the indicated time and then examined JAK2 and STAT3 phosphorylation by western blot analyses . We found that lethal toxin treatment had no detectable effect on the levels of phosphorylated JAK2 or STAT3 after IL-23 treatment ( Fig 3D ) . Thus , lethal toxin did not interfere with the JAK-STAT pathway , the canonical IL-23 signaling pathway in ILC3s . In other immune cells , particularly macrophages , lethal toxin is known to cleave MEK1 and MEK2 and lead to degradation of these proteins [33] . Thus we also examined MEK1 and MEK2 levels in MNK-3 cells after incubation with lethal toxin . Treatment of MNK3 cells with lethal toxin , but not toxin lacking enzymatic activity , led to a time-dependent reduction in MEK1 and MEK2 levels ( Fig 3E ) . Lethal toxin has also been reported to affect other MEK kinases , including MKK3 and MKK6 [46 , 47] . Therefore , we also examined MKK3 and MKK6 levels in MNK-3 cells after lethal toxin treatment . We found that lethal toxin led to reduced levels of MKK3 and MKK6 , albeit with slower kinetics than that observed for MEK1 and MEK2 ( Fig 3E ) . Together , these data show that lethal toxin treatment leads to loss of MEK1 , MEK2 , MKK3 and MKK6 in ILC3-like MNK-3 cells . These data are also consistent with lethal toxin mediating the degradation of these proteins . MEK1/2 and MKK3/6 are MAPK kinases upstream of several MAPK pathways , including ERK1/2 and p38 , respectively , and have well described roles in many immune cells [48] , but this has not been studied in IL-23-stimulated ILC3s . IL-23 treatment of MNK-3 cells led to temporal phosphorylation of ERK1/2 ( Fig 4A ) . ERK1/2 activation was rapid , and could be detected within 5 min of IL-23 addition , suggesting a direct activation of ILC3s by IL-23 . This activation was dependent on intact IL-23 , as heat-treated ( HT ) IL-23 was unable to phosphorylate ERK1/2 ( Fig 4B ) . Furthermore , neutralization of IL-23 inhibited IL-23-mediated ERK1/2 phosphorylation ( Fig 4B ) . Importantly , the phospho-ERK1/2 signal was completely abrogated upon lethal toxin treatment , which also correlated with loss of MEK1 and MEK2 ( Fig 4A ) . In addition to the mouse cell line , we also examined whether IL-23 treatment results in ERK1/2 phosphorylation in human ILC3s . As these are very rare cells , we first expanded primary sorted ILCs in vitro to obtain larger numbers of ILCs ( S4 Fig ) . IL-23 stimulation of the expanded human ILCs led to increased phosphorylation of ERK1/2 , which did not occur when the cells were treated with lethal toxin ( Fig 4C ) . Similar results were obtained with expanded mouse ILCs ( Fig 4D ) . Thus , IL-23 directly activates the ERK1/2 signaling pathway in ILC3s and this activation is inhibited by lethal toxin . Unlike IL-23-mediated ERK1/2 activation , p38 activation was not modulated by IL-23 but it was modulated by lethal toxin in MNK-3 cells . Phosphorylated p38 was found in unstimulated MNK-3 cells , suggesting basal levels of p38 activation in cells that is not readily increased by IL-23 stimulation ( Fig 4A ) . However , this phosphorylated p38 was undetectable in lethal toxin treated cells ( Fig 4A ) , correlating with lack of MKK3 and MKK6 , suggesting the toxin interfered with p38 activation through upstream interference of MKK levels . As p38 activation is often associated with IL-1β signaling , and IL-1β can also induce IL-22 in ILC3s , we also examined the effect of lethal toxin on IL-1β-mediated activation of ILC3s . We found that lethal toxin suppressed IL-22 production ( S5A and S5B Fig ) and also ablated IL-1β-mediated phoshphorylation of p38 through loss of total p38 ( S5C Fig ) . Together , these data show that treatment of ILC3s with lethal toxin prevented activation of ERK1/2 and the maintenance of p38 , which correlated with the loss of their upstream activators , MEK1/2 and MKK3/6 . As lethal toxin treatment led to reduced levels of MEK1/2 and MKK3/6 and reduced phosphorylation of ERK1/2 and p38 , we examined the role of these two MAPK kinases in IL-22 production in ILC3s . To this end , we made use of two widely used small molecule inhibitors , PD98059 and SB203580 . PD98059 is a selective inhibitor of MEK1/2 and therefore has downstream indirect inhibition of ERK1/2 [49] and SB203580 selectively inhibits p38 activation [50] . Cells were treated with an inhibitor or vehicle only control and then were stimulated with IL-23 . Both inhibitors significantly reduced IL-23-mediated IL-22 production from Rag1-/- splenocytes ( Fig 5A and 5B ) . When we examined IL-22 secretion from purified mouse ILCs , we found that SB203580 down-regulated IL-23-mediated IL-22 production by approximately 50% whereas the effect of PD98059 was a reduction of 25% ( Fig 5C ) . This indicated that between the two MAPK pathways , p38 may be the more important for IL-22 production in ILC3s . Thus , the p38 pathway is a positive regulator of IL-23-mediated IL-22 production in ILC3s whereas the ERK1/2 pathway appears to play a minor role . Our results suggest that lethal toxin from B . anthracis subverts ILC3 function by modulating the p38 signaling pathway . This finding may have implications for the host immune response or barrier function during infection . To translate our in vitro studies in mouse and human ILC3s to an intact organism , we examined if lethal toxin modulated ILC3 function in mice . Mice were intravenously injected with 100 ug each of lethal factor and protective antigen and then 48 hr later we examined the numbers of ILC3s in different tissues as well as the capacity of these ILCs to produce IL-22 or GM-CSF ex vivo . Rag1-/- mice were used in order to examine ILCs in the absence of IL-22-producing T cells . We found no change in the total number of ILCs ( Lin- CD45 . 2+ Thy1 . 2+ CD127+ ) in the spleens , lungs or livers of toxin treated mice compared to mice that were injected with vehicle control ( Fig 6A and S6A Fig ) . To examine the capacity of these cells to produce cytokine upon ex vivo stimulation , we stimulated the cells with IL-23 , PMA and ionomycin and examined IL-22 production by intracellular cytokine staining ( Fig 6B and S6B Fig ) . In ex vivo culture , a low percentage of ILCs produced IL-22 . Upon IL-23 stimulation , ILCs isolated from untreated mice had a greater percentage of cells expressing IL-22 than lethal toxin treated mice ( Fig 6C ) . These data suggest that lethal toxin in vivo negatively modulates ILC3 function by decreasing the capacity of ILC3s to produce the key cytokine , IL-22 . In addition to IL-22 , ILC3s also secrete other effector molecules , including the cytokine GM-CSF . In our ex vivo stimulated cells we also examined whether lethal toxin modulated GM-CSF production , which is induced in ILC3s by IL-1β [51] . Most ILCs that produced IL-22 also produced GM-CSF , but not all GM-CSF expressing cells produced IL-22 ( Fig 6B ) . Significantly fewer ILCs from mice treated with lethal toxin produced GM-CSF compared to ILCs from control mice ( Fig 6B and 6C ) . Therefore , in vivo lethal toxin administration can reduce the ability of ILC3s in several different tissues to produce IL-22 and GM-CSF .
We have found that B . anthracis lethal toxin down-regulated IL-23-mediated Il22 expression and IL-22 secretion in vitro and IL-22 production in ILC3s ex vivo . Lethal toxin had no effects on the canonical IL-23 signaling pathway , via JAK2/STAT3 , but did mediate loss of MEK1/2 and MKK3/6 from ILC3s . Reduced MEK1/2 and MKK3/6 levels correlated with reduced activation of the downstream MAPK signaling molecules ERK1/2 and p38 , respectively . Experimental inhibition of MEK signaling pathways in ILC3s likewise resulted in reduced IL-22 production . Thus , MAPK signaling pathways , particularly the MKK3/6-p38 axis , are important in IL-23-mediated IL-22 production in ILC3s . Our studies were performed using mouse and human immune cells and included cell lines , primary cells and expanded primary cells providing robust results . Thus , our data are complementary and show that MAPK is a central signaling pathway in IL-23-mediated IL-22 production in ILC3s , suggesting that this is an evolutionarily conserved and important pathway . MAPK are important for mediating signaling of IL-1β- or RET receptor-mediated IL-22 production , but have not yet been identified in IL-23-mediated signaling in ILC3s [15 , 52] . Activation of multiple signaling pathways may allow cells to finely tune expression of effector molecules as well as provide redundancy and resiliency . During infection this allows immune responses to better counteract the virulence factors produced by pathogens . Bacterial toxins are valuable molecular tools for elucidating signaling pathways in cells [53] . By probing the biology of B . anthracis lethal toxin we have identified MEK1/2-ERK1/2 and MKK3/6-p38 as important signaling pathways in the production of IL-22 in ILC3s . We found that IL-23 stimulation of ILC3s led to rapid phosphorylation of ERK1/2 , suggesting a direct effect of IL-23 on ERK1/2 activation . ERK1/2 and p38 phosphorylation was inhibited in the presence of lethal toxin , which we showed caused loss of the upstream MAPK kinases , MEK1/2 and MKK3/6 [33 , 46 , 47] . IL-23 signaling strongly activates the STAT3 signaling pathway , which is critical in ILC3s for IL-22 production [27] . However , other signaling pathways , including MAPK , are less well understood . There is one report of MEK1/2 inhibitors modulating IL-23 signaling in DCs [54]; however , MAPK signaling is not a well-appreciated IL-23-mediated pathway in ILC3s . ILC3s are rare , but can nevertheless have critical roles in the early immune response to pathogenic bacteria at mucosal barriers . Experimental elimination of these cells in a variety of bacterial infections worsens infection , especially in the absence of adaptive immunity . Mice lacking ILCs are more susceptible than control mice to such pathogenic bacteria as Citrobacter rodentium or K . pneumoniae [40 , 55] . Less is known regarding the role of innate lymphocytes in B . anthracis infection . One study found that in a model of B . anthracis gastrointestinal infection that colonic ILC2s , a Th2-like innate lymphocyte subset with some similarities to ILC3s , were reduced in number [56] . For the ILC2s that did persist , a smaller percent produced the key ILC2 cytokines IL-5 and IL-13 . Lethal toxin was shown to decrease the cytotoxicity of natural killer ( NK ) cells [57] . Combined with our data on ILC3s , it appears that B . anthracis modulates many subsets of innate lymphocytes , potentially through conserved mechanisms . Lethal factor directly effects endothelial and epithelial cells to reduce barrier integrity during infection [5] . We have shown here another potential mechanism for how lethal toxin perturbs barrier function through the down-regulation of a key effector molecule , IL-22 , in mucosal barrier maintenance and wound repair . Bacterial toxins are a key virulence factors in that modulate host signaling pathways [41] . Pathogenic bacteria and viruses have evolved strategies to disarm the innate immune system during infection [3] . As secreted proteins , bacterial toxins can have far-reaching effects within the host . In our in vivo toxin model we examined the systemic effects of lethal toxin on ILC3s . ILC3s isolated from the lungs , livers and spleens of lethal toxin treated mice were found to have reduced capacity to produce IL-22 and GM-CSF . This finding has important implications on the ability of the host to prevent bacterial dissemination and reduce and/or eliminate pathogen burden . IL-22 is one of the most important effector molecules produced by ILC3s . IL-22 signaling in non-hematopoietic cells up-regulates expression of antimicrobial peptides , such as β-defensin , lipocalin-2 , RegIIIβ , and RegIIIγ [20 , 25 , 58] . These antimicrobial peptides help regulate the commensal flora that helps prevent pathogen colonization , as well as directly counteract pathogens [59] . IL-22 is a potent inducer of mucins , the large glycoproteins that create a thick , often impenetrable barrier , on mucosal surfaces [60 , 61] . IL-22 also plays a role in epithelial cell repair and has direct effects on the intestinal stem cells [22–24] . Mice deficient in IL-22 or ILC3s are more susceptible to many different bacterial infections , especially mucosal-associated pathogens . The role of IL-22 has been studied in a similar spore-forming mucosal Gram-positive bacterium , C . difficile . IL-22 deficient mice are more susceptible to C . difficile infection [21] and both ILC1s and ILC3s are important in controlling infection [62] . Our results show that reduction in IL-22 production by a bacterial toxin is an excellent strategy on the part of the pathogen to establish infection in the host . Reduced IL-22 in both the inflammatory milieu and systemically would have far reaching effects on the effectiveness of the immune response to combat infection . In addition to IL-22 , ILC3s produce other effector molecules such as IL-17 and GM-CSF . IL-17 is produced mainly by pathogenic ILC3s associated with chronic inflammation and cytokine dysregulation [11] and we did not readily detect IL-17 in our studies . GM-CSF is produced by ILC3s after stimulation [51 , 63] . It is a potent cytokine in the development , further activation and trafficking of macrophages , DCs and other innate cells . GM-CSF also promotes an inflammatory environment for the adaptive arm of the immune system , especially in Th17 cell biology [64 , 65] . In bacterial infections , GM-CSF is important for neutrophil and macrophage influx to sites of infection [63 , 66] . In this study we have shown that a bacterial toxin perturbs signaling pathways in ILC3s , down-modulating the innate immune response . Our data implicate p38 and MAPK in IL-23-mediated signaling in ILC3s , identifying a new pathway to target in the regulation of ILC3 function . Identification of IL-23-mediated MAPK signaling in IL-22 production may lead to new or co-opted therapeutics . IL-22 is likely a beneficial cytokine in B . anthracis infection , but in chronic inflammatory conditions such as inflammatory bowl disease or psoriasis , IL-22 can have pathogenic consequences [67 , 68] . Understanding the environmental and molecular factors that regulate IL-22 will be essential for developing more focused approaches to target the biology of IL-22 .
Recombinant protective antigen and wild-type lethal factor or lethal factor mutant ( E687C ) were obtained from List Biologicals ( Campbell , CA ) or BEI Resources ( Manassas , VA ) . PD98059 and SB203580 were both from Cell Signaling Technology ( Danvers , MA ) . Rag1-/- mice on a C57BL/6 background were obtained from The Jackson Laboratory ( Bar Harbor , ME ) [69] . Mice were housed in an AAALAC-accredited Helicobacter-free rodent barrier facility . All studies were approved by the OUHSC Institutional Animal Care and Use Committee . Tonsils were received after being discarded from pediatric surgery at OU Children’s Hospital ( Oklahoma City , OK ) . Institutional Review Board approval was obtained prior to the initiation of our studies . MNK-3 cell line [45] and clone B3 were maintained in DMEM ( Corning; Tewksbury , MA ) with 10% heat-inactivated FBS ( Gemini Bio-Products; West Sacramento , CA ) , 2 mM GlutaGro ( Corning ) , 1 mM sodium pyruvate ( GE Healthcare HyClone; Logan , UT ) , 55 μM β-mercaptoethanol ( Sigma ) , 10 mM HEPES ( Corning ) , 50 μg/ml gentamycin ( Amresco; Solon , OH ) , 100 U/ml penicillin ( Gemini Bio-Products ) , 100 U/ml streptomycin ( Gemini Bio-Products ) and 10 ng/ml recombinant mouse IL-7 and IL-15 ( eBioscience; San Diego , CA or Peprotech; Rocky Hill , NJ ) . Spleens were excised and single cell suspensions were made by disruption of the spleen on wire mesh using the plunge of a 3 ml syringe . Cells were centrifuged and the cell pellet was resuspended . Red blood cells were lysed in ACK lysis buffer ( 0 . 83% NH4Cl , 0 . 5% KHCO3 , 0 . 5 μM EDTA ) for 2 min and then neutralized with PBS or media . Cells were counted using trypan blue exclusion staining and a hemocytomer . Tonsillar lymphocytes were isolated by passing minced tonsil pieces ( approximately 1 mm ) through a 70 μM cell strainer and washed with HBSS supplemented with 100 U/ml penicillin , 100 U/ml streptomycin , 5 μg/ml gentamicin , and 0 . 5 μg/ml amphotericin B ( Corning ) . Tonsillar lymphocytes were isolated by layering single cell suspension over a Ficoll gradient ( GE Healthcare Life Sciences; Pittsburgh , PA ) . After isolating cells from the interface , which are mostly leukocytes , RBCs were lysed . Cells were then washed in HBSS for three times to remove contaminating Ficoll . Tonsillar lymphocytes were aliquoted and stored in liquid nitrogen until future use . 100 , 000–500 , 000 splenocytes per well were incubated in a round bottom 96 well plate in IMDM media supplemented with 10% FBS , 100 U/ml pencillin , 100 U/ml streptomycin , IL-2 ( 20 ng/ml ) and IL-7 ( 10 ng/ml ) . Human tonsillar lymphocytes ( 2x106 ) were plated in 200 μl RPMI media supplemented with 10% FBS , 100 U/ml penicillin , 100 U/ml streptomycin , non-essential amino acids , sodium pyruvate , IL-2 ( 20 ng/ml ) , IL-7 ( 10 ng/ml ) and IL-1β ( 20 ng/ml ) . For initial lethal toxin experiments splenocytes/tonsillar lymphocytes were cultured in media containing in 0 . 1% serum to minimize the effect of serum on lethal factor enzyme activity . After the initial period of lethal toxin treatment ( 3 hrs ) , serum was replenished to a full concentration of 10% . Cells after toxin treatment were stimulated with recombinant mouse or human IL-23 ( 50 ng/ml ) ( eBioscience ) for 18 hrs . Cell supernatants were harvested by centrifugation at 1 , 500 rpm for 5 minutes and then used for the measurement of IL-22 . MNK-3 cells ( an ILC3 cell line ) were cultured in MNK-3 media ( DMEM ( High glucose ) , 10% FBS , 100 U/ml penicillin , 100 U/ml streptomycin , 2 mM Glutagro , 1 mM sodium pyruvate , 10 mM HEPES , 55 μM β-mercaptoethanol , 5 μg/ml gentamicin , 10 ng/ml IL-7 and 10 ng/ml IL-15 ) . For lethal toxin experiments MNK-3 cells were cultured in media containing 10 ng/ml IL-7 . Cells were treated with lethal toxin for 2 hrs followed by IL-23 stimulation for 6 hrs . Cell lysates were analyzed for RNA and supernatants were analyzed for secreted IL-22 by ELISA . Splenocytes were isolated from Rag1-/- mice and cultured in IMDM ( Corning ) containing 0 . 1% FBS , 100 U/ml penicillin , 100 U/ml streptomycin and 2 mM glutamine supplemented with IL-2 ( 20 ng/ml ) and IL-7 ( 10 ng/ml ) . Cells were incubated with control ( no PA ) or PA-AlexaFluor647 at increasing concentrations ( 0–10 μg/ml ) . Cells were incubated at 37°C for 2 hrs . Cells were washed with 1% BSA and stained with surface markers for ILC3 . Cells were analyzed by flow cytometry . Mouse or human IL-22 ELISAs ( Antigenix America; Huntington Station , NY ) were performed according to the manufacturer’s protocols . Cell were stimulated as described for 5 hr in the presence of brefeldin A ( eBioscience ) for the last 4 hr . Intracellular cytokine staining was performed according to the manufacturer’s protocol with fluorophore conjugated mAbs to IL-22 ( clone Il22JOP ) or GM-CSF ( clone MP1-22E9 ) . Cells were analyzed on a Stratedigm S1200Ex flow cytometer ( Stratedigm , San Jose , CA ) and data were analyzed using FlowJo v . 9 . 6 ( Tree Star; Ashland , OR ) . Single cell suspensions were washed in PBS and then stained for 20–30 min on ice in the dark with fluorophore-labeled Abs ( for a complete list see Tables 1 and 2 ) and fixable viability dye ( eBioscience ) . Samples were washed with PBS with 1% BSA . Samples were either analyzed immediately or fixed with 2% paraformaldehyde ( PFA ) and stored at 4°C until analysis by flow cytometry . Apoptosis was monitored by Annexin V and 7-AAD staining following manufacturers’ protocols ( BD Biosciences or BioLegend ) . Briefly , cells were treated or not with lethal toxin for time indicated . After 6 hr , cells were centrifuged and the supernatant was removed and stored . Cell pellets were washed with PBS . Cells were surface stained to identify ILCs along with a fixable viability dye eFluor780 ( eBioscience ) . Cells were then stained with Annexin V and 7-AAD . Stained cells were immediately analyzed by flow cytometry . For isolating mouse primary ILCs , single cell preparations of Rag1-/- splenocytes were surface stained for NK1 . 1 and CD127 . Cells were sorted as ( CD127+ NK1 . 1- ) and ( CD127- NK1 . 1+ ) using a FACSAria . For sorting human ILCs , tonsillar lymphocytes were depleted of B cells using CD19 magnetic beads ( eBioscience ) . The B cell depleted fraction was then surface stained for lineage markers ( CD3 , CD19 , CD14 ) , CD127 and cells were sorted ( Lin- CD127+ ) . Sorted human ILC3s ( Lin- CD127+ ) were cultured at 2 , 000–5 , 000 cells per well of a 96 well plate with irradiated ( 30 Gy ) OP9-DL1 feeders [70] . Cells were cultured in RPMI with 10% FBS , 100 U/ml penicillin , 100 U/ml streptomycin , 1 mM sodium pyruvate , non-essential amino acids , 50 μM β-mercaptoethanol , 2mM glutamine and recombinant human IL-2 ( 20 ng/ml ) , IL-7 ( 20 ng/ml ) , SCF ( 20 ng/ml ) , FLT3L ( 10 ng/ml ) , IL-15 ( 10 ng/ml ) for up to three weeks . For mouse ILC3s , splenocytes from Rag1-/- mice were sorted ( Lin- ( CD3 , CD45R , CD11c , GR-1 , NK1 . 1 ) c-kit+ Thy1 . 2+ ) and cultured as described for human cells using recombinant mouse cytokines . Cells were harvested in Trizol ( Life Technologies; Carlsbad , CA ) or TriPure ( Roche; Nutley , NJ ) and RNA was prepared according to the manufacturers’ protocols . RNA was DNase treated ( Roche ) and cDNA was reverse transcribed using Transcriptor ( Roche ) with oligo dT as the primer . cDNA was used as template in a real time PCR reaction using ABI Taqman primer-probes sets ( Table 3 ) on a ABI 7500 Fast real time PCR machine ( Life Technologies ) . cDNA was semi-quantitated using the ΔΔCT method with Hprt ( mouse ) or HPRT ( human ) as an internal control for all samples . After stimulation , cells were washed with PBS . Cells were lysed with TN1 lysis buffer ( 50 mM Tris , 125 mM NaCl , 1% Triton X-100 , 10 mM EDTA , 10 mM sodium fluoride and 10 mM sodium pyrophosphate ) supplemented with protease inhibitor cocktail ( 1 . 2 mM AEBSF , 0 . 46 μM aprotinin , 14 μM bestatin , 12 . 3 μM E-64 , 112 μM leupeptin , 1 . 16 μM pepstatin ) ( Amresco; Solon , OH ) ) and 1 mM sodium orthovanadate ( Enzo Life Sciences; Farmingdale , NY ) . Cell lysates were centrifuged at 15 , 000 rpm for 5 min . Cell supernatants were separated by SDS-PAGE on a 4–15% gradient gel ( Bio-Rad; Hercules , CA ) . Proteins were transferred to an Immobilion-P PVDF membrane ( EMD Millipore; Billerica , MA ) using a wet transfer method . The protein-transferred membrane was blocked with 5% milk and then incubated with the manufacturers’ recommended concentration of primary antibody overnight at 4°C ( Table 4 ) . Blots were then washed and incubated with the appropriate species-specific-HRP secondary antibody ( 1:1000 ) for 1 hr . Blots were developed using Pierce ECL2 Western Blotting Substrate ( Thermo Scientific; Waltham , MA ) and imaged using a FluorChemQ ( Alpha Innotech; San Leandro , CA ) . Images were semi-quantitated using open source Image J software . For reblotting with another antibody , blots were stripped using a Restore Western Blot Stripping Buffer ( Thermo Scientific ) and then washed and re-blocked and used as indicated above . Age- and sex-matched Rag1-/- mice were injected intravenously with 100 μg lethal factor and 100 μg protective antigen in vehicle or as a control vehicle only via the lateral tail vein . Forty-eight hr later , mice were euthanized and lymphocytes were isolated from the spleens , livers and lungs . Cells were restimulated for 5 hr in the presence of BFA with or without 5 μg/ml phorbol 12-myristate 13-acetate ( PMA ) ( Sigma ) , 0 . 5 μg/ml ionomycin ( Sigma ) and 50 ng/ml IL-23 . Cells were then surface stained , subjected to intracellular cytokine staining and analyzed by flow cytometry as described above . Livers were excised and homogenized into a single cell suspension . Lymphocytes were isolated from the liver as previously described [71] . Liver homogenate was incubated with 100 U/ml collagenase ( Sigma; St . Louis , MO ) and 20 μg/ml DNase I ( Sigma ) for 40 min at 37°C . To remove hepatocytes , homogenates were centrifuged at 300 rpm for 3 min , and then supernatants were centrifuged at 1500 rpm for 10 min . The cells were resuspended in 1 ml complete media and 4 ml of 30% OptiPrep ( Sigma ) in a sodium phosphate buffer and 1 ml of media was carefully layered on top . Cells were centrifuged at 2 , 700 rpm for 20 min . The top layer and interface were harvested as the liver lymphocyte population . Lung lymphocytes were isolated as previously described [72] . Briefly , lungs were excised and mechanically homogenized into fragments of 1 mm . Homogenized tissue was transferred into 15 ml conical tube containing 5 ml of lung digestion medium ( DMEM supplemented with 10% FBS , 100 U/ml penicillin , 100 U/ml streptomycin , 50 μM β-mercaptoethanol , 250 U/ml collagenase IV , 25 U/ml DNase I ) . Tissue was digested at 37°C for 20 mins . The digested tissues were pushed through a 70 μM cell strainer to generate a single cell suspension . The single cell suspensions were washed with serum free RPMI medium and centrifuged at 400Xg for 5 mins at 4°C . Cell pellets were then resuspended in 40% Percoll . Cells were then differentially centrifuged at 400Xg for 10 mins at 4°C without braking . Cell supernatants were discarded and cell pellets were lysed of RBCs and then used for staining with antibodies for flow cytometry analysis . All animal experiments were conducted in accordance with the Animal Welfare Act and the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The University of Oklahoma Health Sciences Center ( OUHSC ) animal facilities have full accreditation from the Association for Assessment and Accreditation of Laboratory Animal Care and are PHS-assured ( Assurance Number: # A3165-01 ) . All animal procedures were approved by the OUHSC Animal Care and Use Committee ( IACUC ) under protocol 14–094 and the Office of Animal Welfare Assurance ( OAWA ) , which oversees the administration of the IACUC at OUHSC . Values are expressed as mean±SD . For two-way comparisons , a standard paired t test was used . For multiple comparisons , one-way ANOVA with Tukey’s post hoc analysis was used . For the in vivo experiment , two-way ANOVA corrected for multiple comparisons using Sidek’s multiple comparison test was used . Significance was defined as a value of p<0 . 05 . | Bacillus anthracis , the bacterium that causes the deadly disease anthrax , is commonly known for its use in bioterrorism . We have used B . anthracis to study the effect of MAPK signaling pathways in group 3 innate immune lymphocytes ( ILC3s ) . B . anthracis enters the host through the lungs or gastrointestinal ( GI ) tract . To cause disease , the pathogen requires translocation across barrier tissue layers to disseminate . During this infection process , the bacterium produces many virulence factors , including a lethal toxin , that allow it to subvert the host immune response . In many immune cells , lethal toxin turns off signaling events through degradation of intermediate signaling components . In this study , we show that lethal toxin interferes in a specific signaling pathway , MAPK , which is significant because it is not usually associated with IL-23-mediated signaling . We specifically studied the interference of MAPK in ILC3s , rare immune cells found in the lungs and GI tract . These cells secrete high levels of interleukin-22 ( IL-22 ) , a key cytokine for maintaining barrier tissues during inflammation . Lethal toxin significantly reduced in vitro IL-23-mediated IL-22 production in mouse and human ILC3s . Furthermore , in vivo administration of lethal toxin reduced IL-22 production in ILC3s . This negative effect on IL-22 implicated MAPK signaling as one of the required pathways in ILC3s for IL-22 production , which means that B . anthracis may inhibit ILC3 function during infection , potentially aiding its bacterial dissemination . These findings increase our understanding of the molecular factors that control IL-22 regulation in ILC3s and aid in development of immune therapeutics and preventive measures . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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"methods"
] | [] | 2017 | Bacillus anthracis lethal toxin negatively modulates ILC3 function through perturbation of IL-23-mediated MAPK signaling |
De novo biosynthesis of lipids is essential for Trypanosoma brucei , a protist responsible for the sleeping sickness . Here , we demonstrate that the ketogenic carbon sources , threonine , acetate and glucose , are precursors for both fatty acid and sterol synthesis , while leucine only contributes to sterol production in the tsetse fly midgut stage of the parasite . Degradation of these carbon sources into lipids was investigated using a combination of reverse genetics and analysis of radio-labelled precursors incorporation into lipids . For instance , ( i ) deletion of the gene encoding isovaleryl-CoA dehydrogenase , involved in the leucine degradation pathway , abolished leucine incorporation into sterols , and ( ii ) RNAi-mediated down-regulation of the SCP2-thiolase gene expression abolished incorporation of the three ketogenic carbon sources into sterols . The SCP2-thiolase is part of a unidirectional two-step bridge between the fatty acid precursor , acetyl-CoA , and the precursor of the mevalonate pathway leading to sterol biosynthesis , 3-hydroxy-3-methylglutaryl-CoA . Metabolic flux through this bridge is increased either in the isovaleryl-CoA dehydrogenase null mutant or when the degradation of the ketogenic carbon sources is affected . We also observed a preference for fatty acids synthesis from ketogenic carbon sources , since blocking acetyl-CoA production from both glucose and threonine abolished acetate incorporation into sterols , while incorporation of acetate into fatty acids was increased . Interestingly , the growth of the isovaleryl-CoA dehydrogenase null mutant , but not that of the parental cells , is interrupted in the absence of ketogenic carbon sources , including lipids , which demonstrates the essential role of the mevalonate pathway . We concluded that procyclic trypanosomes have a strong preference for fatty acid versus sterol biosynthesis from ketogenic carbon sources , and as a consequence , that leucine is likely to be the main source , if not the only one , used by trypanosomes in the infected insect vector digestive tract to feed the mevalonate pathway .
Trypanosoma brucei is a hemoparasitic unicellular eukaryote that causes Human African Trypanosomiasis ( HAT ) , also known as sleeping sickness . The disease , fatal if untreated , is endemic in 36 countries in sub-Saharan Africa , with about 70 million people living at risk of infection [1] . The T . brucei life cycle is complex and the parasite must adapt to several dynamic micro-environments encountered both in the insect vector , tsetse fly , and in the mammalian hosts . This leads to substantial morphological and metabolic changes , including adaptation of their lipid and energy metabolism . Here , we will focus on the insect midgut procyclic stage ( PCF ) of the parasite by providing a comprehensive analysis of its fatty acid and sterol de novo biosynthesis from available carbon sources . In glucose-rich mammalian blood , the metabolism of T . brucei bloodstream forms ( BSF ) relies on glucose , while the poor availability of this carbohydrate in the tsetse fly midgut constrains PCF to use other carbon sources . In this context , PCF have developed an energy metabolism based on amino acids , such as proline and threonine [2 , 3] . However , in the standard SDM79 glucose-rich medium , PCF preferentially utilize glucose through glycolysis [4 , 5] , which is converted into the excreted succinate and acetate end products [6 , 7] . Acetate is synthetized into the mitochondrion from glucose and threonine-derived acetyl-CoA by two mitochondrial redundant enzymes , i . e . acetyl-CoA thioesterase ( ACH , EC 3 . 1 . 2 . 1 , Tb927 . 3 . 4260 , http://www . genedb . org/genedb/tryp/ ) and acetate:succinate CoA transferase ( ASCT , EC 2 . 8 . 3 . 8 , Tb927 . 11 . 2690 ) ( see Fig 1 ) [8 , 9] . While most of the acetate is excreted by the parasite , a significant part is converted back to acetyl-CoA by the cytosolic acetyl-CoA synthetase ( AceCS , EC 6 . 2 . 1 . 1 , Tb927 . 8 . 2520 ) [10] . This unusual acetyl-CoA transfer system only described in trypanosomatids so far , replaces the canonical citrate shuttle required to transfer acetyl-CoA from the mitochondrion to the cytosol , which is well known in most eukaryotes . The acetate-based acetyl-CoA transfer system is essential for the parasite to feed de novo fatty acid biosynthesis . This is due to the cytosolic localization of the first step of both the mitochondrial type II fatty acid synthase system ( FASII ) and the microsomal elongase pathway ( ELO ) , i . e . acetyl-CoA carboxylase ( EC 6 . 4 . 1 . 2 , Tb927 . 8 . 7100 ) , which produces the malonyl-CoA precursor from acetyl-CoA [11] . Trypanosomes have developed a unique microsomal elongase pathway to produce most of their fatty acids [12 , 13] , whereas in other eukaryotes elongases only extend pre-existing long chain fatty acids [14] . Another specificity of T . brucei remains in the nature of the CoA primer for this elongase pathway , which depends on butyryl-CoA rather than acetyl-CoA . In addition to the ELO system , FASII contributes to approximately 10% of fatty acids by producing long chain fatty acids such as palmitate , as well as specific ones such as octanoic acid for lipoic acid biosynthesis [15 , 16] . Both biosynthetic pathways are essential for growth of PCF , although trypanosomes have developed multiple ways to scavenge fatty acids present in the serum via uptake of protein-bound fatty acids and lysophospholipids [17] . As described for other cells , T . brucei membranes contain sterols , which regulate membrane fluidity and contribute to the organization of membrane domains . Sterols are acquired from both exogenous ( lipoprotein-cholesterol endocytosis ) and endogenous ( de novo biosynthesis ) sources [18] . Unlike mammalian cells but similar to fungi and Leishmania , trypanosomes synthetize ergosterol or ergosterol-like sterols instead of cholesterol [19] . However , the diversity of extracellular precursors and the sterol biosynthesis pathway were poorly investigated so far . Leishmania mexicana promastigotes use leucine as substrate to be incorporated efficiently into sterols and this pathway , even less effective , was also described in Trypanosoma cruzi [20 , 21] . More recently , leucine has been described as a precursor for sterol biosynthesis in PCF trypanosomes [22] , however , the degradation pathway leading to its integration into sterols , and its relative contribution to sterol ( as well as fatty acid ) de novo biosynthesis have not been investigated so far . Ketogenic carbon sources degraded into acetyl-CoA can theoretically feed both fatty acid and sterol de novo biosynthesis in trypanosomes , as observed for glucose , threonine and acetate [23] . According to genome analyses , T . brucei lacks the enzymatic capacity to degrade other ketogenic amino acids , such as lysine , phenylalanine , tryptophan and tyrosine , into acetyl-CoA [24 , 25] . However , in addition to glucose , threonine and acetate , proline is a potential lipid precursor because of its partial degradation into excreted acetate [5 , 26] . In addition , the T . brucei genome contains gene candidates for all enzymes involved in degradation of isoleucine into acetyl-CoA [24 , 25] , although experimental evidences of its role in lipid metabolism are missing . Acetyl-CoA could also theoretically be produced by ß-oxidation of fatty acids , as reported for Leishmania [27 , 28] , however , analysis of a knock-out mutant of the single gene possibly encoding the trifunctional enzyme involved in this catabolic pathway ( TFEα1 , Tb927 . 2 . 4130 ) strongly supports the view that ß-oxidation does not occur in PCF under standard growth conditions [29] . Here , we have investigated for the first time the relative contribution of all possible carbon source candidates ( proline , threonine , glucose , acetate , leucine , isoleucine and valine ) to lipid production in PCF grown in rich culture medium and have characterized the 2-amino-3-ketobutyrate CoA transferase ( AKCT , EC 2 . 3 . 1 . 29 , Tb927 . 8 . 6060 ) , which catalyses the second step of the threonine degradation pathway . We have also characterized two enzymes , isovaleryl-CoA dehydrogenase ( IVDH , EC 1 . 3 . 99 . 10 , Tb927 . 11 . 1540 ) and sterol carrier protein 2 thiolase ( SCP2-thiolase , EC 2 . 3 . 1 . 9 , Tb927 . 8 . 2540 ) , involved in sterol biosynthesis from leucine or ketogenic carbon sources ( glucose , threonine and acetate ) , respectively . Moreover , we have shown that the reverse reaction of ASCT contributes to sterol biosynthesis from acetate . We have also highlighted a metabolic redistribution in favour of fatty acid biosynthesis when acetyl-CoA production from different carbon sources is reduced . Finally , experimental infection of tsetse flies with the IVDH and AKCT null mutant parasites has further evidenced the biosynthetic pathway requirements and carbon sources availability in the midgut of the insect vector .
Both the de novo biosynthesis of fatty acids and the mevalonate pathway leading to sterol production have been demonstrated to be essential for growth of the procyclic trypanosomes ( PCF ) , however a systematic analysis of possible extracellular precursors feeding lipid biosynthesis has not been performed so far [13 , 30] . To determine the carbon source preference for biosynthesis of both sterols and fatty acids , PCF were incubated overnight in SDM79 medium containing 4 mM of each carbon source analyzed ( acetate , threonine , glucose , proline , leucine; except for isoleucine and valine , at 1 mM ) with one of them being radio-labelled . Then , [14C]-labelled fatty acid methyl esters produced by transesterification and sterols were separated by HPTLC and quantified . This analysis shows that acetate and threonine are the preferred carbon sources for de novo synthesis of both the fatty acids and sterols , while glucose is used in a lower extent , as previously reported [23] . It is noteworthy that contribution of glucose is certainly underestimated here ( up to two-fold ) , since expression of these data as nanomoles of glucose incorporated into sterols or fatty acids does not take into account that up to two acetyl-CoA molecules could theoretically be produced per glucose consumed [31] , as opposed to acetate and threonine , which are converted into a single acetyl-CoA molecule . Proline , whose contribution to central metabolism is down-regulated in rich-medium [5] , is not incorporated into fatty acids and its incorporation into sterols is close to the background level . Leucine is only a precursor of sterols and does not contribute to fatty acid biosynthesis , while incorporation of radio-labelled isoleucine into sterols is 20-fold lower compared to leucine ( Fig 2A and 2B ) . Incorporation of [14C]-labelled valine , which can theoretically be converted into acetyl-CoA [24] , is not detected in sterols and fatty acids . To further characterize the de novo synthesized sterol and fatty acid molecules , incorporation of [13C]-labelled acetate , threonine , glucose , leucine and proline into lipids of PCF incubated overnight in SDM79 medium containing 4 mM of each carbon source , was determined by gas chromatography-mass spectrometry ( GC-MS ) . This approach allowed us to estimate the incorporation of [13C]-enriched precursors into different lipids whose nature is determined by their absolute mass . Five sterol molecules , including cholesterol and two ergosterol derivatives ( ergosta-5 , 7 , 24 ( 25 ) -trienol and ergosta-5 , 7 , 25 ( 27 ) -trienol ) , were identified by GC-MS . In the absence of [13C]-labelled precursors ( control ) , less than 2% of each identified sterols were [13C]-enriched , which corresponds to natural 13C-enrichment ( Fig 2C ) . As expected , none of the [13C]-labelled precursors were incorporated into cholesterol , which was taken from the medium with no detectable de novo biosynthesis in trypanosomatids [18] . In contrast , [13C]-acetate , [13C]-threonine , [13C]-glucose and [13C]-leucine precursors were incorporated into the other sterols , with similar incorporation profiles . This included ergosterol derivatives , which have been described as the end products of the sterol biosynthetic pathway in trypanosomatids [22] . In agreement with HPTLC data , [13C]-proline was not significantly incorporated into sterols ( Fig 2C ) . However , no significant incorporation of [13C]-acetate and [13C]-glucose into fatty acids has been observed by GC-MS , in agreement with previous LC-MS/MS data [32] . This is certainly due to a combination of ( i ) the relatively high background corresponding to natural [13C]-enrichment ( in the range of 1 . 5% in this series of experiments ) , and ( ii ) the large amount of cellular fatty acids , which are 5- to 10-fold more abundant than sterols in trypanosomes [33] . The role of the threonine dehydrogenase ( TDH , EC 1 . 1 . 1 . 103 , Tb927 . 6 . 2790 ) in acetate production from threonine , by catalyzing the first step of the pathway , has been previously shown [23] . Here , we have investigated the second step of the pathway ( production of acetyl-CoA and glycine from 2-amino-3-ketobutyrate ) catalyzed by the 2-amino-3-ketobutyrate CoA transferase ( AKCT ) potentially encoded by a single gene in the T . brucei genome ( Tb927 . 8 . 6060 ) . To confirm the role of the AKCT gene in acetate production , both alleles were replaced by homologous recombination with resistance markers ( Fig 3A ) . The AKCT activity in the AKCT null mutant ( Δakct ) is close to the background level ( 1 . 2 ± 2 . 6 nmol min-1 mg-1 of proteins ) and 10-fold reduced compared to the parental cell line ( 12 . 0 ± 1 . 6 nmol min-1 mg-1 of proteins ) , while the malic enzyme ( EC 1 . 1 . 1 . 39 ) activity was similar in both samples ( 91 . 0 ± 10 . 3 versus 78 . 4 ± 12 . 1 nmol min-1 mg-1 of proteins , respectively ) . This shows that the AKCT gene is responsible for the cellular AKCT activity . It is noteworthy , that the Δakct cell line showed no growth delay in normal SDM79 , nor in SDM79 lacking glucose and acetate . NMR spectrometry analysis of excreted end products from threonine and glucose metabolism of parental and Δakct cells was performed as previously described [23] . Cells were incubated in PBS with equal amounts ( 4 mM ) of D-[U-13C]-glucose and non-enriched threonine , in order to perform a quantitative analysis of threonine-derived and glucose-derived acetate production by 1H-NMR . [13C]-Acetate derived from D-[U-13C]-glucose ( annotated A13 in Fig 3B ) is represented by two doublets , with chemical shifts at around 2 . 0 ppm and 1 . 75 ppm , respectively , while the central resonance ( 1 . 88 ppm , annotated A12 ) corresponds to threonine-derived non-enriched acetate . Acetate production from threonine is abolished in the Δakct mutant , whereas [13C]-acetate is still produced from D-[U-13C]-glucose ( Fig 3B ) . As expected , production of [13C]-succinate ( annotated S13 ) from D-[U-13C]-glucose is not affected by AKCT gene deletion . The residual non-enriched acetate ( A12 ) and succinate ( S12 ) observed in the Δakct mutant ( A12 and S12 ) and the parental cells ( S12 ) is probably derived from an unknown internal carbon source [23] . Analysis of [14C]-labelled lipids of the mutant and Δakct cell lines incubated with L-[U-14C]-threonine showed that incorporation of radio-labelled carbons into both sterols and fatty acids is abolished in the Δakct cell line , while , incorporation of [1-14C]-acetate into lipids is not affected ( Fig 3C ) . Altogether these analyses demonstrate that AKCT is involved in lipid biosynthesis from threonine through production of acetate , as previously showed for TDH [23] . To determine the subcellular localization of AKCT , we performed in situ tagging in the hemizygous AKCT knock out cell line by adding a TY1 epitope tag to the 3'-extremity of the remaining AKCT allele . This cell line was then used for immunofluorescence microscopy in order to validate the subcellular localisation of the AKCT protein , which co-localizes with the mitochondrial marker protein ASCT [8] ( Fig 3D ) . The mitochondrial localization of AKCT is consistent with the prediction with a high probability ( 0 . 91 ) by the MitoProt program ( http://ihg . gsf . de/ihg/mitoprot . html ) of a mitochondrial targeting signal corresponding to the first 15 N-terminal amino acids of the protein , as well as with the presence of AKCT in previously published mitochondrial proteomes [34–36] . Leucine has previously been described as a precursor for sterol biosynthesis in Leishmania spp . and T . cruzi , as well as in PCF of T . brucei [20–22 , 37] . However , the first steps leading to HMG-CoA have not been investigated in trypanosomatids so far . According to the current model , leucine is converted in the mitochondrion through a 5-step process into 3-hydroxy-3-methylglutaryl-CoA ( HMG-CoA ) , which is located at a branching point between the different carbon sources used for sterol biosynthesis ( Fig 1 ) . To investigate leucine degradation in PCF , the third enzyme of the pathway ( isovaleryl-CoA dehydrogenase , IVDH ) , which oxidizes isovaleryl-CoA into 3-methylcrotonyl-CoA , was functionally expressed in Escherichia coli to determine its activity and raise anti-IVDH antibodies . The specific activity of the his-tagged recombinant T . brucei IVDH purified to homogeneity for its substrate ( isovaleryl-CoA ) is 270 ± 40 nmol min-1 mg-1 of proteins , which corresponds to a rate of 0 . 2 conversions per active site per sec . This confirms that our candidate gene is a bona fide IVDH . To specifically block sterol biosynthesis from leucine , both alleles encoding IVDH were replaced by the BSD and PAC markers to produce the Δivdh-C3 and Δivdh-C8 cell lines . Deletion of both IVDH alleles was confirmed by PCR and western blotting analyses ( Fig 4A and 4B ) . Indeed , the anti-IVDH immune serum produced against the T . brucei recombinant IVDH gene product ( 44 . 8 kDa ) recognizes a 45 kDa protein by western blotting in the procyclic parental cells , which is no more detectable in the Δivdh cell lines . Analysis of [14C]-labelled sterols of the mutant and parental cell lines incubated with L-[U-14C]-leucine showed that incorporation of radio-labelled carbons into sterols is abolished in both Δivdh cell lines . However , incorporation of L-[U-14C]-threonine into fatty acids is not affected , which demonstrates that IVDH is involved in sterol biosynthesis from leucine ( Fig 4C ) . Interestingly , incorporation of L-[U-14C]-threonine into sterols increased by 2- and 1 . 5-fold in the Δivdh-C3 and Δivdh-C8 cell lines , respectively . The MitoProt program predicts with a high probability ( 0 . 93 ) that the first 33 N-terminal amino acids of IVDH constitute a mitochondrial targeting signal . The intracellular localization of IVDH was investigated by immunofluorescence microscopy of PCF expressing the full-length IVDH containing or lacking the first 33 N-terminal residues and fused with the EGFP at its C-terminal extremity ( IVDH-1/412-EGFP and IVDH-34/412-EGFP , respectively ) . Immunofluorescence analyses revealed co-localization of IVDH-1/412-EGFP with ASCT , a known mitochondrial protein [8] , while the IVDH-34/412-EGFP recombinant protein lacking the predicted mitochondrial targeting signal shows a cytosolic-like fluorescence signal ( Fig 4D ) . These data are consistent with the presence of IVDH in previously published mitochondrial proteomes [34 , 36] . Glucose , threonine and acetate are precursors for biosynthesis of both fatty acids and sterols ( Fig 2 ) . The metabolic pathway leading to production of fatty acids in the endoplasmic reticulum and the mitochondrion , through the elongase system and FASII , respectively , has been described before [13 , 16 , 23] . However , the link between glucose-derived , threonine-derived and acetate-derived acetyl-CoA and HMG-CoA , for sterol biosynthesis , has not been investigated so far ( see Fig 1 ) . The T . brucei genome contains candidate genes for this 2-step metabolic pathway involving a thiolase ( SCP2-thiolase ) and HMG-CoA synthase ( HMGS , EC 2 . 3 . 3 . 10 , Tb927 . 8 . 6110 ) previously identified [24 , 38 , 39] . The T . brucei SCP2 recombinant protein , expressed and isolated from E . coli , shows thiolase activity in both the biosynthetic and degradative directions [39] . To determine the role of the SCP2-thiolase in this connecting pathway , we analyzed the impact of RNAi down-regulation of SCP2-thiolase expression ( Fig 5A ) on sterol and fatty acid biosynthesis from different carbon sources . As expected , fatty acid biosynthesis from threonine and glucose is not affected in the RNAiSCP2 . i cell line ( Fig 5B , top panel ) , however , incorporation of D-[U-14C]-glucose , L-[U-14C]-threonine and [1-14C]-acetate into sterols is abolished in the mutant cell line ( Fig 5B , lower panel ) . This demonstrates that SCP2-thiolase is involved in HMG-CoA production from ketogenic carbon sources , with no alternative thiolase-like activity expressed by the parasite to produce acetoacetyl-CoA from acetyl-CoA . The reduction of radiolabel incorporation into sterols in the non-induced RNAiSCP2 . ni cell line is due to the strong reduction of SCP2-thiolase expression without induction ( Fig 5B ) . It is also noteworthy that leucine is not a precursor for fatty acid biosynthesis ( Figs 2A and 5B ) , implying that the SCP2-thiolase/HMGS pathway is not reversible in the PCF trypanosomes . Acetate is the preferred precursor for both fatty acid and sterol biosynthesis , when PCF are incubated with the same amounts of all four known precursors ( Fig 2 ) . Acetate is converted into malonyl-CoA in the cytosol to feed both the microsomal and mitochondrial fatty acid biosynthetic pathways . However , to feed sterol synthesis , acetate has to be converted into acetyl-CoA in the mitochondrion , possibly by the previously characterized ACH and/or ASCT [8 , 9] ( see Fig 1 ) . To address this question , incorporation of [1-14C]-acetate into lipids of the previously produced Δach and Δasct knock-out cell lines [9] was compared to the parental cell line . Fig 5B shows that de novo sterol biosynthesis from acetate is abolished in the Δasct mutant , but is not affected in the Δach mutant ( lower panel ) . As expected de novo fatty acid biosynthesis from acetate , glucose and threonine , as well as sterol biosynthesis from glucose and threonine are not affected in both mutant cell lines . This clearly demonstrates that mitochondrial production of acetyl-CoA from acetate solely requires ASCT , which belongs to the family I CoA-transferases previously described to catalyze reversible transfer of coenzyme A groups from CoA-thioesters to free fatty acids [40] . This also confirms that ACH irreversibly converts acetyl-CoA into acetate [9] . Reduction of SCP2-thiolase expression in the non-induced ( . ni ) and induced ( . i ) RNAiSCP2 cell line leads to an increase of L-[U-14C]-leucine incorporation into sterols , probably as a consequence of reduction/abolition of glucose- , threonine- and acetate-derived acetyl-CoA incorporation into sterols ( Fig 5B , lower panel ) . To further study this adaptive flux redistribution , we analyzed de novo lipid biosynthesis in mutant cell lines affected in acetyl-CoA production from glucose and threonine . For this analysis , we have selected knock-out and/or knock-down mutant cell lines affecting expression of the E2 subunit of the pyruvate dehydrogenase complex ( PDH-E2 , EC 2 . 3 . 1 . 12 , Tb927 . 10 . 7570 ) ( Δpdh ) , threonine dehydrogenase ( TDH ) ( RNAiTDH ) and both PDH-E2 and TDH ( RNAiTDH/RNAiPDH ) , previously generated [23] ( Fig 6A ) . In the same line as observed for the RNAiSCP2 cell line , L-[U-14C]-leucine incorporation into sterols was increased in the RNAiTDH/RNAiPDH mutant , probably to compensate for the reduced contribution of glucose and threonine to sterol biosynthesis . It is noteworthy that the incorporation of [1-14C]-acetate into fatty acids was increased by ~3-fold in the RNAiTDH/RNAiPDH . i double mutant , certainly as a consequence of the abolition of glucose and threonine incorporation into fatty acids , with an overall incorporation of ketogenic carbon sources in fatty acid similar to the parental cell line ( Fig 6B ) . We also investigated the fate of radio-labelled glucose incorporation into lipids , when acetyl-CoA conversion into acetate is affected in the Δach/RNAiASCT cell line . Since growth of this cell line stops after 13 days of induction before dying two weeks later [9] , the Δach/RNAiASCT . i mutant was analyzed 8 and 15 days post-induction . As previously observed , D-[U-14C]-glucose incorporation into fatty acid-containing lipids is strongly reduced , however , contribution of D-[U-14C]-glucose to sterol biosynthesis increased 3 . 7-fold compared to the parental cells 8 days post-induction ( Fig 7 ) . The important reduction of D-[U-14C]-glucose incorporation into sterols after 15 days of induction is probably due to a general down-regulation of biosynthetic pathways consecutive of growth arrest . In addition , distribution of glucose- , threonine- and acetate-derived acetyl-CoA between the fatty acid and sterol biosynthetic pathways , seems to be under regulation . As expected , incorporation of D-[U-14C]-glucose and L-[U-14C]-threonine into lipids ( fatty acids and sterols ) is abolished in the Δpdh and RNAiTDH . i cell lines , respectively ( Fig 6B , indicated by an arrow ) . Interestingly , incorporation of radio-labelled threonine is increased into fatty acids and decreased into sterols of the Δpdh mutant , while the same data are observed for the RNAiTDH . i cell line incubated with radio-labelled glucose ( Fig 6B ) . This suggests that the metabolic flux from these two carbon sources is redistributed towards fatty acid biosynthesis , at the expense of sterol biosynthesis . This relative flux redistribution appears clearly in the RNAiTDH/RNAiPDH . i double mutant , which shows a 3 . 2-fold increase of radio-label incorporation from [1-14C]-acetate into fatty acids , while de novo synthesis of sterols from acetate is almost completely abolished ( Fig 6B ) . Since , none of the PDH and TDH steps belong to the metabolic pathways leading to lipid biosynthesis from acetate ( see Fig 1 ) , we interpret this data as a consequence of the reduced mitochondrial acetyl-CoA pool , which is no more fed by glucose and threonine degradation . In other words , we propose that PCF trypanosomes favor fatty acid biosynthesis at the expense of sterol biosynthesis from glucose , threonine and acetate , when degradation of ketogenic carbon sources is considerably reduced . We then attempted to determine whether the abolition of the incorporation of leucine and threonine in lipids had an effect on tsetse infections . A total of 850 flies ( 50 to 100 individuals per strain and per replicate ) were fed with either the parental , Δakct , Δivdh-C3 or Δivdh-C8 PCF cell lines . After 2 weeks , the midgut infection rates were 16% and 6% with the parental EATRO125 . T7T and Δakct procyclic cell lines , respectively , with no significant differences ( Fig 8 ) . Similarly , deletion of the IVDH gene did not affect the midgut infection rates with both the Δivdh-C3 and Δivdh-C8 cell lines ( 13% and 21% , respectively ) . To further address the role of IVDH in the mevalonate pathway , glucose and acetate were removed from the medium , while threonine was reduced down to 150 μM to maintain protein biosynthesis ( Fig 9 ) . The growth of the parental , Δivdh-C3 and Δivdh-C8 cell lines was not affected in the absence of these three carbon sources , suggesting either that the mevalonate pathway is not essential or that uptake of extracellular fatty acids provided by 10% fetal calf serum ( FCS ) can feed this pathway . Indeed , FCS contains fatty acids ( free or associated with phospholipids and other lipids ) , which can theoretically be converted into acetyl-CoA to feed the mevalonate pathway . To address this question , growth of the Δivdh and parental cell lines were compared in SDM79 containing delipidated FCS and depleted for glucose , threonine and acetate . The parental cells were not affected , while the growth of both Δivdh mutants stopped after 15 days of incubation , which is consistent with the essential role of the mevalonate pathway ( Fig 9 ) . It is noteworthy that the observed growth recovery of the Δivdh mutants 5 days later may be due to an adaptation to the low amounts of lipids , that are only ~5-fold reduced in the commercial delipidated FCS . To confirm this growth phenotype , an IVDH ectopic copy was re-introduced in situ in one IVDH locus of the Δivdh-C3 mutant cell line ( Δivdh-C3/IVDH ) . The expression of IVDH was confirmed by western blotting ( Fig 9 , inset ) . As expected , growth of the Δivdh-C3/IVDH rescue cell line was restored in the delipidated SDM79 depleted for glucose , threonine and acetate ( Fig 9 ) .
The human and livestock pathogen Trypanosoma brucei has maintained and developed the ability to produce de novo fatty acids through the mitochondrial FASII and microsomal elongase system , as well as sterols through the mevalonate pathway , which are all essential for growth of the mammalian and insect stages of the parasite ( for reviews see [16 , 17 , 30 , 42] ) . Here we have performed a comprehensive metabolic analysis of all possible carbon sources feeding fatty acid and sterol biosynthesis in PCF , with the aim to complete the metabolic map , to study the regulatory interplay between the different branches of the corresponding metabolic network , and to determine the real contribution of each branch in vivo in the insect vector . The T . brucei genome potentially encodes for all enzymes responsible for conversion of glucose , acetate , threonine , leucine , isoleucine , valine , proline and fatty acids into acetyl-CoA and/or HMG-CoA , the precursors for biosynthesis of fatty acids and sterols . Among them , only the four former carbon sources can support lipid biosynthesis . The absence of isoleucine and valine incorporation into lipids is consistent with previous data obtained in Leishmania promastigotes [20] . Proline degradation into sterols is negligible , since it solely contributes to 0 . 5% of sterol biosynthesis in medium containing equimolar amounts of all known precursors ( Fig 2 ) , while it is not a carbon source for fatty acid biosynthesis in PCF . These data are consistent with previous data reported for Leishmania promastigotes [20 , 43] and with the fact that proline is primarily converted into glutamate and succinate , with barely no acetate ( and acetyl-CoA ) produced by PCF grown in glucose-rich conditions [5 , 26] . However , PCF use proline as a main carbon source in the glucose-depleted environment of the insect midgut [3 , 44] . Surprisingly , removing the main ketogenic carbon sources ( glucose , threonine and acetate ) from the culture medium did not significantly increase proline incorporation into lipids ( Fig 2 ) , confirming that proline-derived acetyl-CoA is not used for lipid biosynthesis . Acetyl-CoA could also theoretically be produced by ß-oxidation of fatty acids , as reported for Leishmania [27 , 28] , however , analysis of a knock-out mutant of the single gene possibly encoding for two ß-oxidation steps strongly supports the view that ß-oxidation does not occur in PCF under standard and glucose-depleted growth conditions [29] . Interestingly , the growth defect observed for the two Δivdh cell lines incubated in the glucose/threonine/acetate/lipid-depleted medium , but not in the glucose/threonine/acetate-depleted medium , suggest that ß-oxidation of fatty acids may contribute to feed the mevalonate pathway in the absence of the other ketogenic carbon sources . These preliminary data suggest that ß-oxidation occurs in PCF grown in the presence of limited amounts of lipid precursors , albeit this hypothesis needs to be further investigated . Leucine was previously described as a precursor for sterol biosynthesis in trypanosomatids [20–22 , 37] . In PCF grown in rich medium , the leucine contribution to sterol biosynthesis is 2- and 3-fold lower than glucose and acetate , respectively [22] , which is consistent with our data ( Fig 2 ) . We have experimentally validated the in silico deduced leucine degradation pathway by demonstrating that the enzymatic activity of the IVDH gene product corresponds to the third enzymatic step of the pathway and that deletion of the IVDH gene abolished incorporation of radio-labelled leucine into sterols . The mitochondrial localization of the GFP-tagged IVDH is consistent with the potential mitochondrial targeting sequence located at the N-terminal extremity of the five enzymes potentially involved in HMG-CoA production from leucine , as well as the recent characterization of the gene encoding the mitochondrial 3-methylglutoconyl-CoA hydratase , which produces HMG-CoA from the IVDH reaction product [45] . The following step consisting on conversion of HMG-CoA into mevalonate by HMG-CoA reductase also occurs in the mitochondrial matrix [46] . This contrasts with all other eukaryotic cells , which express HMG-CoA reductase anchored to the endoplasmic reticulum membrane with its active site facing the cytosol . Thus , one may consider that the direct incorporation of leucine into sterols , unique in trypanosomatids , is the direct consequence of the unusual localization of HMG-CoA reductase in the parasite mitochondrial matrix , where HMG-CoA is produced from leucine . In other eukaryotes , HMG-CoA first needs to be converted in the mitochondrion into acetyl-CoA , which is then transferred to the cytosol through the citrate shuttle , before being converted back in the cytosol into HMG-CoA . Our data demonstrate that the mevalonate biosynthetic pathway , leading to sterol biosynthesis , is essential for the PCF growth since the Δivdh cell line is lethal in the absence of ketogenic carbon sources , including fatty acids , and since we were not able to generate the Δivdh/RNAiSCP2 double mutant despite several attempts . This importance is certainly due to the use of mevalonate to synthesize several ubiquitous families of essential molecules such as dolichols , ubiquinones and carotenoids , that are essential for many cellular functions [47] . Mevalonate production is probably also essential for de novo biosynthesis of sterol , which would be consistent with previous reports showing that sterol uptake cannot compensate for lack of de novo synthesis [48–50] . In addition , part of de novo-produced ergosterol derivatives , which are not present in scavenged LDL ( only cholesterol ) , may also serve as cell proliferation signals , as proposed for the bloodstream trypanosomes [49] . In addition to leucine , PCF trypanosomes use ketogenic carbon sources for sterol biosynthesis through conversion of acetyl-CoA into HMG-CoA by SCP2-thiolase [39] and HMGS [38] , as demonstrated here by abolition of incorporation of radio-labelled glucose , threonine or acetate into sterols in the RNAiSCP2 mutant . However , the parental cells are unable to incorporate radio-labelled leucine into fatty acids in the presence or in the absence of the main ketogenic precursors of fatty acids ( glucose , threonine and acetate ) ( Fig 2 ) . This implies that the 2-step bridge between the fatty acid and sterol biosynthetic pathways is unidirectional toward HMG-CoA production , even when fatty acid biosynthesis is strongly affected in the absence of ketogenic carbon sources . This observation is consistent with the 7-fold higher specific activity of T . brucei SCP2-thiolase in the synthetic direction ( production of acetoacetyl-CoA from acetyl-CoA ) compared to the thiolytic direction [51] . These data also suggests the absence of a HMG-CoA lyase activity ( EC 4 . 1 . 3 . 4 ) , converting HMG-CoA into acetoacetate and acetyl-CoA , although the T . brucei genome encodes a putative mitochondrial HMG-CoA lyase ( Tb927 . 4 . 2700 ) [24] , which was detected in the PCF proteome [52] . As discussed above , four carbon sources are used in vitro by PCF grown in rich medium to produce lipids , i . e . leucine only involved in sterol biosynthesis and three ketogenic sources ( glucose , threonine and acetate ) precursor of both sterols and fatty acids ( Fig 10 ) . It is to note that extracellular fatty acids ( free fatty acids and/or phospholipids ) contribute to lipid biosynthesis in the Δivdh cell lines grown in glucose/threonine/acetate-depleted conditions . However , their contribution in the wild type cells or in rich medium has not been investigated yet . Analysis of utilization of these carbon sources in mutants affecting different entry points of the de novo lipid biosynthetic pathways showed three kinds of flux redistribution between the mevalonate and malonyl-CoA branches . The first and most intuitive adaptation concerns up-regulation of leucine incorporation into sterols in mutants showing reduced contribution of ketogenic sources to sterol biosynthesis , such as in the RNAiSCP2 and RNAiTDH/RNAiPDH cell lines ( Fig 10A and 10B ) . An equivalent flux redistribution was observed for T . cruzi epimastigotes incubated with a specific inhibitor of HMG-CoA synthase ( L-659 , 699 ) , which causes an increase of leucine contribution to sterol biosynthesis to compensate for blocking the “acetyl-CoA/HMG-CoA bridge” required for acetate incorporation into sterols [21] . The increase of leucine contribution to sterol biosynthesis , as a consequence of limited ketogenic source availability , is an expected adaptation to maintain the essential mevalonate pathway . The second adaptation , is the increase of threonine contribution to sterol biosynthesis in the Δivdh cell lines , which further supports the essential role of the mevalonate pathway in PCF ( Fig 4C ) . The third type of metabolic adaptation clearly observed in the RNAiTDH/RNAiPDH double mutant concerns shift in utilization of ketogenic carbon sources under limited carbon source degradation , i . e . when glucose and threonine contribution to acetyl-CoA production is impaired , acetate contribution to fatty acid production is increased at the expense of the abolished sterol production . Indeed , in the presence of all of the three ketogenic carbon sources at 4 mM each , 108 parental PCF cells incorporate 10 and 12 nmol of acetate into fatty acids and sterols per hour , respectively ( Fig 2 ) , while the whole biosynthetic flux from acetate is redirected towards fatty acid production in the RNAiTDH/RNAiPDH cell lines ( Fig 7 ) . This fatty acid preference would not affect the overall lipid biosynthesis , since , as mentioned above , leucine degradation can be up-regulated to fulfill the essential mevalonate demand . This adaptation is particularly relevant to sustain de novo biosynthesis of fatty acids by the mitochondrial FASII , which is essential for the parasite [13] . Altogether , these three levels of metabolic regulation by flux redistribution allow a high level of flexibility depending on carbon source availability to feed the mevalonate and malonyl-CoA pathways . Although the exact composition of the tsetse midgut content is poorly known , it has been reported that this amino acid-rich environment contains at least leucine and threonine in the 100 μM range in addition to proline [53] . However , glucose is apparently absent between blood meals , and the presence of acetate and fatty acids/phospholipids has not been investigated so far . Thus , among the ketogenic carbon sources used by PCF trypanosomes , and compared to the high concentrations of all three ketogenic sources in rich cell culture conditions ( 4 mM each ) , only relatively low amounts of threonine have been detected in the tsetse midgut so far . From our in vitro analyses , one may consider that the possible low amounts of ketogenic carbon sources present in the insect digestive tract could be mostly , if not exclusively , used to feed fatty acid biosynthesis . In this context , leucine would be the only carbon source used by PCF in the fly midgut to feed the mevalonate pathway . Incidentally , the Δivdh mutant cell lines established fly infections as efficiently as the parental cell line , suggesting that ketogenic carbon sources feed the mevalonate pathways in vivo , at least when contribution of leucine to this pathway is impaired . However , we don't know yet how the IVDH null mutants would behave in the other organs of the fly . An exhaustive quantitative analysis of all possible carbon sources in the midgut , as well as in the other infected organs of the tsetse fly is now required to address this question . In total , our observations emphasize the remarkable plasticity of the metabolic networks in this eukaryotic organism . We propose that the acetyl-CoA/HMG-CoA bridge could be used by trypanosomes to adapt to glucose/threonine/acetate-rich micro-environmental niches in the insect vector and/or in specific tissues of the mammalian hosts , by redistributing metabolic flux from ketogenic carbon sources towards the mevalonate pathway , as in the standard in vitro growth medium . In this context , flux through this bridge needs to be regulated as a function of carbon source availability and/or lipid requirement . Because of its key branching position , acetyl-CoA is the most promising “sentinel” candidate to relay metabolic information from this metabolic network to influence protein expression and/or activity . In favor of this hypothesis , redistribution of PCF glycolytic flux towards acetate production in the Δpepck ( phosphoenolpyruvate carboxykinase , EC: 4 . 1 . 1 . 32 ) mutant induced a 2-fold down-regulation of TDH expression , interpreted as a consequence of acetyl-CoA accumulation [23] . The role of acetyl-CoA in regulation of gene expression and activity through protein lysine acetylation was described decades ago . However , the intricate link between lysine acetylation and cellular metabolism is an emerging concept supported by the recent development of high throughput analyses of acetylome [54 , 55] . For instance , Wang et al . , showed that carbon source utilization and metabolic flux in Salmonella is coordinated by acetylation of key enzymes [56] . Beside histone acetylation , nothing is known about the trypanosomatid acetylome , although their genome encodes lysine acetylases and deacetylases [57–59] .
T . brucei EATRO1125 . T7T ( TetR-HYG T7RNAPOL-NEO ) PCF were cultured at 27°C in SDM79 medium containing 10% ( v/v ) heat-inactivated FCS and 3 . 5 mg/l hemin [60] . Alternatively , PCF cells were cultivated into SDM79 medium depleted from glucose and/or acetate , and supplemented with 50 mM N-acetylglucosamine , a specific inhibitor of glucose transport , that prevents consumption of the fetal calf serum-derived glucose ( 0 . 5 mM final ) [29 , 61 , 62] , and with 150 μM of threonine ( instead of 3 . 4 mM in standard SDM79 ) to sustain protein biosynthesis . This glucose/threonine/acetate-depleted SDM79 medium was also prepared with heat-inactivated commercial delipidated FCS ( Cocalico Biologicals ) that contains ~5-times less sterol and phospholipids compared to regular FCS . Replacement of the gene encoding the PDH complex E2 subunit ( PDH-E2 , EC 2 . 3 . 1 . 12 , Tb927 . 10 . 7570 ) , ASCT ( ASCT , EC 2 . 8 . 3 . 8 , Tb927 . 11 . 2690 ) and ACH ( ACH , EC 3 . 1 . 2 . 1 , Tb927 . 3 . 4260 ) by the blasticidin ( BSD ) and puromycin ( PAC ) resistance markers via homologous recombination was described before ( Δpdh , Δasct and Δach cell lines , respectively ) [9 , 23] . Replacement of the isovaleryl-CoA dehydrogenase gene ( IVDH , EC 1 . 3 . 99 . 10 , Tb927 . 11 . 1540 ) and the 2-amino-3-ketobutyrate CoA transferase gene ( AKCT , EC 2 . 3 . 1 . 29 , Tb927 . 8 . 6060 ) by BSD and PAC resistance markers via homologous recombination was performed with DNA fragments containing a resistance marker gene flanked by the IVDH or AKCT UTR sequences , as performed before [63] . Briefly , the pGEMt plasmid was used to clone a HpaI DNA fragment containing the BSD or PAC resistance marker gene preceded by the IVDH 5’UTR fragment ( 668 bp ) and followed by the IVDH 3’UTR fragment ( 524 bp ) . For the AKCT knock-out the same procedure was performed with AKCT 5’UTR ( 535 bp ) and AKCT 3’UTR ( 510 bp ) fragments . The IVDH and AKCT knock-out were generated in the EATRO1125 . T7T parental cell line , which constitutively expresses the T7 RNA polymerase gene and the tetracycline repressor under the control of a T7 RNA polymerase promoter for tetracycline inducible expression ( TetR-HYG T7RNAPOL-NEO ) [64] . Transfection and selection of drug-resistant clones were performed as reported previously [65] . Transfected cells were selected in SDM79 medium containing hygromycin B ( 25 μg/ml ) , neomycin ( 10 μg/ml ) , blasticidin ( 10 μg/ml ) and puromycin ( 1 μg/ml ) . The selected cell lines TetR-HYG T7RNAPOL-NEO Δivdh::BSD/Δivdh::PAC and TetR-HYG T7RNAPOL-NEO Δakct::BSD/Δakct::PAC are called Δivdh and Δakct , respectively . Re-introduction of an ectopic IVDH copy in one IVDH locus was performed by transfecting the Δivdh-C3 mutant cell line with a DNA fragment containing the IVDH 5’-UTR ( 346 bp ) followed by the phleomycin resistant gene ( BLE ) , the aldolase 3'UTR ( 146 bp ) , the actin 5'UTR ( 132 bp ) , the IVDH gene and the IVDH 3’UTR ( 362 bp ) . Transfected cells were selected in SDM79 medium containing hygromycin B ( 25 μg/ml ) , neomycin ( 10 μg/ml ) , blasticidin ( 10 μg/ml ) , puromycin ( 1 μg/ml ) and phleomycin ( 5 μg/ml ) . The inhibition by RNAi of gene expression was performed by expression of stem-loop “sense/anti-sense” RNA molecules of the targeted sequences introduced in the pLew100 ( kindly provided by E . Wirtz and G . Cross ) [66] or the pHD1336 ( kindly provided by C . Clayton , ZMBH , Heidelberg , Germany ) expression vectors , as previously described . The PCF transfected cells were selected in SDM79 containing 5 μg/ml of phleomycin ( pLew100 ) or 10 μg/ml of blasticidin ( pHD1336 ) . Down-regulation of gene expression by RNAi of threonine dehydrogenase ( TDH , EC 1 . 1 . 1 . 103 , Tb927 . 6 . 2790 ) and PDH-E2 in the EATRO1125 . T7T cell lines is described elsewhere ( RNAiTDH and RNAiPDH/RNAiTDH ) [23] . Similarly , production of the Δach/RNAiASCT cell line has been described before [9] . Construction of pLew-SCP2-SAS used to down-regulate the SCP2-thiolase ( SCP2-thiolase , EC 2 . 3 . 1 . 9 , Tb927 . 8 . 2540 ) by RNAi was done before [39] . For heterologous expression in E . coli , the pET28 vector was used to express the T . brucei IVDH missing the N-terminal 13 amino acids , responsible for mitochondrial import . The IVDH gene ( from position 41 to 1234 bp ) containing 6 histidine codons at its 3’-extremity was inserted in the NheI and BamHI restriction sites of the pET28 vector to produce the pET28-IVDH plasmid , which was used to transform the E . coli BL21 ( DE3 ) strain harboring pGro7 plasmid ( Takara Bio Inc . , Japan ) . MZ9B culture media ( 1 liter ) supplemented with 34 μg/ml chloramphenicol , 30 μg/ml kanamycin and 0 . 3 mg/ml L-arabinose ( induction of the expression of GroEL-ES complex ) was inoculated with 10 ml of overnight culture grown in LB medium and incubated at 37°C until the culture reached 0 . 6 OD600 . The culture was cooled down to 20°C and the protein expression was induced by addition of IPTG to a final concentration of 0 . 1 mM . The growth was continued overnight at 20°C . The bacterial cells were harvested by centrifugation at 3635 g for 60 min at 4°C and suspended in sodium phosphate buffer ( 50 mM NaH2PO4 , 0 . 3 M NaCl , 10% glycerol pH 8 . 0 ) supplemented with 10 mM imidazole in 0 . 1 g/ml ratio and stored to -70°C . After thawing the cell suspension , lysozyme , ATP and FAD were added to a final concentration of 0 . 1 μg/ml , 5 mM and 50 μM , respectively . The cells were disrupted by sonication and the soluble fraction was separated by centrifuging 30 , 000 g for 45 min at 4°C . The supernatant was mixed with 1 ml of NiNTA beads ( Qiagen ) and end-over-end rotated for 30 min at 4°C . The column material was allowed to settle , the unbound fraction was collected and the beads were washed with 100 column volumes of sodium phosphate buffer with 20 mM imidazole . Bound protein was eluted with 10 column volumes of sodium phosphate buffer with 250 mM imidazole . The fractions containing the recombinant IVDH were pooled and imidazole was removed by using PD-10 column according to manufacturer’s instructions ( GE Healthcare ) . Concentrated sample was applied to Superdex 200 HiLoad prep grade 16/60 gel-filtration column ( GE Healthcare Life Sciences ) equilibrated with the sodium phosphate buffer at 4°C . The purity of the sample was checked by SDS-page , showing only one prominent band . Fractions containing the IVDH were pooled , concentrated and protein concentration was determined with Nanodrop ( Thermo Scientific ) . The anti-IVDH immune serum was raised by Covalab in rabbit by four injections at 14-day intervals of 50 μg of IVDH recombinant nickel-purified proteins , emulsified with complete ( first injection ) or incomplete Freund’s adjuvant . For enzymatic activity measurements , PCF cells were washed in PBS ( 10’ , RT , 900 g ) , resuspended in assay buffer and lysed by sonication ( Bioruptor , Diagenode; high intensity , 5 cycles , 30sec/30sec on/off ) . Samples were supplemented with ‘Complete EDTA-Free’ protease-inhibitor cocktail ( Roche ) and the protein amount determined with the Pierce protein assay in a FLUOstar Omega plate reader . The 2-amino-3-ketobutyrate CoA transferase ( AKCT ) activity was measured as described before in [67] . The assay buffer contained 100 mM K-phosphate buffer pH 7 . 4 , 2 . 5 mM EDTA , 0 . 1 mM Acetyl-CoA , 300 mM glycine , 0 . 1 mM 5 , 5'-dithiobis- ( 2-nitrobenzoic acid ) ( DTNB ) . The reactions were started by injection of glycine . The activity was measured in the reverse direction , i . e . the acetyl-CoA consuming direction . Freshly produced free thiol groups react with DTNB and the resulting TNB2− dianion can be quantified by measuring light absorption at 405 nm . The malic enzyme activity was determined as a quality control of the cellular extracts as described before [68] . The specific activity of the isovaleryl-CoA dehydrogenase ( IVDH ) was measured in 100 mM KPi buffer pH 7 . 0 in a total volume of 0 . 5 ml at 25°C using a Jasco-V660 ( Jasco Corporation , Japan ) spectrophotometer . The reaction mixture contained 50 μM 2 , 6-dichloroindophenol , 1 . 5 mM phenazine methosulfate , 10 μM FAD and 2 . 6 μg of IVDH . In the reference cuvette holder 50 μM 2 , 6-dichloroindophenol in 100 mM KPi buffer pH 7 . 0 was used . The baseline reaction was measured for 3 min and the reaction was started by adding isovaleryl-CoA ( 52 . 5 μM , Sigma , ref I9381 ) . The decrease in absorbance at 600 nm was monitored during 3 min . The conversion rate was then determined from the linear part of the reaction curve and the specific activity was calculated using the molar extinction coefficient of 20 . 6 mM-1 cm-1 for 2 , 6-dichloroindophenol [69] from four separate measurements . Total protein extracts of parental or mutant PCF ( 5x106 cells ) were size-fractionated by SDS-PAGE ( 10% ) and immunoblotted on PVDF membrane ( Biorad ) [70] . Immunodetection was performed as described [70 , 71] using as primary antibodies , the rabbit anti-SCP2-thiolase ( diluted 1:50 ) [39] , the rabbit anti-ASCT ( diluted 1:100 ) [8] , the immunopurified rabbit anti-ACH ( diluted 1:10 ) [9] , the rabbit anti-TDH ( diluted 1:500 ) [23] , the rabbit anti-GPDH ( glycerol-3-phosphate dehydrogenase , Tb927 . 8 . 3530 , EC 1 . 1 . 1 . 8; diluted 1:100 ) [72] , the rabbit anti-ENO ( enolase/2-phospho-d-glycerate hydrolase , Tb927 . 10 . 2890 , EC 4 . 2 . 1 . 11; diluted 1:20 , 000 ) [73] , the rabbit anti-IVDH ( diluted 1:100 ) , the mouse anti-PDH-E2 ( diluted 1:500 ) [63] , the mouse anti-hsp60 ( diluted 1:10 , 000 ) [74] and the mouse monoclonal anti-PFR ( paraflagellar rod protein 2 , L8C4 , 1:1000 ) [75] , and as secondary antibodies , anti-rabbit or anti-mouse IgG conjugated to horseradish peroxidase ( Sigma , 1:5000 dilution ) . Revelation was performed using the SuperSignal West Pico Chemiluminescent Substrate as described by the manufacturer ( Thermo Scientific ) . Images were acquired and analyzed with the ImageQuant LAS 4000 ( GE Healthcare Life Sciences ) . Recombinant fragments corresponding to the N-terminal extremity of IVDH gene , with or without the N-terminal mitochondrial signal ( 33 residues ) , followed by the enhanced green fluorescent protein ( EGFP ) was expressed in PCF using the pLew100 vector . PCR fragments corresponding to the first 412 amino acids of IVDH ( pLew-IVDH-1/412 . EGFP ) and the same region deleted for the N-terminal 33 residues ( pLew-ACH-34/412 . EGFP ) were inserted between the XhoI and XbaI restriction sites of the pLew100-EGFP1 plasmid described before [76] . For in situ TY1 tagging of AKCT , we used a modified pEnT6P vector kindly provided by M . Gould and M . Boshart [77] . Three PCR-amplified fragments were sequentially inserted into this vector to C-terminally tag the AKCT gene with a TY1 tag: the 5’ end of the downstream ORF ( Tb927 . 8 . 6070 ) ( PCR1 , 221 bp ) flanked by the HindIII ( 5' extremity ) and NotI/SpeI ( 3' extremity ) restriction sites , the 3’ end extremity of the AKCT gene without the stop codon ( PCR2 , 267 bp ) flanked by the NotI and SpeI restriction sites , and the full length 3’UTR of AKCT ( PCR3 , 751 bp ) flanked by the BamHI and SphI restriction sites . The resulting plasmid was sequenced and verified , before transfection of the EATRO1125 . T7T hemizygous AKCT knock out procyclic cell line ( TetR-HYG T7RNAPOL-NEO Δakct::BSD ) with the NotI-linearized plasmid . For immunofluorescence analyses , all cell lines were fixed with 2% formaldehyde in PBS , permeabilized with 0 . 2% Nonidet NP-40 and spread on poly-L-lysine coated slides . After incubation for 30 min in PBS containing 3% BSA , slides were incubated with rabbit anti-ASCT ( diluted 1:100 ) [8] and mouse anti-TY1 ( BB2 , diluted 1:200 ) [78] followed by ALEXA Fluor 594-conjugated goat anti-mouse secondary antibody ( diluted 1:100 ) and/or ALEXA Fluor 488-conjugated goat anti-rabbit secondary antibody ( diluted 1:100 ) ( Molecular Probes ) . Cells were viewed with a Leica DM5500B microscope and images were captured by an ORCA-R2 camera ( Hamamatsu ) and Leica MM AF Imaging System software ( MetaMorph ) . Processing was performed with ImageJ . To analyse the carbon source preference ( Fig 2 ) , 108 cells in the late exponential phase were incubated for 16 h in 5 ml of modified SDM79 medium containing 4 mM of each carbon source ( acetate , glucose , threonine , leucine , proline; except for isoleucine and valine , 1mM ) and one radio-labelled source ( 10 μCi of [1-14C]-acetate ( 55 . 3 mCi/mmol , Perkin-Elmer , Ref NEC084 ) , 5 μCi of D-[U-14C]-glucose ( 300 mCi/mmol , Perkin-Elmer , Ref NEC042 ) , 2 . 5 μCi of L-[U-14C]-threonine ( 175 mCi/mmol , American Radiolabeled Chemicals , Ref ARC0677 ) , 2 μCi of L-[U-14C]-leucine ( 328 mCi/mmol , Perkin-Elmer , Ref NEC279 ) , 9 μCi of L-[U-14C]-proline ( 271 mCi/mmol , Perkin-Elmer , Ref NEC285 ) , 1 μCi of L-[U-14C]-isoleucine ( 329 mCi/mmol , Perkin-Elmer , Ref NEC278 ) or 1 μCi of L-[U-14C]-valine ( 271 mCi/mmol , Perkin-Elmer , Ref NEC291 ) . For other experiments , cells were incubated in modified SDM79 medium containing 4 mM glucose , 4 mM threonine and either 5 μCi of D-[U-14C]-glucose or 2 . 5 μCi of L-[U-14C]-threonine or 1 mM acetate with 10 μCi of [1-14C]-acetate or 1 mM leucine with 2 μCi of L-[U-14C]-leucine . Cells were checked microscopically for viability several times during the incubation . Subsequently , cells were spun down at 1000 g for 10 min , resuspended in 200 μl of H2O and lipids were extracted by 2 ml of chloroform:methanol ( 2:1 , v/v ) for 30 min at room temperature , and then washed three times with 1 ml of 0 . 9% NaCl . Half of the washed lipid extracts were evaporated and then dissolved in 400 μl of chloroform:methanol ( 2:1 , v/v ) ( Fractions 1 ) . The other half was also evaporated and lipids were dissolved in 1 ml of methanol/H2SO4 ( 40:1 , v/v ) for trans-esterification of the fatty acids part of lipids at 80°C for 60 min . After cooling the samples , 400 μl of hexane ( 99% pure ) and 1 . 5 ml of H2O were added , and the mixture was homogenized vigorously during 20 sec . The samples were then centrifuged 5 min at 1-000 g to separate phases , and the hexane upper phases containing fatty acid methyl esters ( FAMEs ) were recovered without contact with the lower phases ( Fractions 2 ) . Aliquots ( 10–50 μl ) of Fractions 1 and Fractions 2 ( FAMEs ) were then loaded onto distinct HPTLC plates ( Merck ) developed in hexane/ethylether/acetic acid ( 90:15:2 , v/v ) to isolate and quantify sterols ( RF 0 . 20 ) , and FAMEs ( RF 0 . 90 ) , respectively . Sterols and FAMEs were identified by co-migration with known standards . Their radio-labelling was then determined with a STORM 860 ( GE Healthcare ) . For the calculation of the number of nanomoles of precursor incorporated into fatty acids or sterols , we considered that one acetyl-CoA molecule is produced per molecule of acetate , threonine and glucose consumed . When all data were normalized to the results of the parental cell line set at an arbitrary value of 100 , for the PCL samples , the differences between PCL samples did not exceed ±75% of values presented in Fig 2 . To characterize the de novo synthesized sterol and fatty acid molecules ( Fig 2C ) , 108 cells in the late exponential phase were incubated for 16 h in 5 ml of modified SDM79 medium containing 4 mM of non-enriched glucose , proline , acetate , leucine and threonine ( control ) . For 13C conditions , each non-enriched carbon source was individually substituted by its 13C equivalent: either 4 mM of D-[U-13C]-glucose ( Cambridge Isotope Laboratories ) or 4 mM of L-[U-13C]-proline ( Sigma-Aldrich Chemistry ) , 4 mM of L-[U-13C]-leucine ( Sigma-Aldrich Chemistry ) , 4 mM of L-[U-13C]-threonine ( Sigma-Aldrich Chemistry ) or 4 mM of [U-13C]-sodium acetate ( CortecNet ) . Cells were checked microscopically for viability several times during the incubation . Cells were then spun down at 1000 g for 10 min . A saponification step was performed , after total evaporation of the solvent , by adding 1 ml of ethanol with the internal standard α-cholestanol ( 5 μg ) and 100 μl of 11 N KOH , followed by 4 h of incubation at 80°C . After the addition of 1 ml of hexane and 2 ml of water , the sterol-containing upper phase was recovered and the solvent was evaporated under an N2 gas stream . Sterols were derivatized by N , O‐bis ( trimethylsilyl ) trifluoroacetamide ( BSTFA ) for 15 min at 100°C . After complete evaporation of BSTFA under N2 gas , sterols were resuspended in 200 μl of hexane before analysis by GC-MS . GC-MS was performed using an Agilent 6850 gas chromatograph and coupled MS detector MSD 5975-EI ( Agilent ) . An HP-5MS capillary column ( 5% phenyl-methyl-siloxane , 30-m , 250-mm , and 0 . 25-mm film thickness; Agilent ) was used with helium carrier gas at 2 ml/min; injection was done in splitless mode; injector and mass spectrometry detector temperatures were set to 250°C; the oven temperature was held at 50°C for 1 min , then programmed with a 25°C/min ramp to 150°C ( 2-min hold ) and a 10°C/min ramp to 320°C ( 6-min hold ) . Quantification of sterols was based upon peak areas that were derived from the total ion current . For determination of the stable IEF ( isotope enrichment factors ) , the extent of 13C incorporation was determined by GC/MS according to the calculations reported before [79] . 108 T . brucei procyclic cells were collected by centrifugation at 900 g for 10 min , washed once with PBS and incubated for 6 h at 27°C in 5 ml of incubation buffer ( PBS supplemented with 5 g/l NaHCO3 , pH 7 . 4 ) , with D-[U-13C]-glucose ( 4 mM ) in the presence of threonine ( 4 mM ) . The integrity of the cells during the incubation was checked by microscopic observation . Fifty microliters of maleate ( 20 mM ) were added as internal reference to a 500 μl aliquot of the collected supernatant and 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 [80] . Acquisition conditions were as follows: 90° flip angle , 5000 Hz spectral width , 32 K memory size , and 9 . 3 s total recycle time . Measurements were performed with 256 scans for a total time close to 40 min . Before each experiment , the phase of the ERETIC peak was precisely adjusted . Protons linked to acetate carbon C2 generate by 1H-NMR five resonances , a single peak ( non-enriched acetate ) flanked by two doublets ( [13C]-acetate ) . The central resonance ( 1 . 88 ppm ) corresponding to non-enriched acetate , in the Δakct mutant probably derives from an unknown internal carbon source . Teneral males of Glossina morsitans morsitans from 8 to 96 hours post-eclosion were first infected through a silicone membrane with T . brucei PCF at 5 x 106 parasites/ml in SDM79 culture medium . Tsetse flies were subsequently maintained in Roubaud cages for 15 days at 26°C and 60% hygrometry and fed twice a week through a silicone membrane with mechanically defibrinated fresh sheep blood as previously described [41] . Flies were starved for at least 48 hours before being dissected . Tsetse alimentary tracts from the proventriculus to the hindgut were dissected and arranged lengthways in a PBS drop . The presence of parasites was assessed by microscopic examination as previously described [41] . A total of 850 flies were dissected in three independent replicates in order to compare the infection rates obtained with the four strains . Tsetse fly mortality and midgut infection rates were subjected to statistical analyses with the software XLSTAT Version 2016 . 05 . 34687 ( Addinsoft ) integrated to Excel 15 . 31 ( Microsoft ) . A two-way ANOVA test including 3 parameters ( mortality rates , infection rates and number of dissected flies ) was performed with intergroup comparisons by Tukey ad hoc post-tests with α = 0 . 05 . Infection rates were plotted as mean ± SD . | In this study , we have ( i ) determined the carbon sources used by the Trypanosoma brucei procyclic insect form to feed the essential lipid biosynthetic pathways , ( ii ) further characterized the metabolic pathways leading to their degradation into acetyl-CoA ( fatty acid precursor ) and 3-hydroxy-3-methylglutaryl-CoA ( sterol precursor ) and ( iii ) showed that reduction of the ketogenic carbon sources degradation , favors their incorporation into fatty acids , instead of sterols . This fatty acid preference is compensated by an increase of leucine incorporation into sterols , which highlights the parasite adaptation capacity regarding carbon source availability by modulating the metabolic flux between branches within the network . This metabolic flexibility is particularly relevant for the insect stages of trypanosomes that evolve in the midgut and the salivary glands of their blood-feeding insect vector . One may also consider that , the metabolic flow redistribution towards the mevalonate pathway ( sterol production ) described in vitro also occurs in vivo , depending on the carbon source composition of the tsetse fly micro-environment , which may considerably vary along the digestive tract and depending on the fly feeding status , as well as in the other infected fly organs . | [
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"che... | 2018 | De novo biosynthesis of sterols and fatty acids in the Trypanosoma brucei procyclic form: Carbon source preferences and metabolic flux redistributions |
Two recombinant Fasciola hepatica antigens , saposin-like protein-2 ( recSAP2 ) and cathepsin L-1 ( recCL1 ) , were assessed individually and in combination in enzyme-linked immunosorbent assays ( ELISA ) for the specific serodiagnosis of human fasciolosis in areas of low endemicity as encountered in Central Europe . Antibody detection was conducted using ProteinA/ProteinG ( PAG ) conjugated to alkaline phosphatase . Test characteristics as well as agreement with results from an ELISA using excretory–secretory products ( FhES ) from adult stage liver flukes was assessed by receiver operator characteristic ( ROC ) analysis , specificity , sensitivity , Youdens J and overall accuracy . Cross-reactivity was assessed using three different groups of serum samples from healthy individuals ( n = 20 ) , patients with other parasitic infections ( n = 87 ) and patients with malignancies ( n = 121 ) . The best combined diagnostic results for recombinant antigens were obtained using the recSAP2-ELISA ( 87% sensitivity , 99% specificity and 97% overall accuracy ) employing the threshold ( cut-off ) to discriminate between positive and negative reactions that maximized Youdens J . The findings showed that recSAP2-ELISA can be used for the routine serodiagnosis of chronic fasciolosis in clinical laboratories; the use of the PAG-conjugate offers the opportunity to employ , for example , rabbit hyperimmune serum for the standardization of positive controls .
In Central Europe , the most frequently encountered autochthonous helminthic infections that require appropriate immunodiagnostic support include both forms of echinococcosis ( Echinococcus multilocularis and Echinococcus granulosus ) , toxocarosis ( Toxocara spp . ) , trichinellosis ( Trichinella spp . ) , ascariosis ( Ascaris lumbricoides , A . suum ) and fasciolosis ( Fasciola hepatica ) . Other helminthoses are diseases encountered in the context of travel medicine and sojourn in tropical or subtropical areas . Generally , the immunodiagnosis of helminthic infections is challenged particularly by the problem of high serological cross-reactivity when using crude or inadequately purified antigens . Another serodiagnostic problem relates also to cancer patients who raise antibodies against predominantly carbohydrate epitopes that might be common to helminth antigens [1] , [2] , [3] , as exemplified e . g . by cross-reactive anti-P1 antibodies that can be elevated in some cancer patients as well as in echinococcosis and fasciolosis patients [4] , [5] . Thus far , immunodiagnostic tools/methods for echinococcosis [6] , [7] , toxocarosis [8] , trichinellosis [9] and ascarosis [10] that achieve measures of specificity and sensitivity permissible for routine use or commercialization have been developed . However , the immunodiagnosis of fasciolosis , in Central European regions of low endemicity , has remained a major challenge , and routine diagnostic laboratories are struggling with the selection of a suitable and reliable test . Nevertheless , recent improvements have been published , mainly by Latin and North American groups on the use of purified antigens , such as Fas2 [11] , CL1 [12] or FhSAP2 [13] , [14] . To date , these antigens have not yet been ( i ) validated according to the standard/s required of routine diagnostic laboratories operating under Central European infectiological conditions and ISO 17025 norms , ( ii ) assessed in relation to specificity ( e . g . , considering cancer patients ) or ( iii ) directly compared with each other for diagnostic performance . Based on a review of the literature , we selected two promising but different recombinant Fasciola antigens , the F . hepatica saposin-like protein-2 antigen ( SAP2 ) [15] and the cathepsin L1 cysteine proteinase ( CL1 ) [16] to establish and subsequently assess an optimized ELISA for the serodiagnosis of human fasciolosis . In this assessment , an emphasis was placed on the immunodiagnostic discrimination from other ( hepatic ) parasitological problems encountered in Central Europe , such as alveolar echinococcosis , toxocarosis and ascariosis , but also other parasitic diseases acquired during overseas travel . In addition , one of the most frequently encountered differential diagnostic problems in hepatic and other organ disorders are tumors , which even upon use of various imaging procedures , may not be readily discriminated from particular parasitoses . Moreover , sera from cancer patients are also known sometimes to cause serological cross-reactivity , as has been documented , e . g . for echinococcosis serology [1] , [2] , [3] , [17] , [18] . Therefore , one of the crucial considerations for the present study was the inclusion of sera from 121 cancer patients that had already been previously investigated for their putative cross- or non-specific reactivity with Echinococcus antigens [2] , [3] . The working hypothesis of the present study was that , if both recombinant antigens exhibit a similarly high specificity , then their direct combination might yield a higher diagnostic sensitivity than when employed as single antigens . Therefore , we compared the ELISAs using recSAP2 , recCL1 and recSAP2 plus recCL1 with the conventional ELISA ( ISO-17025 ) using excretory-secretory products from adult F . hepatica ( Fh_E/S ) . In preliminary experiments with the conventional FhES-ELISA , we had shown that a conventionally used anti-huIgG-alkaline phosphatase conjugate exhibited the same diagnostic performance as a ProteinA-ProteinG-AP-conjugate [PAG-AP] ( Gottstein et al . , unpublished ) . Based on these findings and the fact that for PAG-AP a positive control serum of animal origin can be used , we elected to conduct the present study using PAG-AP .
All serum samples from humans were collected as part of public health and clinical diagnostic activities , were available prior to the commencement of this study and were treated anonymously , Samples from blood donors were obtained under informed written consent and provided by the Swiss Blood Transfusion Center ( SRK ) . This study was approved by the IPA Review Board of the Vetsuisse Faculty of Bern , Switzerland . Excretory-secretory products ( FhES ) from F . hepatica were prepared as described elsewhere [20] . Briefly , adult flukes were collected from the bile ducts from sheep livers obtained from a slaughterhouse and were washed several times in 0 . 01 mol/L phosphate-buffered saline ( PBS ) , pH 7 . 4 at room temperature . The flukes were incubated under sterile conditions at 37°C for 24 h in serum-free RPMI-1640 medium supplemented with 25 mmol/L HEPES buffer , 7 . 5% sodium bicarbonate , containing 100 µL penicillin and 100 µg/mL streptomycin . The medium was then sedimented ( 5 , 000× g for 10 min at 4°C ) to remove any remaining particles . The supernatants were collected and then concentrated using an YM-10 membrane filter system ( Amicon Corp . , Lexington , MA ) . Protein concentrations were assessed with a Bradford based protein assay ( BioRad Laboratories , Cressier , Switzerland ) . A fresh , morphologically intact and viable adult F . hepatica was isolated from an ovine bile duct and immediately put into RNA later ( Invitrogen ) for storage . Using peqGold RNAPure and peqGOLD OptiPure ( both PeqLab ) , RNA was isolated according to the manufacturer's manual and by using a poly-T primer cDNA was prepared with the Omniscript RT kit ( Qiagen ) . The coding sequence of the saposin-like protein-2 antigen ( recSAP2 ) was amplified by PCR ( initial denaturation: 98°C - 3 min , amplification: 25×98°C–20 sec , 58°C–20 sec , 72°C–30 sec , and a final 72°C step for 5 min ) using the primers FhSAP-forward ( 5′-CACCAACCCACTGTTCGTGTTAATG ) and FhSAP-reverse ( 5′-CTAGCACAGCTTGATTAAACG ) . Primer FhSAP-dw contained a N-terminal CACC stretch needed for the directional in-frame cloning of the amplicon ( 306 bp ) into the Champion pET Directional Topo Expression Kit ( Invitrogen ) . Insertion was verified by sequencing , and clones containing a perfect matching sequence were used for pilot experiments of expression . The clone expressing the highest level of recSAP2 was then used for large scale expression: 10 ml of overnight culture were diluted in 1 l Luria Bertani ( LB ) medium containing 100 µg/ml ampicillin ( Sigma ) and shaken at 37°C until the OD600 reached 0 . 5 . The protein expression was then induced by adding 1 mg IPTG . After shaking for 3 . 5 h at 37°C , the cells were pelleted by sedimentation ( 15 min , 4 , 000×g ) and the recSAP2 was isolated under denaturating conditions using 2 Protino Ni-IDA 1000 packed columns ( Machery-Nagel ) according to the manufacturer's instructions , with the following exception . After washing , under denaturating conditions , the columns were washed with 10 ml non-denaturating buffer ( 50 mM NaH2PO4 , 300 mM NaCl ) , and the recombinant protein was eluted three times with 1 ml non-denaturating elution buffer ( 50 mM NaH2PO4 , 300 mM NaCl , 250 mM imidazole , pH 8 . 0 ) . To reach ELISA-stage , the recSAP2 was precipitated with saturated ammonium sulfate solution , and the precipitate dissolved in ELISA coating buffer ( 100 mM sodium carbonate , pH 9 . 6 ) . Storage prior to use for ELISA was at −80°C . The purity and antigenicity of the recSAP2 were assessed by silver-staining of SDS-PAGE gels [21] and Western blot analyses , as described previously for recP29 , a recombinant antigen of E . granulosus [22] . The complete cDNA sequence encoding F . hepatica secreted CL1 was retrieved from GenBank . Forward ( 5′- GTACCCGACAAAATTGACTGG-3′ ) and reverse ( 5′- TCACGGAAATCGTGCCACCAT-3′ ) primers were designed to amplify the appropriate region of the protein ( 220 amino acid ) , without the C-terminal propeptide ( 55 amino acid ) . A CACC-tag was added to the 5′ end of the forward primer for further cloning into the Champion pET Directional Expression kit ( Invitrogen ) . The cDNA encoding the CL1antigen ( 24 . 2 kDa ) was amplified by PCR of 250 ng of F . hepatica cDNA as a template ( the same as used to amplify recSAP2 ) , 200 µM dNTPs , 0 . 5 µM of each forward and reverse primer , in a total volume of 50 µl with 1 U of Phusion High-Fidelity DNA polymerase ( New England Biolabs ) . The amplification was carried out using an initial denaturation of 98°C for 1 min , followed by 25 cycles of denaturation at 98°C for 30 s , annealing at 58°C for 30 s and an extension at 72°C for 30 s . The final polymerization was carried out at 72°C for 5 min . The 658 bp PCR product was purified using High Pure PCR Product Purification Kit ( Roche ) and then cloned into Champion pET expression vector . Competent E . coli ( TOP10 ) cells were transformed using the manufacturer's instructions ( Invitrogen ) . The transformed bacteria were incubated on LB plates containing 100 µg/ml of ampicillin at 37°C overnight , and colonies containing the insert were identified by colony PCR . Five positive clones were grown overnight in LB medium containing ampicillin , and then the plasmids were isolated using a QIAprep Spin Miniprep Kit ( Qiagen ) according to the manufacturer's protocol . Each vector construct was sequenced to ensure an open reading frame . Recombinant CL-1 was expressed as a fusion protein with His-Tag in E . coli BL21 as described above for the recSAP2 . RecCL1 from E . coli was purified under denaturing conditions ( 8M Urea ) using packed columns ( Protino Ni-IDA 150 , Macherey & Nagel ) according to the instructions of the manufacturer . The eluate was passed through PD10 desalting columns ( GE Healthcare ) and then was dialyzed against PBS . Purified protein samples were examined in silver-stained SDS-PAGE gels [21] and by Western blot [22] . FhES- , recSAP2- and ecCL1-ELISAs were carried out essentially as described for Echinococcus antigens [23] . Briefly , sera were diluted 1∶100 and tested using the following antigens ( at optimized coating concentrations ) : FhES-antigen ( 10 µg protein per ml ) ; recSAP2-antigen ( 0 . 1 µg protein per ml ) ; recCL1-antigen ( 0 . 1 µg protein per ml ) . A fourth ELISA included a double-coating with a mix of 0 . 1 µg protein of recSAP2 and recCL1 per ml . As a conjugate , an anti-human-IgG-alkaline phosphatase [αhuIgG-AP] conjugate ( Sigma; 1∶1'000 dilution ) or a ProteinA-ProteinG-AP-conjugate [PAG-AP] ( Thermo Scientific no . 32391; 1∶10'000 dilution ) was used . The four Fasciola-antigen-ELISAs validated here were first calibrated , in order to determine the optimal threshold ( cut-off value ) for the discrimination between positive and negative findings . The individual cut-off value was thus determined by testing blood donor sera and tumor patients' sera and potentially cross-reactive sera ( from patients with other parasitic diseases ) as a one group together , thus reaching a representative average number of the “negative samples” encountered in a routine laboratory . Inter-test and intra-test variations in test results were calculated as coefficients of variation for reference negative and positive sera , all tested in triplicate on each test plate; variation of ≤15% was recorded , which is considered acceptable for serodiagnostic assays [2] . For statistical analyses , the samples from the 30 patients with confirmed F . hepatica infection represented the positive status ( 1 ) , whereas the 20 healthy individuals , the 121 cancer patients and the 87 patients infected with potentially cross-reacting or diagnostically relevant other parasitic infections , all represented the negative status ( 0 ) . The comparative evaluation of the four assays ( i . e . FhES-ELISA; recSAP2-ELISA; recCL1-ELISA; recSAP2-recCL1-ELISA ) was carried out using this classification . To quantify the linear numerical correlation between the raw data measurements of the three assays , Spearman rank correlation coefficients were derived using all 248 samples . The distribution of OD405 nm-values for the samples in the three assays was displayed using dot plots and box plots . In a Receiver-Operator-Characteristic ( ROC ) analysis , threshold ( cut-off ) values for the following four conditions were derived according to: In addition , modified two-graph ROC curves were drawn for the different assays , and the areas under the ROC curves ( AUC ) were statistically compared for significant differences . Descriptive statistics , plots and ROC analyses were done Microsoft Excel 2010 ( www . microsoft . com ) and the statistical software package NCCS 8 ( www . ncss . com ) . The highest potential for a false positive interpretation of test results relates to sera from patients suffering from other parasitic diseases . In Table 1 , we present the different parasitic diseases and their rate of cross-reactivity determined in the different ELISA-types , yielding a relative specificity index linked to cross-reactivity . To determine the threshold between positive and negative results , the cut-off value was arbitrarily set at the Youdens J maximum value , calculated as described above ( MedCalc software version 12 . 7 . 5 . 0; http://www . medcalc . org ) . GenBank accession number for the used cDNA-SAP2 stretch: AF286903 . 1 . GenBank accession number for the complete cDNA sequence encoding F . hepatica secreted CL1: U62288 . 2 .
Five batches of recSAP2 and five batches of recCL1 were both independently produced and analysed by silver staining ( data not shown ) . As all batches yielded identical purities , they were pooled to obtain two single working batches , respectively . These batches were each assessed with known fasciolosis-sera by Western blot to verify the antigenicity of the two single bands of expected relative mobilities of Mr 15000 and Mr 26000 for recSAP2 and recCL1 , respectively ( data shown for recSAP2 only , Figure 1 ) . Fasciolosis-sera were selected , such as to cover the whole range of antibody levels measured by the conventional FhES-ELISA . The relationship between “banding intensity” and FhES-ELISA antibody level was relative . Serum a1 has the highest levels in FhES-ELISA ( >100 AU , relative antibody units: The quantification of these ELISA-results , expressed in relative antibody units [AU] , arises from the routine serology carried out at the Institute of Parasitology in Bern , and is not further specified in this article ) , and exhibited also the strongest staining intensity by Western blot . Sera nos . a2–a5 yielded medium FhES-ELISA antibody levels ( 70 , 40 , 51 and 66 AU ) , while staining intensity in Western blot varied considerably and appeared not to be directly linked to the recSAP2-ELISA findings . Serum a6 was very weak in FhES-ELISA , and was not detectable by Western blot analysis . However , the same serum ( a6 ) was , nevertheless , also weakly positive in recSAP2-ELISA ( Figure 1 ) . The distribution of absorbance values varied considerably between test positive and negative samples , as to be expected , but also between different assays ( Figure 2 ) , and different optimal cut-off values were established for the four tests ( see section of ROC analyses ) . The highest overall accuracy ( agreement between references status and test result ) reached was 0 . 984 , while the highest combined sensitivity and specificity ( Youdens J ) was 0 . 905 ( Table 2 , Figure 3 ) . The recSAP2-ELISA cut-off 0 . 084 showed the best combination of sensitivity ( 0 . 867 ) and specificity ( 0 . 989 ) of the three recombinant-antigen assays ( Table 2 ) . For this assay , the empirical values for sensitivity , specificity , overall accuracy and Youdens J , as a function of the cut-off value , were plotted in a multi-line ROC graph ( Figure 4 ) to illustrate the pattern of these test characteristics over a range of cut-off values . When comparing the AUC values of the four tests , test results of the recCL1 assay were significantly lower than for all other assays ( p<0 . 001 ) , while results of the FhES assay were significantly higher , even when compared with the second best assay ( recSAP2; p<0 . 047 ) . Cross-reactions , as a results of a positive-negative discrimination based on the cut-off value selected , are presented according to the different parasitic disease groups investigated ( Table 1 ) . One serum ( a case of alveolar echinococcosis ) consistently cross-reacted in all four ELISAs , whereas all other cross-reactions were individually scattered among individual ELISAs . The best score in specificity ( 99% ) was achieved by recSAP2-ELISA , with a single instance of cross-reactivity ( described above ) . Using the selected cut-offs , FhES exhibited the best level of diagnostic sensitivity ( 93% ) ( 28 positive sera of 30 Fasciola-cases ) , followed by recSAP2 ( 26 of 30 cases; 87% ) , while the sensitivities achieved using recCL1 and the combination of recCL1 and recSAP2 were all below 77% for all elaborated cut-offs ( Table 2 ) .
Coprological diagnosis , based on the identification of F . hepatica eggs found in stools , duodenal contents or bile analysis is still commonly employed as a “gold standard” to detect human fasciolosis . This is the case , despite the consensus that this method is not entirely reliable [24] for reasons such as: ( i ) eggs are not detected until the patent period of infection , when much of the liver damage has already occurred by the migration of juvenile flukes in the liver parenchyma , ( ii ) eggs are released sporadically from the bile ducts and , hence , stool samples from infected patients may not necessarily contain eggs [25] . Therefore , serological techniques play an important complementary role in the diagnosis of clinical cases of fasciolosis . In the situation of low endemicity , such as encountered in many countries of Central Europe , serological methods require not only a good diagnostic sensitivity , but more importantly also a high specificity . The reason is that potentially cross- or false-positively reacting sera will be much more frequently found in routine diagnosis than actual true cases of fasciolosis . This has to be considered particularly in the context of a differential diagnosis , predominantly related to any other hepatic disorders resembling , symptomatically , those of fasciolosis . Serodiagnosis of fasciolosis of humans has been successfully performed by employing several antigens ( antigen fractions ) of F . hepatica , where , to date , ES products have become the most commonly used antigen in-house ELISAs [24] . Nevertheless , the use of FhES is associated with several problems when used for routine diagnostic laboratory conditions , including ( 1 ) a dependence on the availability of living flukes and ( 2 ) representing an antigen mixture that is subjected to variations due to natural and artificial conditions ( e . g . time between slaughter and cultivation ) . This makes antigen standardization between diagnostic laboratories difficult , whereas a recombinant single component antigen exhibits a constant composition . With regard to recombinant antigens , cathepsin L1 ( CL1 ) and saposin-like protein 2 ( SAP2 ) from F . hepatica are of the most frequently referenced candidates being used for detecting anti-Fasciola antibodies in different epidemiological situations [12] , [15] , [16] , [26] , [27] . For the present study , we selected both SAP2 and CL1 as key candidates to be compared , alone or in combination , in the form of bacterially-expressed recombinant antigens ( recSAP2 and recCL1 ) . As a serological standard , we used a conventionally employed F . hepatica ES antigen ( FhES ) . In order to improve routine applicability of any of these tests , we carried out a preliminary study comparing the efficacy of anti-human-IgG-alkaline phosphatase and a ProteinA-ProteinG-alkaline phosphates conjugate ( PAG-AP ) to detect anti-Fasciola-antibodies ( data not shown ) . We documented a comparable performance of both conjugates ( even slightly improved for PAG , although statistically not significant ) . PAG-AP offers the considerable advantage that it binds also to IgG of several animal species , including , for example , rabbit IgG . The availability of sufficient positive control serum for routine diagnostic application under ISO 17025 accreditation conditions is one of the big problems in routine diagnostic laboratories , as the procurement of such serum ( in larger quantities ) from fasciolosis patients is difficult or even impossible in countries of low endemicity . Alternatively , hyperimmune serum from rabbits or other appropriately immunized animals can assume the role of positive control reagents , thus considerably facilitating the establishment of standardized operating procedures ( SOPs ) of Fasciola-serology . The results of the present , comparative study demonstrated similar performances of the FhES and the recSAP2 antigen with regard to diagnostic sensitivity and specificity , whereas the assay using antigen recCL1 did not reach the expected performance . Our initial working hypothesis had been based on the assumption that a combination of recSAP1 and recCL1 would increase diagnostic sensitivity , provided specificity could be maintained . This hypothesis was justified by an advanced appraisal of previous publications on the topic . Carnevale et al . [26] , who used a recombinant CL1 containing the proregion of the protein , reported 100% sensitivity and 100% specificity . In this study , however , the selection criteria , from both clinical and epidemiological perspectives , have not been described , such that one cannot elucidate whether 100% sensitivity represents a true diagnostic sensitivity , as encountered in a routine diagnostic laboratory . Similarly , Tantrawatpan et al . [28] , who used a peptide-based form of CL1 deduced from F . gigantica , reported 100% sensitivity and 99 . 7% specificity . However , in the latter publication , the pre-selection criteria for the fasciolosis sera were not documented as to allow an assessment of the actual diagnostic sensitivity . Such tests should also include sera from acute cases , for which infection had been proven but eggs could not be detected repeatedly using a coprological sedimentation technique . For example , sera from acute cases were not included in the study by O'Neill et al . [16]; these authors reported 100% sensitivity for an ELISA using CL1 expressed in Saccharomyces cerevisiae ( yeast ) and an anti-IgG4-detection systems following the testing of sera from 26 cases with egg-excretion . However , these authors did not evaluate the true sensitivity by testing sera from cases representing various forms of infection and disease stages . A relatively recent study on recombinant CL1 was carried out by Gonzales Santana et al . [27] upon use of Pichia pastoris for expressing recFhCL1 . Conversely to our study , the antigen demonstrated not only an excellent diagnostic sensitivity , but also an optimal specificity . The reason for the difference encountered between these study findings and our study may be the found by the fact that expression of a metazoan gene such as cl1 in P . pastoris may much better lead to carboxylation of the antigen , and thus to an improved formation of relevant epitopes within this proteinic antigen . We will address this important feature in our next studies . Another reason why the diagnostic sensitivity of recCL1 was higher in other studies [16] may have been the use of a subclass-specific anti-IgG4-conjugate . The same approach ( anti-IgG4-conjugate ) was chosen by Tantrawatpan et al . [28] , who furthermore employed a peptide-based synthetic FhCL1-antigen . Although we know that the protein A and protein G used in our study , both principally bind to human IgG4 ( http://www . amsbio . com/brochures/Protein-A-G%20-Affinity-for-IgG-subclasses . pdf ) , it will be nevertheless interesting to compare , in future studies , both conjugate types directly with regard to the diagnostic sensitivity yield . In our study , bacterially expressed recCL1 exhibited two related problems , which finally rendered this antigen not useable for our purpose . First , in comparison to recSAP2 , recCL1 displayed relatively high background reactivity with both , sera from cancer patients and sera from patients suffering from other parasitoses ( see Figure 2 ) . This translated into a relatively high cut-off level for the discrimination between seropositivity and seronegativity and , thus , resulted in a relatively low diagnostic sensitivity . Nevertheless , we raised the question whether , among the four recSAP2-seronegative fasciolosis patients , one or more of them would be recCL1-positive , thus justifying a possible combination of the two antigens . However , this was not the case . In this context , it is also important to mention that the two FhES-negative fasciolosis patients were also seronegative against the recSAP2 and recCL1 antigens . Consequently , an overall appraisal of the results did not suggest a routine application of recCL1 antigen for the serodiagnosis of human fasciolosis . Nevertheless , as other previous reports clearly documented a good diagnostic performance of recCL1 if produced by a different expression system [16] or as a synthetic polypeptide [28] , we will , in future studies , switch from bacterial expression to the other expression/synthesis systems . A detailed comparison between the diagnostic operating characteristics of the recSAP2-ELISA and the other Fasciola-ELISAs included in our study demonstrated clearly an excellent performance of recSAP2 in relation to both specificity and cross-reactivity ( specificity 99% , see Tab . 1 ) . Regarding diagnostic sensitivities , among the 30 fasciolosis patients available for our study , only one patient with a coprologically confirmed fasciolosis remained consistently negative in all four tests , including the recSAP2-ELISA . Due to the lack of clinical data from the respective patient , the reason for this false-negative result could not be established . Here , the lack of clinical and radiological evidence might indicate a chronic infection status with a low infection intensity , accompanied by a decline or even disappearance of parasite-specific antibody levels . In this respect , it is possible that especially the hepatic parenchymal migration stage of juvenile flukes early during infection induces the strongest immune response , whereas a few adult worms remaining in the bile ducts during the chronic phase of infection might not be sufficient to sustain an antigen stimulus to maintain a detectable serum antibody level . Overall , sera from four fasciolosis patients were test-negative in both recombinant antigen-based assays , while three of them were clearly seropositive in the conventional FhES-ELISA . However , this slight diagnostic inferiority of recSAP2 as compared with FhES was largely compensated by other parameters that favored SAP2 as a routine serodiagnostic tool , particularly when applied in a low endemicity area . In such a situation , the comparatively higher specificity of recSAP2 ( 99% versus 95% , see Table 1 ) might be superior to the comparatively higher sensitivity of FhES ( 87% versus 93% ) . Importantly , in contrast to FhES , recSAP2 did not exhibit occasional cross-reactions with sera from neuro-cysticercosis and filariosis patients ( see Table 1 ) . Such cross-reactions might hamper the diagnostic performance in cases where other clinical data are inconclusive , and where serology becomes a crucially important diagnostic tool . In conclusion , we consider the recSAP2-PAG-AP-ELISA as serological test system for routine diagnosis of human fasciolosis , particularly if test results are supported by clinical history and the use of other serological tests controlling for possible cross-reactions due to antibodies induced by other helminths . In addition , this test system might serve as an excellent serodiagnostic tool for epidemiological studies of human fasciolosis , particularly in the context of outbreaks , or accumulated case numbers , for example , as observed recently in Switzerland [19] . Our conclusions are in perfect agreement with a previous report from Figueroa-Santiago et al . [15] , who were the first authors to document the excellent diagnostic performance of the recSAP2-ELISA . | To improve the serodiagnosis of human fasciolosis caused by Fasciola hepatica , we comparatively evaluated the accuracy of two different enzyme-linked immunosorbent assays ( ELISAs ) based on the use of two published recombinant antigens . The best performance was achieved with the recombinant F . hepatica saposin-like protein-2 antigen ( recSAP2 ) . Although the F . hepatica E/S antigen exhibited a slightly higher diagnostic sensitivity , the higher specificity performance of recSAP2 renders this antigen very suitable for application in low endemic areas , especially when coupled to an easy and standardized production facility as compared to the relatively complex production procedure for an E/S antigen . Conclusively , the recSAP2-ELISA can be used as a routine individual serodiagnostic test for human fasciolosis , especially when backed up by a compatible clinical history together with other serodiagnostic technique for other helminth infections of the liver , e . g . alveolar or cystic echinococcosis . | [
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] | 2014 | Comparative Assessment of ELISAs Using Recombinant Saposin-Like Protein 2 and recombinant Cathepsin L-1 from Fasciola hepatica for the Serodiagnosis of Human Fasciolosis |
Somatosensory thalamocortical ( TC ) neurons from the ventrobasal ( VB ) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex , and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep . This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes , called tonic firing and low-threshold bursting . Although the general properties of TC neurons are known , we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus . The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats . We recorded the electrical activity of VB neurons ( N = 49 ) and reconstructed morphologies in 3D ( N = 50 ) by applying standardized protocols . After identifying distinct electrical types , we used a multi-objective optimization to fit single neuron electrical models ( e-models ) , which yielded multiple solutions consistent with the experimental data . The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting . A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features . The e-models , when tested in combination with different morphologies , showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology . The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior , such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies .
Thalamocortical ( TC ) neurons are one of the main components of the thalamus and have been extensively studied in vitro and in computo , especially in first order thalamic nuclei in different species [1] . One of these nuclei , namely the ventral posterolateral nucleus ( VPL ) , relays somatosensory , proprioceptive , and nociceptive information from the whole body to the somatosensory ( non-barrel ) cortex [2] . The VPL is located close to ventral posteromedial nucleus ( VPM ) , which transmits information from the face to the barrel cortex . The VPL and VPM nuclei constitute the ventrobasal ( VB ) complex of the thalamus [3] . Despite its key role in sensory functions , a systematic characterization of the cellular properties of the VB complex is still missing . The morphologies of VPL neurons in adult rats were described in early anatomical studies but were limited to two-dimensional drawings of Golgi-impregnated cells [4] . The general electrical properties of TC neurons maintained in vitro are known and similar in different thalamic nuclei and species with respect to the generation of two distinct firing modes , called tonic firing and low-threshold bursting [5–8] . However , a systematic description on the electrical types in the VB thalamus in the rodents is still missing . Collecting morphological and electrophysiological data , by following standardized experimental procedures , is essential for the definition of cells types and it is the first step to constrain computational models of single neurons [9 , 10] . Although models of TC neurons have been published previously , they were typically aimed at studying specific firing properties and their parameters were hand tuned to achieve the desired result [11–15] . The purpose of our study is to systematically define the morphological and electrical types by collecting in vitro experimental data and to constrain biophysically detailed models of VB TC neurons of the juvenile rat . To the best of our knowledge , automatic parameter search has not been applied , thus far , to capture complex firing behavior in thalamic neurons , in particular low-threshold bursting and tonic firing . We defined the electrical and morphological types of TC neurons through in vitro patch-clamp recordings and 3D morphological reconstructions . We then extended an existing method [16] to account for their distinctive firing properties . These electrical models ( e-models ) were constrained by the electrical features extracted from experimental data [9 , 10 , 17 , 18] . Other experimental data were used to assess the generalization of the models to different stimuli and morphologies . We further performed a sensitivity analysis by varying each parameter at a time by a small amount and recording the resulting electrical features . This analysis provided an assessment of the robustness of the models and a verification that the selected features provided sufficient constraints for the parameters .
We characterized TC neurons in slices of the rat VB thalamus , by combining whole-cell patch-clamp recordings , biocytin filling and 3D Neurolucida ( MicroBrightField ) reconstruction , along with anatomical localization in a reference atlas [19] ( Fig 1 ) . Visual inspection of 50 reconstructed morphologies ( 24 from the VPL , 26 from the VPM nuclei ) revealed variability in the number of principal dendritic trunks and their orientation , in agreement with previous anatomical studies [4] . The maximum radial extent of the dendrites ranged between 120 and 200 μm and they started to branch between 20 and 50 μm from the soma ( S1 Fig ) . We then analyzed the morphologies with two methods in order to quantitively classify different morphological types . We used algebraic topology to extract the persistent homology of each morphology and to visualize the persistence barcode [20] ( Fig 2A , see Methods ) . Each horizontal bar in the persistence barcode represents the start and end point of each dendritic component in terms of its radial distance from the soma . The barcodes of all the morphologies followed a semi-continuous distribution of decreasing length . To quantify the differences between the barcodes , we computed the pairwise distances of the persistence images ( see Methods and S1 Fig ) . We found that they were in general small ( <0 . 4 , values expected to vary between 0 and 1 ) . These findings indicate that the morphologies cannot be grouped in different classes based on the topology of their dendrites . Furthermore , we performed Sholl Analysis [21] to compare the complexity of the dendritic trees ( Fig 2B ) . We observed that all the morphologies had dense dendritic branches , with a maximum number of 50–100 intersections between 50–80 μm from the soma . When comparing the Sholl profiles for each pair of neurons we could not find any statistically significant difference ( S1C Fig ) . Considering the results of topological and Sholl analyses , we grouped all the morphologies in one morphological type ( m-type ) called thalamocortical ( TC ) m-type . We used an adaptive stimulation protocol , called e-code , consisting of a battery of current stimuli ( see Methods for details ) , where the stimulation amplitude was adapted to the excitability of different neurons . This standardized protocol has previously been used to build biophysically-accurate models of cortical electrical types ( e-types ) [16] . However , TC neurons from different thalamic nuclei and species fire action potentials in two distinct firing modes , namely tonic firing , when stimulated from a relatively depolarized membrane potential or low-threshold bursting , from a hyperpolarized membrane potential [5–8] . We thus extended the e-code to include two different holding currents . All the neurons recorded in this study displayed tonic and burst firing , when stimulated with the appropriate holding current ( Fig 1B ) . Moreover , we were able to classify different e-types by considering the voltage traces recorded in tonic mode in response to step current injections ( Fig 1B ) . The majority of the cells ( 59 . 3% ) showed a non-adapting tonic discharge ( continuous non-adapting low-threshold bursting , cNAD_ltb e-type ) while others ( 40 . 7% ) had higher adaptation rates ( continuous adapting low-threshold bursting , cAD_ltb e-type ) , as reflected by the adaptation index ( Fig 1C ) . We followed the Petilla convention [22] for naming the tonic firing discharge ( cNAD or cAD ) , extending it to include “_ltb” for the low-threshold bursting property . In some rare examples , we noticed acceleration in the firing rate with decreasing inter-spike intervals ( ISIs ) towards the end of the stimulus . Similar adapting and accelerating responses have already been described in the VB thalamus of the cat [7] . We also observed stereotypical burst firing responses within the same cell , with variation of the number of spikes per burst in different cells , but the burst firing responses alone were insufficient to classify distinct e-types . Multi-compartmental models comes with the need of tuning a large number of parameters [23] , therefore we constrained the models as much as possible with experimental data . We first combined the morphology and the ionic currents models in the different morphological compartments ( soma , dendrites and axon ) . Given that the reconstruction of the axon was limited , we replaced it with a stub representing the initial segment [16] . We used previously published ionic current models and selected those that best matched properties measured in rat TC neurons ( see Methods ) . The kinetics parameters were not part of the free parameters of the models . The distribution of the different ionic currents and their conductances in the dendrites of TC neurons is largely unknown . The current amplitudes of the fast sodium , persistent and transient ( A-type ) potassium currents were measured , but only up to 40–50 μm from the soma [24] . Indirect measures of burst properties [15] or Ca2+ imaging studies [25] suggest that the low-threshold calcium ( T-type ) channels are uniformly distributed in the somatodendritic compartments . We thus assumed different peak conductance in the soma , dendrites and axon for all the ionic currents , except for ICaT , which had the same conductance value in the soma and dendrites . We then extracted the mean and standard deviation ( STD ) of different electrical features in order to capture the variability of firing responses from different cells of the same e-type [9 , 10 , 17] ( Fig 3 ) . We observed that some features extracted from tonic firing responses had distinct distributions between the cAD_ltb and cNAD_ltb e-types ( Fig 3A ) . For optimizing the models’ parameters , we chose features that quantified passive ( input resistance , resting membrane potential ) , burst and tonic firing properties ( number of spikes , inverse of inter-spike intervals , latency to first spike , adaptation index ) , action potentials shape ( amplitude , half-width , depth of the fast after-hyperpolarization ) . We aimed at finding the minimal set of features that captured the most important properties in the two firing modes . This set was a trade-off between comprehensively describing the experimental data ( i . e . extracting all possible features ) , which can lead to over-fitting and loss of generalizability , and a too small set that would miss some important characteristics . For the tonic firing responses , we used three stimulation amplitudes ( 150% , 200% , 250% of firing threshold ) which have been shown to reproduce the complete input-output function of the neurons [16 , 17] . Responses to two hyperpolarizing steps of different amplitudes ( −40% and −140% threshold ) constrained the input resistance ( conductance of the leak current ) and the conductance of currents activated in hyperpolarization , for example the h-current , Ih ( sag_amplitude feature ) . We included baseline voltage values to the optimization objectives to ensure that the models were in the right firing regime and spike count to penalize models that were firing in response to the holding currents . To constrain the low-threshold burst we used features ( such as number of spikes ) which are influenced by specific ionic currents , for example the low-threshold calcium current , ICaT . The average value and STD of each feature were used to calculate the feature errors ( Fig 4C ) . Each error measured how much the features of the models deviated from the experimental mean , in units of the experimental STD . We used a multiobjective optimization approach ( MOO ) , where each error was considered in parallel . To rank the resulting models after optimization , we considered model A better than model B if the maximum error of all the features of A was smaller than the maximum error of all the features of B . By applying this MOO procedure , we generated multiple models with distinct parameter combinations for each of the twenty-two free parameters ( Fig 4B ) . The free parameters were allowed to vary between the upper and lower bounds shown in Fig 5B . The models reproduced well the key firing dynamics observed in the experimental recordings . They showed a low-threshold burst when stimulated from a hyperpolarized membrane potential , crowned by a variable number of sodium spikes ( Fig 4B ) . In the tonic firing regime , they reproduced adapting and non-adapting firing discharges as observed in the two e-types . These results indicate that the ion channels included in the models were sufficient to reproduce the experimental firing properties and that different e-types in TC neurons could be generated by different ion channel densities . We found that different sets of parameter values reproduced the target firing behavior ( Fig 5B ) . We further analyzed models that had all the feature errors below 3 STD . Models’ voltage responses reflected the characteristic firing properties of TC neurons ( S3 Fig ) , indicating that the selected set of features and ion channels were sufficient to capture the two firing modes , in both the adapting and non-adapting e-types . The voltage traces from different models showed small differences in spike amplitude , firing frequency , and depth of the after-hyperpolarization , as reflected by the variability of features values ( Fig 5C ) , arising from differences in ion channel densities between models . Spike-shape related features ( e . g . AP . amplitude ) in the different models covered the space of the experimental variability , while for some features ( e . g . input resistance , Rinput ) , all models tended to cluster on one of the tails of the experimental distribution . Rinput relates to the neuron passive properties and depends both on the number of channels open at rest ( inverse of the leak conductance in the model ) and the size of the cell . Given that all the models for a given e-type were constrained on a single morphology , this result is not surprising . Other features , such as sag amplitude were less variable in the models compared to experiments . We hypothesized that this depended on the variable stimulation amplitudes applied to different experimental cells , while all the models were stimulated with the same current amplitudes . Some other features were systematically above or below the experimental values in both e-types . We suggest that this depend on the exact dynamics of some specific ion channels . For example , the amplitudes of the first and second spikes in the burst tended to be similar or above and below the experimental values , respectively . This can depend on the specific activation/inactivation properties of some ionic currents , for example the transient sodium current ( INaT ) and delayed potassium current ( IKd ) . During the rising phase of the low-threshold spike , INaT in the model is readily activated and generated a first spike with higher amplitude , but is not repolarized enough by the activation of IKd . At higher potentials , reached towards the peak of the low-threshold spike , the availability of INaT and other depolarizing currents seemed reduced and generated a spike with smaller amplitude . Sensitivity analysis confirmed that INaT and IKd had an influence on the amplitude of the first and second spike in the burst . Furthermore , these two currents operate together with currents that generate the burst , such as the low-threshold calcium current ( ICaT ) and the Ih in shaping the amplitude of the second spike in the burst . Interestingly , the models also tended to have lower instantaneous frequency of the first two spikes in the burst ( Inv . 1st ISI ) and this feature had similar sensitivity ( but of opposite signs ) to the amplitude of the second spike in the burst . Another possible explanation is the lack of some ionic currents in the model , for example some specific subtype of potassium channels that promote higher firing rates ( Kv3 . 1 and Kv3 . 3 ) . While neurons of the thalamic reticular nucleus are known to express this channel subunit [26] , the expression in TC neurons has not been confirmed yet . The dynamics of IKd could also explain why the after-hyperpolarization ( AHP depth ) tended to be smaller in the models compared to the experimental values . AHP depth is also influenced by other ionic currents , such as high-threshold calcium current ( ICaL ) , calcium-activated potassium current ( ISK ) and the intracellular calcium dynamics . The number of action potentials ( Num . of APs ) in different conditions ( No stim , Ihold ) ensured that the models did not spike in the absence of a stimulus or in response to the holding current . For this reason , all the experimental and model feature values in Fig 5C are equal to 0 . We examined the diversity of the parameter values with respect to the initial parameter range ( Fig 5B ) . Most of the optimized parameter values spanned intervals larger than one order of magnitude . On the other hand , some parameter values were restricted to one order of magnitude , for example the permeability of the low-threshold calcium current PCaT . This result is in agreement with experiments showing a minimum value of ICaT is critical to generate burst activity and this critical value is reached only at a certain postnatal age [27] . The value of PCaT was constrained by features measuring burst activity ( such as number of spikes , frequency , etc . ) . We used different stimuli for model fitting ( current steps ) and for generalization assessment ( current ramps and noise ) . We simulated the experimental ramp currents , by stimulating the models with the appropriate holding currents for the two firing modes and a linearly increasing current . We first compared visually the model responses with the experimental recordings ( Fig 6A ) . In burst mode , the models reproduced the different behaviors observed experimentally: absence of a burst , small low-threshold spike , burst ( S4A Fig ) . Moreover , the latency of burst generation substantially overlapped with the experimental one . However , a small fraction of models ( 1 . 2% ) generate repetitive burst that we have never observed in the experimental recordings ( S4B Fig ) . These models were quantitatively rejected by considering the number of spikes and the inter-spike intervals . In tonic mode , the latency to first spike , the voltage threshold , the shape of the subsequent action potentials and the increase in firing frequency were comparable with the experimental recordings ( Fig 6A ) . In addition , we quantified the generalization error to ramp stimuli ( Fig 6C ) , by considering the latency to first spike , firing frequency increase over time ( tonic mode ) or number of spikes ( burst mode ) . Although conductance-based models can be fit by using step and ramp currents , these stimuli are different from synaptic inputs , which can be simulated by injecting noisy currents . To test the response to such network-like input , we used a noisy current varying accordingly to an Ornstein-Uhlenbeck ( OU ) process [28] to compare models’ responses with the experimental data . Each experimentally recorded cell was stimulated with the same OU input , scaled by a factor w . Experimentally , w was calculated by evaluating the responses to previous stimuli . We developed a similar approach to generate the noise stimuli in silico ( see Methods ) . The noise current was injected on top of the holding currents used during the optimization . We found that the models reproduced well the subthreshold potential , spike times and the distribution of single spikes and bursts ( Fig 6B ) . Moreover , we quantitatively evaluated the generalization to the noise stimulus by extracting features ( e . g . number of spikes ) and comparing them with the experimental mean . We computed generalization errors for each model , which were calculated similarly to the optimization errors ( Fig 6C ) . We considered a model acceptable after generalization if it had all generalization errors <3 STD and we found that the majority of the models ( >90% ) passed the generalization test ( Fig 6D ) . We assessed the robustness of the models to small changes in their parameter values . To that end , we varied each parameter at a time by a small amount ( ± 2 . 5% of the optimized value ) and computed the values of the features . A sensitivity value of 2 between parameter p and feature y means that a 3% change in p caused a 6% change in f . We ranked the parameters from the most to the least influential and the features from the most sensitive to the least sensitive . Some features resulted to be more sensitive to parameter changes , both in term of magnitude of the sensitivity and number of parameters ( e . g . adaptation index , inverse of inter-spike intervals , ISIs , AHP depth ) . Most of these features describe the model firing pattern , which depend more on the interplay between the different ionic currents than on the specific activation/inactivation dynamics . Conversely , spike shape-related features were less sensitive to parameter changes ( e . g . AP half-width , AP amp . ) because they depend more on specific ionic current dynamics ( e . g . IKd , IL , INaT , ) . Some features were very weakly influenced by small parameter changes , e . g . baseline voltage , which depend more on the holding current amplitude , than on the model parameters ( Fig 7A ) . The conductance of the leak current gleak emerged as the most influential parameter ( Fig 7A ) . An increase in gleak caused a decrease in firing frequency ( inverse of ISIs ) in both the tonic and burst firing modes . These results are easy to interpret when considering Ohm’s law: increasing gleak means decreasing the input resistance of the model , so that for the same input current the voltage response becomes smaller . The second most influential parameter was the conductance of the persistent sodium current gNaP in the dendrites , which increased the tonic firing rate as expected from a depolarizing current . Interestingly , gNaP had an effect on the late phase of the low-threshold burst ( inverse last ISI—burst ) , suggesting that the low-threshold burst is initiated by the activation of IT and modulated by INaP . An increase in the permeability of the low-threshold calcium current PCaT , known to be one of the main currents underlying low threshold bursting , enhanced burst firing responses ( it increased the inverse of ISIs , i . e . the firing frequency ) and had effects on some of the tonic features . Increasing the somatic permeability of the high threshold calcium current PCaL decreased the tonic firing rate , despite being a depolarizing current . Increasing PCaL means higher Ca2+ influx and higher activation of the Ca2+-activated potassium current ( ISK ) . The parameter gSK had indeed a similar effect on the features and thus clustered together with parameters regulating the intracellular calcium dynamics γCa and τCa ( Fig 7B ) . Sag amplitude , that is known to depend on the activity of Ih , was mainly influenced by change in gleak , PCaT and gh . In summary , each parameter influenced at least one feature . These results indicate that the model ability to generate tonic and burst firing is robust to small changes in parameter values and that all the parameters were constrained during the optimization by one or more features . We then analyzed which features depended similarly on parameter changes , as they may add superfluous degrees of freedom during parameters search . Fig 7B shows the same sensitivities as in Fig 7A , clustered by their similarities ( see Methods ) . Features clustered together if they were sensitive to similar parameter combinations and parameters clustered based on their similar influence on the features . Not surprisingly , the same tonic features measured at different level of current stimulation clustered together ( e . g . AP amplitude and half-width , AHP depth , latency of the first ISI ) and tonic firing features belonged to a cluster that was different from burst features . Some features measured in tonic mode ( such as AP half-width and AP amp . ) clustered together because they depended mainly on the dynamics of INaT and IKd: increasing the conductance of INaT increased the amplitude of the APs and decreased its duration . This was also true for the amplitude of the 1st AP in the burst . Features measured in burst mode had similar sensitivities because they depend on currents that are active at relatively hyperpolarized potential ( such IH and ICaT ) . We optimized the parameters for the adapting and non-adapting e-models in combination with two different experimental morphologies selected at random and then tested them with the other 48 morphologies . Considering that morphologies could not be classified in different m-types based on topological analysis of their dendrites and that TC neurons have been shown to be electrically compact [15] , we expected the electrical behavior to be conserved when changing morphology . Nonetheless , different neurons vary in their input resistance Rinput and rheobase current Ithr due to variation in the surface area . Variation in Rinput and Ithr made the current amplitude applied during the optimization inadequate to generate the appropriate voltage trajectories . We thus devised an algorithm to search for the holding current to obtain the target holding voltage ( for example −64 mV or −84 mV for tonic and burst firing , respectively ) and Ithr from the desired holding voltage . The different e-model/morphology combinations ( me-combinations ) were evaluated by computing the same feature errors calculated during optimization ( Fig 8A ) . For each morphology , we selected the e-model that generated the smallest maximum error . We chose the value of 3 STD as the threshold to define which me-combinations were acceptable [29] , yielding 48 acceptable me-combinations out of the 48 tested ( Fig 8A ) . All me-combinations reproduced burst and tonic firing ( Fig 8B ) . Given that the generalization of the electrical models to the other 48 morphologies worked well , we can conclude that the morphological properties of the modeled neurons are very similar , at least for properties that have an impact on the electrical models ( e . g . surface area , diameters of the compartments ) .
Our objective was to apply and extend an existing data-driven pipeline to identify the cell types and build models of VB thalamocortical neurons that reproduced the multiple firing modes that have been experimentally observed . We successfully modelled these novel firing types , by including additional stimulation protocols and features to constrain the low-threshold burst . Our morphological and electrical data were used to define the properties of VB TC neurons in the rat . We found two electrical types ( e-types ) of TC neurons , but no objectively different morphological types ( m-types ) were revealed either using Sholl analysis [21] or topological analysis of dendritic branching [20] . We cannot exclude that refinements to these methods will reveal different m-types similar to the ones described in the visual thalamus of the mouse [30] . We also showed that automatic parameter search can be applied to build biophysically and morphologically detailed models . This method was already applied to model canonical firing behavior in cortical [9 , 10 , 16 , 17] , hippocampal [31] , cerebellar granule neurons [32] and corticospinal neurons [33] . To the best of our knowledge , such an automatic parameter search has not been used previously to capture different firing modes and complex firing behavior such as low-threshold bursting in thalamic neurons . Standardized electrophysiological protocols allowed us to identify for the first time in juvenile rat adapting and non-adapting e-types of TC VB neurons that were previously observed in other species [7] . This finding suggests that the intrinsic properties of TC neurons contribute to adaptation , a key phenomenon for filtering out irrelevant stimuli , before sensory information reaches the neocortex . Further experiments are needed to elucidate the relative contribution of intrinsic mechanisms and network properties to adaptation in somatosensory systems . We named the two main e-types continuous non-adapting low-threshold bursting ( cNAD_ltb ) and continuous adapting low-threshold bursting ( cAD_ltb ) by following and extending existing conventions [16 , 22 , 31] . In this study , we improved upon previous morphologically and biophysically detailed models of tonic and burst firing in TC neurons [12 , 13 , 15] by explicitly constraining the parameters with experimental data , without hand-tuning of their values . Unlike previous models , we chose a multi-objective optimization for a methodological and a scientific reason: it is more time-efficient , reproducible , and it approximates the variability in ionic channel expression of biological neurons [31 , 34–36] , as shown by the family of acceptable solutions we found . However , experiments aimed at quantifying ion channel conductances are essential to assess if these solutions fall between biological ranges . Furthermore , we tested the generalization capability of the models and found that more than 90% of the models were comparable with the experimental data . Nonetheless , we noticed some inaccuracies when comparing the voltage traces with the experimental data when assessing the generalization of the models . For instance , some models tended to generate small transient oscillations in response to ramp stimuli in burst mode . This result is not surprising , considering that the exact kinetics for all the ionic currents are not available and that there are known limitations in models of ionic channels derived from the literature or from other models [37 , 38] . In particular , modifications to the kinetics of the low-threshold calcium current was shown to explain the propensity to generate oscillatory bursts in TC neurons of other nuclei and species [39] . More generally , we included ion channels that were used in previous models and that were validated with experimental data whenever possible . We undertook an extensive literature review to use channel kinetics derived from recordings in rat TC neurons from the ventrobasal ( VB ) thalamus or other first-order thalamic nuclei , whenever the data was available ( see Methods ) . Moreover , we cannot exclude that some ionic currents were missing from our models and that they could have improved their fitness . TC neurons have been shown to be electrically compact [15] and could , in principle , be modeled as a single compartment . However , active mechanisms need to be located in the dendrites in order to ensure synaptic integration and amplification [40] . Information regarding specific conductances or firing properties in the dendrites of TC neurons is limited . For this reason , dendritic parameters in our models may be underconstrained . However , the sensitivity analysis revealed that dendritic parameters did not appear to be the least constrained because they influenced different tonic and burst-related features . We included in the model fitting and validation pipeline a sensitivity analysis , which is often neglected in computational neuroscience [41] . Although we cannot use our simple univariate approach to explore multidimensional parameter correlations and principles of co-regulation of ion channels expression , it is useful to find better constraints for parameters optimization . The selection of the features is indeed a step that still requires care and experience by modelers . Furthermore , this type of sensitivity analysis allows to identify parameters that can be traded-off during the optimization and that can be removed in order to reduce the dimensionality of the problem . In our study , parameters related to the calcium dynamics were shown to influence the features in a very similar fashion . This type of analysis is of particular importance in future work aimed at using the full diversity of ion channels that can be inferred from gene expression data . Gene expression data could also provide additional constraints on the choice of ion channels and indicate the ones that are missing in our models . More in detail , we propose that sensitivity analysis should be a fundamental tool in selecting which conductances are successfully optimized by the available experimental constraints . The example we showed is a local approach , applied to a specific solution to the optimization problem , which showed that our models are robust to small parameter changes . This analysis can be extended to study how the sensitivities vary in the neighborhood of different solutions . In conclusion , we systematically studied the morphological and electrical properties of VB TC neurons and used these experimental data to constrain single neuron models , test their generalization capability and assess their robustness . Further work will validate these models in response to synaptic activity , in order to include them in a large-scale model of thalamocortical microcircuitry [16] .
Experimental data were collected in conformity with the Swiss Welfare Act and the Swiss National Institutional Guidelines on Animal Experimentation for the ethical use of animals . The Swiss Cantonal Veterinary Office approved the project following an ethical review by the State Committee for Animal Experimentation . All the experiments were conducted on coronal or horizontal brain slices ( 300 μm thick- ness ) from the right hemisphere of male and female juvenile ( P14-18 ) Wistar Han rats . The region of interest was identified using the Paxinos and Watson rat brain atlas [19] . After decapitation , brains were quickly dissected and sliced ( HR2 vibratome , Sigmann Elektronik , Germany ) in ice-cold standard ACSF ( in mM: NaCl 125 . 0 , KCl 2 . 50 , MgCl2 1 . 00 , NaH2PO4 1 . 25 , CaCl2 2 . 00 , D- ( + ) -Glucose 50 . 00 , NaHCO3 50 . 00; pH 7 . 40 , aerated with 95% O2 / 5% CO2 ) . Recordings of thalamocortical neurons in the VB complex were performed at 34°C in standard ACSF with an Axon Instruments Axopatch 200B Amplifier ( Molecular Devices , USA ) using 5–7 MΩ borosilicate pipettes , containing ( in mM ) : K+-gluconate 110 . 00 , KCl 10 . 00 , ATP-Mg2+ 4 . 00 , Na2-phosphocreatine 10 . 00 , GTP-Na+ 0 . 30 , HEPES 10 . 00 , biocytin 13 . 00; pH adjusted to 7 . 20 with KOH , osmolarity 270–300 mOsm . Cells were visualized using infrared differential interference contrast video microscopy ( VX55 camera , Till Photonics , Germany and BX51WI microscope , Olympus , Japan ) . Membrane potentials were sampled at 10 kHz using an ITC-18 digitizing board ( InstruTECH , USA ) controlled by custom-written software operating within IGOR Pro ( Wavemetrics , USA ) . Voltage signals were low-pass filtered ( Bessel , 10 kHz ) and corrected after acquisition for the liquid junction potential ( LJP ) of −14 mV . Only cells with a series resistance <25 MΩ were used . After reaching the whole-cell configuration , a battery of current stimuli was injected into the cells and repeated 2–4 times ( e-code ) . During the entire protocol , we defined offset currents in order to keep the cell at −50 mV ( tonic firing ) or −70 mV ( burst firing ) before LJP correction and applied them during the entire protocol . The step and ramp currents were injected with a delay of 250 ms in the experiment . In the models , the stimuli were injected with a delay of 800 ms , to allow for the decay of transients due to initialization . Each stimulus was normalized to the rheobase current of each cell , calculated on-line as the current that elicited one spike ( stimulus TestAmp , duration 1350 ms ) . The stimuli used in the experiments , for fitting and testing the models were: Neurons that were completely stained and those with high contrast were reconstructed in 3D and corrected for shrinkage as previously described [16] . Reconstruction used the Neurolucida system ( MicroBrightField ) . The location of the stained cells was defined by overlaying the stained slice and applying manually an affine transformation to the Paxinos and Watson’s rat atlas [19] . Electrical features were extracted using the Electrophys Feature Extraction Library ( eFEL ) [42] . We calculated the adaptation index ( AI ) from recordings in tonic mode ( Step 200% threshold ) and classified TC VB neurons into adapting ( AI> = 0 . 029 ) and non-adapting ( AI<0 . 029 ) electrical types . AI was calculated using the eFEL feature adaptation_index2 and corresponded to the average of the difference between two consecutive inter-spike intervals ( ISI ) normalized by their sum . The cut-off value was calculated after fitting a Gaussian mixture model to the bimodal data , using available routines for R [43 , 44] . In order to group data from different cells and generate population features , we normalized all the stimuli by the rheobase current Ithr of each cell . To calculate Ithr , we used IDRest and IDThresh and selected the minimal amplitude that evoked a single spike . Along with the voltage features , we extracted mean holding and threshold current values for all the experimental stimuli . Description of the features and the details on their calculation are available on-line [42] . Current stimuli applied during the optimization and generalization were directly obtained from the experimental values or automatically calculated by following the experimental procedures ( e . g . noise stimulus ) . Reconstructed morphologies were analyzed to objectively identify different morphological types . The Sholl profiles of each pair of cells was statistically tested by using k-samples Anderson-Darling statistics . This test was preferred to the most common Kolmogorov-Smirnov test , because it does not assume that the samples are drawn from a continuous distribution . The different Sholl profiles are indeed an analysis of the intersections with discrete spheres . To compare the topological description of each morphology we transformed the persistence barcodes into persistence images and calculated their distances as in [20] . Briefly , we converted the persistence barcode , which encodes the start and end radial distances of a branch in the neuronal tree , into a persistence diagram . In the persistence diagram , each bar of the barcode is converted into a point in a 2D space , where the X and Y coordinates are the start and end radial distances of each bar . The persistence diagram was then converted into a persistence image by applying a Gaussian kernel . We used the library NeuroM [45] to perform Sholl and morphometrics analyses . We used Hodgkin-Huxley types of ionic current models , starting from kinetics equations already available in the neuroscientific literature . Along with kinetics of the ionic currents , we stored information on the experimental conditions , such as temperature and LJP , by using the software NeuroCurator [46] . Whenever the data was available , we compared simulated voltage-clamp experiments to experimental data from juvenile rats . Ionic currents Ii were defined as functions of the membrane potential v , its maximal conductance density gi and the constant value of the reversal potential Ei: Ii=gimixhiy ( v−Ei ) mion and hion represent activation and inactivation probability ( varying between 0 and 1 ) , with integer exponents x and y . Each probability varied according to: n′ ( v ) = ( n∞ ( v ) −n ) /τn ( v ) where n∞ ( v ) is a function of voltage that represents the steady-state activation/inactivation function ( normally fitted with a Boltzmann curve ) and τn ( v ) is a voltage-dependent time constant . Exceptions to this formalism are ionic currents that do not inactivate ( y = 0 ) and ionic currents with ( in ) activation processes mediated by two or more time constants . Calcium currents ( ICaT and ICaL ) were modeled according to the Goldman-Hodgkin-Katz constant field equation and had permeability values instead of conductance [47] . NEURON 7 . 5 software was used for simulation [56] . We used NEURON variable time step method for all simulations . For the sake of spatial discretization , each section was divided into segments of 40 μm length . The following global parameters were set during optimization and generalisation: initial simulation voltage ( −79 mV ) , simulation temperature ( 34°C ) , specific membrane capacitance ( 1 μF/cm2 ) , specific intracellular resistivity 100 Ωcm for all the sections , equilibrium potentials for sodium and potassium were 50 mV and −90 mV , respectively . BluePyOpt [18] with Indicator Based Evolutionary Algorithm ( IBEA ) were used to fit the models to the experimental data . Each optimization run was repeated with three different random seeds and evaluated 100 individuals for 100 generations . The evaluation of these 300 individuals for 100 generations was parallelized using the iPython ipyparallel package and took between 21 and 52 h on 48 CPU cores ( Intel Xeon 2 . 60 GHz ) on a computing cluster . Each optimization run typically resulted in tens or hundreds of unique acceptable solutions , defined as models having all feature errors below 3 STD from the experimental mean . We performed a sensitivity analysis of an optimization solution by varying one parameter value ( pm ) at a time and calculating the electrical features from the voltage traces ( y+ and y- ) . We defined the sensitivity as the ratio between the normalized feature change and the parameter change , which for smooth functions approximates a partial derivative [57 , 58] . The features changes were normalized by the optimized feature value . For small changes of parameter values , we assumed that the features depend linearly on its parameters . We could thus linearize the relationship between the features and the parameters around an optimized parameter set and calculate the derivatives . The derivatives were calculated with a central difference scheme [57] . We collected the derivatives ( sensitivities ) in the N X M Jacobian matrix , with N representing the number of features and M the number of parameters . To rank parameters and features we computed their relative importance by calculating their norms ( the square root of the summed squared values ) from the Jacobian columns and rows , respectively . To cluster parameters based on similar influences on the features and to cluster features that were similarly dependent on the parameters , we used angles between columns ( or rows ) to compute distances D between parameters ( or features ) : D=1−|cosθ| Features where thus considered similar if they depended in a similar manner on the parameters , independent of sign or magnitude . | Thalamocortical neurons are one of the main components of the thalamocortical system , which is implicated in key functions including sensory transmission and the transition between brain states . These functions are reflected at the cellular level by the ability to generate action potentials in two distinct modes , called burst and tonic firing . Biophysically-detailed computational modeling of these cells can provide a tool to understand the role of these neurons within thalamocortical circuitry . We started by collecting single cell experimental data by applying standardized experimental procedures in brain slices of the rat . Prior work has demonstrated that biological constraints can be integrated using multi-objective optimization to build biologically realistic models of neurons . Here , we employed similar techniques , but extended them to capture the multiple firing modes of thalamic neurons . We compared the model results with additional experimental data , test their generalization and quantitatively reject those that deviated significantly from the experimental variability . These models can be readily integrated in a data-driven pipeline to reconstruct and simulate circuit activity in the thalamocortical system . | [
"Abstract",
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] | [
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"biophysics"... | 2019 | Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons |
Male breast cancer accounts for approximately 1% of all breast cancer . To date , risk factors for male breast cancer are poorly defined , but certain risk factors and genetic features appear common to both male and female breast cancer . Genome-wide association studies ( GWAS ) have recently identified common single nucleotide polymorphisms ( SNPs ) that influence female breast cancer risk; 12 of these have been independently replicated . To examine if these variants contribute to male breast cancer risk , we genotyped 433 male breast cancer cases and 1 , 569 controls . Five SNPs showed a statistically significant association with male breast cancer: rs13387042 ( 2q35 ) ( odds ratio ( OR ) = 1 . 30 , p = 7 . 98×10−4 ) , rs10941679 ( 5p12 ) ( OR = 1 . 26 , p = 0 . 007 ) , rs9383938 ( 6q25 . 1 ) ( OR = 1 . 39 , p = 0 . 004 ) , rs2981579 ( FGFR2 ) ( OR = 1 . 18 , p = 0 . 03 ) , and rs3803662 ( TOX3 ) ( OR = 1 . 48 , p = 4 . 04×10−6 ) . Comparing the ORs for male breast cancer with the published ORs for female breast cancer , three SNPs—rs13387042 ( 2q35 ) , rs3803662 ( TOX3 ) , and rs6504950 ( COX11 ) —showed significant differences in ORs ( p<0 . 05 ) between sexes . Breast cancer is a heterogeneous disease; the relative risks associated with loci identified to date show subtype and , based on these data , gender specificity . Additional studies of well-defined patient subgroups could provide further insight into the biological basis of breast cancer development .
Breast cancer does not exclusively affect females . Around 300 men in the UK and 1 , 900 men in the US are diagnosed with the disease each year [1] . The average age at incidence of male breast cancer is somewhat different to that seen for female breast cancer , with the disease typically affecting men 5–10 years later than women . Perhaps because male breast cancer is not common , few risk factors have been demonstrated to influence disease risk , but tentative associations with obesity , lack of exercise , excess alcohol consumption , gynaecomastia , past benign breast disease , past liver disease , infertility , diabetes and exposure to ionising radiation have been suggested [2] , [3] . Investigation of susceptibility genes for male breast cancer has been limited . It has however been shown that approximately 10% of men with breast cancer carry BRCA2 mutations , while mutations in BRCA1 are exceedingly rare [4] . The relative risk of breast cancer in men associated with BRCA2 mutations is high [5] . Recently the CHEK2 1100delC variant has been found to give a 10-fold risk of male breast cancer independent of BRCA1 or BRCA2 [6] . Mutations in these genes are rare in the general population and it is likely that much of the genetic contribution to female breast cancer risk can be attributed to the co-inheritance of multiple low risk common variants [7] . Recent genome-wide association studies ( GWAS ) have shown associations between single nucleotide polymorphisms ( SNPs ) mapping to a dozen or more loci and female breast cancer risk in European populations , each conferring odds ratios ( ORs ) of 1 . 04–1 . 43 [8]–[14] . To explore the possibility that the same risk variants influence male breast cancer risk , we conducted a case-control study of male breast cancer , genotyping 12 SNPs annotating the loci that have the strongest and most consistent associations with female breast cancer .
457 cases of male breast cancer were recruited in a population-based case-control study of the genetic , environmental and behavioral causes of male breast cancer being conducted in England and Wales . Potential cases were all men resident in these countries aged 18–79 with newly diagnosed breast cancer since January 1st , 2005 , identified through notifications by treatment centres and systematic regular listings of cases from regional cancer registries . 98% of cases for whom registry data has been received have been histologically confirmed . The median age at diagnosis of cases was 65 . 5 years ( interquartile range: 59–72 ) . A total of 1608 unmatched controls were available for genotyping; 535 men were ascertained through our ongoing breast cancer studies and a further 1073 were healthy male and female individuals from the UK Genetic Lung Cancer Predisposition Study ( GELCAPS ) [15] . The decision to include a second control set was made a priori , with the aim of increasing statistical power . We saw no evidence for an effect of control group on the overall effect estimate for each SNP . Collection of blood samples from all subjects was undertaken with informed consent and relevant ethical review committee approval . DNA was extracted from venous blood samples using conventional methodologies and quantified by Picogreen ( Invitrogen , Carlsbad CA ) . SNPs were chosen for analysis on the basis of validated associations with female breast cancer from recent GWAS [8]–[14] . Genotyping of rs11249433 , rs13387042 , rs4973768 , rs10941679 , rs16886165 , rs9383938 , rs13281615 , rs865686 , rs2981579 , rs3817198 , rs3803662 and rs6504950 was performed by allele-specific PCR using KASPar chemistry ( Kbioscience , Hertfordshire , UK ) . Each DNA plate contained 5% sample duplication to assess genotyping concordance between duplicate pairs . We attempted to genotype 2119 samples ( including duplicates , n = 54 ) and excluded samples ( n = 49; 11 cases , 34 controls and four members of a duplicate pair ) in which no-calls were observed for two or more SNPs . Genotyping QC statistics were therefore computed on 2070 samples ( Figure S1 ) . Final locus and sample completion rates were >99 . 9% . The mean genotype concordance between duplicate pairs was 99 . 8% . We excluded a further 18 subjects due to self-reported non-European ancestry ( 13 cases and 5 controls ) . No SNP genotypes showed significant deviation from the proportions expected under Hardy-Weinberg equilibrium in controls ( Table S1 ) . ORs and 95% confidence intervals ( CI ) were calculated using unconditional logistic regression . The odds ratio for each SNP was determined by fitting multiplicative and unconstrained genetic models . P-values were computed from likelihood ratio test statistics . Case-only unconditional logistic regression was used to test the significance of association with age at diagnosis . Deviation of genotype proportions from Hardy-Weinberg equilibrium was assessed in controls using an exact test [16] . To compare formally the ORs in males with the equivalent published ORs for female disease , we assumed both sets of ORs were log-normally distributed . Then under the null hypothesis that the OR in males is equal to the OR in females , the difference between the estimated log ORs is normally distributed with mean zero and variance equal to the sum of the squared standard errors of the two estimates . From this we obtained a χ2 statistic for each comparison ( 1 degree of freedom [d . f . ] ) and from the sum of the χ2 statistics a global test for all comparisons ( 12 d . f . ) . Statistical analyses were performed using the Genotype Libraries and Utilities ( GLU ) package ( http://code . google . com/p/glu-genetics ) and R [17] .
433 male breast cancer cases and 1569 controls were successfully genotyped according to our predefined QC criteria . The majority of cases were diagnosed with invasive breast cancer ( n = 399 ( 92% ) ) while a further 31 ( 7% ) were ductal carcinoma in situ . Three cases ( <1% ) were of unknown histology . Table 1 shows the OR for male breast cancer associated with each of the 12 SNPs previously reported to be associated with female breast cancer risk . For five SNPs , rs13387042 ( 2q35 ) , rs10941679 ( 5p12 ) , rs9383938 ( 6q25 . 1 ) , rs2981579 ( FGFR2 ) and rs3803662 ( TOX3 ) , the risk allele for female breast cancer was associated with increased risk of male breast cancer ( p<0 . 05 ) . Two SNPs , rs13387042 ( 2q35 ) and rs3803662 ( TOX3 ) , remained significant below the Bonferroni adjusted threshold for independent tests of p<4 . 12×10−3 . Comparing ORmale estimates with those for female breast cancer ( ORfemale ) there were two SNPs , rs13387042 ( 2q35 ) and rs3803882 ( TOX3 ) for which the ORmale was significantly higher than the ORfemale , albeit not after adjusting for multiple testing ( rs13387042 , ORmale:ORfemale p = 0 . 03; rs3803882 , ORmale:ORfemale p = 0 . 04; Table 2 ) . rs3803662 ( TOX3 ) showed the strongest association with male breast cancer ( ORmale = 1 . 48; 95% CI 1 . 26–1 . 75 , p = 4 . 04×10−6 ) with an excess relative risk that was more than twice the female estimate ( ORfemale = 1 . 20; 95% CI 1 . 16–1 . 24 ) [9] . Similarly , the excess risk conferred by rs13387042 ( 2q35 ) in males ( ORmale = 1 . 30; 95% CI 1 . 11–1 . 51 , p = 7 . 98×10−4 ) was more than double that observed in females ( ORfemale = 1 . 12; 95% CI 1 . 09–1 . 15 ) [11] . For one SNP ( rs6504950 , COX11 ) the ORmale was in the opposite direction to that reported for female breast cancer ( ORmale = 0 . 90; 95% CI 0 . 76–1 . 06 , ORfemale = 1 . 05; 95% CI 1 . 03–1 . 07 ) [8] , [9] and was inconsistent with the female estimate ( ORmale:ORfemale p = 0 . 04; Table 2 ) . For the other nine SNPs that we tested the ORmale estimates were consistent with the ORfemale estimates . Comparing the combined estimates of all 12 SNPs , however , there was nominal evidence that the male ORs differed from the female ORs ( p = 0 . 03; Table 2 ) . The frequency of female breast tumors that are estrogen receptor ( ER ) positive varies , particularly according to menopausal status at diagnosis [18] . Based on a sample of almost 3 , 000 patients the proportion is typically between 64% and 79% [18] . In contrast , male breast tumors , tend to be overwhelmingly ER-positive ( >90% ) [19] . In the current study estrogen receptor status was known for 251 male breast cancer cases , 246 ( 98% ) of which had ER-positive tumors . For nine of the 12 SNPs that we genotyped , ORfemale estimates stratified according to ER status have been reported for Caucasian populations ( Tables S2a and S2b ) . In females , the OR for ER-positive disease is stronger than the OR for ER-negative disease for all nine of these loci and this difference is significant for all but two of them ( rs16886165 ( MAP3KI ) and rs3817198 ( LSP1 ) [8] , [11] , [20] . Given the predominance of ER-positive tumors in male disease we also compared the ORmale with the ORfemale for ER-positive disease ( Table S2a ) . There was nominally significant evidence overall that the male ORs differed from those for ER-positive female disease ( p = 0 . 05 ) . We also tested for a difference between the ORmale estimates for these nine SNPs and the ORfemale estimates for ER-negative disease ( Table S2b ) ; there was stronger evidence of a difference ( p = 0 . 01 ) . Finally , we assessed the relationship between genotype and age at onset of male breast cancer ( Table S3 ) for each of the 12 loci . There was no evidence for a trend with age at diagnosis . We have shown , for the first time that common genetic variants influence susceptibility to male breast cancer . Furthermore we have demonstrated that for at least a subset of known susceptibility loci the risk allele for female breast cancer is also associated with increased risk of disease in males . To our knowledge these 433 male breast cancer cases represent the largest single series to date; despite this , we lacked power to detect modest relative risks for all but the most common variants . For example we had only 40% power to detect an OR of 1 . 15 for a variant with a minor allele frequency of 30% at a significance level of 5% . The lack of a statistically significant association with male breast cancer risk for seven of the 12 SNPs that we tested may , therefore , simply reflect a lack of power . Notably , for two of the three SNPs for which the ORmale was inconsistent with the ORfemale ( rs13387042 ( 2q35 ) and rs3803882 ( TOX3 ) ) the association in males was stronger than that in females . While the ORmale estimates were slightly closer to the ORs for ER-positive disease in females , it is noticeable that these are the two SNPs that show the largest effects on ER-negative disease risk in females . Although the significance of this observation , if any , is not yet clear , our data on male breast cancer alongside the published associations with female breast cancer , clearly implicate the 2q35 and 16q12 . 1 loci in the aetiology of breast cancer , irrespective of gender and tumor pathology . Given that the majority of female breast cancer risk loci identified to date demonstrate a degree of specificity for ER-positive or ER-negative disease [8] , [11] , [20] , [21] it seems likely that subtype specific GWAS will lead to the identification of additional risk loci . Our analyses suggest that GWAS of male breast cancer may also lead to the identification of novel breast cancer risk loci in males and that these should provide further insight into the biological basis of male and female breast cancer development . | Breast cancer is the most common female cancer in the United Kingdom but also occurs in men , albeit at a much lower frequency . Relatively little is known regarding risk factors for male breast cancer . Here , we examine the effect of common genetic variants that are known to be associated with female breast cancer to determine whether they also affect risk of male breast cancer . We show that certain of these variants are also associated with male breast cancer risk but that the magnitudes of their effects differ in males from females . Future analyses of the genetics of male breast cancer may shed light on the biology of both male and female breast cancer . | [
"Abstract",
"Introduction",
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] | [
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] | 2011 | Genetic Variants at Chromosomes 2q35, 5p12, 6q25.1, 10q26.13, and 16q12.1 Influence the Risk of Breast Cancer in Men |
Porphyromonas gingivalis is a major pathogen in severe and chronic manifestations of periodontal disease , which is one of the most common infections of humans . A central feature of P . gingivalis pathogenicity is dysregulation of innate immunity at the gingival epithelial interface , including suppression of IL-8 production by epithelial cells . NF-κB is a transcriptional regulator that controls important aspects of innate immune responses , and NF-κB RelA/p65 homodimers regulate transcription of IL8 . Phosphorylation of the NF-κB p65 subunit protein on the serine 536 residue affects nuclear translocation and transcription of target genes . Here we show that SerB , a haloacid dehalogenase ( HAD ) family serine phosphatase secreted by P . gingivalis , is produced intracellularly and can specifically dephosphorylate S536 of p65 in gingival epithelial cells . A P . gingivalis mutant lacking SerB was impaired in dephosphorylation of p65 S536 , and ectopically expressed SerB bound to p65 and co-localized with p65 in the cytoplasm . Ectopic expression of SerB also resulted in dephosphorylation of p65 with reduced nuclear translocation in TNF-α-stimulated epithelial cells . In contrast , the p105/50 subunit of NF-κB was unaffected by SerB . Co-expression of a constitutively active p65 mutant ( S536D ) relieved inhibition of nuclear translocation . Both the activity of the IL8 promoter and production of IL-8 were diminished by SerB . Deletion and truncation mutants of SerB lacking the HAD-family enzyme motifs of SerB were unable to dephosphorylate p65 , inhibit nuclear translocation or abrogate IL8 transcription . Specific dephosphorylation of NF-κB p65 S536 by SerB , and consequent inhibition of nuclear translocation , provides the molecular basis for a bacterial strategy to manipulate host inflammatory pathways and repress innate immunity at mucosal surfaces .
Many of the mucosal surfaces of humans are colonized by a diverse and abundant microbiota . In most instances the host remains healthy , in large part due to numerous innate and acquired immune mechanisms that limit microbial intrusion and rapidly kill organisms that traverse epithelial barriers . In the periodontal tissues of the oral cavity the epithelium of the subgingival compartment plays a central role in orchestration of innate immunity . While this tissue is relatively porous , gingival epithelial cells secrete high levels of IL-8 and consequently large numbers of neutrophils are recruited into the periodontal area where they serve to constrain the microbial challenge [1] . Successful periodontal pathogens , such as Porphyromonas gingivalis , are capable of disrupting innate defenses , and indeed one virulence determinant of P . gingivalis is inhibition of IL-8 production by gingival epithelial cells , a strategy known as localized chemokine paralysis [2] , [3] . Moreover , periodontal diseases are multispecies infections involving pathogenic communities in which the microbial constituents exhibit polymicrobial synergy . Consistent with this , P . gingivalis can antagonize IL-8 secretion in the presence of stimulatory organisms [2] , a property that will allow P . gingivalis to enhance the pathogenicity of the entire multispecies periodontal community and which contributes to its designation as a keystone pathogen [4] . The P . gingivalis serine phosphatase SerB is required for IL-8 suppression , and in a murine model of disease a mutant lacking SerB induces higher levels of neutrophil recruitment into gingival tissues compared to the parental strain [5] . Additionally , loss of SerB attenuates alveolar bone destruction in animal infection models demonstrating that SerB , and its associated anti-inflammatory action , is required for P . gingivalis to realize its full pathogenic potential [5] . The mechanistic basis for the SerB-dependent inhibition of IL-8 remains undetermined . P . gingivalis is an intracellular pathogen and epithelial cell entry is accomplished by a very limited number of bacterial effectors . The major fimbriae mediate attachment to integrin receptors and this leads to remodeling of the host cell cytoskeleton . SerB is secreted by P . gingivalis and facilitates invasion through dephosphorylation and activation of the host actin depolymerizing protein cofilin [6] , [7] , [8] . SerB is a haloacid dehalogenase ( HAD ) family enzyme that is functionally versatile and can impact the dynamics of both the host microfilament and the microtubule cytoskeleton [8] , [9]; however , the complete range of host cell serine phosphoproteins that can be dephosphorylated by the enzyme is unknown . At the transcriptional level , the predominant control over IL8 is exerted by the eukaryotic transcription factor NF-κB [10] . NF-κB can be comprised of homo- or hetero-dimers containing combinations of the subunits RelA/p65 , c-Rel , RelB , p100 , p105 and p50 , each of which contains a Rel homology domain that mediates subunit binding [10] . The non-Rel NF-κB subunit , RPS3 , guides NF-κB to specific κB sites on the chromosome and contributes to regulatory specificity [11] . In unstimulated cells , NF-κB is generally sequestered in the cytosol and bound to inhibitory IκB proteins [12] . In the canonical pathway stimuli , including bacterial products and proinflammatory cytokines , activate IκB kinases ( IKKα and β , along with the regulatory subunit IKKγ ) that phosphorylate IκB and induce ubiquitination and proteosomal degradation of IκB [13] , [14] . The free homo- or hetero-dimer complex then rapidly enters the nucleus and initiates transcription of downstream effector genes [15] . Noncanonical activation pathways also exist in which there is no sequestration of NF-κB by IκB proteins , and in one example active p52-RelB complexes can be generated by IKKα-mediated phosphorylation of p100 [16] . Many considerations contribute to the transcriptional activation of NF-κB target genes , including the nature of the homo- or hetero-dimers , the involvement of basal transcription factors and coactivators , and histone modifications in the promoter region [10] . In addition , posttranslational modifications of NF-κB subunits , including phosphorylation , acetylation and methylation , have been shown to contribute to regulation of transcriptional activity [17] . The p65 subunit , for example , can be phosphorylated on serine ( S ) 276 in the Rel homology domain as well as on S468 and S536 in the C-terminal transcriptional activation domain , in response to a variety of stimuli [17] . Phosphorylation on these sites can have different functional consequences depending on the nature of the stimulus , the kinase involved and the target gene . S536 can be phosphorylated by IKKs in response to TNF-α , LPS , Helicobacter pylori or HTLV1 TAX protein , resulting in elevated function [17] . The enhanced transcriptional activity following p65 S536 phosphorylation may ensue from a conformational change affecting binding to other subunits or decreased affinity for the IκBα inhibitor [18] , [19] . S536 phosphorylation can also impact the kinetics of nuclear import of NF-κB p65 and cytoplasmic retention of IκBα [20] . Furthermore , there is evidence that S536 phosphorylation represents a noncanonical activation pathway whereby phosphorylated p65 can translocate to the nucleus independent of IκBα regulation [21] . In this study we found that SerB-mediated inhibition of IL-8 production by epithelial cells involved suppression of NF-κB activation . SerB was capable of binding to the p65 subunit of NF-κB and dephosphorylated p65 at S536 through the action of the HAD-family enzyme motifs . SerB thus prevented nuclear translocation of NF-κB p65 and subsequent transcription of the IL8 gene . This work provides insights into both the immune-subversive processes of P . gingivalis , and the development of polymicrobial synergy in pathogenic communities .
Previous reports have established that P . gingivalis can inhibit the accumulation of IL-8 in the supernatants of gingival epithelial cells [2] . The secreted serine phosphatase SerB is required for maximal inhibition of IL-8 production [9] , and to explore whether regulation of IL-8 by SerB occurs at the transcriptional level we transfected telomerase immortalized gingival keratinocytes ( TIGKs ) with the reporter plasmid pIL-8κB-Luc in which the luciferase gene is controlled by the NF-κB-regulated IL8 promoter . Transfected cells were then infected with P . gingivalis ATCC 33277 wild type ( WT ) or its isogenic ΔserB mutant at MOI 10 for 16 h , stimulated with tumor necrosis factor ( TNF-α ) and assayed for luciferase activity at 3 h after stimulation ( Figure 1A ) . IL8 promoter activity was increased 5-fold by TNF-α stimulation , and infection with P . gingivalis WT significantly reduced IL8 promoter activity to 3 . 6-fold over unstimulated basal levels . In contrast , the ΔserB mutant was unable to antagonize TNF-α stimulated IL8 promoter activity . These results suggest that the ability of SerB to diminish IL-8 secretion by epithelial cells involves inhibition of NF-κB dependent transcription of the IL8 gene . Recent reports have documented that phosphorylation of NF-κB p65 at the S536 residue in Jurkat cells induces IL8 transcription following stimulation by ionomycin [21] . We hypothesized therefore that p65 S536 might be a target for dephosphorylation by P . gingivalis SerB . Immunoblots ( Figure 1B ) with scanning densitometry ( Figure 1C ) showed that after 10 min of TNF-α stimulation the levels of p65 S536 phosphorylation in TIGKs were reduced over 4-fold by P . gingivalis WT . The ΔserB mutant had a diminished capacity to reduce the phosphorylation of the NF-κB p65 S536 residue . P . gingivalis expressing the SerB enzyme can thus induce dephosphorylation of p65 S536 . P . gingivalis is efficiently internalized by gingival epithelial cells [22] , [23] , and intracellular invasion is required for NF-κB suppression [2] . To establish that SerB is produced and secreted by intracellular P . gingivalis , we infected TIGKs with P . gingivalis expressing SerB fused to a FLAG epitope [6] . Intracellular bacteria were detected with DAPI , to avoid antibody reaction with extracellular antigens . The detection of infected bacteria with DAPI was found to be as efficient and specific as with P . gingivalis antibodies ( Figure S1 ) . As shown in Figures 1D and 1E , confocal microscopy with FLAG antibodies detected SerB within the cytoplasm of TIGKs . Additionally , fractionation of P . gingivalis infected TIGK cells showed that SerB could be detected in both the membrane and cytosol compartments ( Figure S2 ) . Hence , SerB is accessible for direct interactions with host cytosolic phosphoproteins following P . gingivalis infection . To investigate the function of SerB independent of other P . gingivalis molecules , we constructed a mammalian expression vector bearing a fusion protein , Myc-SerB , which was transiently expressed in TIGK cells . Using the cross-linking agent DSP to stabilize enzyme-substrate interactions , we found that Myc-SerB , but not Myc alone ( empty vector ) , co-immunoprecipitated with endogenous NF-κB p65 ( Figures 2A and 2B ) . Co-precipitation , albeit less efficient , was also observed in the absence of cross-linking reagent ( Figure S3 ) . These results indicate that SerB can interact directly with p65 or a p65 complex , but do not exclude the possibility that other P . gingivalis molecules can also interact with NF-κB components . To further examine the interaction of SerB with NF-κB p65 , GFP tagged NF-κB p65 was generated and introduced into TIGK cells along with Myc-SerB . We then visualized co-localization of Myc-SerB and GFP-NF-κB p65 in TIGKs . Figures 2C and 2D show that when Myc-SerB and GFP-NF-κB p65 were ectopically expressed in TIGKs , Myc-SerB co-localized with GFP-NF-κB p65 in the cytoplasmic area . We then confirmed that the phosphorylation status of NF-κB p65 is necessary for SerB binding by constructing a constitutively-active phosphomimic p65 S536D [21] . TIGKs were transfected with GFP-NF-κB p65 or GFP-NF-κB p65 S536D , along with Myc-SerB , and left unstimulated to maintain the phosphorylation of wild type p65 at basal levels . Immunoprecipitation with Myc antibodies revealed that Myc-SerB co-immunoprecipitated with GFP-NF-κB p65 S536D whereas co-precipitation of Myc-SerB with wild type , unphosphorylated NF-κB p65 was below detection thresholds ( Figures 2E and 2F ) . This finding demonstrates that the phosphorylation status of NF-κB p65 S536 is significant for the binding of SerB to NF-κB p65 . Next we investigated whether binding of SerB to p65 effectuates dephosphorylation of S536 . Myc-SerB was ectopically expressed in TIGKs and after stimulation with TNF-α , the kinetics of NF-κB p65 phosphorylation were examined by immunoblot analysis with quantitative densitometry . As shown in Figures 3A and 3B , TIGKs expressing Myc-SerB displayed a lower level of phosphorylated NF-κB p65 from 5–30 min after TNF-α stimulation compared to cells transfected with empty vector . After 1 h , levels of phospho-p65 declined in cells expressing both Myc-SerB and Myc alone as a result of the transient effect of TNF-α . These findings indicate that SerB can dephosphorylate p65 at the S536 residue . To address the specificity of SerB action , we investigated whether the enzyme can also dephosphorylate the S276 and S468 residues . SerB did not significantly decrease the level of phospho-NF-κB p65 S276 or S468 induced by TNF-α stimulation ( Figures S4 and S5 ) . In addition , the effect of SerB on phosphorylation of the NF-κB p105 subunit at S933 was assessed . Similarly , SerB did not dephosphorylate the NF-κB p105 S933 residue ( Figures 3C and 3D ) . Phosphorylation of NF-κB p105 leads to processing into the p50 subunit [24] , [25] and hence we also confirmed that the amount of NF-κB p50 was unchanged in TNF-α stimulated TIGKs expressing Myc-SerB compared to the empty vector control ( Figures 3C and 3E ) . Thus , SerB does not impact the phosphorylation status of NF-κB p65 S276 , p65 S468 , or p105 S933 , and does not affect processing of phospho-NF-κB p105 into NF-κB p50 . The action of SerB on NF-κB thus appears to be specific for p65 S536 . The phosphorylation status of NF-κB p65 Ser 536 has been shown to control the kinetics of NF-κB p65 nuclear import in Jurkat cells [20] . Based on this observation , we postulated that SerB can block the translocation of NF-κB p65 to the nucleus in stimulated cells . To test this idea , we transfected TIGKs with either GFP-tagged NF-κB p65 or GFP- tagged phosphomimic NF-κB p65 S536D . Cells were co-transfected with Myc-SerB and the location of GFP-NF-κB p65 determined by confocal microscopy . Upon stimulation with TNF-α , 68% of counted control cells were positive for GFP-NF-κB p65 in the nucleus , confirming nuclear translocation of the NF-κB p65 subunit ( Figures 4A and 4B ) . However , in stimulated TIGKs expressing Myc-SerB and GFP-NF-κB p65 fusions , only 26% of counted cells were positive for GFP-NF-κB p65 in the nucleus . In cells expressing Myc-SerB and GFP-NF-κB p65 S536D , the number of counted cells positive for nuclear NF-κB p65 was similar to the level in cells without SerB , demonstrating that translocation of the constitutively active p65 subunit is unaffected by SerB . These results suggest that dephosphorylation of S536 by SerB inhibits nuclear translocation of NF-κB p65 . As controls , we examined nuclear translocation of GFP-NF-κB p50 or GFP-NF-κB p105 fusions in TIGKs co-transfected with Myc-SerB . The presence of SerB did not affect the nuclear translocation of p50 or p105 ( Figures 4C and 4D ) , further evidence that the action of SerB is specific for the p65 subunit . It is reported that phospho-NF-κB p65 does not associate with NF-κB p50 , and that NF-κB p50-independent NF-κB p65 can be recruited to the IL8 promoter following stimulation by ionomycin in Jurkat cells [21] . We hypothesized , therefore , that dephosphorylation of NF-κB p65 by SerB would be sufficient to prevent transcription of the IL8 gene . To test this possibility , Myc-SerB and the luciferase IL8 reporter plasmid , pIL8κB-Luc , were co-expressed in TIGKs . In response to TNF-α , luciferase activity of cells expressing Myc-SerB was significantly reduced compared to cells transfected with empty vector ( Figure 5A ) . We then examined the ability of excess exogenous p65 to partially relieve suppression of the IL8 reporter by introducing GFP-NF-κB subunits into the cells containing the reporter plasmid . Additional co-transfection of GFP-p65 with Myc-SerB increased promoter activity from 0 . 76- to 55-fold of the control level compared with cells expressing Myc-SerB and GFP at 3 h after stimulation by TNF-α ( Figure 5A ) . In contrast , exogenous p50 subunit did not relieve SerB-mediated suppression of the IL8 promoter ( Figure 5A ) and indeed decreased luciferase activity indicating that overexpression of p50 subunits can interfere with the formation of p65 homodimers . Levels of TNF-α stimulated IL8 promoter activity in the presence of GFP-p65 and Myc-SerB were significantly lower than those obtained with GFP-p65 and Myc alone ( Figure S6 ) , consistent with the data in Figure 4 . To confirm the relevance of the interaction between SerB and NF-κB p65 for IL-8 production , IL-8 secreted into TIGK culture media was measured by ELISA . Two hours after TNF-α stimulation , IL-8 production was decreased by 74% in cells expressing Myc-SerB , and at 4 h after stimulation the IL-8 level was reduced by 65% ( Figure 5B ) . Co-transfection with GFP-NF-κB p65 partially relieved the inhibition of TNF-α induced IL-8 secretion , whereas GFP-NF-κB p50 had no effect ( Figure 5C ) . SerB possesses 4 HAD family motifs which determine phosphatase activity [26] , along with an ACT small molecule binding domain . To assess the contribution of these domains , we performed a structure-function analysis of SerB with deletion and truncation constructs lacking either the HAD domains or the ACT domain ( Figure 6A ) . These constructs were expressed in TIGKs which were then stimulated with TNF-α to induce NF-κB p65 phosphorylation . As shown in Figures 6B and 6C , Myc-SerB 1–197 and Myc-SerB Δ198–358 , which do not possess the HAD family motifs , were incapable of inducing dephosphorylation of NF-κB p65 . In contrast , Myc-SerB 198–413 which lacks the ACT domain but not the HAD domains reduced the level of phospho-p65 to a similar level compared to full length SerB . Additionally , Myc-SerB Δ198–358 was unable to co-precipitate with NF-κB p65 ( Figure 6D ) . Next we performed a luciferase reporter assay to examine the activity of Myc-SerB Δ198–358 on the IL8 promoter using reporter plasmid pIL8κB-Luc . Compared with cells expressing Myc-SerB , luciferase activity of cells expressing Myc-SerB Δ198-358 increased from 0 . 30- to 1 . 93-fold of the control following stimulation with TNF-α ( Figure 6E ) . We also performed ELISA to quantify secreted IL-8 in supernatants of TIGKs expressing Myc-SerB Δ198-358 . IL-8 secretion was significantly increased in cells expressing Myc-SerB Δ198-358 compared to cells expressing full length Myc-SerB ( Figure 6F ) . These results illustrate the necessity of the HAD-family motifs for SerB phosphatase activity against NF-κB p65 S536 , and for the consequent effects on transcription of the IL8 gene and the secretion of IL-8 induced by TNF-α . To support the notion that the HAD-family motif is key for SerB activity , we performed immunofluorescence assays in TIGKs expressing GFP-NF-κB p65 and either of Myc-SerB or Myc-SerB Δ198–358 . At 30 min after stimulation with TNF-α , 72% of counted cells transfected with empty vector were positive for GFP-NF-κB p65 in the nucleus ( Figures 7A and 7B ) . In TIGKs expressing Myc-SerB , 30% of counted cells were positive for GFP-NF-κB p65 in the nucleus , while in cells expressing Myc-SerB Δ198–358 , 78% of counted cells were positive , indicating that loss of the HAD-family motifs prevents SerB from blocking nuclear translocation of NF-κB p65 . Collectively these results indicate that SerB binds to the NF-κB p65 subunit in TIGKs and through the action of the HAD-family motifs dephosphorylates the S536 residue . Dephosphorylation of NF-κB p65 impedes nuclear translocation and activity of NF-κB p65 on the IL8 promoter , which is the mechanism that causes abrogation of IL-8 production by P . gingivalis .
Periodontal diseases are characterized by destruction of the gingival tissues including the alveolar bone , with eventual exfoliation of the tooth . These are among the most common infections of humans , and in developed countries over half of the adult population will experience some form of periodontitis [27] . The disease initiates at the epithelial surfaces of the subgingival compartment , and gingival epithelial cells play a central role in responding to microbial infection and orchestrating immune responses [1] . Mucosal inflammatory responses involve coordinated expression of cytokines and chemokines , and the NF-κB transcriptional regulator exerts control over a number of inflammation-associated genes . P . gingivalis is a major pathogen in the initiation and progression of severe and chronic forms of periodontal disease . A feature of P . gingivalis infection is dysregulation of innate immunity [3] , including suppression of IL-8 production from gingival epithelial cells . The functionally versatile serine phosphatase SerB of P . gingivalis is required for optimal invasion of P . gingivalis into epithelial cells [6] , [8] and is also necessary for IL-8 suppression [9] . In this report we provide several lines of evidence that SerB antagonizes IL-8 production through dephosphorylation of the serine 536 residue of the RelA/p65 subunit of NF-κB . This evidence includes: infection of epithelial cells with a P . gingivalis ΔserB mutant induces less dephosphorylation of NF-κB p65 S536 and higher levels of IL8 promoter activity compared to parental P . gingivalis; ectopically expressed SerB binds to and dephosphorylates p65 in epithelial cells stimulated with TNF-α; exogenous SerB impedes nuclear translocation of p65; and SerB blocks IL8 promoter activity and secretion of IL-8 from epithelial cells . Given the role of NF-κB in directing cytokine production and innate immune responses , it is not surprising that many human pathogens have evolved sophisticated mechanisms to interfere with NF-κB activity and disrupt host immunity [28] . Bacterial disruption of NF-κB can occur at multiple points in the activation pathway . Several organisms target the IKK complex , a central node that integrates the input from a number of upstream pathways . For example , the InlC internalin of Listeria monocytogenes can bind to IKKα and block IκBα phosphorylation [29] , while the NleH1 and NleH2 proteins of EHEC impede IκB ubiquitination [30] . Specific bacterial effectors can also target TLR signaling upstream of the IKK complex [31] , and NF-κB-dependent transcription downstream of IKK [32] . While the latter mechanism is less commonly recognized , it is less likely to impact other signaling pathways that intersect with TLR signaling and with the IKK complex [33] , and thus provide a more precise redirection of host immune responses . For example , the NleH1 effector of EHEC also binds to NF-κB RPS3 and prevents phosphorylation [32] . NleH1 can thus selectively reduce the transcription of RPS3-dependent target genes , including IL8 and TNF , without attenuating promoter activity of other NF-κB-dependent genes . The ability of P . gingivalis SerB to prevent nuclear translocation of p65 is a previously unrecognized bacterial NF-κB subversion strategy involving specific dephosphorylation of a NF-κB subunit . Phosphorylation of p65 at S536 can elevate nuclear translocation and transcriptional activity of NF-κB [34] . However , the full extent of NF-κB regulation that is dependent on serine phosphorylation of p65 remains to be fully defined , and may vary according to cell type and phosphorylation mechanism [17] . Various studies have shown both inducible phosphorylation of S536 and a pool of constitutively phosphorylated p65 [21] , [35] . Our data show that in gingival epithelial cells , S536 phosphorylation is induced by TNF-α and that nuclear translocation of p65 is enhanced by phosphorylation . Immunoblot analyses did not reveal a pool of phospho-p65 in the absence of stimulation , similar to the situation in intestinal epithelial cells [36] . Among the pathogenic constituents of the periodontal polymicrobial community , P . gingivalis has a unique ability to selectively antagonize IL-8 production in epithelial cells , even in the context of co-infection with other stimulatory bacteria [2] , [9] . Conversely , P . gingivalis can induce the secretion of other proinflammatory cytokines [37] . These properties are consistent with a specific action of P . gingivalis on NF-κB p65 . It has been reported that the IL8 promoter is regulated by p65 homodimers , and that neither p105/p50 homodimers nor p105/p50 - p65 heterodimers , can activate transcription from the IL8 promoter [38] . In addition , in this study we found that overexpression of NF-κB p65 , but not p50 , induces the secretion of IL-8 ( Figure 5C ) . Crystallization studies show that NF-κB p65 homodimers bind to a pseudosymmetric sequence in the enhancer region of the IL8 gene [39] . Moreover , phospho-p65 can be selectively recruited to the NF-κB site on the IL8 promoter independent of p50 [21] . Hence , dephosphorylation of p65 and inhibition of nuclear translocation will allow P . gingivalis to selectively manipulate different aspects of innate immunity . NF-κB also controls expression of a number of host genes that govern cell proliferation and programmed cell death [10] , and thus broadly based inhibition of NF-κB would be predicted to elevate apoptotic cell death . P . gingivalis , however , suppresses apoptotic cell death in epithelial cells and accelerates progression through the cell cycle [40] . Collectively these results point toward an exquisite specificity of the interaction between P . gingivalis and NF-κB , allowing process-specific manipulation . P . gingivalis is an intracellular organism which lacks the machinery and effector molecules of the type III secretion system that are utilized by many invasive pathogens [41] . Rather , P . gingivalis relies on a more limited number of multifunctional effectors , such as the HAD family SerB , to direct entry into host epithelial cells [8] . While the SerB deficient mutant of P . gingivalis has a diminished capacity to invade and survive within epithelial cells [8] , the ability of ectopically expressed SerB to dephosphorylate p65 establishes that abrogated p65 dephosphorylation by the SerB mutant is not a reflection of lower levels of intracellular bacteria , but due to the absence of the enzyme-substrate reaction . Furthermore , although SerB can dephosphorylate a variety of host cell proteins , such as cofilin [6] , its impact on the NF-κB pathway appears to be restricted to the p65 subunit . The effect of SerB on p65 phosphorylation , nuclear translocation and transcription of the IL8 gene all require the HAD enzyme domains . Eukaryotic HAD family enzymes have been shown to regulate innate immunity by modulating the phosphorylation state of signal transducers involved in host responses to viral infection [42]; however , this is the first example of a bacterial HAD family phosphatase that can divert innate immunity through dephosphorylation of host signaling molecules . The ability of P . gingivalis SerB to specifically target IL8 transcription through dephosphorylation of p65 provides a molecular basis to the localized chemokine paralysis induced by P . gingivalis at mucosal surfaces . Many bacteria possess SerB homologs which are generally annotated as metabolic enzymes . It will be interesting to discover whether secretion of this enzyme and activity against NF-κB are features of pathogens , particularly keystone pathogens in mixed communities .
Wild-type ( WT ) P . gingivalis ATCC 33277 , isogenic ΔserB and serB::FLAG were cultured anaerobically as described previously [6] , [8] . TIGKs , telomerase immortalized gingival epithelial cells were generated by immortalization of primary gingival epithelial cells [22] with bmi1/hTERT [43] , and were maintained in Keratinocyte-SFM ( Invitrogen ) supplemented with 5 ng/ml Human Recombinant Epidermal Growth Factor and 50 µg/ml Bovine Pituitary Extract . Mouse monoclonal anti-Myc and mouse monoclonal anti-β-actin were from Sigma-Aldrich; mouse monoclonal anti-NF-κB p65 , rabbit monoclonal anti-phospho-NF-κB p65 S536 , rabbit monoclonal anti-NF-κB p65 S468 , rabbit monoclonal anti-NF-κB p105/p50 , rabbit monoclonal anti-phospho-NF-κB p105 S933 , and rabbit monoclonal anti-calnexin were from Cell Signaling Technology; rabbit polyclonal anti-NF-κB p65 S276 was from Abcam; rabbit monoclonal anti-β-tubulin was from Imgenex; and mouse monoclonal anti-FLAG was from Invitrogen . P . gingivalis whole cell antibody and SerB antibodies have been described previously [6] , [9] . Rhodamine Red-X-conjugated secondary antibody ( goat anti-mouse IgG ) and Alexa Fluor 488-conjugated secondary antibody ( goat anti-rabbit IgG ) from Invitrogen were used for fluorescent microscopy . Horseradish peroxidase ( HRP ) -conjugated secondary antibodies ( goat anti-mouse IgG and goat anti-rabbit IgG , Cell Signaling Technology ) were used for immunoblotting . FITC-conjugated phalloidin ( Sigma-Aldrich ) was used to stain actin for fluorescent microscopy . TNF-α ( PeproTech ) was used for cell stimulation . A chimeric construct of Myc-SerB was constructed by cloning PCR amplified serB from P . gingivalis into pCMV-Myc ( Clontech ) using exogenously added EcoRI sites . Myc-tagged SerB mutants consisting of amino acids 1–197 , 198–413 and a deletion of amino acids 198–358 were produced using PCR and inserted into pCMV-Myc using exogenously added EcoRI sites . GFP-tagged NF-κB p65 was produced from TIGK cDNA and inserted into pAcGFP1-C1 ( Clontech ) using exogenously added KpnI sites . A point mutation of GFP-tagged NF-κB p65 to S536D was introduced by overlapping fusion PCR . GFP-tagged NF-κB p105 was produced from TIGK cDNA and inserted into pAcGFP1-C1 using exogenously added EcoRI sites . GFP-tagged NF-κB p50 was produced from amino acids 1 to 438 of NF-κB p105 and cloned into pAcGFP1-C1 according to the published sequence [44] . The pIL-8 κB-Luc reporter plasmid was designed according to the published sequence [38] . The enhancer sequence of IL8 was cloned into pGL4 . 23 [luc2/minP] ( Promega ) using exogenously added KpnI and NheI sites . Primer sequences are listed in Table S1 . All PCR products and mutations were confirmed by sequencing . Transient transfection of TIGK cells was performed using FuGENE 6 Transfection Reagent ( Promega ) . TIGKs were fixed with 3% paraformaldehyde in PBS for 30 min at room temperature , permeabilized with 0 . 1% Triton X-100 in PBS for 5 min at room temperature and blocked with 0 . 1% gelatin in PBS for 20 min at room temperature . Primary antibodies were diluted 1∶400 in PBS , and Rhodamine Red-X-conjugated secondary antibodies were diluted 1∶400 in PBS . Antibody incubations were for 1 h at room temperature , followed by six washes in PBS . Cells were mounted onto glass slides using Vectashield Mounting Medium with DAPI ( Vector laboratories ) to label the bacterial and cellular DNA , and examined using laser scanning confocal microscopy ( FV1000; Olympus ) . Images acquired and analyzed using FluoView software ( Olympus ) . TIGKs were transiently transfected for 36 h and stimulated with TNF-α for 30 min ( except where indicated ) . GFP and Myc tags were detected by confocal microscopy . At least 90 GFP/Myc positive cells were counted in each condition . TIGKs were washed with PBS and suspended in homogenization buffer ( 3 mM imidazole [pH 7 . 4] , 250 mM sucrose , 0 . 5 mM EDTA ) . Cells were mechanically disrupted by vigorous passage through 23- and 27-gauge needles 8 times on ice . The sample was centrifuged at 3 , 000 g for 15 min at 4°C to remove the pellet of bacteria , unbroken TIGKs , host nuclei and cytoskeletal components . The sample was further centrifuged at 17 , 400 g for 30 min at 4°C and the supernatant was used as the cytosolic fraction . The pellet was lysed in lysis buffer ( 2 M thiourea , 7 M urea , 3% CHAPS , 1% Triton X-100 ) on ice for 30 min . After centrifugation at 17 , 400 g for 30 min at 4°C , the supernatant was used as the membrane fraction . Cells were lysed , clarified by centrifugation , separated by SDS-PAGE and transferred to nitrocellulose membranes . Membranes were blocked with PBST ( PBS and 0 . 1% Tween 20 ) containing 1% skim milk for 1 h at room temperature , and were then incubated for 1 h at room temperature with primary antibodies diluted in PBST . Membranes were washed three times with PBST , incubated for 1 h at room temperature with a 1∶5 , 000 dilution of HRP-conjugated secondary antibodies in PBST , and washed three times with PBST . Immunoreactive bands were detected using Pierce ECL Western Blotting Substrate ( Thermo Scientific ) and ChemiDoc XRS Plus ( Bio-Rad ) . Images were acquired with Image Lab Software version 3 . 0 ( Bio-Rad ) . Cells were cross-linked with 0 . 25 mM dithiobis ( DSP , Thermo Scientific ) in PBS for 5 min at room temperature . After washing cells with quenching buffer ( 1 M glycine in PBS [pH 7 . 4] ) 4 times , cell proteins were extracted in lysis buffer . Cell lysates were subjected to pull down reactions using the Anti-c-Myc Immunoprecipitation Kit ( Sigma-Aldrich ) according to the manufacturer's protocol . Proteins bound to the anti-c-Myc agarose were analyzed by immunoblotting . TIGKs were transfected with pIL-8 κB-Luc , pRL-CMV Vector ( Promega ) , and various combinations of expression plasmids . Total plasmid amounts were equalized in each transfection . Cells were lysed and reporter activity was determined using the Dual-Glo Luciferase Assay System ( Promega ) . Firefly luciferase activity was normalized on the basis of Renilla luciferase activity in the same extracts . IL-8 concentrations in TIGK culture supernatants were determined using the Quantikine Human CXCL/IL-8 ( R&D Systems ) according to manufacturer's protocol . p-value was determined using two-tailed t test ( closed testing procedure ) and p<0 . 05 was considered significant . | Periodontal diseases are one of the most common infections of humans , and are characterized by gingival inflammation and destruction of the hard and soft tissues that support the tooth , eventually causing tooth loss . Porphyromonas gingivalis is a major pathogen in periodontal diseases and a key pathogenic attribute of this organism is the ability to disrupt host innate immunity . Infection of gingival epithelial cells by P . gingivalis suppresses production of the neutrophil chemokine IL-8 . This inhibitory process is associated with the P . gingivalis serine phosphatase , SerB . In this study we show that SerB has a potent and specific ability to inhibit activation the NF-κB transcription factor which regulates IL-8 production . Mechanistically , SerB binds to and dephosphorylates the p65 subunit of NF-κB which prevents nuclear translocation and subsequent transcription of the IL8 gene . Targeting the NF-κB p65 subunit allows P . gingivalis to dampen IL-8 dependent inflammatory responses , facilitate survival and potentially to establish a favorable niche for the entire periodontal microbial community . | [
"Abstract",
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] | 2013 | The Serine Phosphatase SerB of Porphyromonas gingivalis Suppresses IL-8 Production by Dephosphorylation of NF-κB RelA/p65 |
Core protein of Flaviviridae is regarded as essential factor for nucleocapsid formation . Yet , core protein is not encoded by all isolates ( GBV- A and GBV- C ) . Pestiviruses are a genus within the family Flaviviridae that affect cloven-hoofed animals , causing economically important diseases like classical swine fever ( CSF ) and bovine viral diarrhea ( BVD ) . Recent findings describe the ability of NS3 of classical swine fever virus ( CSFV ) to compensate for disabling size increase of core protein ( Riedel et al . , 2010 ) . NS3 is a nonstructural protein possessing protease , helicase and NTPase activity and a key player in virus replication . A role of NS3 in particle morphogenesis has also been described for other members of the Flaviviridae ( Patkar et al . , 2008; Ma et al . , 2008 ) . These findings raise questions about the necessity and function of core protein and the role of NS3 in particle assembly . A reverse genetic system for CSFV was employed to generate poorly growing CSFVs by modification of the core gene . After passaging , rescued viruses had acquired single amino acid substitutions ( SAAS ) within NS3 helicase subdomain 3 . Upon introduction of these SAAS in a nonviable CSFV with deletion of almost the entire core gene ( Vp447Δc ) , virus could be rescued . Further characterization of this virus with regard to its physical properties , morphology and behavior in cell culture did not reveal major differences between wildtype ( Vp447 ) and Vp447Δc . Upon infection of the natural host , Vp447Δc was attenuated . Hence we conclude that core protein is not essential for particle assembly of a core-encoding member of the Flaviviridae , but important for its virulence . This raises questions about capsid structure and necessity , the role of NS3 in particle assembly and the function of core protein in general .
The genus pestivirus , together with the genera hepacivirus , flavivirus and the newly proposed genus pegivirus [1] , constitutes the family Flaviviridae . Cloven-hoofed animals are affected by pestiviruses , which cause severe diseases like classical swine fever ( CSF ) and bovine viral diarrhea ( BVD ) . Pestiviruses possess a single stranded RNA genome of positive polarity with one open reading frame ( orf ) encoding approximately 4000 amino acids ( aa ) . The resulting polyprotein is processed co- and posttranslationally into at least 12 viral proteins by three viral and two cellular proteases [2] . Pestiviral particles are enveloped and contain three virus-encoded glycoproteins , Erns , E1 and E2 . Erns is unique for pestiviruses and is the only known viral structural protein with an uridinylate specific RNase domain belonging to the T2 RNase family [3] , [4] . E1 and E2 or analogous proteins ( prM , E ) are encoded by all members of the Flaviviridae . Inside the virus particle , the viral genome is accompanied by a core protein . However , members of the proposed genus pegivirus , GBV- A and GBV- C [reviewed by 1] , do not appear to encode a core protein . Pestiviruses encode a small , basic core protein , which , in contrast to hepaci- and flaviviruses , does not possess any predicted regular secondary structure and is intrinsically disordered [5] , [6] . The pestiviral core protein has RNA chaperone activity [6] and its implicated functions are condensation of the viral RNA genome and subsequent packaging into virions . Its ability to bind RNA relies on the overall protein charge , which results in an unspecific affinity for nucleic acids [5] . The pestiviral core protein is processed at its N-terminus by the autoprotease Npro [7] , whereas the C-terminus is generated by signal peptide peptidase ( SPP ) cleavage [8] . Recent findings revealed that deletion of basic areas of classical swine fever virus ( CSFV ) core protein ( aa 213–231 of the viral polyprotein ) results in a ten-fold reduction of virus output , whereas deletion of small , less charged stretches ( aa 194–198 and aa 208–212 ) leads to a more than 1000-fold drop in virus output [9] . This implicates a more complex mechanism of core function in particle morphogenesis , which is not solely relying on overall protein charge . Duplication and triplication of the CSFV core protein gene as well as integration of up to 3 yellow fluorescent protein ( YFP ) genes between 2 core coding regions yielded replication competent viruses whose virus output was approximately 100-fold reduced in comparison to wildtype , revealing a high tolerance of core protein to size increase . We also reported the rescue of a CSFV encoding an YFP-core fusion protein by a single amino acid substitution in the NS3 helicase domain ( N2256Y ) [9] . This finding points to an ability of NS3 to substitute core functions . For all members of the Flaviviridae , there is increasing evidence that nonstructural proteins are required for virus morphogenesis [reviewed by 10] . Single amino acid residues in the NS3 helicase domain of yellow fever virus ( YFV ) and hepatitis C virus ( HCV ) have been described as important for particle formation [11] , [12] . Apart from NS3 , p7 , NS2 and NS5A have been reported as factors involved in HCV particle generation [13]–[17] . The pestiviral NS3 is a multifunctional molecule possessing protease , NTPase and helicase activity [18]–[22] and shares similarity with the analogous protein of hepaci- and flaviviruses . Its uncleaved precursor , NS2–3 , has been reported to be essential for particle formation [23] , [24] . In the present study , we describe the ability of CSFV NS3 to compensate for functionally compromised core proteins and even the deletion of nearly 90% of the core gene by acquisition of single codon rescue mutations in its helicase subdomain 3 . These findings provide strong evidence for a major role of the NS3 helicase domain in pestiviral particle assembly and implicate questions about the function of core protein . As members of the newly proposed genus pegivirus [1] – namely GBV- A and GBV- C - do not encode an obvious core protein , we provide experimental evidence that loss of the core coding region is tolerated by another member of the Flaviviridae .
Recently , we reported that a single amino acid substitution ( SAAS ) ( N2256Y ) in the helicase domain of NS3 rescued a poorly growing CSFV construct ( Vp447Yc ) that encoded a core protein of which the N-terminus was fused to YFP [9] . This unexpected result prompted us to investigate spontaneously occurring revertants of a CSFV mutant in detail that initially was designed to determine requirements for core processing by signal peptide peptidase ( SPP ) . Replacement of most of the signal peptide ( aa 250–261 ) by a stretch of 8 leucine residues ( Figure 1B ) led to a poorly growing virus ( 4 . 5×103 ffu/ml ) ( Vp4478leu ) that showed a more than 200-fold rise in titer upon passaging in SK6 cells . To identify the genomic change ( s ) leading to virus rescue , virus progeny was repeatedly plaque-selected . Interestingly , sequence analysis of these selected viruses did not reveal changes in the genomic sequence of the mutated core . Rescue mutations were identified by reintroducing genomic fragments ( nt31–1580; nt1480–3970; nt 3900–5570; nt 5500–8590; nt8330–10510; nt 10420–12290 ) of the rescued viruses into the parental plasmid p4478leu . Only introduction of a genomic fragment nt 5500–8590 encoding parts of NS3-NS4B ( aa 1730–2656 of the polyprotein ) into the parental plasmid resulted in rescue after transfection of the respective viral genomes . Upon sequencing of this fragment one SAAS was found in each clone tested in NS3 helicase subdomain 3 ( namely E2160G , N2177Y , Q2189K , P2200T and N2256D ) ( Figure 1B ) . To prove that these SAAS were indeed responsible for the rescue , the respective mutations were each engineered into the full-length cDNA construct of Vp4478leu ( p4478leuE2160G , p4478leuN2177Y , p4478leuQ2189K , p4478leuP2200T , p4478leuN2256D ) . After transfection , the resulting viruses grew to titers exceeding 105 ffu/ml without the need for passaging ( Table 1 ) . Growth characteristics are shown for the virus growing to highest titers ( Vp4478leuN2177Y ) ( Figure 2A ) . The overall titer of Vp4478leuN2177Y was about one log10 below the one of Vp447 . In the background of the parental Vp447 , the N2117Y substitution led to a more than 20- fold decrease of virus output ( Vp447N2177Y ) in comparison to Vp447 ( Table 1 ) . To assess whether acquisition of SAAS in NS3 helicase subdomain 3 might be a general mechanism of CSFV to overcome defects in the core gene , rescue experiments with a different loss of core function mutant were attempted . An initially poorly growing CSFV ( 7 . 1×102 ffu/ml 24 h after transfection ) encoding an internal deletion ( aa 208–212 ) in the core gene ( Vp447Δ208–212 ) ( Figure 1C ) was passaged in SK6 cells until an increase in virus growth was observed . Using the same approach as described above , a SAAS at position N2177H was identified . After introducing this SAAS N2177H into parental plasmid , virus titer ( Vp447Δ208–212N2177H ) rose to 7 . 9×105 ffu/ml 24 h after transfection of the respective virus genome in SK6 cells ( Table 1 ) . Apparently single amino acid substitutions in the C-terminal subdomain of the NS3 helicase compensate for functionally compromised core mutants that are compromised by N-terminal fusion to YFP ( Vp447Yc ) , defective C-terminal processing ( Vp4478leu ) or an internal deletion ( Vp447Δ208–212 ) , respectively . Core protein can be detected in lysates of cells transfected with genome of Vp447 and in pelleted virions of Vp447 ( Figure 2B ) . Surprisingly , Western blot analysis of cell lysate and pelleted virus particles revealed that neither Vp447N2177Y nor Vp4478LeuN2177Y contained detectable levels of core protein in concentrated virus preparations . Core protein could be detected in lysates of SK6 cells transfected with genome of Vp447N2177Y , but not after transfection of genomes of Vp4478leu and Vp4478leuN2177Y . Mutations within the NS3 helicase subdomain 3 allowed the rescue of viruses with compromised core function . To examine whether the core-coding region is dispensable altogether , almost the entire core gene ( aa170–246; 77 of the 86 codons ) was deleted in p447 , yielding p447Δc ( Figure 1D ) . Nine C-terminal amino acids ( 247–255: LEKALLAWA ) were preserved as part of the signal sequence ( aa 247–269 ) to ensure translocation of Erns into the ER lumen . While this construct lacking the core-coding region was not viable , introduction of above described SAAS in NS3 into p447Δc ( p447ΔcE2160G , p447ΔcN2177H , p447ΔcN2177Y , p447ΔcQ2189K , p447ΔcP2200T , p447ΔcN2256D ) led to the release of infectious virus with titers of at least 1×104 ffu/ml 24 h after electroporation of the respective transcripts ( Table 1 ) . Highest titers were observed for Vp447ΔcN2177Y and Vp447ΔcP2200T ( 4 . 0×105 and 2 . 3×105 ffu/ml 24 h after transfection in SK6 cells ) , thus being 30–50 -fold below Vp447 titer ( Figure 2A ) . Hence , SAAS in the helicase domain of NS3 can not only compensate for functionally compromised , but even completely absent core protein . No upstream open reading frame longer than 15 codons that might provide the virus with an alternative core protein could be identified . As expected , no core protein could be detected in either cell lysate or supernatant of RNA cells transfected with Vp447ΔcN2177Y or Vp447ΔcP2200T ( Figure 2B ) . To exclude a possible function of the C-terminal core aa 247–269 in Vp447ΔcN2177Y , they were replaced by the signal peptide of bovine CD46 , a cell surface glycoprotein ( Vp447ΔcN2177YCD46SP ) . Progeny virus production of Vp447ΔcN2177YCD46SP was slightly reduced ( 1×105 ffu/ml 24 h after transfection ) in comparison to Vp447ΔcN2177Y . Analysis of cell lysate of Vp447ΔcN2177Y 72 h after transfection of SK6 cells did not reveal differences in the relative presence and processing of NS2–3 , NS5B , Erns and E2 in comparison to wildtype ( Figure S1 ) . This suggests that cellular protein expression and polyprotein processing is neither affected by the lack of core protein nor by the presence of a SAAS in the NS3 helicase . The relative reduction of protein expression in Vp447Δc genome transfected cells results from its inability to spread . No changes in the regions surrounding the deletion of the core gene and NS3 were detected after ten passages of Vp447ΔcN2177Y in SK6 cells ( data not shown ) . Introduction of combinations of the described amino acid exchanges in NS3 helicase of Vp447Δc showed no additive effect but rather resulted in a 10–100 fold drop in virus titer ( data not shown ) . The lack of a structural component of the virus particle may result in altered phenotypic properties of the virus . We therefore assessed virus infectivity , morphology , and physical stability of Vp447ΔcN2177Y compared to wildtype Vp447 . The presence of viral genome in cells transfected with genomic RNA of Vp447 or Vp447ΔcN2177Y or infected with Vp447 or Vp447ΔcN2177Y was assessed by Northern blot analysis . Genomes could be detected for Vp447ΔcN2177Y ( 12059 nt ) and Vp447 ( 12293 nt ) ( Figure 3A ) , but the size difference of 234 nt could not be resolved . To verify that the infectivity of Vp447ΔcN2177Y is due to proper virus particles , not secreted replication complexes , neutralization assays were performed . Incubation of Vp447ΔcN2177Y with either a monoclonal antibody against E2 ( A18 ) or sera of one vaccinated animal ( S98 ) and one vaccinated and subsequently CSFV infected animal ( S05 ) neutralized infectivity in the same fashion as observed for the parental Vp447 ( Figure 3B ) . Next , specific infectivity in the supernatant was assessed . To allow for strict discrimination between both viruses on the level of RNA , a modified Vp447ΔcN2177Y , encoding for 5 alanine residues between the Npro C-terminus and core residue 247 ( Vp447Δc+5AlaN2177Y ) was generated . The resulting PCR assay specifically amplified either Vp447 or Vp447Δc+5AlaN2177Y genomes ( Figure S2 ) . In cell culture , growth of Vp447Δc+5AlaN2177Y was slightly improved in comparison to Vp447ΔcN2177Y . With this approach , we determined a specific infectivity ( ratio of virus genomes versus infectivity in cell culture supernatant ) of 23 genomes/ffu ( SD±14; n = 3 ) for Vp447 and 131 genomes/ffu ( SD±61; n = 3 ) for Vp447Δc+5AlaN2177Y . To determine density and size of Vp447 in comparison to Vp447ΔcN2177Y , equilibrium density centrifugation and size exclusion chromatography was performed . The densities of Vp447 and Vp447Δc+5AlaN2177Y were compared by separation in individual , continuous sucrose gradients ( 10–60% ) and equilibrium centrifugation . 30 fractions of 360 µl each were harvested by bottom puncture . In repetitive experiments , infectivity peaked at a density of 1 . 104–1 . 111 g/ml for Vp447 and of 1 . 099–1 . 112 g/ml for Vp447Δc+5AlaN2177Y ( Figure 4A ) . RNA levels , determined by virus specific real-time RT-PCR , peaked at a density of 1 . 10 g/ml for Vp447 and at 1 . 09–1 . 11 for Vp447Δc+5AlaN2177Y ( Figure 4A ) . Peak E2 levels were detected from 1 . 10–1 . 14 g/ml for both viruses , but E2 was present in all fractions ( Figure 4B ) . To avoid variations between two gradients , 106 ffu of both viruses were mixed and layered on top of the same sucrose gradient . As described above , 30 fractions of 360 µl each were harvested by bottom puncture . Again , E2 was detectable over a wide range of the gradient ( 1 . 04–1 . 18 g/ml sucrose ) ( Figure 4C ) and infectivity peaked at a density of 1 . 105–1 . 113 g/ml ( Figure 4D ) . Highest levels of Core protein were detectable at a density of 1 . 09–1 . 10 g/ml . RNA levels of either virus matched with infectivity and peaked in the same fraction ( 1 . 105 g/ml ) ( Figure 4D ) . To address the effect of the SAAS N2177Y in Vp447Δc on particle formation , Vp447Δc+5AlaN2177 was created . 75 ml of supernatant of SK6-cells 48 h after transfection with genomes of either Vp447Δc+5AlaN2177 or Vp447Δc+5AlaN2177Y were subjected to equilibrium centrifugation ( Figure S3 ) . Highest levels of infectivity were recorded at a density of 1 . 117 g/ml for Vp447Δc+5AlaN2177Y and at 1 . 102 g/ml for Vp447Δc+5AlaN2177 . Both infectivity and RNA-levels were reduced more than 400-fold in Vp447Δc+5AlaN2177 in comparison to Vp447Δc+5AlaN2177Y in all fractions tested . Overall , E2 levels were comparable between both viruses and peaked at 1 . 12–1 . 14 g/ml . However , the ratio of E2 homo- to heterodimer seemed to differ between the two viruses , as did the E2 levels at a density of 1 . 10 g/ml . The nucleocapsid of Vp447 is likely composed of core protein and the viral genome but so far has not been characterized . To gain at least preliminary information about the nucleocapsid of Vp447 and whether an analogous structure exists in Vp447Δc+5AlaN2177Y , either virus was treated with a nonionic detergent ( 0 . 5% NP40 ) to remove the envelope prior to equilibrium centrifugation as described above . The treatment completely abrogated infectivity in the fractions recovered and viral RNA levels were reduced more than 100-fold for either virus in comparison to untreated virus . RNA levels were just above background and peak levels occurred at densities of 1 . 05 g/ml and 1 . 2 g/ml for Vp447Δc+5AlaN2177Y whereas a broad peak of genomic RNA could be detected at densities of 1 . 11–1 . 2 g/ml for Vp447 ( Figure 4A ) . To increase precision of the analysis , both viruses were mixed , treated with 0 . 5% NP40 and analyzed in the same gradient . The E2 signal was shifted towards the top of the gradient ( 1 . 04–1 . 14 g/ml ) , whereas weak core signals could be detected at higher densities ( 1 . 13–1 . 18 g/ml ) ( Figure 4C ) . Viral genome of Vp447 was detected in highest amounts at densities of 1 . 14–1 . 2 g/ml , whereas highest levels of Vp447Δc+5AlaN2177Y genome were now observed at densities of 1 . 17–1 . 19 g/ml and 1 . 22 g/ml ( Figure 4D ) . These results indicate that detergent treatment of Vp447 in fact releases nucleocapsids of higher density . This assay is complicated by the RNase activity of the structural protein Erns , which might result in degradation of the viral genome after lysis of the lipid envelope . Hence , both Vp447 ( Vp447_H30K ) and Vp447Δc+5AlaN2177Y ( Vp447Δc+5AlaN2177Y_H30K ) with an exchange of Erns residue histidine 30 to arginine , destroying the active centre of its RNase , were generated [25] . This aa exchange did not affect the amount of progeny virus produced ( Figure S4 , Figure S5 ) . Both viruses were subjected to equilibrium density centrifugation to compare them with the respective parental virus . No differences were present regarding the amount and distribution of E2 ( Figure S4; data for Vp447Δc+5AlaN2177Y_H30K not shown ) . After detergent treatment , RNA levels of Vp447 and Vp447_H30K as well as of Vp447Δc+5AlaN2177Y and Vp447Δc+5AlaN2177Y_H30K remained at low levels ( Figure S4 , Figure S5 ) . Size exclusion chromatography was performed to directly compare the Stokes diameter of Vp447 and Vp447Δc+5AlaN2177Y . For this purpose , a mixture of 108 ffu of each Vp447 and Vp447Δc+5AlaN2177Y was subjected to gel filtration using Superose 6 . Infectivity was detectable in fractions 40–78 . Real-time RT-PCR ( as described above ) differentiating Vp447 from Vp447Δc+5AlaN2177Y allowed detection of viral genomes in fractions 43–78 . Peak levels of genomes of either virus were observed in fractions 59–61 and coincided with peak infectivity ( Figure 5 ) . For electron microscopic inspection , virus was produced in SK6 cells in serum free medium , concentrated by ultracentrifugation and inspected by TEM . The identity of the virions was confirmed by immunogold ( 10 nm ) staining with a monospecific rabbit serum against Erns ( for specificity of this serum , see Figure S6 ) . In both preparations , pleomorphic particles of about 50 nm were detectable . No morphological changes were apparent between Vp447 and Vp447ΔcN2177Y particles ( Figure 6 ) . Mean size of Vp447 particles was 51 . 9 nm ( standard deviation 8 . 9 nm; n = 43 ) and of Vp1017 particles 50 . 1 nm ( standard deviation 9 . 3 nm; n = 34 ) . However , no exact size comparison or tomographic particle analysis was possible since required particle quantity , quality and purity was not achieved . To address whether the absence of core protein in the virus particle affects physical stability of Vp447ΔcN2177Y , the kinetics of inactivation of Vp447 and Vp447ΔcN2177Y at 37°C and 39 . 5°C were determined . No major differences in thermal stability were observed between the two viruses ( Figure S7 ) . Physical stability was also assessed by freezing and thawing of defined virus preparations . After thawing , 19% of the initial virus input could be recovered for Vp447 and 13% for Vp447ΔcN2177Y ( Figure S7 ) . CSF is a disease of pigs with strain dependent virulence . Vp447 represents a moderately virulent strain [26] , causing mortality rates >50% . To assess virulence of Vp447ΔcN2177Y , a small-scale animal experiment was conducted . Two groups of two pigs each were injected intramuscularly with 5×106 TCID50 of Vp447 or Vp447ΔcN2177Y . Two days later , a sentinel pig was added to each group . Animals were evaluated according to a standard clinical scoring system [27] , rectal temperature and leukocyte counts . Vp447 infected animals exhibited febrile temperatures ( >40°C ) on day 7–10 after infection and from day 13 after infection until the end of the experiment ( Figure 7A ) . One Vp447 infected pig ( wt2 ) had to be euthanized on day 21 after infection , with a clinical score of 10 . The other Vp447 infected pig ( wt1 ) had a clinical score between 2 . 5 and 4 . 5 on days 17 , 18 and 21–27 . Severe leukopenia ( leukocyte count below 10 Giga/l ) , a typical symptom of CSF [reviewed with other clinical symptoms by 28] , was present in wt1 and wt2 from day 4 after infection , with further declining leukocyte counts until the end of the experiment ( Figure 7B ) . The sentinel animal ( wtS ) housed together with the Vp447 infected pigs developed febrile temperatures from day 14 after infection until the end of the experiment and leukopenia was present on day 21 and 28 of the experiment . Virus could be isolated from Vp447 infected animals on days 4 , 7 , 10 and 14 after infection ( Table 2 ) . Virus isolation was not possible from the sentinel animal on days 4 , 7 , 10 and 14 after infection of the other pigs . Neutralizing antibodies could not be detected in Vp447 infected animals and their sentinel on days 10 , 14 and 21 after infection ( Table 3 ) . No apparent signs of disease ( clinical score = 0 ) were observed for Vp447ΔcN2177Y infected animals ( Δc1 and Δc2 ) and their sentinel ( ΔcS ) throughout the experiment . With the exception of one day of slightly elevated body temperature ( Δc2 on day 8 ) and mild leukopenia of animal Δc1 on day 21 , no fever or leukopenia were present in Vp447ΔcN2177Y infected animals ( Δc1 and Δc2 ) and their sentinel ( ΔcS ) . We were unable to reisolate Vp447ΔcN2177Y from sera ( Table 2 ) and leukocytes ( not shown ) of infected animals on days 2 , 4 , 7 , 10 and 14 after infection . However , viral genomes could be amplified from leukocytes until day 7 and neutralizing antibodies could be detected beginning with day 14 after infection ( Table 3 ) .
Key findings of this study are that ( 1 ) a pestivirus lacking almost the entire core coding region is viable and that ( 2 ) viability depends on single point mutations in the helicase domain of NS3 . This finding questions the general assumption that a core protein is a specific and essential structural element of enveloped RNA viruses and is supported by the existence of GBV- A and GBV- C , which do not encode an obvious core protein [reviewed by 1] . Further to this , the data support a central role of the multifunctional NS3 protein in virus particle assembly . During the characterization of different loss - of - function manipulations of the core gene of CSFV , we observed that some replicative but initially poorly growing viruses generated increased amounts of progeny virus after extended incubation periods of the transfected cells . The responsible gain-of-function mutations could not be mapped to the locus of the manipulated nucleotide sequence . Instead , single nucleotide exchanges clustered within a stretch of approximately 300 nucleotides of NS3 helicase subdomain 3 , about 6000 nucleotides downstream of the core gene . The occurrence of second site mutations in NS3 upon loss of core protein function differs from results described for tick-borne encephalitis virus . In this model , the deletion of parts of the internal hydrophobic domain led to the acquisition of hydrophobic residues in the core gene itself [29] . To confirm that the observed infectivity of core deficient viruses was due to proper virus particles , Vp447 and Vp447ΔcN2177Y were compared with regard to sensitivity towards neutralizing antibodies . In both cases , infectivity was blocked by hyperimmune sera from pigs or a monoclonal antibody directed against viral E2 . Differences in the stability of particles of Vp447 and Vp447ΔcN2177Y with regard to infectivity were not observed upon freezing - thawing and heat exposure . Electron micrographs of Vp447 and Vp447ΔcN2177Y were obtained from concentrated serum-free cell culture supernatants and the structures observed were immunogold labelled with a monospecific rabbit serum against Erns . This was necessary because pestivirions in general lack a characteristic morphology . No morphological differences between Vp447 and Vp447ΔcN2177Y particles were apparent . Precise determination of structure and size would require cryo EM to avoid preparation dependent artifacts and also larger numbers of particles . With regard to particle sizes no apparent differences in Stokes diameter could be detected between Vp447 and Vp447ΔcN2177Y particles in gel filtration experiments . Both viruses eluted from the column in the same fractions . Due to difficulties in comparing different gel filtration runs , it was mandatory to separate Vp447 and Vp447ΔcN2177Y side by side . To distinguish between both viruses by real time RT-PCR , a modified Vp447ΔcN2177Y was constructed , which encodes an additional sequence of five alanines between Npro C-terminus and signal peptide ( Vp447Δc+5AlaN2177Y ) . This construct was also employed for determination of virus density in linear sucrose gradients . After it was evident from individual gradient experiments that infectivity and genomic RNA comigrated at densities from 1 . 10–1 . 11 g/ml ( Figure 4 ) , both viruses were mixed and analyzed in the same gradient . Again RNA , E2 and infectivity accumulated at the same densities . A surprising finding was that core protein showed highest concentration at slightly lower densities than peak RNA and infectivity levels . As E2 can be detected in the supernatant of cells transfected with the genome of Vp447Δc , it was of interest to compare the suspected pseudoparticles with regard to density and genome integration to Vp447ΔcN2177Y . Hence , equal volumes of supernatant of cells either transfected with Vp447Δc+5AlaN2177 or Vp447Δc+5AlaN2177Y genomes were subjected to density gradient centrifugation . The reduction of infectivity of Vp447Δc+5AlaN2177 in comparison to Vp447Δc+5AlaN2177Y correlated with the reduction of genome levels at the densities tested , suggesting that the SAAS N2177Y is critical for the integration of the virus genome into the particles during assembly if core protein is not present . Overall , E2 levels between the two viruses were comparable both with regard to total amount in the supernatant and distribution according to density . However , the ratio of E2 homo- to heterodimer , as well as the amounts of E2 at a density of 1 . 1 g/ml and the density of peak infectivity differed between the two viruses , which might indicate differences in particle composition . To determine whether core protein actually is a component of a nucleocapsid structure , the envelope of the virus particles was removed by treatment with a non-ionic detergent ( Nonidet P40 ) . NP40 treated viruses were layered on top of sucrose gradients as before and the position of infectivity , E2 , core and RNA were recorded after equilibrium centrifugation . Infectivity could be abolished completely by NP40 treatment . The signal of E2 shifted towards the top of the gradient ( 1 . 04–1 . 14 ) whereas the core protein signal shifted to higher densities ( 1 . 13–1 . 18 ) . Peak values of viral genomes coincided with core signal in Vp447 , which might implicate the presence of a nucleocapsid like structure of higher density . For Vp447Δc+5AlaN2177Y , signals for viral genome were low , with a slight elevation at the tube bottom if the virus was separately run on a gradient . Hence , we were unable to assign the genome to a discrete density . In contrast , a slight peak of viral RNA , comparable in density to Vp447 , was observed if both viruses were separated in the same gradient . One could speculate that this effect is due to a redistribution of core protein between viral genomes after detergent treatment . Overall , the amounts of RNA determined by real time RT-PCR were 102–104 lower than with intact viruses , which can be taken as evidence for RNA degradation . A comparable experiment for HCV determined only a six-fold reduction of genomic RNA after NP40 treatment [30] . A major difference between HCV and CSFV is the presence of the potent ribonuclease Erns in the virus envelope [31] . However , after mutational disruption of the RNase active centre of Erns [25] , we did not observe changes in the levels of viral genome detectable in comparison to virus with intact RNase . This suggests that the analytic system itself , by employing sucrose , contains RNases , which together with the long centrifugation time ( 24 h ) , are sufficient to degrade most of the viral genomes present in the sample . To address this technical problem , improved separation methods have to be established to minimize RNA degradation . However , the relatively higher amount of viral genome detectable for Vp447 in comparison to Vp447ΔcN2177Y suggests a protective function of core protein against RNase . The absence of core protein and thus a known proteinaceous component of the nucleocapsid questions the way how a linear viral RNA molecule of approximately 3 µm is condensed in order to fit into the virus particle of less than 50 nm diameter . Further to this , the genome has a negative charge that is partially neutralized by a usually positively charged ( nucleo- ) protein . Strikingly , introduction of the single amino acid substitution N2177Y into the parental Vp447 ( Vp447N2177Y ) reduced virus growth and abrogated the detectable incorporation of core protein into the virus particles , while at the same time the core protein accumulated intracellularly . This points to an ability of modified NS3 to counteract core particle integration , probably by modulation of core-RNA-interaction . This finding also raises the question whether NS3 might replace core in the virus particle . So far , we were unable to detect any NS3 in purified virus preparations , but we cannot exclude that a small number of molecules is packaged . As we have no evidence for other virally encoded proteins for replacement of the missing core protein , it is conceivable that host cellular proteins , for example cytoplasmic RNA chaperones or nuclear RNA binding proteins , compensate for the lack of core protein . The association of cellular proteins with virus particles has been described for RNA and DNA viruses , like hepadnaviruses [32] , rabies virus [33] , filoviruses [34] , respiratory syncytial virus [35] and HCV [36] . Interestingly , HSP70 or HSP90 were most often found associated with virus particles . An important task will therefore be a proteome analysis of highly purified virus particles of Vp447 and Vp447ΔcN2177Y . Epitope tagged viruses - as described for HCV [37] , [38] and BVDV [39] - may be useful for such an investigation . NS3 is functionally well conserved among members of the Flaviviridae and significant sequence conservation is apparent . It is a multifunctional protein that contains several enzymatic activities , such as serine protease , NTPase and RNA helicase [18]–[22] . Its involvement in particle assembly has been suggested for HCV [11] , [13] and YFV [12] , [40] , [41] . The conserved helicase motifs are located in subdomains 1 and 2 of the NS3 helicase [42] . NS3 helicase subdomain 3 is the least conserved stretch in NS3 of Flaviviridae , both with regard to amino acid sequence and structure [43] . Although it is not present in all superfamily 2 helicases [44] , it is essential for NS3 helicase activity . Analysis of all single aa substitutions in the putative CSFV NS3 helicase subdomain 3 , which were able to rescue Vp447ΔcN2177Y , did not reveal an obvious pattern with regard to amino acid identity , charge or polarity , hence we are not able to draw conclusions about the mode of action by analysis of the sequence identities . So far , the 3D-structure of pestiviral NS3 helicase is not known and the sequence homology to HCV NS3 is too low to draw conclusions . All rescue mutations were located in regions aligning with alpha helices both in dengue virus [45] and HCV [46] , [47] ( Figure S8 ) . All but one aa substitution identified were located in stretches reported to be important for NS3 helicase protein-protein-interaction and optimal replication of HCV [48] . So far , there is no mechanistic explanation how the described mutations in NS3 helicase domain 3 allow for the rescue of Vp447Δc . Structural and functional analysis of the modified NS3 proteins are needed to elucidate the gain of function in particle assembly . Finally , the virulence of Vp447ΔcN2177Y in comparison to Vp447 was assessed in a small scale animal experiment . The parental CSFV strain used for this study causes disease in pigs with a case fatality rate of >50% [26] . While the two pigs infected with Vp447 and the sentinel housed together with these two pigs developed typical signs of CSF , the pigs infected with Vp447ΔcN2177Y and the respective sentinel animal stayed completely healthy although they were injected with the same dose of virus . Neither fever nor leukopenia was observed in pigs infected with Vp447ΔcN2177Y . Detection of genomic RNA in leukocytes up to day 7 p . i . and the appearance of CSFV neutralizing antibodies in both Vp447ΔcN2177Y infected animals beginning at day 14 suggest that a limited replication took place in the animals , despite our inability to reisolate Vp447ΔcN2177Y from serum or blood cells . This indicates that the lack of core protein leads to a strong attenuation of the virus . The sentinel pig developed no neutralizing antibodies , which can be taken as evidence that Vp447ΔcN2177Y is not or inefficiently transmitted . All this points to an important role of pestiviral core protein in vivo . Further effort will be put in the characterization of Vp447ΔcN2177Y in primary cells of its natural host to elucidate the mechanisms underlying its attenuation .
All animal work was conducted according to the legal regulations of the German Animal Welfare jurisdiction ( Tierschutzgesetz ) . The animal experiment was subject to authorization and was recorded after approval under reference number AZ 06/1105 at the Lower Saxony State Office for consumer protection and food safety . The internal reference was V2006-6 . Sequence modifications were introduced into the core or NS3 protein of CSFV Alfort/Tübingen recombinant full length cDNA clone ( p447 ) by site directed mutagenesis or end to end ligation , utilizing Pfu-DNA polymerase ( Promega , Mannheim , Germany ) ( Primers are available upon request ) . Sequence analysis was employed to confirm the generated constructs ( Quiagen , Hilden , Germany ) . SK6-cells were grown in Dulbecco's modified Eagle's medium supplemented with 10% fetal calf serum at 37°C under 5% CO2 . Virus cDNA was transcribed into RNA using SP6-polymerase ( NEB , Frankfurt am Main , Germany ) and , typically , 2 . 5 µg RNA were electroporated into 5×106 SK6-cells ( Bio-Rad Gene Pulser ) . Replication was assessed 14 h after electroporation via immunohistochemistry using monoclonal antibody A18 , directed against the CSFV E2 protein . Virus titer was determined in focus-forming units/ml ( ffu/ml ) 24 h after electroporation . For this purpose , supernatant was harvested , clarified ( 5 min at 3 , 000×g ) , and seeded on SK6-cells , employing 10-fold dilution steps . After 14 h , cells were fixed and stained for E2 as mentioned above . Antigen-positive foci of infected cells were counted using a Nikon Eclipse TS100 microscope and the titer was calculated . All virus titers were confirmed by multiple experiments ( more than two ) . For virus passaging , cell culture supernatant was harvested 72 h after electroporation of genomic RNA and clarified by centrifugation ( 5 min at 3 , 000×g ) . Consecutively , 2×105 SK6-cells were infected with 1 ml of supernatant of the previous passage . This procedure was repeated every 3 to 4 days along with the determination of virus titers . Virus neutralization was tested according to [49] . Briefly , serum samples from a CSFV vaccinated ( S05 ) and a vaccinated and infected ( S98 ) animal , as well as cell culture supernatant containing an anti-E2 antibody ( A18 ) and a serum of an animal neither infected nor vaccinated against CSFV were diluted 2-fold in duplicates on a 96well plate ( sera were kindly provided by the Community Reference Laboratory for CSF , Hannover ) . Thereafter , a defined virus suspension of Vp447 was added to each well and the plate was incubated for 1 h at 37°C . Subsequently , the employed virus suspension was back titrated on the plate , a suspension of SK6-cells ( 3×105 cells/ml ) was added to each well and the plates were incubated at 37°C for 72 h . Virus infection was detected by immunohistochemistry as described above . TCID50/ml of the employed virus suspension and ND50/ml were calculated according to [49] . Western blotting was done essentially as described by ( 8 ) . Briefly , 24 h–72 h after electroporation , cells were lysed in Tris-EDTA buffer containing 2% SDS , subjected to SDS-PAGE on 7 . 5 , 10 or 12% polyacrylamide gels using Tris-tricine buffers , and blotted to nitrocellulose . As primary antibody , mouse monoclonal antibody A18 ( anti-E2 ) , 5H4 ( anti-Core ) , 24/16 ( anti-Erns ) , code 4 ( anti-NS3 ) , 6B2 ( anti-NS5B ) or anti-β-actin antibody ( A5441; Sigma-Aldrich ) was utilized . Horseradish peroxidase-coupled goat anti-mouse antibody served as secondary antibody ( Dianova , Hamburg , Germany ) . Signals were revealed using chemiluminescence ( ThermoFisher , Bonn , Germany ) and exposure to Kodak BioMax film . Virus-containing supernatants were concentrated for immunoblotting by clarification for 5 min at 3 , 000×g , followed by pelleting of 1 . 2 ml in a TL100 Beckmann ultracentrifuge at 45 , 000 rpm for 1 h . After removal of the supernatant , the pellet was resuspended in 10 µl Tris-EDTA buffer containing 2% SDS and further processed as described for the cell lysate . Signals were quantified employing ImageJ ( http://rsbweb . nih . gov/ij/index . html ) . All constructs were confirmed by sequencing ( Quiagen , Hilden , Germany ) . Revertant viruses were analyzed by sequencing after reverse transcriptase ( RT ) -PCR and cloning into the pGEM-T vector ( Promega , Mannheim , Germany ) using standard primers ( oligonucleotide sequences are available upon request ) . Continuous sucrose gradients ( 10%–60% w/v sucrose in 50 mM Tris , pH 7 . 4 ) of 11 ml were generated with a GP250 gradient programmer in conjunction with two Pharmacia P500 pumps at a flow rate of 1 ml/min . In a volume of 400 µl , 106 ffu of each Vp447 and a Vp447 with a deletion of core protein ( aa 170–246 of the polyprotein ) and a five alanine linker between Npro C-terminus and signal peptide ( Vp447Δc+5AlaN2177Y ) were layered on top of the gradient and centrifuged in a Beckman SW41 rotor at 180 . 000 g ( 32 . 00 rpm ) for 24 h . 30 fractions of 360 µl each were collected by bottom puncture and the refractive index was determined . 30 µl of each fraction were used for titration on SK6-cells and 20 µl of two fractions pooled were subjected to Western blot analysis . Viral RNA was purified utilizing the QuiaAmp Viral RNA kit ( Quiagen , Hilden , Germany ) according to the manufacturer , reverse transcribed employing the Quanti Tect Reverse Transcription kit ( Quiagen , Hilden Germany ) with the same reverse primer ( rev: CATCCCGCGTATCTCTT ) and subjected to qPCR ( Quanti Tect SYBR Green PCR kit , Quiagen , Hilden , Germany ) in a StepOnePlus real-time PCR system ( Applied Biosystems , Darmstadt , Germany ) , using forward primer specific for either Vp447 ( for_wt: CAAGCCACCAGAGTCCAG; fragment size 258 nt ) or Vp447Δc+5AlaN2177Y ( for_Δc: TGCGGCCGCAGCTCTAGA; fragment size 246 nt ) and the reverse primer already employed in the reverse transcription reaction . 1×108 ffu of each Vp447 and Vp447Δc+5AlaN2177Y were pelleted at 100 , 000×g for 1 h in a 45Ti rotor in a Beckman L8–70 ultracentrifuge . The pellet was resuspended in 550 µl 1xTNE buffer overnight at 4°C on a shaker . The complete volume was loaded onto a Pharmacia XK16 gel chromatography column , packed with Superose 6 ( prep grade , GE Healthcare , Munich , Germany ) with a total volume of 138 ml ( determined by dextran-blue ) including the void volume of 41 . 5 ml ( determined by 10% acetone in H2O and subsequent measurement of optical density at 280 nm ) . The column was calibrated employing IgM ( size 21 nm ) , which was subsequently measured in the elution fractions by agar gel diffusion ( Novartis , Marburg , Germany ) . The chromatography was performed at a flow rate of 6 ml/h generated by a LKB P-1 pump with 1xTNE buffer . 80 fractions of 2 ml each were collected by a LKB superfrac collector . Collector tubes were blocked with 1xTNE containing 1% BSA fraction 5 for 10 min at room temperature . RNA was prepared from the resulting fractions by QuiaAmp Viral RNA kit ( Quiagen , Hilden , Germany ) and analyzed for the presence of viral genome by above described real-time RT PCR for the presence of either Vp447 or Vp447Δc+5AlaN2177Y genome . SK6 cells transfected with either Vp447 or Vp447ΔcN2177Y genome were seeded on 10 143 cm2 cell culture plates each in medium containing FCS . 18 h after transfection , the cells were washed twice with PBS and the medium was replaced by a serum free medium for MDBK cells ( Sigma-Aldrich , Munich , Germany ) . 48 h after transfection , the supernatant was harvested and cellular debris was removed by centrifugation ( 5 min at 3 , 000×g ) . Subsequently , virus was pelleted at 25 . 000 rpm in a TI45 rotor for 8 h . Thereafter , the pellet was resuspended in PBS for 12 h at 4°C . Virus preparations were mounted on glow discharged , pioloform and carbon coated copper-rhodium grids . After saturation using 1% ( w/v ) bovine serum albumin ( BSA ) in PBS grids were transferred to droplets of the first antibody: monospecific rabbit serum anti Erns , 1∶200 in PBS , 0 . 5% ( w/v ) BSA for 1 h in a humid chamber . After 5 washing steps on droplets of PBS immune labeling was completed using goat anti-rabbit IgG conjugated to 10 nm colloidal gold ( Plano , Wetzlar , Germany ) 1∶25 in PBS , 0 . 5% ( w/v ) BSA . The preparation was finished by 5 washing steps on PBS followed by short incubation on distilled water and negative staining using 2% methylamine tungstate ( Plano , Wetzlar , Germany ) . Air dried grids were examined in a Zeiss EM910 transmission electron microscope at 80 kV at an instrumental magnification of 31 . 500 and 50 . 000 and micrographs taken on Kodak SO-163 negative film . Six weaner pigs were purchased from a commercial piggery and tested negative for infection with Pestiviruses by RT-PCR and serum neutralization test . The pigs were kept in two separately housed groups under high containment conditions . Two pigs of each group were either infected intramuscularly with 5×106 TCID50 Vp447 or Vp447 with a deletion of core amino acids 170–246 ( position in the polyprotein ) ( Vp447ΔcN2177Y ) . Two days after infection , the previously separated sentinel animal was returned to each group . The animals were monitored daily for clinical signs of CSFV according to a modified clinical score developed by [27] and body temperature was recorded . The clinical score is calculated by scoring each parameter ( liveliness/body tension/body shape/breathing/walking/skin/eyes+conjunctiva/appetite/defecation ) from 0–3 ( no signs of disease – severe signs of disease ) , followed by addition of all values obtained . As the animals were housed in groups in this experiment , the parameter “leftovers in feeding trough” could not be evaluated for an individual animal . EDTA blood samples were taken on days 2 , 4 , 7 , 14 , 21 and 28 after infection . The leukocyte fraction was isolated from EDTA blood by addition of 6 . 25% ( v/v ) 5% EDTA-Dextran solution , followed by sedimentation and several wash steps with PBS [49] and the leukocyte count was determined in a Neubauer chamber . Animals were euthanized because of animal welfare reasons ( clinical score >20 or severe disease ) during the experiment or at the end of the experiment . | Virus particles of members of the Flaviviridae consist of an inner complex of viral RNA genome and core protein that together form the nucleocapsid , and an outer lipid layer containing the viral glycoproteins . Functional analyses of core protein of the classical swine fever virus ( CSFV ) , a pestivirus related to hepatitis C virus ( HCV ) , led to the observation that crippling mutations or even complete deletion of the core gene were compensated by single amino acid substitutions in the helicase domain of non-structural protein 3 ( NS3 ) . NS3 is well conserved among the Flaviviridae and acts as protease and helicase . In addition to its essential role in RNA replication , NS3 apparently organizes the incorporation of RNA into budding virus particles . Characterization of core deficient CSFV particles ( Vp447Δc ) revealed that the lack of core had no effect with regard to thermostability , size , density , and morphology . Vp447Δc was fully attenuated in the natural host . Our results provide evidence that core protein is not essential for virus assembly . Hence , Vp447Δc might help to explain the enigmatic existence of GB viruses -A and -C , close relatives of HCV that do not encode an apparent core protein . | [
"Abstract",
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"Results",
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"medicine",
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] | 2012 | The Core Protein of Classical Swine Fever Virus Is Dispensable for Virus Propagation In Vitro |
Tungiasis is a parasitic skin disease caused by penetrating female sand fleas . By nature , tungiasis is a self-limiting infection . However , in endemic settings re-infection is the rule and parasite load gradually accumulates over time . Intensity of infection and degree of morbidity are closely related . This case series describes the medical history , the clinical pathology , the socio-economic and the environmental characteristics of very severe tungiasis in five patients living in traditional Amerindian communities in the Amazon lowland of Colombia . Patients had between 400 and 1 , 300 penetrated sand fleas . The feet were predominantly affected , but clusters of embedded sand fleas also occurred at the ankles , the knees , the elbows , the hands , the fingers and around the anus . The patients were partially or totally immobile . Patients 1 and 3 were cachectic , patient 2 presented severe malnutrition . Patient 3 needed a blood transfusion due to severe anemia . All patients showed a characteristic pattern of pre-existing medical conditions and culture-dependent behavior facilitating continuous re-infection . In all cases intradomiciliary transmission was very likely . Although completely ignored in the literature , very severe tungiasis occurs in settings where patients do not have access to health care and are stricken in a web of pre-existing illness , poverty and neglect . If not treated , very severe tungiasis may end in a fatal disease course .
Tungiasis ( sand flea disease ) , one of the most neglected tropical diseases ( NTDs ) , is caused by female sand fleas ( Tunga penetrans and more rarely T . trimamillata ) penetrated into the skin . The disease is prevalent in resource-poor communities in South America , the Caribbean , sub-Saharan Africa and Madagascar [1–5] . Children , the elderly and persons with disabilities bear the highest disease burden [6 , 7] . Intensity of infection varies widely and correlates to severity of disease [1 , 8] . Tetanus is a known sequel in individuals with insufficient immunization [3 , 9] . In endemic communities light infections with 5 to 10 embedded sand fleas predominate . [8] . Reports on very severe tungiasis with a hundred or more embedded sand fleas are scanty in the current literature [10–12] . In contrast , in publications from the end of the 19th to the middle of the 20th century , very severe tungiasis was frequently reported [13–26] . Here we report five cases of very severe tungiasis in inhabitants of four Amerindian communities in Vaupés Department , in the Amazon lowland of Colombia . The patients shared a spectrum of pre-existing medical conditions facilitating constant re-infection and presented socio-economic and environmental characteristics which together influenced the development of tungiasis into a life-threatening condition .
Vaupés Department is situated in the Southeast part of Colombia and covers an area of 54 , 134 km2 . Geographically , it belongs to the Amazon basin and is almost completely covered with dense rain forest . The Río Vaupés is an affluent of the Río Negro , a major tributary to the Amazon River . Vaupés Department is inhabited by about 35 , 000 people of whom 12 , 000 live in the capital Mitú . At least 220 Amerindian communities are spread all over the department but only a minority can be accessed by boat or small aircraft . Rarely , communities have more than 200 inhabitants . People live from fishing , hunting and collection of edible plants in the forest and subsistence cultivation of cassava ( Manihot esculenta ) . The only hospital of the department is located in Mitú . A couple of primary health care centers are dispersed in the municipalities of Carurú and Taraira . They serve communities which can be reached by forest tracks or boat . During a period of 12 weeks , five patients with very severe tungiasis were observed . Four patients were seen at the emergency unit of Mitú hospital , one in the community she was living in . Patient 1 and 2 were from Wacará ( N01°14ʹ45 . 19ʺ , W07°00ʹ37 . 20ʺ , patient 3 from Nuevo Pueblo ( N00°51ʹ55 . 01ʺ , W69°33ʹ52 . 01ʺ ) , patient 4 from Los Angeles ( N0°34ʹ26 . 31ʺ , W70°07ʹ28 . 98ʺ ) , and patient 5 from Puerto Pinilla ( N0°55ʹ39 . 30ʺ , W69°57ʹ33 . 15ʺ ) . Patient 1 was completely immobile and had to be carried in a hammock from her community to the Vaupés River for six hours and from there she was transported by boat to Mitú . Patient 2 had considerable pain while walking and was hobbling slowly on the lateral rim of his feet . Patient 3 and 4 were completely immobile . They were carried in a hammock to a small airstrip by community members and then transported by aircraft to Mitú . Patient 5 was found living in an isolated place at a small tributary of the Vaupés River . As the access was extremely difficult and her condition was relatively good , she was examined and treated at home . Patients were undressed , the whole body was washed and carefully examined for the presence of embedded sand fleas . The skin was also inspected for signs of bacterial and fungal infection . Severity of tungiasis was determined using a previously established score [27] . Staging was performed according to the Fortaleza classification [28] . Immediately after the examination , the patients were treated topically with NYDA , a formula containing two dimeticone ( silicone ) oils with low viscosity ( Pohl-Boskamp GmbH & Co . KG , Hohenlockstedt , Germany ) [29] . Due to its physical mode of action , NYDA is registered as a class II medical device [29] . Treatment with dimeticones is considered as the reference treatment of tungiasis by the Ministry of Health and Social Protection of Colombia . Affected body areas were carefully wetted with the dimeticone . In areas with hyperkeratotic skin and several layers of sand fleas situated on top of each other , the oil was vigorously rubbed into the skin . The treatment was repeated after 24 hours . In patient 4 , an additional application was made 1 week after the initial treatment . The tetanus-vaccination status was verified and patients were vaccinated against tetanus if necessary . Because of severe anemia , patient 3 received 2 × 250 ml red blood cell concentrate . Patient 1 was treated with oxacillin intravenously ( twice one gram per day for nine consecutive days ) due to severe bacterial superinfection of lesions at the feet . Patient 2 , 4 and 5 were treated with albendazole ( 400 mg per day for three days ) and tinidazole ( three tablets of 500 mg per day for two days ) to eliminate intestinal helminths . Patient 3 was treated with metronidazole ( 500 mg every 12 hours for five days ) , oxacillin ( twice one gram per day for nine days ) , gentamycin intravenously because an infection with gram-negative bacteria was suspected ( 160 mg every six hours for seven days ) and albendazole ( 400 mg per day for three days ) . Patients were monitored for up to 15 days and changes in their clinical condition were documented . Since the patients did not speak Spanish , questions were translated by health assistants speaking the same language as the patients . The study was performed as part of routine health care provided by public health personnel of Vaupés Health Department in Mitú Hospital . The examination of the skin for the diagnosis of tungiasis is part of the routine health care and was carried out with the aim to cure patients from a life-threatening condition . All patients provided oral consent . The objective of the examination as well as the risks and benefits of treatment were explained to each patient and relatives/caregivers present . The examination of minors was made with the authorization and in the presence of at least one of their parents . In accordance with Resolution 008430 of 1993 , of the Ministry of Health and Social Protection of Colombia , which regulates research in humans , the study is classified as a low risk study .
Demographic , socio-economic and clinical characteristics are depicted in Table 1 . Patient 1 ( female , 72 years ) lived together with patient 2 , her grandson , in a small hut without a solid floor . She was suffering from gonarthrosis for long . Since she could not work anymore , she rested day and night in her hammock . Food was provided by her son once a day , but food shortage was common . Several dogs belonged to the household . Patient 2 ( male , 16 years ) was the grandson of patient 1 . He suffered from bilateral deafness and mental retardation since birth and had never left the village he was born in . He was unable to take care for himself or to accompany his father for hunting or gathering food . Instead he waited the whole day inside the hut , crouching on his heels or directly squatting on the ground next to his grandmother . The only piece of clothing he had were torn shorts . Patient 3 ( male , 69 years ) lived in a very remote community located near the frontier with Brazil . He belonged to the Iupdah-Maku ethnicity , a group of Amerindians who only recently became sedentary . Iupdah people entirely live from hunting and edible fruits they collect in the rain forest . The patient was suffering from gonarthrosis and therefore was unable to walk . He entirely depended on food provided by relatives . His daughter , who had taken care of him , had moved away to another community a couple of months ago . The patient was left alone in a shelter without walls , where he spent the whole day in the hammock . Two dogs were his only companions . Patient 4 ( male , 81 years ) lived in a small community located at the Brazilian border . He belonged to the Tuyuca ethnic group . He suffered from gonarthrosis since long and was cared for by his daughter . However , the daughter had moved away some time ago . The eldest son should have taken care of the patient , but was unable to provide sufficient food even for his own family . Patient 5 ( female , 94 years ) lived in an isolated dwelling at a small tributary of the river Vaupés . She belonged to the Siriana ethnic group and lived with her oldest son , who had no wife . Her mobility and vision were restricted . A week before the patient was identified by the medical team , she had been treated by a relative who had applied a plant extract of unknown origin on the feet . This had reduced the number of viable lesion from around 1000 to about 50 . Patient 1 and 3 had an extremely severe form of tungiasis with approximately 1 , 000–1 , 300 embedded sand fleas in all stages of development . The soles and the lateral rims of both feet were covered with several layers of embedded sand fleas on top of each other and closely-packed ( Figs 1 and 2 ) . Clusters of embedded sand fleas existed at the ankles , lower legs , knees , at the elbows and around the anus ( Figs 3 and 4 ) . The palm , the back of the hand and the fingers were also affected ( Figs 5 and 6 ) . The feet emitted a strong odor of necrotizing flesh . Patient 1 and 3 were anemic , dehydrated and cachectic . Their weight was 35 kg and 39 kg , respectively . Patient 1 was intensely infested with head lice and also had myiasis at the right foot . Patient 2 had approximately 250 embedded sand fleas in all stages of development of which the great majority had penetrated at the feet . The density of parasites was particularly high in the interdigital area of the soles ( Fig 7 ) . A small cluster of embedded sand fleas was detected around the anus . Most of the sand fleas were viable . The patient weighed only 23 kg . Patient 4 had approximately 400 embedded sand fleas in all stages of development . Lesions were located on the soles , the lateral rim of the feet , ankles , knees , elbows and hands . On the soles lesions occurred in three layers on top of each other . Patient 5 had approximately 1 , 000 lesions , of which 950 consisted of decaying or dead sand fleas at the time of examination . The laboratory findings are summarized in Table 2 . Patient 1 and 3 had a severe anemia . Patient 2 had a leukocytosis of 12 , 700 cells/μl . Patient 2 and 4 showed a hypereosinophilia ( 2 , 540 and 2 , 520 eosinophils/μl , respectively ) . After two applications of the dimeticone oil , patients recovered rapidly . After three to four days , inflammation of the skin had regressed considerably ( Fig 8 ) and patients could place their feet on the ground without feeling pain . After one week all lesions had developed into crusts and patients were transferred to a rehabilitation centre for Amerindians at the periphery of Mitú . At the end of the rehabilitation , i . e . 15 to 20 days after the first treatment with dimeticone , the patients had increased their weight Patient 1: from 35 to 41 kg; patient 2: from 23 to 28 kg; patient 3: from 39 to 45 kg; patient 4: from 33 to 41 kg ( not data available from patient 5 ) .
It has been shown that age-specific prevalence curves show a peak in children and adults > 60 years and that the elderly always bear a high disease burden [1 , 37] . Due to age-related poor sight elder people are unable to identify exactly where the parasite is located , and by consequence need help from family members to remove embedded sand fleas with a sharp instrument , the traditional treatment still in use in Amerindian communities [11] . Even if vision is adequate , elder people will usually be unable to bend down to the feet . Besides , elder people are less mobile and tend to stay at intradomiciliary transmission sites for many hours of the day . Another characteristic is that elder people frequently live alone and need to be cared for by relatives . Patient 1 and 2 were wholly dependent on food provided by the only healthy adult person in the household . Patient 3 , 4 and 5 were taken care for by their daughters . When the daughter left the community , the elder sons barely managed to provide food for their own family and usually nothing was left for the patients . In Amerindians of the Amazon basin it is a question of survival for the family that old or handicapped people , who cannot provide food for themselves , become gradually separated from their families and are no longer cared for . This explains the malnutrition and cachexia observed in patients 1 , 2 and 3 . Medical conditions which cause patients to involuntarily spend many hours in direct contact with the soil , such as sleeping sickness , mental disorders , alcoholism or Klippel-Trenaunay-Syndrome , are known since long as factors predisposing to very severe tungiasis [38 , 39] . The same holds true when patients do not perceive pain or itch as in leprosy [38] . Patient 2 exposed skin of the buttocks for many hours of the day when crouching on his heels or squatting directly on a dirt floor in his ragged shorts . Persistently exposing skin at other areas than the feet facilitates the penetration of sand fleas at ectopic sites , such as around the anus and the inguinal area [13 , 14 , 22 , 40] . Four patients had a pre-existing medical condition which restricted their mobility and left them laying in their hammock most time of the day . In the Amazon lowlands hammocks are installed such that they swing 20–40 cm above the floor . People rest in the hammock with a hand , an elbow , the lateral rim of the foot and/or the knee outside very close to the floor and place these body areas from time to time on the ground . By consequence , rather large areas of the skin are exposed to sand fleas which , in turn , explains the high parasite burden and the ectopic localizations ( Figs 1B , 1C , 2B and 3B ) [41 , 42] . Since tungiasis of the feet impairs mobility due to severe pain , a vicious cycle develops in embedded sand fleas accumulating over time eventually resulting in very severe tungiasis and total immobility , if patients do not have access to treatment . Another predisposing factor for a continuous accumulation of a high parasite load is intradomiciliary transmission . In the setting of the five patients , intradomiciliary transmission was very likely: In Amerindian communities , a fire is lit just below the hammock to warm up the sleeping person the whole night . This makes the surrounding earth floor dry and causes cracks in the soil , a perfect niche for the completion of the off-host cycle of T . penetrans . Dogs are frequently infected with T . penetrans and usually spend the night inside the dwelling next to the fire place [11 , 13 , 36] . Thus , eggs expelled from embedded sand fleas develop into adults in the area directly below the hammock or next to it . Hence , the probability to get infected is high as soon as the feet of a person are placed on the ground . In Vaupés Amerindians strongly believe that severe tungiasis is due to an oath ( maldad or incantation ) and , hence , they are convinced that the disease cannot be cured . By consequence , people think that the afflicted person will be devoured by the parasite sooner or later and that only a traditional healer ( payé ) can interrupt this process . This explains the attitude that community members think that it is better to stay away from the affected person and why the patients of this study were secluded by the community . The web-of-causation making tungiasis a life-threatening condition is depicted in Fig 9 . Lack of knowledge of the pathogenesis of tungiasis was identified as the leading cause of very severe disease and death in colonial times when Spanish and Portuguese conquistadors penetrated into the interior of the yet unknown continent and were confronted with pathogens they had never met before [43] . The scenario was similar in Africa , after T . penetrans arrived in Angola in 1875 and then rapidly spread along trading routes and with military missions [14 , 17 , 21 , 23 , 24 , 33]; in natives of Madagascar after the disease all of a sudden appeared on the island [18 , 25]; in troops deployed for the first time in an endemic area , such as French soldiers in Mexico in 1862 [16 , 31] . That the lack of knowledge of tungiasis rapidly leads to very severe disease and eventually to death is best demonstrated by the history of 100 Irish settlers who moved from the Coast of French Guyana to the interior of the country in 1852 . They became so heavily infected with sand fleas that 70 settlers died within a year and the remaining 30 came back to the coast in a very debilitated condition [25] . Lack of knowledge also seemed to play a role when tungiasis spread in Central Africa in the 1920s [33 , 34] . However , lack of knowledge does not seem to play a role in the five patients . Tungiasis is known since pre-colonial times in Amerindians and local people clearly understand the pathogenesis of the disease [44] . The spectrum of pathology associated with very severe tungiasis and its consequences are known since long . Already in 1900 L . L . Decle , a physician of the British army noted: “Ulcers caused by Tunga penetrans were the most frequent ailment treated and second only to smallpox as a cause of death in Luanda’s hospital between July and October 1877…” “Never in my life have I seen such awful ulcers . Some of the men had their bone of their big toe protruding fleshless for more than an inch; others had quite a square inch of the bone of the heel exposed . Even when tungiasis did not cause death , the parasite paralyzed movement . ” The author continued:”In some villages of Uduhu , I found the people starving , as they were so rotten with ulcers from jiggers that they had been unable to work in their fields , and could not even go to cut the few bananas that had been growing . ” [20] Morbidity associated with tungiasis and its sequels is depicted in Table 3 . The patients in this study showed a few peculiar findings . Patient 2 and 3 had a cluster of lesions around the anus , an ectopic localization which has never been described before . Patient 1 and 3 showed a severe anemia which required immediate blood transfusion in patient 3 . There is reason to believe that the anemia was the consequence of the high parasite load . First , severe infestation with Ctenocephalides felis in animals causes a significant anemia [45 , 46] . Second , in contrast to most other Siphonaptera , which are only temporary ectoparasites , T . penetrans sucks blood almost permanently . Third , significant anemia was present in a very severe case of tungiasis from Tanzania with about 1 , 100 lesions [42] . With a persistent high parasite burden , the chronic blood loss will be substantial over time and eventually results in life-threatening anemia . Of course , the co-existing hookworm infection also may have contributed to anemia . Hitherto , the only available treatment of tungiasis in Amerindian communities is surgical removal of embedded sand fleas using inappropriate instruments such as thorns , sharpened wooden sticks , knives etc . It goes without saying that this procedure is painful and always bears the risk of bacterial or fungal superinfection of the sore . Even in hospital settings , surgical extraction is virtually impossible if a great number of sand fleas are located in clusters or on top of each other in hyperkeratotic skin as in our patients . The medical device NYDA contains two dimeticones with different viscosity and rapidly enters into tiny openings and covers microscopic surfaces [47] . The formula is used for the treatment of headlice in more than 20 countries . Its mode of action is purely physical . The topical application of this product has been proved highly effective in randomized control trials in Kenya and Uganda in patients with up to 30 embedded sand fleas [29 , 48] . Here we show that the formula is also effective in patients with several hundred of embedded sand fleas located in clusters and in several layers on top of each other in hyperkeratotic or partially necrotizing skin . While in simple tungiasis an application of a few drops of the dimeticone—targeted to the abdominal cone which protrudes through the skin–are sufficient [48] , in very severe tungiasis the skin needs to be intensively wetted and treatment should be repeated after 24 hours . Taken together , this case-series shows that very severe tungiasis still occurs in Amerindian communities . The true frequency of this devastating condition is probably underestimated . A characteristic pattern of pre-existing medical conditions and socio-economic and environmental factors determines whether tungiasis develops into a life-threatening condition . Obviously , most of these factors are related to extreme poverty . Our findings are also a good argument to make a call for action for those countries in which tungiasis occurs in remote settings and where health coverage is poor . Dimeticone should be made available to treat patients in an early stage of disease to avoid life-threatening sequels . | Tungiasis ( also called sand flea disease ) is a neglected tropical disease ( NTD ) caused by the penetration of female sand fleas in the skin , typically at the toes , the sole or the heel . Once embedded in the upper strata of the skin , the parasite hypertrophies , enlarging its body size by a factor of 2000 within ten days . This causes intense inflammation with pain and itching , eventually leading to impaired mobility . During a period of three weeks , eggs are expelled through a tiny opening in the skin . When the last egg has been released into the environment , the parasite shrinks and eventually dies . Hence , by nature tungiasis is a self-limited infection . However , in endemic settings re-infection is the rule and parasite load gradually accumulates over time . Here we report five cases with extremely severe tungiasis in patients with 400 to 1 , 300 embedded sand fleas . Not only the feet were affected , but clusters of parasites also occurred at the ankles , the knees , the elbows , the hand , the fingers and around the anus . The patients were partially or totally immobile . Two patients were cachectic and one required a blood transfusion . All patients showed a characteristic pattern of pre-existing medical conditions and culture-related behaviour facilitating continuous re-infection . | [
"Abstract",
"Introduction",
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... | 2019 | Very severe tungiasis in Amerindians in the Amazon lowland of Colombia: A case series |
Most angiosperm nuclear DNA is repetitive and derived from silenced transposable elements ( TEs ) . TE silencing requires substantial resources from the plant host , including the production of small interfering RNAs ( siRNAs ) . Thus , the interaction between TEs and siRNAs is a critical aspect of both the function and the evolution of plant genomes . Yet the co-evolutionary dynamics between these two entities remain poorly characterized . Here we studied the organization of TEs within the maize ( Zea mays ssp mays ) genome , documenting that TEs fall within three groups based on the class and copy numbers . These groups included DNA elements , low copy RNA elements and higher copy RNA elements . The three groups varied statistically in characteristics that included length , location , age , siRNA expression and 24∶22 nucleotide ( nt ) siRNA targeting ratios . In addition , the low copy retroelements encompassed a set of TEs that had previously been shown to decrease expression within a 24 nt siRNA biogenesis mutant ( mop1 ) . To investigate the evolutionary dynamics of the three groups , we estimated their abundance in two landraces , one with a genome similar in size to that of the maize reference and the other with a 30% larger genome . For all three accessions , we assessed TE abundance as well as 22 nt and 24 nt siRNA content within leaves . The high copy number retroelements are under targeted similarly by siRNAs among accessions , appear to be born of a rapid bust of activity , and may be currently transpositionally dead or limited . In contrast , the lower copy number group of retrolements are targeted more dynamically and have had a long and ongoing history of transposition in the maize genome .
Most DNA within angiosperm genomes is repetitive , typically representing active transposable elements ( TEs ) or DNA derived from formerly active TEs . This repetitive component is the primary determinant of genome size ( GS ) variation across species , constituting ∼20% of small genome species like rice and A . thaliana but >85% of larger genomes like that of maize ( Zea mays ssp . mays ) , barley and wheat [1] . The preponderance of TE-derived DNA suggests superficially that TEs reign unchecked within plant genomes , but this is of course untrue because natural selection acts both to attenuate TE activity and to remove them from genomes and populations [1] , [2] . TE activity is also attenuated by the plant host , which uses small interfering RNAs ( siRNAs ) to silence TEs both before and after transcription . Many of the molecular details of this host response remain unclear , but the general mechanism of pre-transcriptional silencing is now well known [3]–[5] . TEs are first recognized by the host , probably via double-stranded RNAs that originate either as a consequence of a hairpin structure in the RNA or by complementary transcripts from different strands . These double-stranded RNAs are cleaved by DICER complexes into 24 nucleotide ( nt ) fragments , and the 24 nt siRNAs are loaded onto an Argonaut complex , which migrates to a precise chromosomal location based on homology between the DNA-target and the 24 nt siRNA . The Argonaut complex then attracts methylation machinery , leading to de novo TE methylation and silencing . Post-transcriptional silencing is not as thoroughly characterized , but it appears to rely primarily on siRNAs of 21 nt in length for most plants but predominantly of 22 nt in length for maize ( Zea mays ssp . mays ) [5] , [6] . The 21/22 nt siRNAs may originate by several mechanisms , including from miRNA genes , from phased processing of RNAs [7] and from digestion and processing of mRNAs [8] , [9] . No matter the source , 21/22 nt siRNAs target mRNA transcripts through homology , with the consequent double-stranded RNA either modified or degraded [3] , [5] . Ultimately the host response leads to the attenuation of TE activity and limits TE copy number . However , TEs may occasionally escape host control , leading to a ‘burst’ of transposition , an increase in copy number and potentially a shift in genome size [10] , [11] . Although not well characterized , bursts of activity may vary by TE type , for at least two reasons . First , TEs have inherently different multiplication capabilities [12] . Cut-and paste class II DNA transposons replicate conservatively , while copy-and-paste class I retroelements have the capability to replicate multiplicatively . Second , the host response can vary with the TE subfamily [13] , [14] . This variation in host response has become obvious in part from the study of methylation mutants . For example , mutants with modified activity of RNA-dependent RNA polymerase 2 ( RDR2 ) produce fewer 24 nt siRNAs than wild type , with a concomitant increase in TE transcription [7] , [13] , [15] . However , in the maize RDR2 ( mop1 ) mutant , TE transcription is actually decreased for a subset of TE subfamilies [13] , illustrating that not all TEs are equal with respect to the mechanisms of the host response . Despite the fact that the interaction between TEs and siRNAs is a critical aspect of genome function and evolution , the co-evolutionary dynamics between these two entities remains poorly characterized . Such characterization requires the study of covariation between siRNA expression and TE copy number . However , the estimation of TE copy numbers is not trivial because “complete” genomes often lack components of repetitive DNA . For example , the maize reference sequence is estimated to be missing ∼11% of the genome [16] , most of which is likely to be repetitive elements . To get around this problem , Tenaillon et al . [17] have developed a method to estimate the TE complement in the maize genome based on high throughput sequencing ( HTS ) of genomic samples . In this method , the HTS reads are mapped against an exemplar set of sequences that represent ∼1500 TE subfamilies in the maize B73 reference genome [16] . By assessing the coverage of each exemplar , researchers have been able to not only to estimate relative contribution of individual TE subfamilies but also to identify some of the repetitive DNA that was missing from the reference [17] , [18] . This study is born from an observation about TE abundance that is based on the data of Tenaillon et al . [17] . In perusing copy number among over ∼1500 TE subfamilies in the maize genome , we have noticed that TEs fall into three distinct groups based on their class and copy numbers . The first group is set of DNA ( class II ) transposons . Another is composed of high copy number retroelements , such as members of the Opie family of the Long Terminal Repeat ( LTR ) Copia superfamily and members of the Cinful family of the LTR Gypsy superfamily . The final group consists of over 300 retrolement subfamilies with lower copy number . This observation suggests that there is a higher-order organization of elements within the maize genome , and it has prompted us to study features of their evolutionary dynamics . To characterize the groups , we first employ bioinformatic and genomic analyses of data from the B73 reference genome . Specifically , we have used newly generated siRNA data to compare and contrast patterns of the siRNA-mediated host response among TE groups . Then , to better understand the evolutionary dynamics of these groups , we compare TE abundances and siRNA profiles among B73 and two additional landraces , Palomero Toluqueño ( PT ) and Olote Colorado ( OAXA ) . We have chosen these samples for two reasons . First , they are roughly equidistant in genetic relationship to the B73 reference; based on SNP data [19] , the two landraces form an ingroup with B73 as the outgroup . The second reason is that they represent extremes of the ∼30% variation in genome size ( GS ) within the species [20] . PT has a genome size of 5 . 58 pg/2C , which is similar to that of the 5 . 64 pg/2C B73 reference genome , whereas the OAXA genome is ∼1 . 3-fold larger , at 7 . 11 pg/2C [20] . This extreme difference in GS enhances the a priori probability that there is , in fact , variation in TE copy numbers and siRNA expression in our sample of germplasm . With genomic and siRNA HTS data from three accessions , we address a set of four questions . First , given that TEs fall naturally into three groups based on their class and copy numbers , do they vary in other characteristics ? If so , what might these characteristics imply about genome organization and the host response ? Second , are these three groups consistent across the maize germplasm , suggesting that this organization is a higher-order property of the maize pan-genome [21] ? Third , do the groups vary in their evolutionary dynamics , as measured by differences in abundance among accessions ? Finally , do shifts in siRNA expression covary with the abundance of the TEs they target ? Our ultimate goal is to begin to unravel the evolutionary dynamics between TEs and the host response in the context of the history and organization of the maize genome .
While surveying copy numbers of TEs within B73 , we observed an interesting phenomenon . The observation began by mapping 18 , 689 , 555 paired-end ( PE ) reads of B73 genomic data to the published Unique TE ( UTE ) database . The UTE consisted of 1514 TEs that was built by filtering the exemplar database of 1526 TEs ( TEdb ) [16] , [22] to reduce cross-homologies between TE exemplars and thereby improve mapping resolution [17] . Plots of the RPKM ( Reads per Kilobase per Million mapped , see Methods ) values for individual TE subfamilies ( RPKMTE ) yielded different distributions between DNA transposons and RNA transposons . The DNA transposons had a unimodal distribution of RPKMTE , while the RNA transposons had a bimodal distribution ( Figure 1a ) . We constructed a Rank-Frequency plot , which is a representation of the Empirical Distribution Function ( EDF ) , for these data and found that DNA ( or class II ) transposons closely matched a log-normal distribution ( Figure 1b ) but RNA elements did not . Instead , the RNA elements fit a mixture of a log-normal distribution and another ( approximately Poisson ) distribution . Based on these distributional properties , we defined three TE groups: group D , which consisted of 841 exemplar DNA elements; group R1 , which included 365 exemplar RNA elements with relatively low abundances; and group R2 , the set of 198 high abundance class I retroelements ( Figure 1ab; Table S1 ) . Note that these three groups do not include 110 exemplar elements for which the RPKMTE data suggested fewer than 2 copies in B73 . Among the three groups , it may not be surprising that the ‘high copy’ R2 group contained retroelements known to be common throughout the maize genome , including Ji and Opie Copia elements and the Cinful , Huck and Prem1 Gypsy elements ( Table 1 ) [22]–[24] . There is nonetheless substantial overlap in the identity of superfamilies between the R1 and R2 classes . For example , the R1 and R2 group include Copia ( n = 95 and n = 52 , respectively ) and Gypsy ( n = 128 and n = 112 , respectively ) exemplars , as well as a wide array of other LTR retroelements and LINE L1 elements ( Table 1 ) . Thus , at the gross levels of TE Order and Superfamily [25] , there was extensive overlap between the R1 and R2 groups . Their primary distinction was abundance . Given noticeable differences in abundance dynamics , we investigated additional characteristics among the three groups ( Figure 1c–f ) - including their genomic properties , siRNA targeting and insertion ages – to help determine whether the groups are differentiated by characteristics beyond abundance . We found that the abundant R2 group of retroelements was longer , on average , than the other two groups ( Figure 1c ) , with the R1 group intermediate in length among the three . The groups also differed in genomic location ( or context ) . We assessed genomic context by mapping paired-reads that did not match the same TE exemplar [17] . That is , if one paired-end matched a known TE exemplar , we could assess whether the second read matched to a second TE subfamily , to a gene in the Filtered Gene Set ( FGS ) or to a reference set of Knob and Centromeric ( KnobC ) repetitive DNA ( see Methods ) . The results indicated that the D group was more often located close to genes [22] , the R2 group was more often located near other TEs , and R1 elements were closer to genes on average than R2 elements ( Figure 1f ) . We assessed one aspect of the host response to these groups by sequencing 22 nt and 24 nt siRNA from B73 leaf tissue , resulting in a total of 9 . 23×106 and 20 . 16×106 reads , respectively , for the two size classes . These siRNA reads were mapped to the TEdb of 1526 elements [16] , and we recorded the number of siRNA hits to each TE exemplar . The mapping results revealed that the R2 group had the highest total siRNA hits , in part due to their higher abundance ( Figure 1d ) . However , when corrected for RPKMTE , these TEs tend to be lowly targeted by both 22 nt and 24 nt siRNAs on a per-copy basis ( Figure 1e ) , perhaps because long retroelements are targeted primarily at their ends rather than across their entire length by siRNAs and methylation marks [26]–[28] . In contrast , the D and R1 TEs were targeted by significantly higher numbers of siRNAs per RPKMTE and also by higher 24 : 22 nt siRNA ratios ( Figure 1e ) . Finally , we summarized insertion time estimates of the R1 and R2 groups , using data from a previous study of the B73 genome [22] ( Figure 2 ) . Both groups exhibited heterogeneity in insertion times , with some elements estimated to be >5 million years ( my ) old . However , the average age of the two groups differed significantly ( p<0 . 001 , Kruskal-Wallis ) , with the R1 groups younger ( average estimated age 0 . 93 my , n = 305 , std . dev . 1 . 11 ) than the R2 group ( average estimated age 1 . 04 my , n = 191 , std . dev . 0 . 84 ) . Moreover , the R1 group included elements with a range of insertion ages that included recent insertion ( 0 . 00 my ) . In contrast , the age distribution of the R2 group suggested that most element proliferation occurred in a well-defined period , with no evidence of insertion in the last 0 . 36 my . To sum: While there is variation within the D , R1 and R2 groups for all measured characteristics ( Figure 1 ) , the three groups nonetheless differed significantly for most measured characteristics , including size , location , age and siRNA targeting . These differences suggest the three groups are biological entities with distinct properties . Given dramatic differences in age and siRNA targeting among groups , we also determined whether the groups differ in expression dynamics . To assess expression , we examined existing RNAseq data from B73 leaf tissue ( see Methods ) . The data indicate that total expression of R2 elements is highest among the three groups , with similar levels of expression for the D and R1 groups ( Figure 3a ) . However when corrected for abundance , the R2 TEs have the lowest expression on a per-copy basis ( Figure 3b ) , consistent with the possibility of copy-number repression [29] , [30] . In contrast , R1 elements exhibit the highest expression on a per-copy basis ( Figure 3b ) . We found similar expression patterns based on germline ( immature tassel ) tissue ( data not shown ) . We also analyzed expression data to assess whether the three groups have different dynamics with respect to an interruption in the host response . To assess this phenomenon , we assessed RNAseq expression data from reference [13] , which generated data from the shoot apical meristems of wild type ( wt ) and RDR2 mop1 mutant plants in the W22 background . Jia et al . [13] reported 373 TE subfamilies with differential expression in the mop1 mutant relative to the wild type ( wt ) . Of these , we selected the 340 TE subfamilies with names that matched the exemplar TEs from the UTE ( Table S2 ) . [For this subset of 340 TEs , we first confirmed that the previous observations about length and other differences among groups continued to hold ( Figure S1 ) . ] We then examined the fold-change ( FCmop ) in expression between wt and mop1 . There were clear trends among groups . On average , expression of the D group was enhanced in the mop1 mutant; for the 109 TE subfamilies in the data set expression increased slightly , ∼0 . 29 log 2 units or ∼1 . 2-fold on average ( Figure 3c ) . The 144 members of the R2 group in the dataset exhibited no strong tendency , with an average 1 . 03-fold shift in expression . In contrast , the R1 group experienced an average −1 . 6-fold decrease in expression in the mop1 mutant , with 80% ( 70 of 87 ) exemplars exhibiting a decrease . The effect of decreased expression was particularly prominent for TE exemplars targeted by high ratios of 24∶22 siRNA , based on our B73 leaf data ( Figure 3d ) . Thus , the puzzling phenomenon of decreased TE expression in a maize RDR2 mutant is due to R1 elements . We questioned whether the three TE groups were unique to the reference genome or a consistent genomic feature across maize sensu lato . To assess TE copy numbers across individuals , we sequenced one lane of genomic DNA from each of the landraces Palomero Toluqueño ( PT ) and Olote Colorado ( OAXA ) . Recall that PT has a genome size of 5 . 58 pg/2C , which is similar to that of the 5 . 64 pg/2C B73 genome , whereas OAXA genome is 7 . 11 pg/2C [20] . Our Illumina sequencing yielded a total of 53 , 535 , 615 and 54 , 318 , 379 paired-end reads , respectively , for the two accessions ( Table S1 ) . These genomic HTS data were mapped to three databases: i ) the Filtered Gene Set ( FGS ) [16] , ii ) the KnobC database and iii ) the UTE . Briefly , the percentage of reads that mapped to the FGS and UTE was similar across accessions: 15 . 0% and 61 . 7% , respectively , for B73; 17 . 0% and 62 . 4% for PT; and 16 . 8% and 55 . 2% for OAXA . The largest difference between accessions was in the percentage of genomic HTS reads that mapped to the KnobC database ( at 6 . 12% for B73 , 1 . 26% for PT and 11 . 14% for OAXA ) . Thus , the most obvious difference between accessions was in heterochromatic sequences , consistent with previous studies suggesting that knob DNA is the primary determinant of GS differences within the genus Zea [18] . Given HTS data , we determined whether the R1 and R2 groups were consistent across accessions or simply a property of the B73 genome . We therefore calculated the RPKMTE values based on reads from PT and OAXA ( Figure S2 ) . For both landraces , the retroelements had a bimodal distribution of copy number , consistent with the B73 analyses ( Figure 1ab ) . Moreover , the same TE subfamilies fell within the two groups: across all three accessions , there was 97 . 3% agreement in classification to the R1 and R2 groups . Given this fact , we used the D , R1 and R2 groupings as defined in B73 for all ensuing analysis . Given the genomic data , we assessed whether the groups evolve similarly by focusing on shifts in abundance among accessions . We did this in two ways . First , for each of the 1514 TE exemplars in the UTE we assessed the number of mapped genomic reads to each exemplar; we then calculated correlations between accessions across all TE exemplars using a logarithmic transformation . The correlation in TE abundance was high for all three pairwise comparisons but highest for the PT and OAXA comparison ( r2 = 0 . 992 versus r2 = 0 . 942 between PT and B73 and r2 = 0 . 939 between B73 and OAXA ) ( Figure 4a ) . Despite these high pairwise correlations there were nonetheless detectable differences in TE abundances for individual TE subfamilies . We applied two statistical tests to assess linear differences between accessions based on the number of hits in each TE exemplar ( Table 2 ) . The first was a standard χ2 ( χ2Std ) that compares the proportion of hits to a particular TE subfamily between two accessions; with a False Discovery Rate ( FDR ) of q<0 . 001 , this method resulted in ( for example ) 834 TE subfamilies with detectable difference in abundance between PT and OAXA ( Table 2 ) . We also devised a novel χ2 ( χ2Corr ) that corrects for the fact that different accessions may have different overall proportions of TEs within those genomes ( see Methods ) . Based on this more appropriate method , 514 TE subfamilies ( 33% ) differed between PT and OAXA , and ∼1000 TE subfamilies differed between B73 and each of the two landraces ( Table 2 ) . These results generated a ranked list of TE subfamilies that are most likely to vary between accessions ( Table S1 ) , but the results require further verification ( see Discussion ) . Second , we assessed whether shifts in copy number were characteristic of the D , R1 and R2 groups . To address this issue , we measured the fold-change in abundance for each TE exemplar , or FCTE , as the log base 2 difference in normalized hits between two accessions ( see Methods and Table S1 ) . Note that FCTE can be either positive or negative , representing increases in copy number for one or the other accession . We then plotted FCTE values for each group and calculated the average FCTE for each group ( Table 3; Figure 5 ) . In all pairwise comparisons between individuals , the average absolute value of FCTE was higher for R1 and R2 than for DNA elements , differing significantly in all comparisons ( p<<0 . 05 , t-test ) . In contrast , the R1 and R2 groups did not differ consistently from one another in average FCTE ( p = 0 . 017 for B73 vs . PT , but p>0 . 05 for the other pairwise comparisons; two-tailed t-test ) , suggesting that the two groups vary similarly in copy numbers between accessions . Thus , fold-change statistics suggest that the R1 and R2 groups varied in abundances more markedly among accessions than did the D group . Because siRNA targeting is an important step in TE silencing and should therefore affect TE activity , we were interested in comparing copy number dynamics with the expression of small RNAs . That is , do copy number and small RNA expression covary ? To address this question , we sequenced two siRNAs libraries from the same tissues ( the third and fourth leaves ) of PT and OAXA , resulting in >37 . 0×106 24 nt siRNAs and >15 . 0×106 22 nt siRNAs for each accession . We mapped siRNAs to the TEdb of 1526 elements , recorded the number of siRNA hits to each TE exemplar , and normalized expression by the upper quartile [31] . We calculated fold-change statistics for 22 nt ( FC22 ) and 24 nt ( FC24 ) siRNA for each TE subfamily in each of the three groups ( Table S1 ) . The results indicated that there were some marked differences in siRNA targeting for some individual D and R1 exemplars , with 222 and 174 subfamilies exhibiting absolute values of FC22 and FC24>2 . 0 , respectively , in the B73 : PT comparison ( Figure 6 ) . However , the variability in FC for the R2 group was relatively small for both 22 nt and 24 nt siRNA expression ( Figure 6 ) . The fold-change patterns based on TEs ( Figure 5 ) and siRNAs ( Figure 6 ) suggest both that siRNA targeting on R2 is highly conserved among accessions and that variation in siRNA expression is decoupled from TE copy number variation . We assessed this more formally using two approaches . The first was to assess the correlation between FCTE vs . FC22 and between FCTE vs . FC24 within groups or across all 1514 TE exemplars . No significant correlations were detected . For example in the B73:PT comparison , FCTE was uncorrelated with FC24 ( r2 = 0 . 002; p = 0 . 10 ) and FC22 ( r2 = 5×10−6; p = 0 . 94 ) across all of the TE exemplars in the R2 group . The second approach was to formulate and conduct statistical test of the hypothesis that TE copy number and siRNA expression change proportionally between individuals . We devised such a test ( χ2Prop ) and applied it to all TE exemplars between accession pairs ( see Methods and Text S1 ) . Based on the χ2Prop test , data from up to 917 TE subfamilies rejected the null hypothesis of proportionality between TE copy number ( RPKMTE ) and 24 nt siRNAs ( Figure 4bc; Table 2 ) . There were fewer rejections between TE copy number and 22 nt siRNAs , but up to 506 between B73 and PT . Thus , the overall pattern for our data is that , for any particular TE subfamily , the expression dynamics of siRNAs that target the TE do not closely mimic shifts in copy number , as measured by HTS data .
With the availability of genomic sequence data from multiple individuals , it has become possible to procure a snapshot of the “pan” ( or whole ) genome of a single species . The pan genome is defined to include a core component that is shared among individuals and also a non-core component that contains strain-specific DNA [21] . For maize , we know that the non-core component is substantial , because GS varies among individuals by up to at least 30% [20] . This and previous studies based on HTS genomic data suggest that the largest share of the non-core component is heterochromatic and knob repeats [18] . The core component is typified first by the genic fraction . For some of our analyses – i . e . , those that employ χ2Corr and χ2Prop – we have assumed that the genic fraction represented by the Filtered Gene Set ( FGS ) is invariable among accessions . Under this assumption , the genic fraction provides an internal control for the ‘coverage’ of a library [17] , [18] . We know that this is not a perfect assumption because the inbred lines B73 and Mo17 are estimated to vary in ∼180 annotated single copy genes and thousands of genes may differ between B73 and other germplasm [32] . It nonetheless seems reasonable to assume that the genic component is relatively static compared to either heterochromatic repeats or TEs . TEs represent both the non-core and the core components of the pan-genome . They are part of the non-core component because they vary remarkably among maize individuals within a syntenous region [33] , because the proportion of TEs within the genome varies among individuals [18] and because individual TE subfamilies vary in copy number between accessions ( Table 2 ) . However , we have also shown that the organization of TEs is core characteristic , in that TEs are conserved in three groups across a small but wide representation of maize germplasm . These three groups are class II DNA elements ( D ) , low copy number class I RNA elements ( R1 ) , and a third set of higher copy RNA elements ( R2 ) . Recognition of this organization , and the consistency of this arrangement among maize genomes , is a novel contribution of this study . To what extent to the three TE groups vary in copy number among accessions ? We took two approaches to assess this question . The first was to compare estimated abundance changes for individual TEs ( Figure 4a and Table 2 ) . While we detect significant differences between accessions for many TE subfamilies , we urge caution in the interpretation of these results . For example , even though we have introduced an improved , modified and more conservative χ2 test , similar approaches are known to have high false positive rates despite the fact they are applied commonly to genomic data [e . g . , 14] . This tendency is perhaps best illustrated by analyses of two biological replicates from reference [18] ( Figure S3 ) , for which we find significant differences in abundance for 331 TE subfamilies based on identical methods ( χ2corr; Table 2 ) . This number provides a ‘baseline’ in which to evaluate our results . For our comparisons , the fewest significant differences were for 514 TE subfamilies between PT and OAXA ( Table 2 ) , suggesting that ∼200 ( = 514-331 ) TE subfamilies still differ in abundance between these accessions . Our second approach was to report fold-change ( FCTE ) statistics that estimate shifts in abundance between accessions for groups of TEs . Our thinking is that FCTE provides a better indication of overall trends by averaging across TE families , but this approach , too , is not without limits ( Figure S3 ) . That said , our analysis of FCTE indicates that the R1 and R2 groups differ ∼1 . 3-fold in copy number on average between the B73 data and the data from the two landrace accessions ( Figure 5 ) . In contrast , the DNA elements vary little among accessions , but this may not be particularly surprising given their conservative mode of replication . FCTE values also suggest that the B73 data differs more from the two landrace than the landraces differ from each other ( Figure 4a ) , with the B73 data having a markedly higher abundance for R1 and R2 elements ( Figure 5 ) . At this point it is not possible to infer whether B73 is an outlier because of genetic differentiation ( i . e . , B73 is the outgroup to the two landraces ) or because of a history unique to B73 , such as inbreeding and intensive selection . In this context , it is worth clarifying that FCTE is designed to measure an outcome – i . e . , differences in abundance – that likely summarize events across a range of mechanistic phenomena . On the one hand , transposition events contribute to differences in copy numbers between and among individuals , and hence FCTE must encompass TE activity and transposition . However , FCTE values may also reflect other processes that shift copy numbers , including phenomena like segmental duplication events , element deletion and natural selection , which likely differentially affects TE subfamilies that are located close to genes [34] . In fact , the Long Terminal Repeat ( LTR ) elements of the sort that constitute much of the R1 and R2 groups are particularly prone to deletion by unequal recombination [35] , [36] , and this process may be quite rapid . It is thus possible that element deletion contributes as much ( or more ) than transposition to FCTE . Although FCTE is not a direct measure of transposition events , it is not apparent that there are better measures to assess TE activity . For example , TE expression is often used as a measure of element activity , but TE transcription often does not reflect actual transposition events [13] , [37]–[39] . There is , in fact , discordance between our estimates of abundance shifts between accessions ( FCTE; Figure 5 ) and expression within B73 ( Figure 3ab ) . This discordance likely reflects that neither measure perfectly assesses transposition; TE expression is a poor measure of transposition activity but FCTE measures an evolutionary outcome ( abundance ) rather than transposition directly . A growing body of literature indicates that silencing mechanisms vary across TEs within the genome . For example , epigenetic modifications may be dependent or independent of siRNAs . The siRNA dependent processes may be , in turn , RDR2 dependent or independent , such as the silencing of MuDR elements by mukiller [40] . Even RDR2 mediated silencing seems to depend on a bevy of other characteristics , including the physical structure ( nested or not ) and chromosomal distribution of TEs [13] , [29]; their copy number , length and age [22] , [26] , [34] , [41] , [42]; and their developmental timing [29] , [39] , [43] . While silencing varies among different TE families , we were interested in whether siRNA expression tracks copy numbers across individuals . We found no evidence that siRNA expression covaries with TE abundance , as shown by the lack of overall correlation between FCTE and either FC22 or FC24 . We also formulated explicit tests of proportionality ( Table 2; Figure 4 ) that demonstrate that siRNA expression and TE abundance often do not covary . This low covariance is somewhat surprising: if shifts in TE abundance are due to element activity , it seems reasonable to assume that more siRNA is needed to silence more TE copies . It is possible that our inferences about siRNA targeting are misled by our focus on leaf , as opposed to germline , tissue . To assess whether siRNA differs substantially among tissues , we reanalyzed siRNA data from previous publications [14] , [44] . These data , which originated from B73 shoot apex and developing ear , were mapped to the TEdb , and then compared between tissues using the standard χ2 approach ( χ2Std ) . Similar to a previous study of methylation patterns [45] , we find that the number of significant siRNA differences between tissues is smaller than that between individuals . We found that the number of TEs ( of 1526 total ) targeted differentially between tissues was 297 and 697 for 22 nt and 24 nt siRNAs , respectively . Notably , these differences may be inflated by the fact that the libraries used for these inter-tissue comparisons came from different growth conditions and even different experimental platforms [14] , [44] . In contrast , ∼500 and ∼900 TE subfamilies are differentially targeted between B73 and the landrace accessions for 22 and 24 nt siRNAs ( Table 2 ) . Thus , while inter-tissue ( or developmental ) variation in siRNA targeting is considerable , it is less substantial than that between individuals , suggesting that the lack of covariance between TE abundance and siRNA expression may not be specific to leaf tissue . Perhaps the most interesting aspect of this study is the previously unrecognized contrast between the R1 and R2 groups of retroelements . These groups consist of comparable Orders and Superfamilies of TEs ( Table 1 ) , and they exhibit similar levels of copy number variation among our sample of accessions ( Figure 5 ) . However , they differ in almost every other measurable characteristic , ranging from average length , to genomic context , to levels of siRNA targeting ( Figures 1 & 6 ) . They even vary as to whether methylation spreads to flanking regions from individual elements , because we have found that this is a phenomenon confined primarily to R2 elements [46] ( data not shown ) . All of these descriptors suggest that the two groups have different dynamics with respect to the host response and also different evolutionary histories . Given all of this information , the R2 group is still surrounded by at least two mysteries . The first is related to the observation that most R2 insertion occurred in a well-defined period , with little additional evidence of recent insertional activity ( Figure 2 ) . This observation suggests that these high-copy elements proliferated in a concerted burst of activity . Since the R2 group encompasses several TE families and Orders ( Table 1 ) , the event that triggered this burst must have had genome-wide effects . Yet the burst is too young to correspond to the ancient polyploid event in the maize lineage [47] and too old to correspond to maize domestication [48]; thus neither seem likely causes . The second mystery is why the age distribution signals little recent insertional activity despite copy number variation ( Figure 5 ) and ongoing expression ( albeit at a low level on a per-copy basis; Figure 3ab ) . If the age summaries are correct , we must conclude that: i ) the tight variation of siRNA expression among individuals ( Figure 6 ) reflects strong transpositional control on this group of elements , despite ongoing transcription and ii ) measured variation in FCTE between individuals reflect rearrangement and deletion events more than active transposition . Based on these considerations , our working hypothesis is that R2 elements are ‘mostly-dead’ ( to paraphrase the 1987 movie ‘The Princess Bride’ ) with respect to ongoing proliferation via transposition . While the R2 group is mysterious , the history of the R1 group is an even bigger puzzle . We initially hypothesized that these were relic elements , for two reasons . First , they have low copy numbers , which is indicative of limited replication . Second , the group is typified by a high proportion of RLX elements ( Table 1 ) , which have the features of class I retroelements but cannot easily be assigned to a particular family because they lack distinguishing structural features [22] . However , the bulk of evidence suggests that our hypothesis was wrong and that the R1 elements remain active . The evidence for this activity includes the fact that R1 elements are variable among individuals , as measured by FCTE ( Figure 5 ) ; are relatively highly expressed on a per-copy basis ( Figure 3b ) ; and are highly targeted by siRNAs relative to R2 elements ( Figure 1e ) . Ongoing activity is also superficially supported by the age distribution of these elements ( Figure 2 ) , for which the mean age of insertion events is significantly lower than that of the R2 group and includes insertion times indicative of recent activity . And yet , somewhat amazingly , 80% of TEs in the R1 group decrease in TE expression , by an average of −1 . 6 fold in shoot apical meristems , when the 24 nt siRNA biogenesis machinery is interrupted by a mop1 mutation [13] ( Figure 3c ) . At present , there is no clear explanation for this unexpected repression of expression , especially when one considers that R1 elements tend to be targeted by a high ratio of 24∶22 siRNAs ( Figure 1e , Figure S2 ) . One possibility is that R1 elements act as a generating source for siRNAs or other methylation signals [6] , not unlike the piRNA loci of Drosophila or zombie elements hypothesized to serve as a source of siRNAs [49] . Under this scenario , their down-regulation in mop1 would be consistent with an interruption of the host response mechanism . If this scenario were true , however , one would expect that the siRNAs that target group R1 TEs should cross-match TEs from other groups at higher than expected levels . We find that the highest percentage of different siRNA cross-matching occurred between R1-generated siRNAs and R2 TEs but at rates ( ∼2 . 0% ) that seem too low to suggest that R1 elements act as a reservoir for the host response . Altogether , our observations indicate that the R1 group is a heterogeneous set of elements that have been transpositionally active more recently than most R2 elements , perhaps for a longer period but at lower rates , as reflected by lower copy numbers . These observations suggest that the R1 group has been a long , slow , ongoing and active component of the maize pan-genome . In contrast , our evidence suggests the R2 group is ‘mostly dead’ , under tight transpostional control and formed of a burst of ancient activity . | Because transposable elements ( TEs ) constitute most angiosperm nuclear DNA , the interaction between TEs and their host genome is a key component for understanding the function and evolution of plant genomes . The diversity of the host response has been studied a great deal , including the biogenesis of small interfering RNAs ( siRNAs ) that target TEs for epigenetic modifications . However , little is known about variation in TE content among closely related genomes and whether siRNA expression tracks this variation . To that end , we surveyed both the copy number and the siRNA targeting of more than 1500 distinct TE subfamilies in the B73 maize reference genome . These surveys indicated that TE subfamilies fall naturally into three distinctive groups based on their class and copy number , but these groups also differ with respect to their location in the genome , their age , their expression and their siRNA regulation . The presence and consistency of these TE groups was also assessed in two genetically distant maize landraces with contrasting genome sizes . The variation in siRNA targeting across different TE groups and families , as well as the lack of correlation between TE and siRNA abundances , argues for the existence of multiple mechanisms and strategies for TE silencing . | [
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] | 2014 | Three Groups of Transposable Elements with Contrasting Copy Number Dynamics and Host Responses in the Maize (Zea mays ssp. mays) Genome |
The molecular mechanisms underlying transcriptional regulation in apicomplexan parasites remain poorly understood . Recently , the Apicomplexan AP2 ( ApiAP2 ) family of DNA binding proteins was identified as a major class of transcriptional regulators that are found across all Apicomplexa . To gain insight into the regulatory role of these proteins in the malaria parasite , we have comprehensively surveyed the DNA-binding specificities of all 27 members of the ApiAP2 protein family from Plasmodium falciparum revealing unique binding preferences for the majority of these DNA binding proteins . In addition to high affinity primary motif interactions , we also observe interactions with secondary motifs . The ability of a number of ApiAP2 proteins to bind multiple , distinct motifs significantly increases the potential complexity of the transcriptional regulatory networks governed by the ApiAP2 family . Using these newly identified sequence motifs , we infer the trans-factors associated with previously reported plasmodial cis-elements and provide evidence that ApiAP2 proteins modulate key regulatory decisions at all stages of parasite development . Our results offer a detailed view of ApiAP2 DNA binding specificity and take the first step toward inferring comprehensive gene regulatory networks for P . falciparum .
Plasmodium falciparum is responsible for the majority of human malaria cases and causes approximately 1 million deaths every year [1] . The complete lifecycle of P . falciparum includes three developmental stages , which occur in its mosquito vector , the human liver , and human blood . Within each developmental stage the parasite undergoes major morphological changes that are accompanied by precisely timed transcription of genes that are necessary for parasite growth , differentiation , and replication . Detailed transcriptome and proteome studies have been conducted across the different stages of the life cycle [2]–[12] . Despite these advances in our understanding of messenger RNA transcript dynamics in P . falciparum , very little is known regarding the mechanism of transcriptional regulation , including transcription factor binding and sequence specificity . Basic transcriptional control in P . falciparum appears to resemble that of other eukaryotic organisms , with general transcription factors coordinating the recruitment of RNA polymerase II to core promoter elements [13]–[15] . Experiments aimed at identifying cis-acting sequences required for gene expression have successfully identified specific enhancer and repressor sequences upstream of the core promoter elements [16]–[26] . In the asexual blood stage , regulatory sequence elements have been identified for the gene encoding the knob-associated histidine-rich protein ( kahrp ) [16] , glycophorin binding protein 130 ( gbp130 ) [18] , cytidine diphosphate-diacylglycerol synthase ( pfcds ) [19] , the DNA polymerase delta gene [20] , a subset of the heat shock protein ( hsp ) family [22] , the rif genes [23] and the falcipains [24] . Additionally , three sequence motifs have been identified upstream of the var genes: the SPE1 , CPE , and SPE2 motifs , of which the SPE2 motif has been hypothesized to be involved in silencing of var gene expression [25] , [26] . In sexual blood stage parasites three distinct short sequence elements have been found to regulate expression of the gametocyte genes pfs16 , pfs25 [17] , and pgs28 [21] . In addition to these experimentally derived motifs , bioinformatic analyses of the P . falciparum genome have identified a number of potential cis-elements that may play a role in gene regulation [27]–[36] . However , attempts to identify trans-factors have been largely unsuccessful [13] , [15] , [37] , [38] , with the exceptions of Myb1 [39] , [40] and the high mobility group box ( HMGB ) proteins [41] , [42] . Recently , a large protein family was identified in P . falciparum , containing Apetala2 ( AP2 ) domains [43] . AP2 domains were originally described in plants as DNA binding domains approximately 60 amino acids in length [44] . In plants , the AP2 family of transcription factors is one of the largest , playing key roles in developmental regulation [44] and stress responses [45] . The Apicomplexan AP2 ( ApiAP2 ) proteins represent a lineage-specific expansion , and are highly conserved across all Plasmodium spp . and in other Apicomplexans including Theileria , Cryptosporidium [43] and Toxoplasma [46] . P . falciparum was initially predicted to contain 26 ApiAP2 factors , each containing one to three AP2 domains [43] , while in Toxoplasma the family is expanded to over 50 ApiAP2 proteins [46] . We have noted a 27th highly conserved ApiAP2 protein ( PF13_0267 ) , which agrees with recent Pfam predictions for this protein [47] . Although other DNA binding proteins have been reported in the literature , ApiAP2 proteins represent the largest family of transcriptional regulators identified in P . falciparum , where they are expressed throughout the entire developmental lifecycle [43] . Previously , we established that two ApiAP2 proteins , PF14_0633 and PFF0200c , bind DNA with high sequence selectivity [48] . Subsequent work demonstrated that the P . berghei orthologue of PF14_0633 ( PBANKA_132980 ) is essential for the formation of sporozoites [49] , and specifically regulates sporozoite target genes by binding to the same GCATGCA motif that we identified [48] . More recently , PFF0200c was shown to function as a DNA tethering protein involved in heterochromatin formation and integrity [50] via binding to the previously identified SPE2 motif [25] . Importantly , PFF0200c does not appear to act as a transcriptional regulator in the blood stage . A third study identified a P . berghei protein , AP2-O [PBANKA_090590 ( PF11_0442 ) ] , as an activator of genes required for invasion of the mosquito midgut during the mosquito stage of the life cycle [51] . Together , these studies highlight the importance of the ApiAP2 DNA binding proteins in modulating stage-specific gene regulation and chromatin integrity . Despite these recent advances , the regulatory function of the majority of ApiAP2 proteins remains unknown . The DNA sequences recognized by the members of this protein family are largely uncharacterized , and the target genes that these ApiAP2 factors bind are undefined . Here we biochemically and computationally characterize the global DNA binding specificities for the entire ApiAP2 protein family from P . falciparum . Our results reveal a complex array of DNA sequence elements , with the majority of proteins binding to unique sequences . We demonstrate several cases where multiple AP2 domains within the same ApiAP2 protein are capable of binding distinct DNA sequences . The identification of these unique sequence motifs sheds light on the molecular mechanisms of transcriptional regulation by assigning correlations between putative cis-acting sequences in Plasmodium and the trans-factors that will bind at those sequences genome-wide . Our data reveal the likely identity of trans-acting ApiAP2 factors that specifically bind to previously described cis-motifs , illuminating some of the previously unknown effectors of plasmodial gene expression . For the many new motifs we identified , we predict putative targets for each of the ApiAP2 proteins . This work represents the first comprehensive analysis of the ApiAP2 DNA binding proteins in P . falciparum and provides a crucial missing link toward understanding their role in the regulation of parasite development .
The 27 plasmodial ApiAP2 proteins vary drastically in size ( Figure S1 ) , however , the predicted 60 amino acid AP2 domains , are well-defined and highly conserved . To determine the DNA binding specificity for the P . falciparum AP2 domains , we used protein binding microarrays ( PBMs ) , which enable simultaneous screening of all possible DNA sequences up to ten nucleotides in length without sequence bias [52] , [53] . Seminal studies from the Bulyk lab have used PBMs to comprehensively characterize individual transcription factors from a diverse array of organisms including yeast , worm , mouse and human [52]–[54]; and we have previously demonstrated its utility for Apicomplexan AP2 DNA binding proteins [48] . We created 50 constructs for PBM screening ( Supplemental Text S1 , Figure S2 ) , including individual domains , full length proteins , and tandem domain arrangements ( two AP2 domains separated by a short conserved linker sequence of 12 to 79 amino acids; designated DLD ) . Our analysis by PBM of these P . falciparum AP2 domains revealed motifs for 20 out of the 27 ApiAP2 proteins ( Figure 1 ) , including a motif for the recently identified ApiAP2 protein , PF13_0267 , helping to confirm the new annotation for this protein . Results from at least two PBM experiments for each AP2 domain were used to generate position weight matrices ( PWMs ) , which represent the DNA binding affinity for a given domain ( Dataset S1 , Figure 1 ) . Replicate experiments had excellent correlation coefficients illustrating the robustness of the PBM methodology ( see Supplemental Text S1 ) . Enrichment scores ( E-scores ) were assigned for each 8-mer ( allowing up to two gaps ) [53] , with a significance cut-off of 0 . 45 ( E-scores range from -0 . 5 to +0 . 5 ) for specific 8-mers enriched above background . The E-score is a rank-based , nonparametric score that is robust to differences in protein concentration and reflects the relative preference for each 8-mer [55] . In total we identified sequence motifs for 24 AP2 domains found in a variety of protein architectures ( Figure 1 ) . While Figure 1 illustrates which motifs are linked to the blood stage of Plasmodium development , several motifs are also associated with ApiAP2 proteins during non-blood stages as well ( see Supplemental Text S1 ) . It is noteworthy that different AP2 domains from the same ApiAP2 protein bind distinct DNA sequence elements . However , we do find several motifs that are recognized by multiple ApiAP2 factors ( see Supplemental Text S1 and Table S1 ) . This complexity may allow for multifaceted transcriptional regulation using a smaller number of individual factors . Protein-DNA interaction specificities are determined by the chemical interactions of amino acids and DNA bases [56] . Side chain flexibility and DNA distortions allow one DNA binding domain to interact with multiple distinct DNA sequences . For several of the AP2 domains there were significant differences among the top scoring 8-mer sequences that were bound , suggesting multi-motif recognition . Using the Seed and Wobble algorithm [57] we identified alternative motifs associated with 8-mers of high signal intensity that could not be explained by the primary motif for 14 AP2 domains ( representing 13 ApiAP2 proteins ) ( Figure S3 , Dataset S2 ) . Some AP2 domains only had a single secondary motif , whereas others had up to four . The secondary motifs can be described based on their relationship with the corresponding primary motifs and fall into the broad categories of end modifications , core changes , variable spacer distances or alternate recognition interfaces [57] ( see Supplemental Text S1 ) . The ability of an individual domain to bind anywhere from one to five different DNA sequences would significantly increase the number of target genes that could be regulated by one factor . We selected two ApiAP2 proteins for confirmation of secondary motif binding by electrophoretic mobility shift assays ( EMSAs ) . Domain 2 of PFD0985w has three predicted secondary motifs in addition to the primary motif . A plot of the E-scores for all ungapped 8-mers reveals that the top 100 matches to both the primary motif and one of the secondary motifs ( Figure 2A ) are relatively equal in E-score . Therefore , PFD0985w_D2 should bind equally well to these two motifs . To test this hypothesis we generated 60 bp oligonucleotides with the specific motif sequence in the center flanked by random sequences . EMSAs with purified PFD0985w_D2 demonstrate that both oligonucleotides are bound equally well , and that the primary motif is capable of out-competing the secondary motif and vice versa ( Figure 2B ) . No binding is observed with an unrelated non-specific oligonucleotide , indicating specificity for the predicted motifs ( data not shown ) . The second ApiAP2 factor that we selected for confirmation of secondary motifs was PFL1900w_DLD . The highest scoring 8-mers for this tandem domain were represented by completely distinct sequences and a plot of all 8-mers and their E-scores revealed preferential binding with a primary , secondary , and tertiary motif ( Figure 2C ) . PFL1900w_DLD was able to shift all three motifs , but with varying affinities ( data not shown for the primary and tertiary motifs ) , and competition between the secondary and tertiary motifs revealed a clear preference for the secondary motif over the tertiary motif ( Figure 2D ) . These results suggest that the secondary motifs detected represent bona fide sequences bound by the AP2 domains and the E-score distributions accurately reflect binding affinities . Both computational predictions and experimental data have identified a number of DNA sequence motifs upstream of genes in Plasmodium [17]–[22] , [27]–[32] , but the specific trans-factors that bind to these motifs have mostly remained elusive . For three cases , we now establish plausible links between the newly identified AP2 DNA sequence motifs and these previous reports . Militello et al . identified a specific motif , ( A/G ) NGGGG ( C/A ) ( called the G-box ) , upstream of 8 out of 18 Plasmodium heat shock genes [22] . The occurrence of this GC-rich motif in the genome is low ( Table S2 ) , suggesting that its presence in upstream sequences may be significant for transcriptional regulation . The sequence motif that we have identified for PF13_0235_D1 is nearly identical to the G-box element ( Figure 3A ) . Furthermore , the expression profiles of pf13_0235 , hsp86 ( pf07_0029 ) , and hsp70 ( pf08_0054 ) , two heat shock genes containing one or more G-boxes exhibit a strong positive correlation ( r = 0 . 93 ) during the asexual blood stage [2] ( Figure 3A ) , suggesting that PF13_0235 may play a role in regulating hsp gene expression . We performed EMSAs using both G-box elements of the hsp86 upstream region and found that PF13_0235_D1 interacts specifically with the G-box and deletion of both G-boxes is required to completely eliminate binding ( Figure 3B , C ) . In the presence of only one G-box , binding is severely reduced ( Figure 3B , C ) suggesting that PF13_0235_D1 preferentially interacts with both G-boxes , perhaps through dimerization ( see below ) . No binding is observed with an unrelated non-specific oligonucleotide at similar protein concentration ( data not shown ) . This result is in agreement with in vivo data from transient transfections , where elimination of G-box 1 substantially reduced luciferase expression , but did not completely abolish it [22] . We also tested the G-box from the 5′ flanking region of hsp70 for in vitro binding by EMSA , and confirmed that PF13_0235_D1 binds this sequence in vitro . It is interesting to note that the binding of this single G-box motif is similar to that seen for hsp86 after deletion of one G-box , suggesting that higher affinity interactions require two occurrences of this motif . Likewise , the sequence element bound by PF10_0075_D3 , GTGCA , is enriched in the upstream sequences of genes involved in merozoite development and invasion [31] , [58] . Using EMSAs we find that PF10_0075_D3 binds to the GTGCA motif upstream of msp1 ( pfi1475w ) , msp10 ( pff0995c ) and rhopH 3 ( pfi0265c ) ( Figure S4A ) , and no binding is observed with an unrelated non-specific oligonucleotide , indicating specificity for the predicted motifs ( data not shown ) . Previous expression studies using a rhoptry gene promoter to drive luciferase expression have demonstrated that the GTGCA motif is important for rhoptry gene-like stage-specific expression [31] . Combined with our EMSA results , this suggests that PF10_0075 may play a role in regulating the expression of invasion-related genes in P . falciparum . Finally , a specific 5 bp motif in the 5′-upstream region of gbp130 ( pf10_0159 ) , GTATT , was previously found to be bound by unknown nuclear factors in a sequence-specific manner [18] . The reverse complement of this 5 bp element is nearly identical to the motif we have identified for PF11_0091 . EMSAs using the promoter region of gbp130 and the purified AP2 domain from PF11_0091 confirm its ability to interact with this sequence ( Figure S4B ) , while no binding was observed with an unrelated non-specific oligonucleotide ( data not shown ) , suggesting it is a possible regulator of GBP130 function . The PBM-derived motifs are useful to suggest putative targets for the ApiAP2 trans-factors , especially where previous characterization is available . However , in vivo assays will be required in all cases to validate these interactions on a protein-by-protein basis . To begin to characterize the functional role of ApiAP2 proteins , we searched the P . falciparum genome for sequences in promoters and untranslated regions that may serve as regulatory sites for ApiAP2 binding . As a first analysis , we used our AP2-specific position weight matrices generated from the PBM data to search the 5′ upstream sequence elements of Plasmodium genes using ScanACE [59] , which lists all matches to our position weight matrices within the user defined threshold . Although putative transcription start sites have been predicted [60] , actual transcription start sites are still poorly defined in P . falciparum [61] . Therefore , we searched 2 kb upstream of the ATG start codon or until an upstream open reading frame was encountered . While this search provides a list of all possible motif occurrences determined from matches to a specific position weight matrix ( Datasets S3 and S4 ) , it is undoubtedly an overestimation of putative target genes . In reality , the presence of a regulatory element upstream of a gene does not confirm a regulatory interaction exists , and many motif occurrences may be inactive [62] . Furthermore , for a regulatory element to be functional , it needs to be accessible for binding , which is in part determined by nucleosome occupancy . Nucleosome occupancy has been mapped during the intraerythrocytic developmental cycle ( IDC ) of P . falciparum [63] and using this data we were able to determine that between 65 and 97% of our ScanACE predicted binding sites are accessible ( nucleosome-free ) at some point during the IDC ( Table S2 ) . This suggests that the majority of our predictions have the potential to be active; however , in vivo binding affinities may differ from in vitro determined affinities , possibly altering the weighting of specific nucleotide positions within the motifs . Ultimately , the actual target sequences of each ApiAP2 protein will need to be individually determined through experimental validation in vivo during the specific lifecycle stage of interest . As a test of the ability of our ApiAP2 proteins to bind to the ScanACE predicted targets we selected a putative target for the newly annotated ApiAP2 protein PF13_0267; pfc0975c has a match to the CTAGAA motif at 1469 bp upstream of the start codon . EMSAs showed that the putative target sequence was bound by the purified AP2 domain from PF13_0267 , while a mutant oligonucleotide lacking the predicted target sequence did not exhibit significant binding ( Figure S5 ) . Although these results demonstrate that our ScanACE-predicted target genes provide a good starting point to search for candidate genes for in vivo testing , this does not indicate if pfc0975c is a true target of PF13_0267 . Indeed the motif bound by PF13_0267 is found upstream of almost all genes and in vivo validation will be required to identify actual targets . Complete AP2 motif occurrence data for the P . falciparum genome are available to the malaria community at PlasmoDB ( www . plasmodb . org ) [64] . While the ScanACE analysis provides a list of all occurrences for each motif , it is unlikely that the ApiAP2 proteins are binding to all possible motif occurrences , and instead that they bind to a smaller subset of promoters . Proteins that are co-localized in the cell or form sub-cellular structures such as the ribosome have been found to be transcriptionally co-regulated in other organisms such as yeast , and often are regulated by the same cis-elements [65] . Genes that are functionally distinct , but are co-expressed can also be regulated by the same cis-elements in their upstream regions . To narrow the ScanACE list to a more informative subset of putative target genes we used relative mRNA abundance profiles to define relationships between co-expressed Plasmodium genes [2] . We used linear regression to determine at each time point the extent to which each AP2 motif contributes to ( or recapitulates ) the overall expression of the genes that contain a given motif in the upstream regions ( see Methods and Supplemental Text S1 for details [66] ) . Thus each motif at each time point is associated with a score ( i . e . the fitted regression coefficient ) , which is positive if genes that have the motif tend to go up at that time point , or negative if they tend to go down . These scores define the predicted motif activity at each time point , and an activity profile across the entire IDC . Activity profiles reflect the predictive effect of individual AP2 motifs on gene expression of a set of target genes at a given IDC timepoint ( Figure 4A ) , and are therefore independent of the mRNA expression profiles of the AP2 genes themselves . The activity profile for each motif was then used to iteratively identify genes containing the target motif in their 5′ upstream regions that share an expression profile similar to the activity profile . This provided a refined list of putative target genes that are co-expressed with one another ( Dataset S5 ) . To illustrate how this approach improved our target predictions we focus on the G-box motif . The ScanACE predicted target list for this motif includes 522 genes ( Dataset S3 ) , and using the activity profile-based approach we refine this list to a set of 124 putative co-expressed target genes ( Table 1 , Dataset S5 ) . A comparison of the average expression profiles for these genes with the expression of pf13_0235 , the gene for the ApiAP2 factor that binds the G-box , shows a strong positive correlation ( 0 . 97; Figure 4 ) . Similar comparisons were made for all ApiAP2 factors and their putative targets ( Figure S6 ) , and we observe either significant positive ( r-values from 0 . 97 to 0 . 43 ) or negative correlations ( r-values from −0 . 94 to −0 . 56 ) for many ApiAP2 factors , implying that this protein family may act as both activators and repressors of target gene expression . Functional annotation of the predicted targets using the DAVID bioinformatics resource [67] identified enrichment of genes involved in specific cellular processes ( Table 1 ) . Targets of PF13_0235_D1 , the ApiAP2 factor that binds the G-box motif , include genes involved in ribosome function or translation and heat shock response genes . Genes involved in these functions have previously been suggested to be regulated via the G-box element [22] , [33] , supporting our target gene predictions . Other notable examples include the enrichment of targets involved in cell invasion and host cell entry for the PF10_0075_D3 motif ( GTGCAC ) and DNA binding for the MAL8P1 . 153 motif ( ACACA ) . The involvement of the GTGCAC motif in invasion related processes has been independently predicted by three bioinformatic studies [31]–[33] , while the ACACA motif was previously associated with DNA replication [31] . Since the majority of these motifs have not been previously described in P . falciparum , our prediction of target gene functions are novel and warrant further characterization . Similarly , we used a recently published P . falciparum growth perturbation dataset [68] as an alternative data source to create activity profiles to refine our target gene predictions ( Figure S7 , Dataset S6 , Supplemental Text S1 ) . Genes that respond in a similar manner to a perturbation are more likely to be regulated by the same factor and we observed narrower target gene lists for each motif , many of which overlap with the predictions made using the IDC co-expression data , and others that are novel target gene predictions ( Table 1 , Figure 4 , Figure S8 ) . Further details on the perturbation refinement of target genes can be found in the Supplemental Text S1 . Combined , refinement of putative targets using the high resolution temporal gene expression data [2] and the perturbation dataset [68] produce manageable gene sets for further analysis . We also looked at motif enrichment in the upstream regions of var genes . There are approximately 60 var genes in P . falciparum that encode the antigenically variant erythrocyte membrane protein 1 ( PfEMP1 ) , which is involved in sequestration of infected red blood cells in the vasculature [69] . The var genes have been divided into groups based on their location along the chromosome ( internal or at chromosome ends ) , the direction of transcription , and the sequences of their intron and 5′ and 3′ untranslated regions [70] . Using our ScanACE prediction of binding sites , we observed a striking pattern of ApiAP2 motifs that were clustered in discrete positions of all three types of var promoters ( Figure 5 ) . In the upsB promoters we observed repeated motifs for PFF0200c that correspond to the previously identified bipartite SPE2 element [25] , located at −2000 to −3000 bp upstream of the ATG start codon . While PF10_0075_D3 binds to a similar sequence as the SPE2 element , recent work has demonstrated that PFF0200c is the primary ApiAP2 factor that binds to this element in vivo [50] . We also predict the SPE1 element of upsB var genes at −1200 bp [25] , which matches to the motif we have identified for PF14_0633 . In addition to these previously identified sequence elements , we predict binding sites for PF11_0442 , which binds to the sequence GCTAGC ( Figure 5 ) . Matches to this motif are conserved in all three major types ( A , B and C ) of var genes . The presence of multiple ApiAP2 binding sites upstream of var genes suggests multiple ApiAP2 factors may be recruited for binding upstream of the var genes . var promoters are silenced by default [71] , and it is possible that this silencing is maintained by the co-ordinated action of multiple ApiAP2 factors . Further investigation of these discrete motif-enriched sites will be required to determine the precise role of additional ApiAP2 proteins in var gene regulation . The IDC transcriptome of P . vivax [72] suggests that the regulation of development follows a similar cascade of gene expression as that seen for P . falciparum [2] , [6] . All P . falciparum ApiAP2 proteins have syntenic homologues in P . vivax and are expressed at a similar stage of development during the IDC with the exception of pf14_0471 ( pv118015 ) which is shifted from trophozoite to late schizont stage [72] ( Figure S9A ) . AP2 domains are highly conserved across all Plasmodium spp . and will likely bind the same motifs . It follows that the ApiAP2 proteins may regulate similar or related target genes in P . vivax compared to P . falciparum . We calculated activity profiles using the P . vivax asexual blood stage transcriptome data [72] ( Figure S9B ) and as seen for P . falciparum , most of the motifs are associated with activation or repression of target genes at one or more timepoints . However , a comparison of the target gene lists for each motif in P . falciparum and P . vivax shows that the conservation of putative targets is low , ranging from 0 to 53% ( Table S3 ) . This implies that although the AP2 DNA binding domains are highly conserved , some of the regulons across these species have diverged and regulation of orthologous genes has evolved independently . It should be noted that a comparison of target genes in non-blood stages , including the mosquito stage , demonstrates substantial statistically significant conservation of motifs among co-regulated genes in P . vivax and P . falciparum [73] . Therefore it will be important to identify the actual target genes bound in vivo for each motif to determine the actual extent of conservation between species . While the above analyses are limited to transcriptional regulation during the blood stage of the lifecycle , ApiAP2 function is not limited to the IDC . Accordingly , we analyzed gene expression data from P . falciparum gametocytes , zygotes and sporozoites [6] , [10] . Activity profiles for ApiAP2 motifs in gametocyte data revealed activity for a number of the PBM derived motifs during gametocytogenesis ( Figure S10A ) . We found two motifs to be active in zygotes , including the previously identified zygote motif for the P . berghei orthologue of PF11_0442 ( PBANKA_090590 ) , as well as the CACACA motif , which is bound by PF13_0026 ( Figure S10A , Dataset S7 ) . Activity profiles for AP2 DNA motifs in sporozoites identify PF14_0633 , PFD0985w_D2 , and PFF0670w_D1 as potentially active AP2 domains during this stage ( Figure S10A ) . Since there is currently no liver stage data available for Plasmodium species that infect humans , we used data from the rodent malaria species P . yoelii [9] . Activity profiles for our PBM derived motifs in the P . yoelii liver stage yielded a number of motifs that are potentially active during this stage ( Figure S11 ) . Further discussion of the non-blood stage active motifs can be found in the Supplemental Text S1 .
Understanding the molecular mechanisms and the regulatory processes that underlie gene expression during the development of P . falciparum is a major challenge in malaria research . The ability to map the recognition sites of DNA binding proteins genome-wide enables an improved understanding of how trans-factors regulate gene expression and has been undertaken for only a few large eukaryotic families of transcription factors [54] , [57] , [74]–[76] . Here , we have comprehensively characterized the P . falciparum ApiAP2 protein family and established their preferred DNA recognition motifs . A number of findings strongly implicate these factors as major regulators of gene expression in P . falciparum . First , the expression of the ApiAP2 genes at distinct times throughout the IDC suggests they are available to regulate target genes throughout the entire 48-hour blood stage of the parasite . Second , the diversity of sequences bound by these domains is sufficient to account for the full cascade of gene expression observed in the IDC . Furthermore , ApiAP2 genes form an interaction network with themselves during the IDC ( Figure S12 ) , suggesting that in addition to regulating target genes they may also regulate their own expression . For all three instances where in vivo data on binding sites for ApiAP2 proteins are available [49]–[51] , our PBM derived motifs are excellent matches , emphasizing the quality of these data and the robustness of the PBMs at identifying preferred sequence elements . Finally , half of the motifs that we identified are nearly identical to motifs that were independently predicted computationally as putative cis-elements in the P . falciparum genome [27]–[29] , [31]–[33] . Our success rate for identifying DNA binding specificities ( 20 out of 27 ApiAP2 proteins , 74% ) using the PBMs is comparable to results obtained using the same technology with other transcription factor families from yeast and mouse ( 40–85% successful ) [54] , [57] , [76] . For those AP2 domains that did not yield a result , there are numerous technical reasons why we may fail to detect binding . Although we were able to successfully express all of our GST-fusion constructs , possible reasons for failure to detect binding events include insufficient protein concentration , low protein stability , binding conditions ( e . g . ionic strength ) used , or low-affinity binding ( undetectable by PBM ) . Another possibility is simply that some predicted AP2 domains lack specific DNA binding activity altogether . As mentioned above , the ApiAP2 proteins vary dramatically in size , including four proteins that are less than 40 kDa ( Figure S1 ) . Only one of the four smallest ApiAP2 proteins ( <300 amino acids ) binds to DNA ( PF13_0026 ) . Despite our testing both full-length ApiAP2 proteins and isolated AP2 domains from these small proteins , we could detect no DNA binding by PBM . This is surprising given their lack of any other predicted functional domains . Both computational prediction of motifs [22] , [31]–[33] and experimental data [25] , [49]–[51] have identified a number of regulatory elements involving repeated iterations of the same motif . One established way to recruit multiple copies of the same factor to a particular site in the genome is through the formation of dimers or multimers of the same protein . A crystal structure has recently been solved of the AP2 domain from PF14_0633 , which reveals that the AP2 domain forms a dimer when bound to DNA [77] . This structure suggests that other ApiAP2 proteins may also form homo- or heterodimers [78] , thereby recognizing multiple DNA sequence motifs in concert . Further support for this idea is seen in our EMSA analysis of PF13_0235_D1 , which shows that higher affinity binding to the G-boxes upstream of hsp86 ( Figure 3 ) occurs when multiple copies of the motif are present . Similar results were obtained for PF10_0075_D3 and the predicted rhopH 3 target ( Figure S4A ) suggesting that AP2 domain dimerization may enhance binding to these sequences . Our ScanACE predictions of target genes identify motif repeats in upstream regions ( Dataset S3 ) , which may allow for tighter control of target gene expression by the ApiAP2 factors . Similarly , heterodimer formation may facilitate combinatorial regulation of gene expression at co- motifs . Evidence for such interactions can be found in yeast two-hybrid assays that have detected associations between ApiAP2 proteins [79] . Taken together , our genome-wide motif predictions and the ability of plasmodial AP2 domains to form dimers implies that these factors likely work together to regulate target gene expression . The extremely high level of conservation ( >95% identity ) for each AP2 domain across all Plasmodium spp . [43] suggests that orthologues from other species will bind to similar DNA sequence motifs . Indeed , motifs for the P . falciparum AP2 domains of PF14_0633 and PF11_0442 are matches to the experimentally determined motifs for their P . berghei orthologues ( AP2-Sp and AP2-O , respectively ) [49] , [51] . However , while the cis-elements bound by individual AP2 domains may be conserved across species , our predicted IDC target gene sets for ApiAP2 proteins appear to differ extensively . This is common in other eukaryotic organisms , where DNA binding domains are highly conserved across species , but downstream target genes are divergent [80]–[85] . Our data suggests that this may be true for Plasmodium spp . , as demonstrated by the divergence of IDC target gene sets in P . vivax and P . yoelii compared to P . falciparum , which contrasts with the almost perfect conservation of AP2 domains and the similar temporal expression of ApiAP2 genes . We previously showed that the orthologous AP2 domains from PF14_0633 in P . falciparum and its distant Apicomplexan relative C . parvum ( cgd2_3490 ) bind virtually identical sequence elements [48] , but their predicted regulons had virtually no overlap . However , there are some examples of transcription factor binding site conservation among specific groups of target genes . For example , the G-box element has been predicted to regulate heat shock genes in both C . parvum [86] and P . falciparum [22] , and both the PF14_0633 ( TGCATGCA ) and PF10_0075_D3/PFF0200c_DLD ( GTGCAC ) motifs are conserved among sporozoite and merozoite invasion genes in P . falciparum , P . vivax , P . yoelii , and P . knowlesi [87] . A better assessment of target gene conservation between species will be possible with more accurate target gene lists from chromatin immunoprecipitation experiments for each individual ApiAP2 factor . Indeed , this has recently been demonstrated in the asexual blood stages for PFF0200c [50] , and only a subset of predicted targets were actually bound in vivo by this factor . This is also likely to be true for other ApiAP2 factors and further work identifying functional binding sites will clarify the level of conservation of transcription factor binding sites among the different Plasmodium spp . Combinatorial gene regulation is an important aspect of transcription in many organisms . It controls the level of gene expression , the precise timing of expression , and determines the ability of a regulatory circuit to respond differently to a wide variety of extracellular signals . The finding that there are a relatively small number of specific transcription factors in P . falciparum prompted the hypothesis that combinatorial gene regulation plays an important role in the parasite [29] . Our finding that some ApiAP2 proteins can bind more than one sequence element significantly increases the potential complexity of the regulatory network . We identified secondary DNA binding preferences for 14 AP2 domains . These secondary motifs allow one AP2 domain to regulate a much broader range of targets than initially predicted from our ScanACE analysis using only the primary motifs . Precedent for this has been seen in yeast where the transcriptional activator , HAP1 , binds two completely different regulatory sequences , allowing for the regulation of target genes with different promoter elements [88] . A similar observation was made in a recent PBM analysis of 104 mouse transcription factors , where it was found that almost half of the TFs recognized multiple sequence motifs [57] . A re-analysis of previously generated chromatin immunoprecipitation – microarray ( ChIP-chip ) data illustrated that these newly identified alternate motifs were also bound in vivo [57] . Similar in vivo data will be required to fully elucidate the role of individual ApiAP2 motif occurrences on target gene functions; however it is evident from our data that the ApiAP2 factors have sufficient diversity in sequence recognition to potentially regulate all P . falciparum genes . While it is clear that the ApiAP2 factors bind DNA and recent work has begun to explore their contribution to transcriptional regulation [49] , [51] , these factors are also capable of binding DNA as scaffolding and recruitment proteins [50] . This finding opens up new possible functions for this family of DNA binding proteins . The next step in understanding ApiAP2 function will be to address the role of other domains in these proteins . The majority of ApiAP2 proteins are predicted to encode extremely large proteins , yet nothing is known regarding other functional domains outside of the AP2 domain . Yeast two-hybrid assays have identified interactions between ApiAP2 proteins and chromatin modifying proteins [79] , which could help recruit ApiAP2 proteins to target sites in the genome . How and when ApiAP2 proteins are targeted to the nucleus and if this is actively regulated also remains unanswered . Our global mapping of ApiAP2 motif preferences represents the first characterization of a Plasmodium family of DNA binding proteins and provides a starting point to investigate transcriptional control in P . falciparum . These results provide an important step toward understanding the role of these proteins as major regulators throughout all stages of parasite development in Plasmodium spp . and other related Apicomplexan species .
Domain boundaries were defined as in [43] and extensions were made based on sequence homology both 5′ and 3′ of the AP2 domains amongst Plasmodium spp . orthologues , as well as using structural predictions from the online secondary structure prediction server Jpred3 [89] . In total 77 different versions of ApiAP2 domains from P . falciparum as well as one from P . berghei were cloned into pGEX-4T1 ( GE Life Sciences ) to create N-terminal glutathione S-transferase ( GST ) fusions ( Figure S2 ) . Proteins were expressed in BL21-CodonPlus ( DE3 ) -RIL cells ( Stratagene ) with 0 . 2 mM IPTG at room temperature and affinity purified using glutathione resin ( Clontech ) . The purity of each protein was estimated by silver stained SDS-PAGE and yields were calculated based on absorbance at 260 nm and specific molar extinction coefficients . All protein concentrations include contaminating products and will be an overestimation of the actual AP2 domain amounts . All constructs in Figure S2 were tested at least once on the PBMs , and positive motifs were confirmed at least twice on independent arrays . The PBM experiments were performed as previously described [53] . Briefly , custom designed oligonucleotide arrays are double-stranded using a universal primer , incubated with GST-AP2 fusion proteins , visualized with Alexa-488 conjugated anti-GST antibody , and scanned using an Axon 4200A scanner . Proteins were used at the maximum concentration obtained from purification and represent one-fifth of the total reaction volume used on the PBM . In this study three different universal platforms were used covering all contiguous 8-mers as well as gapped 8-mers spanning up to 10 positions . After data normalization and calculation of enrichment scores [53] , [55] the “Seed-and-Wobble” algorithm was applied to combine the data from two separate experiments and create position weight matrices ( PWMs ) [55] . An enrichment score cut-off of 0 . 45 was used to distinguish high affinity binding data from low affinity and non-specific binding . The score for each 8-mer reflects the affinity of a DNA binding domain for that sequence , with higher scores representing tighter interactions [55] . Secondary motifs were identified by running the “rerank” program until E-scores below 0 . 45 were obtained [55] . The PBM analysis suite was downloaded from the Bulyk lab ( http://the_brain . bwh . harvard . edu/PBMAnalysisSuite/index . html ) . For public access , all motifs have been deposited in the UniPROBE database [90] . N-terminal GST fusions of the ApiAP2 domains were purified as described above . Single-stranded HPLC purified 5′ biotinylated oligonucleotides were purchased from Integrated DNA Technologies ( http://www . idtdna . com ) and annealed with complementary oligonucleotides to create double-stranded probes ( Table S4 ) . EMSAs were performed using the LightShift Chemiluminescent EMSA kit ( Pierce ) . Briefly , purified protein was incubated with 50 ng/µL of poly ( dI-dC ) and 10 fmol of biotinylated probes . Competitor DNA was added in 50 or 250 fold molar excess . All reactions were incubated in the kit EMSA buffer with 2 . 5% glycerol , 5 mM MgCl2 , 10 mM EDTA , 50 mM KCl , and 0 . 05% NP-40 at room temperature for 20 minutes . Electrophoresis and transfer to Nylon membrane ( Hybond ) was performed according to the manufacturer's instructions . The Chemiluminescent Nucleic Acid Detection Module ( Pierce ) was used according to the manufacturer's instructions to visualize the probes . 2 kb-long upstream regions ( or up to the nearest ORF ) were first extracted from whole genome sequences and associated gene annotation data ( PlasmoDB 6 . 0 ) . PBM motifs were trimmed down to their 6 most informative consecutive motif positions ( motif cores ) . Then , we determined all core PBM motif occurrences in the 2 kb regions using the ScanACE approach [91] . G+C content was set to 13 . 1% in P . falciparum , 42 . 8% in P . vivax and 20 . 1% in P . yoelii . Score threshold was set to the average score of randomly drawn sequences from the PBM PWMs , minus two standard deviations ( this is the default ScanACE setting ) . In order to identify candidate target genes for each AP2 , we reasoned that these target genes should be co-expressed and should share the AP2 binding site we identified using PBMs . Thus , we first identified groups of genes that are co-expressed across multiple experimental conditions or multiple time points ( e . g . , in the IDC ) . Then for each AP2 motif , we determined in which of the groups the motif was over-represented . For each of these groups , we extracted the genes associated with one or more motif occurrences . Thus , target genes in this definition can come from multiple co-expression groups ( and not all genes in these co-expression groups end up in the target list because not all genes in these groups will have a motif occurrence in their promoter ) . In order to define co-expressed gene groups , we used the k-means approach together with the Pearson correlation . Motif scanning was performed using the ScanACE approach [91] as described above . In order to determine functional motif score threshold , we used an information-theoretic procedure analogous to that used in FIRE [27]: briefly , we determined the motif score threshold such that the resulting motif occurrences best explain the co-expression clusters obtained by k-means . At a given motif score threshold , motif over-representation in each cluster was assessed using the hypergeometric distribution; to correct for multiple testing ( i . e . multiple clusters being evaluated ) , we used the Benjamini-Hochberg procedure; corrected p-values corresponding to an estimated overall FDR of 0 . 25 were considered significant and the genes associated with motif occurrences in these clusters were extracted . Because the k-means is dependent on initialization , we repeated the entire procedure 10 times; genes extracted 3 times or more ( out of 10 ) were considered as candidate target genes . Thus , only genes associated with the considered motif and that are consistently found as co-expressed together with other genes sharing that same motif end up as candidate target genes in our analysis . More detailed methods are available in the Supplemental Text S1 . PlasmoDB ( www . plasmodb . org ) accession numbers for genes and proteins discussed in this publication are: hsp86 ( PF07_0029 ) ; hsp70 ( PF08_0054 ) ; msp1 ( PFI1475w ) ; msp10 ( PFF0995c ) ; rhopH 3 ( PFI0265c ) ; gbp130 ( PF10_0159 ) ; AP2-O ( PBANKA_090590 ) ; AP2-Sp ( PBANKA_132980 ) . | Plasmodium falciparum is the main cause of the devastating human disease malaria . This parasitic organism has a complex lifecycle spanning a variety of different cell types in the mosquito vector and human host . To adapt and survive in these different environments , the parasite precisely regulates the transcription of genes throughout its lifecycle . However , the mechanisms governing transcriptional regulation in P . falciparum are poorly understood . To date , a single family of specific transcription factors , the Apicomplexan AP2 ( ApiAP2 ) proteins , has been identified . These DNA binding proteins are likely to play a major role in coordinating the development of this parasite and are therefore of major interest . Here , we determine the DNA binding specificities for the entire P . falciparum ApiAP2 family of DNA binding proteins . Our results demonstrate that these proteins bind diverse DNA sequence motifs and co-occur in functionally related sets of genes . By mapping these sequences throughout the parasite genome , we can begin to establish a regulatory network underlying parasite development . This study represents the first characterization of a family of DNA binding proteins in P . falciparum and provides an important step towards understanding gene regulation in this parasite . | [
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] | 2010 | Identification and Genome-Wide Prediction of DNA Binding Specificities for the ApiAP2 Family of Regulators from the Malaria Parasite |
Hypoxia inducible factor 1α ( HIF1α ) is the mammalian transcriptional factor that controls metabolism , survival , and innate immunity in response to inflammation and low oxygen . Previous work established that generation of hypoxic microenvironments occurs within the lung during infection with the human fungal pathogen Aspergillus fumigatus . Here we demonstrate that A . fumigatus stabilizes HIF1α protein early after pulmonary challenge that is inhibited by treatment of mice with the steroid triamcinolone . Utilizing myeloid deficient HIF1α mice , we observed that HIF1α is required for survival and fungal clearance early following pulmonary challenge with A . fumigatus . Unlike previously reported research with bacterial pathogens , HIF1α deficient neutrophils and macrophages were surprisingly not defective in fungal conidial killing . The increase in susceptibility of the myeloid deficient HIF1α mice to A . fumigatus was in part due to decreased early production of the chemokine CXCL1 ( KC ) and increased neutrophil apoptosis at the site of infection , resulting in decreased neutrophil numbers in the lung . Addition of recombinant CXCL1 restored neutrophil survival and numbers , murine survival , and fungal clearance . These results suggest that there are unique HIF1α mediated mechanisms employed by the host for protection and defense against fungal pathogen growth and invasion in the lung . Additionally , this work supports the strategy of exploring HIF1α as a therapeutic target in specific immunosuppressed populations with fungal infections .
Invasive fungal infections continue to take a toll on human health with high mortality and morbidity rates and increasing frequency [1] . The filamentous fungus Aspergillus fumigatus remains the most common cause of airborne invasive fungal infections and is the primary causal agent of invasive pulmonary aspergillosis ( IPA ) [2] . The persistence of sub-optimal IPA clinical outcomes is in part due to a less than optimal understanding of the Aspergillus-host interaction and toxicity associated with current antifungal drugs [3] , [4] , [5] . While much effort for treatment improvement is focused on identification of new antifungal drug targets and compounds , a complementary and important area of investigation is discovering therapies that improve host defense in immunocompromised patients [6] . Host defense to A . fumigatus challenge requires a functioning innate immune response with a strong dependence on neutrophils and other innate immune system effector cells , as their deficiency or defective function is a principal clinical risk factor for IPA [7] , [8] . The timing and magnitude of neutrophil recruitment is pivotal for fungal clearance as a small delay in arrival leads to disease susceptibility [9] , [10] . The recruitment of neutrophils to the site of infection requires a calculated interplay between multiple signaling chemoattractants and receptors that remain to be fully defined in the context of IPA . Alveolar macrophages , likely the first leukocyte exposed to conidia within the lung , induce the expression of pro-inflammatory cytokines , such as TNF , IL-1α/β , IL-6 , GM-CSF , CXCL2 , and CXCL1 in response to engagement of fungal PAMPs by macrophage PRRs such as toll-like receptors ( TLRs ) and dectin-1 [11] , [12] , [13] . Whether through MyD88-dependent or –independent signaling , the expression of these cytokines is mediated through the transcription factors NFκB , NFAT , and IRF's and are indispensible for proper clearance and preventing invasive disease [12] . In particular , the CXC chemokines defined by the amino acid sequence Glu-Leu-Arg ( ELR ) preceding the CXC motif , macrophage inflammatory protein 2 ( MIP-2 , murine CXCL2 ) and keratinocyte-derived chemokine ( KC , murine CXCL1 ) are pivotal factors for neutrophil migration [14] . Neutrophils sense and respond to these inflammatory signals through surface expressed G-protein coupled receptors , cytokine receptors , adhesions ( selectins and integrins ) , Fc-receptors , and innate receptors ( TLRs and C-type lectins ) [15] . Classical chemoattractant receptors expressed on the surface of neutrophils , including leukotriene B4 , platelet-activating factor , and CXCR1 and CXCR2 are required for neutrophil recruitment and migration; mice deficient in these receptors are more susceptible to IPA [10] , [16] , [17] , [18] . Once recruited to the lung and upon contact with hyphae , neutrophils induce degranulation , the respiratory burst , proteases , and antimicrobial peptides leading to both pathogen and host damage [9] , [19] , [20] , [21] . The precise molecular mechanisms involved in neutrophil lung recruitment in response to A . fumigatus infection remain to be fully defined . Pathogen driven inflammation and necrosis of tissue leads to development of microenvironments deficient in oxygen and nutrients , but there is a lack of knowledge regarding how host and pathogen responses to these deficiencies affect the outcome of pathogenesis [21] . Studies have implicated a role for hypoxia inducible factor 1 ( HIF1α ) , a major regulator of the mammalian response to hypoxia , in the regulation of inflammation and host defense responses to microbial pathogens [22] , [23] , [24] , [25] . However , the role of HIF1α in immune responses to lung microbial pathogens , and particularly fungi , is largely unknown . HIF1α is a heterodimeric protein whose α-subunit is stabilized under hypoxic conditions and translocated to the nucleus where it dimerizes with the β-subunit , referred to as the aryl hydrocarbon receptor nuclear translocator ( ARNT , HIF1β ) protein [26] . Once stabilized and in the nucleus , HIF1α binds to hypoxia response elements ( HREs ) of target hypoxia response genes including those involved in the processes of glucose metabolism , hypoxia , apoptosis , angiogenesis , and erythropoiesis [27] . HIF1α is regulated by prolyl hydroxylase's ( PHD ) through hydroxylation of its oxygen-dependent degradation domain and directs HIF1α for ubiquitin-dependent degradation by the von Hippel-Lindau ( vHL ) protein when oxygen is present [26] , [28] . Activation of HIF1α requires basal levels of NFκB and in turn , NFκB activation is controlled by hypoxic inactivation of PHDs [29] , [30] . Moreover , the activation of NF-κB in normoxia leads to up-regulation of HIF1α mRNA levels contrary to hypoxia increased protein levels [30] . This interdependence between HIF1α and NFκB supports a strong link between innate immunity and metabolism in controlling disease . HIF1α plays an important role in innate immunity and host defense as myeloid cells have HIF-dependency for adaptation to hypoxic and inflamed microenvironments that develop during infection . For example , HIF1α is critical for regulating bactericidal activity of phagocytes against Group B Streptococcus [31] . In other pathogens , such as Group A Streptococcus and Staphylococcus aureus , presence and induction of HIF1α in myeloid cells , specifically macrophages and neutrophils , increases phagocytic activity and controls systemic spread of these pathogens [23] , [25] . HIF1α has been suggested to be involved in the suppression of the angiogenic response by A . fumigatus in murine models of IA [32] . More recently , pulmonary HIF1α mRNA levels were observed to increase in response to the human fungal pathogen Coccidioides immitis [33] . Although the roles of HIF1α in myeloid cell bactericidal activity and inflammatory diseases are established , the function of HIF1α in the course of lung infections and particularly with fungi remains unclear . Therefore , we investigated the role of HIF1α following pulmonary challenge with A . fumigatus using a myeloid-specific lysozyme-M cre-recombinase driven HIF1α null mouse ( HIFC ) [22] . We observed that A . fumigatus challenge strongly induces HIF1α stabilization in wild-type immune competent murine lungs and macrophages; a process that is inhibited by administration of corticosteroids . Loss of myeloid HIF1α in otherwise immune competent mice resulted in a dramatic decrease in murine survival when challenged with A . fumigatus conidia . Surprisingly , rather than a role for HIF1α in mediating fungal killing by innate effector cells as previously observed with bacterial pathogens , the reduction in murine survival was in part mediated by a reduced number of neutrophils and innate immune effector cells early following fungal challenge . Reductions in these innate effector cells in the airways and lung in the absence of HIF1α were partially due to defective induction of the chemotactic signal CXCL1 . These results support a role for HIF1α in initiating the correct inflammatory signal and immune response in order to prevent pulmonary fungal growth . Therefore , modulation of HIF1α signaling in specific immunocompromised patient populations is a potential area for therapeutic development .
To determine whether HIF1α stabilization is part of the pulmonary innate defense response to pathogenic fungi , levels of HIF1α mRNA abundance and protein were analyzed in an immune competent murine model of fungal bronchopneumonia initiated by A . fumigatus challenge . Consistent with a potential role for HIF1α in defense against pulmonary fungal disease , A . fumigatus induced a three-fold increase in HIF1α mRNA abundance in the lung compared to PBS inoculated controls ( Figure 1A ) . Accordingly , stabilization of HIF1α protein and increased nuclear localization occurred in murine lungs exposed to A . fumigatus conidia with greater HIF1α protein levels occurring in the cytoplasmic fraction of the PBS inoculated mice ( Figure 1B ) . These results demonstrate two distinct effects of A . fumigatus challenge on HIF1α in the murine lung , the increase of HIF1α mRNA and an increase in nuclear HIF1α protein localization . In addition , HIF1α stabilization occurs in vitro with cultured macrophages exposed to A . fumigatus conidia and germlings in standard tissue culture conditions ( Figure S1A ) . Taken together , in a healthy immune competent murine lung , HIF1α is stabilized in response to A . fumigatus pulmonary challenge , suggesting an important role for this protein in resistance to pulmonary fungal growth and subsequent infection . Consequently , we next sought to determine whether activation of HIF1α was inhibited under conditions known to enhance susceptibility to A . fumigatus pulmonary growth and infection . We determined the effects of corticosteroids on HIF1α stabilization in response to A . fumigatus [34] . Although the effect of steroids on host immune cells has been focused on the role of NFκB , recently , the glucocorticoid dexamethasone was found to abrogate the activation of HIF1α in response to inflammation induced hypoxia [35] , [36] . Intriguingly , corticosteroid treatment of mice significantly reduced mRNA induction of HIF1α two-fold when exposed to conidia of A . fumigatus ( Figure 1A ) . Corticosteroid treatment also resulted in decreased levels of HIF1α protein with overall reduced levels in nuclear extracts in both A . fumigatus inoculated and uninoculated mice compared to immune competent mice ( Figure 1B–C ) . Reductions in nuclear levels of the p65 subunit of NFκB in the corticosteroid treated mice were also observed , which has been reported previously , validating the chosen murine model ( Figure 1B–C ) [37] , [38] . These results suggest that reductions in HIF1α nuclear levels may contribute to susceptibility of corticosteroid treated mice to A . fumigatus . To delineate the function of HIF1α in pulmonary host defense to A . fumigatus , we utilized a conditional knockout system to strongly reduce HIF1α levels in the myeloid compartment [22] . The conditional mice ( HIFC ) expressed Cre recombinase under the control of the lysozyme M promoter in combination with the loxP flanked exon2 of the HIF1α gene . HIF1α levels were significantly reduced in myeloid derived cells , especially macrophages and neutrophils ( Figure S1B ) [22] . Immune competent mice deficient in myeloid HIF1α ( HIFC ) were strikingly more susceptible to A . fumigatus pulmonary challenge compared to littermate controls , with 100% mortality occurring in HIFC mice by day 3 of the experiment and statistically different mortality by day 2 post- fungal inoculation ( p = 0 . 0007 ) ( Figure 2A , p<0 . 0001 ) . HIFC mice had increased fungal burden at 24 and 48 hrs post inoculation compared to littermate controls that began clearing the conidial inoculum after 24 hrs as measured by quantitative real-time PCR analysis of the fungal 18S rDNA gene ( Figure 2B ) [39] . Dissemination to the liver and kidney was also increased in HIFC mice ( data not shown ) . Histopathology of the lungs 8 hrs post-fungal inoculation revealed no apparent difference in the number of conidia in the lungs of HIFC and littermate control mice ( Figure S2A ) . However , decreased levels of cellular infiltrate and inflammation were apparent in HIFC mice A . fumigatus challenged mice ( Figure S2A ) , and this phenotype was also observed at 12 hrs post inoculation ( Figure 2C–D ) . However , the difference in the cellular infiltrate and inflammation was dramatically greater in the littermate controls at 12 hrs post inoculation consistent with a self-resolving fungal bronchopneumonia in these immune competent mice . At later time points post fungal inoculation , 24 and 48 hrs , littermate control mice were able to clear and contain the infection with mostly fungal debris remaining in the lung at 48 hrs ( Figure 2C–D & Figure S2B ) , while the HIFC mice develop invasive disease with uncontrolled hyphal growth . Additionally , the HIFC mice develop increased levels of pulmonary damage , with significantly more lactate dehydrogenase ( LDH ) apparent in bronchoalveolar lavage fluid ( BALF ) at 24 hrs post challenge , but with no marked differences in vascular leakage determined by albumin BALF levels ( Figure 2E–F ) . These results suggest that there is a defect in the innate immune response early during the response to A . fumigatus challenge in the HIFC mice that renders them unable to clear and prevent fungal growth and host tissue damage . Previous reports on HIF1α's function during bacterial infection determined the importance of its activation and presence for neutrophil and macrophage-mediated pathogen killing [22] , [23] , [25] . We therefore sought to determine if the susceptibility of the HIFC mice to A . fumigatus challenge was due to an overall defect in innate effector cell mediated fungal killing . First , utilizing ex vivo bone marrow derived macrophages ( BMDMs ) from HIFC and littermate control mice , no difference in the ability of these cells to phagocytose conidia was observed between genotypes ( Figure 3A ) . Additionally , there was no difference in the BMDM's ability to cause damage to phagocytosed conidia as measured by overall metabolic activity of the conidia phagocytosed by the BMDMs ( Figure 3B ) . To confirm these results in vivo in the context of appropriate inflammatory cues needed for full activation of innate effector cells , we generated a fluorescent Aspergillus reporter ( FLARE ) strain in the CBS144 . 89 ( CEA10 ) wild-type background through ectopic insertion of TdTomato driven by the A . nidulans gpdA promoter . A single ectopic insertion of the construct by Southern blotting and FLARE based viability were confirmed as previously reported for the AF293 FLARE strain ( Figure S3 ) [40] . A . fumigatus FLARE conidia contain two fluorophores that allow leukocytes to be distinguished based on their ability to engulf and/or kill conidia . The TdTomato fluorescence expressed by the conidia is lost upon conidia death , whereas the other fluorophore ( AF633 or BV421 ) coating the conidia is stable even at low pH allowing for tracking of the conidia within leukocytes , whether dead or alive . Utilizing the FLARE strain , which allows real time measurement of conidial uptake and viability ex vivo and in vivo , we observed that HIF1α was not required in bone marrow derived neutrophil ( BMDN ) mediated uptake and killing of A . fumigatus conidia ( Figure 3C–D ) [40] . In agreement with ex vivo observations with single cell types derived from the bone marrow , inoculation of the FLARE strain into the immune competent murine model also revealed no significant difference in the ability of neutrophils or monocytes to engulf or kill conidia ( Figure 3E–G ) . These surprising results demonstrate that HIF1α is not required for innate effector cell mediated killing following challenge with a fungal pathogen , which is in contrast to what has been determined with bacterial pathogens ex vivo and in skin models where HIF1α is critical for bactericidal activity of these effector cells [23] , [25] . These results suggest unique HIF1α mediated mechanisms are employed by the host for protection and defense against fungal pathogen growth and invasion in the lung . One factor determining the outcome of A . fumigatus lung infection is the timing and recruitment of neutrophils into the lung [8] , [9] , [41] . Due to the decrease in inflammation observed in the histology of the HIFC A . fumigatus challenged mice , we quantitatively analyzed the level of innate effector cells through analysis of BALF and lung cellularity at early time points following fungal challenge . Consistent with the qualitative histopathology observations , the overall BALF and lung cellular infiltrates of inoculated littermate mice was greater than the HIFC mice at all time points examined ( Figure 4 ) . At 4 and 8 hrs post fungal inoculation littermate mice displayed higher numbers of monocyte-like cells ( CD11b+Ly6G− ) and macrophages ( CD11c+ ) in the BALF and at 8 hrs within the lung ( Figure 4B–C , E ) . Interestingly , HIFC mock treated mice tended to have modestly higher levels of macrophages than littermate controls , though not always statistically significant ( Figure 4C , E ) . More importantly , the main innate effector cells known to be required for clearance and defense against A . fumigatus , neutrophils and inflammatory monocyte-like cells , were consistently ∼2–3 fold lower in HIFC mice following fungal challenge at 4 , 8 and 12 hrs in the BALF and at 8 hrs in the lung ( Figure 4A , E p<0 . 02 ) . These data suggest that HIFC mice are defective in overall effector cell population numbers early following fungal challenge in the lung and that this may contribute to their inability to control fungal growth and tissue damage . Considering that early following fungal challenge there is precedence for the requirement of neutrophils and inflammatory monocytes in terms of infection outcome , we next sought to determine how HIF1α is involved in the neutrophil response following fungal challenge . During inflammation , the absence of HIF1α in effector cells is characterized by depletion of ATP stores due to a failure to switch from oxidative to glycolytic metabolism , which also results in an increase in toxic levels of ROS [42] . However , neutrophils rely strongly on glycolytic metabolism even though they have the capacity for aerobic respiration , supporting a major role for HIF1α in maintenance of normal neutrophil function [43] . Therefore , we sought to determine whether there was a defect in survival of HIF1α-deficient neutrophils in the presence and absence of fungal stimulation . Agreeing with previously reported data [44] , we determined that murine neutrophils deficient in HIF1α have increased cell death compared to wild-type control neutrophils with HIF1α deficient neutrophils exhibiting increased apoptosis at 5 hrs and increased necrosis at 22 hrs in the absence and presence of stimulation with the fungal β-glucan derivative curdlan ( Figure 5A–B ) . Examination of the cellular infiltrates from BALFs of mice 8 hrs post A . fumigatus challenge revealed an increase in apoptotic neutrophils visualized by increased pyknotic nuclei and karyorrhexis of HIFC compared to wild-type neutrophils ( Figure 5C–D ) [45] , [46] , [47] . There was no difference in the number of wild-type and HIFC neutrophils with ruffled membranes , a marker of neutrophil activation ( Figure 5C–D ) [48] . These data correlate with the increase in LDH observed in the HIFC mice ( Figure 2F ) . Previous reports have identified a role for HIF1α in tissue adhesion , migration and invasion by neutrophils and macrophages at sites of infection and inflammation [22] , [49] . In the HIFC mice , we observed a decrease in the level of margination on the vessel walls compared to the littermate mice ( Figure 5E ) . Margination is a prerequisite for neutrophil transendothelial migration ( TEM ) from the capillaries into pulmonary tissue [50] . There is much controversy as to the requirement for selectin-mediated rolling in order for TEM to occur , but chemokine production due to stimulation from foreign agents is known to increase margination and TEM [51] , [52] . In addition to the lack of margination , the migration of the inflammatory cells away from the vessels is minimal in the HIFC mice compared to littermate controls in which the cells have progressed into the alveoli ( Fig . 2 ) . These results demonstrate a defect in the HIFC neutrophils ability to sense and migrate towards conidia within the lung environment . Therefore , we next sought to determine if there was a defect in neutrophil migration or chemotactic signaling in HIFC mice that could account for decreased neutrophil numbers in vivo in response to A . fumigatus challenge . In response to the general chemotactic signal fetal bovine serum ( FBS ) , HIFC neutrophils were able to migrate to the same capacity as littermate neutrophils ex vivo ( Figure 6A ) . Importantly , this assay was conducted within the time window during which there was no difference in apoptosis between WT and HIFC neutrophils . Consequently , the migration results indicated that a defect in the production of chemotactic/cell survival signals at the infection site or the neutrophil response to tissue-specific signals may be the cause of reduced PMN numbers in vivo . Since HIFC neutrophils were competent to migrate in response to general chemotactic factors ( Figure 6A ) , we next determined if there was a defect in the production of chemotactic signals during fungal pulmonary challenge that would result in decreased migration or cell survival . Utilizing the BALFs of A . fumigatus challenged HIFC and littermate mice as the source of chemotactic factors in the transwell migration assay , we observed that littermate and HIFC neutrophils were defective in migration towards the HIFC BALF , but not towards littermate control BALF . This result indicated there was a missing chemotactic component in the HIFC but not littermate control BALF ( Figure 6B ) . These results support a defect in chemotactic or cell survival signal production in HIFC mice following A . fumigatus pulmonary challenge . Cytokine/chemokine analysis of the 12 hr BALFs from A . fumigatus challenged mice indicated decreased production of the pro-inflammatory cytokines G-CSF , IL-1α , IL-6 , and TNF with no difference in production of the anti-inflammatory cytokine IL-10 ( Figure 6F ) . There was no observed difference in the production of IL-17 and IL-12p40 between the WT and HIFC challenged mice ( Figure 6F ) . The overall response in the HIFC mice at 12 hrs post challenge did not deviate from the usual Th1 protective response with Th2 specific cytokines either unchanged ( IL-10 ) or undetectable ( IL-4 , data not shown ) . The production of one of the major neutrophil chemotactic cytokines CXCL1 was decreased in the HIFC inoculated mice compared to littermate controls early following fungal challenge ( Figure 6C ) . This reduction in CXCL1 correlated directly with the quantitative neutrophil defect found during early time points following fungal challenge ( Figure 4A ) . HIF1α is directly required for CXCL1 mRNA levels as the mRNA abundance in response to A . fumigatus is significantly decreased in macrophages deficient in HIF1α ( Figure 6D ) . Whether the HIF1α regulation of CXCL1 mRNA levels is at the transcriptional or post-transcriptional levels remains unclear , however , analysis of the DNA sequence upstream of the CXCL1 start codon revealed multiple potential HRE elements ( Figure S5A ) . Importantly , the production of CXCL2 [53] and CXCL5 [54] , other neutrophil chemokines detected by CXCR2 , was not different between the WT and HIFC inoculated mice ( Figure 6E ) . Additionally , no statistically significant difference in the mRNA abundance of the receptors CXCR2 , TLR4 , and Dectin-1 were observed between the WT and HIFC neutrophils ( Figure S5B ) . Taken together , these results support a direct role for HIF1α in production of cytokines early in the pulmonary response to A . fumigatus challenge . They also support the hypothesis that the neutrophil quantitative BALF defect in the HIFC mice is due in part to decreased levels of CXCL1 and/or other pro-inflammatory cytokines . We next sought to determine if the HIFC phenotype was due , at least in part , to the marked reduction in CXCL1 levels . Mice deficient in CXCR2 ( ligands CXCL1 ( KC ) , CXCL2 ( MIP2 ) , CXCL5 ( LIX ) ) develop invasive aspergillosis resulting from delayed neutrophil influx that allows conidial germination [10] , [55] . Additionally , the transient expression of CXCL1 during invasive aspergillosis causes an earlier and increased number of neutrophils in the lung that results in improved host defense and outcome of Aspergillus infection [55] . In order to define the involvement of CXCL1 in neutrophil migration , LPS-free recombinant CXCL1 was added to the BALF of A . fumigatus challenged HIFC mice to the level that was determined in the littermate BALF ( Figure 6C ) . The addition of physiological levels of CXCL1 largely restored the neutrophil migration defect observed with A . fumigatus challenged HIFC BALF ( Figure 7A , p<0 . 001 ) . This significant restoration of neutrophil migration by CXCL1 led us to examine if the addition of recombinant CXCL1 to the HIFC A . fumigatus challenged mice could restore neutrophil levels and murine survival . Previously , kinetics of neutrophil recruitment after 4 hrs by the intratracheal instillation of varying amounts of CXCL1 to the lung was demonstrated [56] . Doses of 30 ng and 100 ng of CXCL1 were demonstrated to induce ∼1 . 5 and ∼3 . 5 fold increases in neutrophil recruitment over PBS treatment , respectively [56] . Based on these previously published results , we utilized a dose of 50 ng recombinant CXCL1 for intratracheal instillation . The addition of CXCL1 to the HIFC A . fumigatus challenged mice restored neutrophil numbers in the BALF back to levels of littermate inoculated mice and this restoration correlated with a significant increase in murine survival and improved outcome of infection based on the observed decrease in fungal burden and tissue damage ( Figure 7B–C , E , F ) . The addition of CXCL1 also restored the inflammatory monocyte-like levels back to littermate controls , but not the CD11c+ macrophages , which was expected , as they are known to not respond to CXCL1 ( Figure 7D , & Figure S4 ) . These results demonstrate a unique requirement and mechanism for HIF1α control and induction of CXCL1 in response to A . fumigatus challenge in the lung . Due to the multifunctional role of some cytokines in chemotactic and angiogenic responses and the observed increase in apoptosis/necrosis in the HIFC neutrophils , we determined if the addition of recombinant CXCL1 affected survival of the HIF1α deficient neutrophils . Adding physiologically relevant levels of recombinant CXCL1 to the HIFC neutrophils decreased their time-dependent level of apoptosis and necrosis ex vivo ( Figure 7G ) . This demonstrates that CXCL1 signaling promotes and restores HIF1α neutrophil survival providing support for the requirement of HIF1α-dependent production of CXCL1 .
In this study , we uncover a novel and essential function for myeloid HIF1α in protection against pulmonary A . fumigatus challenge . We present findings that myeloid HIF1α is required for initiating protective inflammatory signals and responses to control A . fumigatus growth and host tissue damage . These data strongly suggest a role for HIF1α in providing host protection to pulmonary fungal disease , provides a deeper understanding of the fungal-host interaction in the lung , identifies a new genetic factor critical for resistance to pulmonary murine fungal infections , and argues for further investigation into the therapeutic potential of HIF1α modulation for fungal disease . Importantly , our results demonstrate a requirement for myeloid HIF1α in murine survival to A . fumigatus pulmonary challenge . The kinetics and final outcome of murine survival in the absence of myeloid HIF1α is striking . Mortality in HIFC mice does not appear to be due to an increased inflammatory response as there is less overall inflammation in the lung at the time points analyzed in our model . Upon examination of the lung , there appears to be no gross defects in the vasculature in the HIFC mice ( also suggested by BALF albumin measurement ) , but as infection progresses and fungal burden increases an increase in the level of damage occurring in the lung in the absence of HIF1α occurs as evidenced by the increase in LDH levels . We cannot currently rule out systemic effects of HIFC loss on murine survival , however , there was a trend toward increased fungal dissemination to the kidneys and liver in the HIFC mice that could contribute to the rapid mortality ( data not shown ) . The increased mortality therefore is likely due to the combination of the dysregulated host response and subsequent increased fungal burden following A . fumigatus challenge . Perhaps most surprisingly , given previous studies in bacterial pathosystems , HIF1α was not required for direct killing of A . fumigatus conidia ex vivo or in vivo by innate effector cells . This is in contrast to the requirement for HIF1α in killing of multiple gram-negative and –positive bacteria in epidermal wound models of infection [22] , [23] , [25] . As HIFC neutrophils are not defective in their ability to elicit the respiratory burst [23] , it is perhaps not surprising that no difference in their ability to kill A . fumigatus conidia was observed due to the known conidial susceptibility to reactive oxygen species [57] , [58] . Additionally , we cannot rule out that the ability to kill conidia may be due to a compensatory HIF2α mechanism , as it has distinct and overlapping biological roles with HIF1α [59] . Importantly , these observations demonstrate the plasticity of HIF1α regulation between different tissue microenvironments and with fungal and bacterial organisms . As the phenotypes of innate cells differ between tissues within the host [60] , it will be important to determine if the findings presented here occur with other pathogens such as the bacteria examined in the epidermal model that are also potent lung pathogens . Though HIF1α does not seem to regulate innate mediated killing of A . fumigatus conidia per se , our data suggest a required role for HIF1α in controlling A . fumigatus infection through modulation of the infection microenvironment that drives the timing , recruitment , and survival of neutrophils at early critical time points following A . fumigatus challenge . Recruitment of myeloid cells during epidermal inflammation is known to require HIF1α and is reported to be due to decreased HIF1-dependent integrin expression [22] , [49] . This may partially account for the decreased margination observed in the HIFC mice challenged with A . fumigatus , however , due to the increased percentage of neutrophils in the pulmonary capillary blood and the smaller size of the capillaries , the importance of integrin and selectin binding for pulmonary TEM to occur is debated [61] , [62] , [63] . Once at the site of inflammation , the ability of neutrophils to remain metabolically competent in low oxygen inflammatory environments is dependent upon the induction of the glycolytic pathway directly regulated by HIF1α [64] . In the absence of HIF1α and oxygen , neutrophils cannot maintain high ATP levels and as a consequence , undergo apoptosis [65] . The delay in the time-dependent recruitment of neutrophils and/or their apoptosis is partially responsible for the increased occurrence of conidial germination in the HIFC mice [9] . The HIFC A . fumigatus challenged mice also had decreased levels of CD11b+Ly6G− inflammatory monocyte-like cells , which have recently been implicated in inflammatory conditioning for neutrophil functions in the lung and conidial killing [41] , and are also likely involved in the HIFC phenotype . The effects of myeloid HIF1α loss on effector cell recruitment and survival appear to be driven in part by defects in the production of pro-inflammatory cytokines . Though our data strongly support a major role for the neutrophil chemoattractant CXCL1 in mediating the HIFC phenotype , other neutrophil chemoattractants/receptors that are known to be involved in neutrophil recruitment during various lung infections may be involved in the HIF1α phenotype including CCL3-CCL6-/CCR1 [66] , C5a/C5aR [67] , and LTB4/LTB4R1 [68] , [69] . However , production of the other CXCR2 ligands CXCL2 [53] and CXCL5 [54] that are known to have roles in neutrophil recruitment were not different within the BALF of WT and HIFC mice . Untangling the signal transduction cascades mediated by HIF1α , including how it is activated in response to A . fumigatus challenge , is an important future direction of this research . Downstream of pathogen recognition , regulation of CXCL1 transcription through NFκB is a known mechanism for CXCL1 induction in response to inflammation [70] . Given the dual regulation of multiple genes by HIF1α and NFκB , it is perhaps not surprising that a role for HIF1α in CXCL1 regulation exists . Loss of CXCL1 mRNA levels in HIF1α macrophages and the presence of putative HREs in the CXCL1 promoter further support a direct role for HIF1α in control of CXCL1 murine lung levels in response to A . fumigatus challenge . Importantly , it appears that in the context of lung infection that HIF1α may play a more dominant role in myeloid activation of CXCL1 and the amplification of the response . This is based on previous demonstration that amplification of CXCL1 levels occurred only in epithelial cells that had constitutively active HIF1α over NFκB activation alone [71] . Given the decrease in IL-1 family members in the HIFC mice , we hypothesize that IL-1 signaling may be play a critical function in these signal transduction cascades . Considering that transgenic expression of CXCL1 during the course of infection improved fungal burden and survival during infection with A . fumigatus [55] , understanding the exact mechanism for HIF1α regulation of CXCL1 is of great importance . Another important future direction is the cell compartment primarily producing CXCL1 and the effects of paracrine and autocrine signaling to regulate the observed effector cell phenotypes . Due to the ability of CXCL1 to reduce the apoptotic phenotype of the HIFC neutrophils , we hypothesize that the HIF-induced CXCL1 responses are not only required for chemotaxis of neutrophils , but also partially involved in inhibiting apoptosis during infection , perhaps consistent with the known angiogenic responses autocrine CXCL1 signaling has in epithelial cells [72] , [73] . Recently , the implication for the requirement of neutrophil transmigration in the transcriptional imprinting of epithelial cells was demonstrated [74] . At the sites of transmigration , the depletion of oxygen by neutrophils in response to either a pathogen or tissue injury , resulted in the stabilization of HIF1α , which was required for mucosal protection and inflammatory resolution [74] . In addition , the release and sensing of VEGF in an autocrine and paracrine manner by monocytes , keratinocytes , and endothelial cells is required for wound healing responses to restore proper tissue homeostasis [75] . Tumors are notorious for exploiting this mechanism to aid in their growth and metastasis , as demonstrated by increased tumor growth with the paracrine signaling of CXCL1 in breast cancer [76] . The increased survival of the HIFC neutrophils with exogenous CXCL1 supports the possibility that decreases in cytokine production in the HIFC mice is due to decreased paracrine signaling responses between the deficient and less abundant myeloid cells and the epithelia/endothelia of the lung . A direct translational outcome from our study that warrants further investigation is the observation that corticosteroids reduced nuclear localization and gene regulation of HIF1α . Given the phenotype of the HIFC mice , it stands to reason that steroid mediated suppression of HIF1α could contribute to aspergillosis susceptibility . Our results demonstrate that steroid treatment does not inhibit overall HIF1α protein levels , but rather reduces the nuclear levels of HIF1α and p65 NFκB . The decrease in nuclear p65 NFκB and subsequent HIF1α mRNA abundance did not cause a decrease in HIF1α protein accumulation in the cytoplasm , indicating that there may be a separate corticosteroid induced NFκB-independent mechanism that hinders the HIF1α induced response to A . fumigatus . Nuclear import of HIF1α is known to rely upon interactions with the septin SEPT9_i1 , a product of transcript SEPT9_v1 that encodes isoform1 , and importin-α [77] . These interactions have been established in the context of tumor and cancer progression , but the interactions during steroid treatment are unknown to our knowledge , and a mechanism for the steroid blockage of HIF1α cytoplasm-nuclear localization is not yet understood . There may also be differential posttranslational regulation of HIF1α under corticosteroid conditions that is impacting the localization , interactions , and nuclear stability of HIF1α in myeloid cells . Interestingly , corticosteroid treated mice were demonstrated to have a two-fold reduction in CXCL1 production compared to immune competent mice following A . fumigatus challenge [78] further supporting a role for HIF1α in steroid induced IPA susceptibility . Taken together , our data support the conclusion that myeloid derived HIF1α is required by effector cells of the innate immune system to prevent A . fumigatus pulmonary infection . As control of metabolism and production of energy in inflammatory , low oxygen infection sites is dependent upon HIF1α , it is in accord that innate inflammatory responses required for clearing and preventing infection are also tied to this important transcriptional regulator . It will be of translational importance to determine if this idea is reflected in the mechanisms underlying the susceptibility of certain patients to A . fumigatus infection . Consequently , it is a high priority to determine if this response can be targeted to reverse the inactive and suppressed effects of the innate cells during IPA in the context of corticosteroid mediated immune suppression . The success of HIF1α agonists in the context of bacterial skin infections is promising in this regard [23] , [25] , [79] .
Aspergillus fumigatus strain CBS144 . 89 ( also known as CEA10 ) was used in all experiments except those involving the FLARE tdtomato strain generated from CEA17 ( uracil auxotroph derived from CEA10 ) . All strains were grown on glucose minimal medium with 1 . 5% agar at 37°C . Conidia were dislodged from plates with a cell scrapper , re-suspended in 0 . 01% Tween-20 , and filtered through miracloth ( EMD chemicals , CalBiochem ) . A . fumigatus strain CEA17 was transformed with a construct consisting of an overlap PCR of the gpdA promoter driven-tdtomato from pSK536 ( gift from Dr . Sven Krappmann ) with A . parasiticus pyrG . The construct was ectopically inserted into the genome using the standard fungal protoplast transformation as previously described [80] . Transformants were initially screened by microscopy and flow cytometry for tdtomato expression . One of five transformants were selected for further use , based on bright fluorescence in all growth stages , comparable radial growth to parental strain , and comparable inflammatory TNF responses by BMDMs ( Figure S3 ) . Copy number was confirmed by Southern analysis with the digoxigenin labeling system ( Roche Molecular Biochemicals , Mannheim , Germany ) as previously described [81] . Generation of FLARE was done as previously described in [40] using Biotin conjugated AF633-streptavidin or Biotin conjugated BilliantViolet421-streptavidin ( BV421 ) ( BioLegend ) . For conidial kill assays , 2 . 5×105 FLARE conidia were incubated in 0 . 2 ml RP10 with 0–10 M H2O2 for 30 min at 37°C , washed , and analyzed by flow cytometry for TdTomato and BV421 fluorescence . Duplicate samples were plated ( at 1∶1 , 000 dilution ) to determine the cfu . CD1 female mice , 6–8 weeks old were used in the corticosteroid ( triamcinolone acetate , Kenalog ) experiments . Mice were obtained from Charles River Laboratories ( Raleigh , NC ) . For the corticosteroid model , mice were immunosuppressed with a single dose of Kenalog ( Bristol-Myers Squibb Company , Princeton , NJ , USA ) injected subcutaneously ( s . c . ) at 40 mg/kg 1 day prior to inoculation . For the immunocompetent experiments , mice 10–12 weeks old with targeted myeloid deletions of HIF1α created via crosses into a background of lysozyme M–driven cre ( HIFC ) expression and littermate controls ( cre-/HIF1α floxed ) were used as described in [22] . For infections , mice were lightly anesthetized and immobilized in an upright position using rubber bands attached to a Plexiglas stand for oropharyngeal aspiration . A blunt 20G needle attached to a 1 ml syringe was advanced into the trachea to deliver the indicated number of conidia ( 3–7×107 ) in a volume of 0 . 05 ml PBS or PBS with 0 . 025% Tween-20 . Nuclear and cytoplasmic proteins were isolated from lyophilized lung tissue , BMDMs , or J774 . 1 macrophages at indicated times . Cells were centrifuged at 1500 rpm for 5 min at 4°C . Supernatant was removed , and the pellet was washed with 5 packed cell volumes ( PCV ) of buffer A [10 mM Tris-HCl ( pH 7 . 5 ) , 1 . 5 mM MgCl2 , 10 mM KCl supplemented with 1M dithiothreitol , 0 . 2 M PMSF , 1 mg/ml leupeptin , 1 mg/ml aprotinin , 1 mg/ml pepstatin , and 0 . 5 M Na3VO4] , resuspended in 4 PCV of buffer A and incubated on ice for 10 min . The cell suspension and lung tissue were homogenized , and nuclei were pelleted by centrifugation at 10 , 000 g for 10 min at 4°C and the supernatant was collected as the cytoplasmic fraction . The cell pellet was resuspended in 3 PCV of buffer C [20 mM Tris-HCl ( pH 7 . 5 ) , 0 . 42 M KCl , 1 . 5 mM MgCl2 , and 20% glycerol] and rotated for 30 min at 4°C . The suspension was centrifuged at 20 , 000 g for 10 min at 4°C . The protein concentration was determined using the Bradford method ( Bio-Rad , Hercules , CA , USA ) . Nuclear and cytoplasmic samples were suspended in 6× SDS sample buffer , boiled for 10 min , and loaded onto a 10% mini-protein precast gels ( Bio-Rad ) for SDS-PAGE . After gel electrophoresis , protein was transferred to a PVDF membrane using the trans-blot turbo transfer system ( Bio-Rad ) . HIF1α and NFκB ( p65 ) were detected using polyclonal rabbit anti-mouse antibodies NB100-449 ( 1∶3000 ) and C-20:sc-372 ( 1∶1200 ) , respectively and an anti-rabbit HRP-conjugated secondary antibody raised in goat ( millipore ) at a 1∶5000 dilution . Chemiluminescence was measured following incubation of blots with Clarity Western ECL substrate ( Bio-Rad ) using a FluorChem FC2 imager ( Alpha Innotech ) . For loading controls , anti-tubulin ( Sigma , T5192 ) ( human ) was utilized . Tissue or BMDMs were re-suspended in Trizol reagent and chloroform to extract RNA . RNA was DNase treated with DNA-free kit ( Ambion ) and reverse transcribed with QuantiTect reverse transcription kit ( Qiagen , USA ) . Primers for all murine genes of interest were designed with PrimerQuest ( IDT ) and manufactured by IDT , USA . Sequences are: hif1α fwd: ATGAGATGAAGGCACAGA , rev: CACGTTATCAGAAATGTAAACC , cxcl1 ( kc ) fwd: TGCACCCAAACCGAAGTCAT , rev: TTGTCAGAAGCCAGCGTTCAC , cxcr2 fwd: TGGCCTAGTCAGTCATCA , rev: CAATCCACCTACTCCCATTC , tlr4 fwd: GTGTGTGTGTGTGTGTTG , rev: AGCTGCTCTGTACACTATTT , dectin1 ( clec7a ) fwd: CCTAGTGTGATCTGTCTTGT , rev: TTTCTGCCCACATATTGATTAG , hprt fwd: GGAGTCCTGTTGATGTTGCCAGTA , rev: GGGACGCAGCAACTGACATTTCTA , rpl13a fwd: CTCTGGAGGAGAAACGGAAGGAAA , rev: GGTCTTGAGGACCTCTGTGAACTT . All reactions were performed on BioRad MyIQ real-time PCR detection system with IQ SYBR green supermix ( Bio-Rad , Hercules , CA ) . The ΔΔCt method was used to assess changes in mRNA abundance , using either hprt or rpl13a as the housekeeping gene . Results presented are the mean and standard deviation from 3 biological and 3 technical replicates . A . fumigatus challenged mice were euthanized at indicated times . For histological studies , the lungs were inflated with 10% buffered formalin , fixed , and embedded in paraffin to generate 4 µm sections stained with hematoxylin and eosin ( H&E ) or Gomori's Methenamine Silver ( GMS ) stain for microscopy by the Dartmouth Immunology COBRE core facility or at Montana State University . Contiguous tissue sections were imaged using a Zeiss Axioscope 2-plus microscope and imaging system ( Zeiss , Jena , Germany ) and a Leica upright DMRXA2 with Leica application suite software and DC500 camera ( Leica Microsystems , Buffalo Grove , IL , US ) . Pathological examination was conducted for apoptosis , necrosis , and vasculature observation . Image analysis was performed using ImageJ software ( v . 1 . 46i ) . For immunohistological studies , the left lung of each mouse was filled with OCT ( frozen tissue matrix ) and after embedding in OCT immediately frozen in liquid nitrogen . The lungs were stained as previously described in [21] , [82] with a rabbit polyclonal antibody to Aspergillus ( Abcam Inc . , Cambridge , MA , USA ) and detected with AlexaFluor488-conjugated goat Anti-rabbit ( Invitrogen , Carlsbad , CA , USA ) diluted 1∶400 . After another washing step , prolong Gold antifade reagent with DAPI ( Invitrogen , Carlsbad , CA , USA ) was added to each section . Microscopic examinations were performed on a Zeiss Axioscope 2-plus microscope and imaging system ( Zeiss , Jena , Germany ) . For each time point , a total of 2 to 4 mice were examined and experiments were repeated in triplicate . To assess fungal burden in lungs , mice were sacrificed at 24 , 36 , or 48 hrs post inoculation , and lungs were harvested and immediately frozen in liquid nitrogen . Samples were freeze-dried , homogenized with glass beads on a Mini- Beadbeater ( BioSpec Products , Inc . , Bartlesville , OK , USA ) , and DNA extracted with the E . N . Z . A . fungal DNA kit ( Omega Bio- Tek , Norcross , GA , USA ) or phenol chloroform extraction . Quantitative PCR was performed as described previously [39] . Cellularity was analyzed on cells from the BALF at specific time points of 4 , 8 , or 12 hrs . Cells isolated from BALFs were enumerated and stained with the following Abs: anti-CD11b ( clone M1/70 ) , anti-CD11c ( clone N418 ) , and anti-Ly6G ( clone 1A8 ) in staining buffer ( PBS supplemented with 2% FBS ) . Neutrophils were identified as CD11b+CD11c−Ly6Ghi , macrophages as CD11c+CD11b−Ly6G− , and inflammatory monocyte-like cells as CD11b+CD11c−Ly6Glo ( negative for NK1 . 1 staining , data not shown ) . A fourth population of cells staining with CD11c+CD11b+Ly6G+ were found , but further determination was not pursued . Flow cytometry data were collected on a MACSQuant 10 ( Miltenyi Biotec ) and analyzed with FlowJo , v . 9 . 4 . 3 ( TreeStar ) . To assess the requirement of CXCL1 for in vivo infection , HIFC mice received 50 µL of 50 ng recombinant CXCL1 ( BioLegend ) 4 hrs following inoculation of 7×107 conidia and were compared to HIFC mice and WT infected mock mice receiving PBS . Separate experiments analyzing survival , cell recruitment by flow cytometry , and fungal burden were conducted . Single-cell lung suspensions were prepared for flow cytometric analysis and classified as described in Hohl et al . ( 2009 ) . Tissue processing did not result in leukocyte uptake of exogenously added FLARE conidia . Lung digest and , if applicable , BALF cells were enumerated and stained with the following Abs: anti-Ly6C ( clone AL-21 ) anti-Ly6G ( clone 1A8 ) , anti-CD11b ( clone M1/70 ) , anti-CD45 . 2 ( clone 104 ) , and anti-Ly6B . 2 ( clone 7/4 ) . PE- and APC-labeled Abs were omitted in FLARE experiments . Neutrophils were identified as CD45+CD11b+Ly6CloLy6G+Ly6B . 2+ cells . Flow cytometry data were collected on a BD LSR II flow cytometer or MACSQuant 10 ( Miltenyi Biotec ) and analyzed with FlowJo , v . 9 . 4 . 3 ( TreeStar ) . Lactate dehydrogenase ( LDH ) using the CytoTox96 non-radioactive cytotoxicity assay kit ( Promega , Cat . No . 573702 ) and albumin assay ( Albumin ( BCG ) Reagent Set , Eagle Diagnostics , Cedar Hill , TX , USA ) were conducted on BALFs from WT and HIFC mice inoculated with 7×107 conidia or PBS according to manufacturers instructions with slight variation . Briefly , 100 µL of the BALF was added to equal volumes of the respective agents and incubated for either 30 min ( LDH ) or 5 min ( Albumin ) and read at 490 nm and 630 nm , respectively . Albumin levels were determined using a standard curve and LDH values for each time point are relative to the WT PBS BALF sample . Bone marrow ( BM ) cells were eluted from tibias and femurs of 8–12 week old Littermate or HIFC mice , lysed of red blood cells , and cultured for macrophages in RP20 ( RPMI , 20% FCS , 5 mM HEPES buffer , 1 . 1 mM L-glutamine , 0 . 5 U/ml penicillin , and 50 mg/ml streptomycin ) supplemented with 30% ( v/v ) L929 cell supernatant ( source of M-CSF ) or neutrophils in murine neutrophil buffer ( HBSS containing 0 . 1% FBS and 1% glucose ) . BM cells for macrophages were plated in a volume of 20 ml at a density of 2 . 5×106 cells/ml in 10 ml petri dishes . The medium was exchanged on day 3 . Adherent BM-derived macrophages ( BMDMs ) were harvested on day 6 . BM- derived cells for neutrophils ( BMDNs ) were suspended in 3 ml 45% percoll and isolated from a 30 min 1600× g percoll gradient ( top to bottom: 3 ml 45% percoll containing BM cells , 2 ml 50% , 2 ml 55% , 2 ml 62% , and 3 ml 81% ) in a Sorvall Legend Mach 1 . 6R benchtop centrifuge , with a BIOshield 600 rotor-75002005 ( Thermo Scientific ) . BMDNs were collected from the 62/81% border and washed with HBSS before live cell counting ( 95% purity , determined by cytospin ) . For the cytokine mRNA abundance quantifying experiments , BMDMs were incubated with A . fumigatus conidia ( strain CBS144 . 89 ) in a 10∶1 ( effector∶target ) ratio 8 hrs . Following the incubation , cells were directly re-suspended in Trizol reagent and chloroform to extract RNA for qRT-PCR . BMDMs were incubated with A . fumigatus conidia in a 9∶1 ( effector∶target ) ratio 3 hrs ( for CFU ) and 16 hrs ( for XTT ) . Following the 3 hr incubation , non-phagocytosed conidia were washed off the cells , serially diluted onto GMM plates in duplicate , and CFU was determined . Following the 16 hr incubation , BMDMs were cold water lysed and the percent damage was quantitated by measuring the OD at 450 nm following a 1 hr incubation with XTT as previously described [82] . Collected BALFs were assayed by ELISA and luminex . Commercially available ELISA kits for CXCL1 ( Assay Biotech , OK-0189 ) , CXCL2 ( R&D systems , DY452 ) , and CXCL5 ( R&D Systems , DY443 ) were used according to the manufactures' instructions . The limit of detection was 15 pg/ml . Luminex analysis was carried out using Bio-Plex Pro Mouse Cytokine immunoassay on a Bio-Plex Array Reader ( Bio-Rad Laboratories Inc . , Hercules , CA ) according to the manufactures' instructions . Bio-Plex Manager software with five-parametric-curve fitting was used for data analysis and procedure was carried out by the Dartmouth Immunology COBRE core . For BMDM cytokine analysis of TdTomato strain , cells were washed and plated in 0 . 2 ml TC medium at a density of 5×105 cells/ml in 96 well plates and co-incubated in a 9∶1 ratio with conidia for 8 hrs . Supernatants were collected for ELISA . A commercially available ELISA kit for TNF ( eBioscience , San Diego , California , US ) was used according to the manufactures' instructions . The limit of detection was 15 pg/ml for TNF . BMDN migration was examined using Costar Transwell plates ( 6 . 5 mm diameter insert , 3 . 0 µm pore size , polycarbonate membrane , Corning Inc . , Corning , NY ) . To determine if migration was defective , 10% FBS was added to the bottom chamber of these plates ( media without FBS was used as a migration control ) . Isolated BMDNs were counted using trypan blue ( Sigma ) , then placed in serum free medium ( SFM ) . Cells were resuspended at 1×106/ml SFM and 250 µl were allowed to migrate for 3 hr at 37°C at 5% CO2 . Following migration , the medium in the top chamber was aspirated and the membrane gently wiped with a cotton swab to remove the cells that did not migrate . The membranes were first rinsed with PBS , the cells were then fixed with 2% formaldehyde in PBS , permeabilized with 0 . 01% Triton X-100 ( Sigma ) in PBS and finally stained with crystal violet ( Sigma ) . Cells that migrated across the membrane were counted . Ten random fields at 40× were counted for each condition using light microscopy . Each experiment was repeated three times . Results are expressed as mean cell migration normalized to media control ± SEM . To determine the role for cytokine signaling in migration defects , a BALF-switch experiment using BALFs from infected HIFC and littermate mice in the bottom chamber was conducted . Briefly , 250 µl BMDNs at 1×106/ml SFM were added to the top chamber of a transwell plate with FN coated membranes with 300 µl of BALF in the bottom chamber and were allowed to migrate for 3 hr at 37°C and 5% CO2 . For add-back experiments , recombinant CXCL1 ( Biolegend ) was added back to the BALFs from infected HIFC mice to the concentration determined by ELISA in the infected littermate BALFs ( 600 pg ) . Neutrophil apoptosis was measured using FACs analysis by staining BMDN's incubated at 37° , 5% CO2 in RP20 medium with annexin V-Pacific Blue ( PB ) ( BioLegend , #640917 ) and PI ( millipore ) . Staining was performed by following the manufacturer's instructions , with minor changes . Briefly , after isolation or incubation for the specified time points , neutrophils were washed twice with ice-cold PBS with 2% FBS and then resuspended in Annexin-V binding buffer ( 0 . 01 M HEPES , pH 7 . 4; 140 mM NaCl; 2 . 5 mM CaCl2 ) . Annexin V-PB and PI were added into the culture tube and incubated for 15 min prior to direct analysis with flow cytometry . Viable neutrophils were defined as negative for annexin V-PB and PI staining; apoptotic neutrophils were defined as positive for annexin V-PB staining but negative for PI staining . Cells positive for both annexin V-PB and PI staining were considered necrotic cells . Cell survival/apoptosis was expressed as a percentage of neutrophils relative to the total number of counted neutrophils . In vivo neutrophils were quantified through analysis of BALF cytospins as previously described [45] , [46] , [47] . Briefly , apoptotic neutrophils were visualized by counting the number of neutrophils with pyknotic nuclei and karyorrhexis out of the total number of neutrophils per frame . Ruffled neutrophils were also quantified and depicted neutrophil activation . Ten random frames at 8 hrs post challenge were analyzed from four HIFC and four WT mice ( analyzed two separate experiments ) . 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 . The animal experimental protocol was approved by the Institutional Animal Care and Use Committee ( IACUC ) at Dartmouth College ( protocol number cram . ra . 1 ) . | Due to the limited treatment options and severity of invasive fungal infections , a better understanding of fungal-host interactions is needed for the development of new therapies . Recent studies have implicated a role for hypoxia inducible factor 1-alpha ( HIF1α ) in the regulation of inflammation and host defense responses to microbial pathogens . In this study , we discover that HIF1α is required for protection and murine survival to Aspergillus fumigatus pulmonary challenge . First , we observed that nuclear HIF1α protein levels are reduced in the murine corticosteroid immunosuppressed model of invasive pulmonary aspergillosis , suggesting its involvement in disease outcome . We then tested the hypothesis that HIF1α is required by innate immune effector cells to control/prevent A . fumigatus growth and invasion . Surprisingly , we observed that the role of myeloid HIF1α is not to mediate innate effector cell A . fumigatus killing directly , but rather to induce and maintain a protective immune response that ensures proper effector cell recruitment and survival at the site of infection . These findings provide a better understanding of host mechanisms involved in thwarting fungal pathogenesis , have implications for host susceptibility , and reveal the potential for novel treatment strategies involving HIF1α mediated signaling in the lung in immune suppressed patients . | [
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"type... | 2014 | Myeloid Derived Hypoxia Inducible Factor 1-alpha Is Required for Protection against Pulmonary Aspergillus fumigatus Infection |
Simple models of therapy for viral diseases such as hepatitis C virus ( HCV ) or human immunodeficiency virus assume that , once therapy is started , the drug has a constant effectiveness . More realistic models have assumed either that the drug effectiveness depends on the drug concentration or that the effectiveness varies over time . Here a previously introduced varying-effectiveness ( VE ) model is studied mathematically in the context of HCV infection . We show that while the model is linear , it has no closed-form solution due to the time-varying nature of the effectiveness . We then show that the model can be transformed into a Bessel equation and derive an analytic solution in terms of modified Bessel functions , which are defined as infinite series , with time-varying arguments . Fitting the solution to data from HCV infected patients under therapy has yielded values for the parameters in the model . We show that for biologically realistic parameters , the predicted viral decay on therapy is generally biphasic and resembles that predicted by constant-effectiveness ( CE ) models . We introduce a general method for determining the time at which the transition between decay phases occurs based on calculating the point of maximum curvature of the viral decay curve . For the parameter regimes of interest , we also find approximate solutions for the VE model and establish the asymptotic behavior of the system . We show that the rate of second phase decay is determined by the death rate of infected cells multiplied by the maximum effectiveness of therapy , whereas the rate of first phase decline depends on multiple parameters including the rate of increase of drug effectiveness with time .
Chronic hepatitis C virus ( HCV ) infection affects between 150 and 180 million people world-wide and is a major cause of chronic liver disease , cirrhosis and hepatocellular carcinoma . A number of agents have been approved for treating HCV infection including pegylated interferon-alpha ( PegIFN ) and ribavirin ( RBV ) ; the HCV protease inhibitors telaprevir , boceprevir , and simeprevir; and the HCV polymerase inhibitor sofosbuvir [1] . A large number of other agents are being tested in clinical trials [2] . An early model of HCV infection and treatment developed by Neumann et al . [3] showed that the effectiveness of antiviral therapy in blocking HCV production from infected cells could be estimated from the kinetics and extent of viral decline during the first few days of therapy . Neumann et al . [3] also showed that if plasma HCV RNA levels were measured frequently after treatment initiation with interferon one observed a biphasic decline after a short delay when the logarithm of HCV RNA/ml was plotted versus time on treatment ( Fig . 1 ) . This type of biphasic decline has now been observed with many different types of HCV treatments including those employing PegIFN and RBV , and a variety of HCV protease and polymerase inhibitors [4]–[10] . The Neumann et al . model [3] assumed that there was delay before interferon became active followed by a period in which it had constant effectiveness . Under reasonable assumptions , this leads to a model described by a set of linear , constant coefficient , ordinary differential equation that can easily be solved [3] . Models , such as that of Neumann et al . , in which the drug effectiveness is constant or constant after a delay have been called constant effectiveness ( CE ) models [11] , [12] . In the case of interferon therapy we now know that the delay is caused by pharmacokinetics of the drug as well as the time needed for the drug to bind cell surface interferon receptors and cause upregulation of interferon stimulated genes , whose gene products then lead to reduced viral replication . For pegylated interferon , which is approved for once weekly dosing , the pharmacokinetics of the drug lead to a loss of effectiveness towards the end of the dosing interval in many patients [13] , [14] . To account for this , a combined pharmacokinetic/viral kinetic model was introduced by Powers et al . [13] and fit to both drug concentration and HCV RNA data by Talal et al . [14] . However , in most clinical studies drug concentration data is not available for each patient . A phenomenological time-varying effectiveness ( VE ) model was therefore introduced by Shudo et al . [11] , [12] and studied numerically . Guedj at al . [6] studying the effects of the HCV protease inhibitor telaprevir on viral decay kinetics showed that a VE model fit clinical data better than a CE model as assessed by the Akaike Information criterion , which allows one to compare the ability of models with different numbers of parameters to fit data [15] . This study was followed by two others by Guedj et al . using VE models to analyze the HCV RNA decay kinetics observed with the nucleoside polymerase inhibitor mericitabine [16] , and with the HCV nucleotide polymerase inhibitors sofosbuvir and GS-0938 [17] . In these cases , the VE model accounted for the fact that these drugs need to be triphosphorylated intracellularly to become active [18] . More recently , Canini et al . [19] used a VE model to analyze the viral kinetics seen in a different set of patients treated with the drug silibinin , which appears to have activity as both a polymerase and entry inhibitor [20] , [21] . In all of these studies employing VE models , numerical methods were used to solve the time-varying equations . Here , we show how a previously used and prototypic VE model can be analyzed mathematically . We obtain an analytic solution to the time-varying problem in terms of modified Bessel functions , and a set of approximate solutions involving exponential decay functions .
We model HCV viral dynamics at the initiation of treatment by modifying the standard constant effectiveness viral dynamic model of Neumann et al . [3] . For infected cells , , and viral load , , the model differential equations are ( 1 ) We assume the number of target cells , , is constant and takes on its pre-therapy steady-state value , . This is an approximation that is commonly made when analyzing clinical trial data obtained over a period of one or two weeks . In the case of Neumann et al . [3] , it was used to analyze data collected over two weeks . In the model given by equation ( 1 ) , target cells are infected by virus , , with mass-action infectivity . Infected cells die at rate per cell and virus clears at rate per virion . The infection process may be hampered by drug treatment; the efficacy of treatment in blocking infection is given by . Infected cells produce virus at rate per cell . Drug treatment may also interfere with viral production , with efficacy . In the constant effectiveness ( CE ) model the drug efficacy is assumed to be constant , . In this case the solution for the viral load dynamics from ( 1 ) is ( 2 ) where is the viral load at , , and [3] , [21] . Here we assume the drug efficacy in blocking viral production , , is time dependent , i . e . , with a build-up of activity to a maximum ( 3 ) where is the maximum drug efficacy obtained with the concentration of drug used and the exponential scale determines the speed at which the drug efficacy reaches its maximum ( ) . In principle , the effectiveness of treatment in blocking infection , , could also be time dependent . Here we have chosen to ignore this possibility as no published data is available to guide such modeling efforts . At treatment initiation ( ) we assume the system is in steady state . Let the initial viral load , i . e . , pre-treatment viral load set-point , be given by . Since we assume that pre-treatment , then and Further , , so that . Since and , Substituting for , our system becomes ( 4 ) Now let and for notational convenience let and with initial conditions and . In the next section we will find an analytic solution for our model using this formulation .
We are interested in solving the system of ODEs ( 5 ) with initial conditions , where is the time-dependent drug efficacy Assume that ; we treat the case separately below . We can re-write this as a linear system , where and . dimensional systems for of the form have solutions , Magnus expansions , that are infinite series , which only collapse to a single term giving a closed for solution if , for any , , [22] , [23] . Since , our system of equations ( 5 ) has no closed form solution . However we can still recover a solution . We begin by writing the system ( 5 ) as a second-order differential equation . First , let Then Our system of equations ( 5 ) then becomes ( 6 ) Since , and from ( 6 ) we recover the second order equation corresponding to the system of ODEs ( 5 ) , ( 7 ) with initial conditions We now employ some convenient changes in the dependent and independent variables . Let Then ( 7 ) becomes Then let ( recall that , so that and , to obtain Finally let , to simplify the equation ( 8 ) Equation ( 8 ) is the modified Bessel differential equation [24] , with solutions where and are the modified Bessel functions of the first- and second-kind of order . As they represent infinite series , Bessel functions are not closed-form solutions . Note that the order is real: since the factor varies between and 1 . Thus Then since , the solution of equation ( 7 ) is ( 9 ) where We can use the solution ( 9 ) and the initial conditions , to solve for the constants , . Let and note that so that . Then , noting that and and Since [24] the constants can be written more simply as ( 10 ) To recover recall that and . Therefore with given by ( 9 ) , Thus the viral load , , is given by ( 11 ) where , are given by ( 10 ) , , and The varying effectiveness model employed above is a simplification of the more general time-varying effectiveness model , ( 12 ) which has been useful in cases where the viral load shows no measurable decay until time [6] , [16] . Since at low values of the effectiveness no change in viral load may be discerned due to low assay sensitivity and noise , one assumes the effectiveness has value at the time viral load declines become measurable . With , , and we recover the simpler form , equation ( 3 ) . The analytic solution for this more general VE model can be found following the approach described above , yielding where is now given by , and the order is ( as before with ) . The constants , are still given by ( 10 ) but with instead . The parameter , , represents the drug's effectiveness in interfering with new cell infection with indicating no efficacy and indicating perfect efficacy . The analytic solution ( 11 ) assumes . Perfect drug efficacy , , is not a biologically reasonable assumption . However , for drugs or drug combinations with very high effectiveness in blocking viral production , viral loads fall profoundly after therapy initiation and new cell infections become rare . Under such circumstances , the solution with ( i . e . no new infections after therapy is initiated ) may be a reasonable approximate model [25] , [26] . Given the equation for infected cells , , from ( 5 ) becomes . With initial condition the solution is . Then the equation for viral load , , from ( 5 ) becomes with initial condition . We can re-write this equation , using an integrating factor , as where is given by ( 3 ) . Integrating , we obtain ( 13 ) For the more general varying effectiveness model given by ( 12 ) , the analytic solution given for and the viral load , , is We note for both VE models there exist three time-scales given by the exponential decay rates , , and . As noted before , in biologically reasonable parameter regimes this model predicts that , after initiation of antiviral therapy , viral load usually undergoes a biphasic decay , consistent with observations on many different types of HCV treatments [3] , [6] , [16] . Examples are given in Figs . 1 and 2 , which show the log of the viral load after treatment initiation at time . The transition time between the fast- and slow- decay phases , marked by a dashed line in Fig . 2 is also of clinical interest . For example , with silibinin treatment the transition time has been shown to vary with the patient's disease progression state ( chronic HCV , compensated/decompensated cirrhosis ) [19] . At the transition time , the viral load curve has maximal curvature ( c . f . Fig . 2 ) . The curvature of the plane curve , , is given by ( 14 ) [27] . Therefore , to calculate the transition time , , we calculate the time when the curvature is maximized . To do this we numerically solve using the analytic solution for where from ( 11 ) . We can use this curvature-based approach to analytically calculate the transition time for the CE model ( 2 ) . Maximizing the curvature ( 14 ) for the CE model ( 2 ) , the transition time is the solution of ( 15 ) for with ( in ( 2 ) , ) . The solution of ( 15 ) is lengthy and is not included here for brevity . Supporting Fig . S1 shows patient data and model fits from [3] with the transition times marked . The model ( 1 ) , with varying drug efficacy ( 12 ) , has been used to investigate a number of drug treatments for HCV . Here we discuss therapy with four drugs: the protease inhibitors ( PIs ) telaprevir and danoprevir , the nucleoside polymerase inhibitor ( NPI ) mericitabine , and silibinin , a compound extracted from milk thistle seed . Silibinin is intriguing because , in addition to interfering with viral production as with the PIs and NPIs , it also appears to have some cell infection interference capabilities [21] , [28] . This additional capability is modeled by the term in ( 12 ) , for telaprevir , danoprevir , mericitabine , and sofosbuvir . Table 1 gives published estimates for model and drug parameters , obtained by fitting VE models to patient data , under the different treatment types , and when available different dosing regimens . The therapy durations were all two weeks or less so the assumption of a constant level of target cells was made in the primary publications from which the parameter estimates were obtained . In the following section we analyze the analytic solution of ( 1 ) , given by ( 11 ) , in order to gain some insight into long- and short-term behavior . Knowledge of the magnitude and relative size of model parameters is very helpful in such analyses . Table 1 reveals that estimates from different studies are not always consistent: observe that estimates for the hepatocyte death rate are an order of magnitude smaller for the mericitabine fits relative to the telaprevir , danoprevir , silibinin , and sofosbuvir . This discrepancy arises from the patient data used in model fitting: patients on telaprevir , danoprevir , silibinin , and sofosbuvir were treatment naïve , while patients put on mericitabine had already experienced PegIFN and RBV treatment failure . Regardless , we note that across all cases . We will exploit these relationships in the asymptotic analysis below . We also note that the rate of effectiveness increase , , can vary by orders of magnitude between drug types . For example , in the case of danoprevir treatment , for telaprevir and silibinin treatments . Analysis of viral dynamics in patients on mericitabine revealed two distinct biphasic viral curve types across patients: the first with a flat second phase , the second with a non-flat ( decaying ) second phase [16] . The covariate distinguishing these two groups remains unclear . But the fits suggest the distinction lies with the parameter , since non-flat second phases have and flat second phase patients have [16] . In the following analysis we will consider across orders of magnitude . The argument of the modified Bessel functions in ( 11 ) is , where . For small we can use the following approximations [24] for modified Bessel functions with small argument , and We will neglect the case since it is highly unlikely that a set of realistic parameters will yield exactly . The approximation for is actually valid for but since we can drop the absolute value signs Since monotonically as we expect the approximations to hold for long times . Note that for and , so we anticipate that the approximations are appropriate even at short times for sufficiently large . Applying the approximations to ( 11 ) , ( 16 ) The order for each treatment regimen shown in Table 1 is given in Supporting Table S1 for reference . Fig . 3a shows a comparison between the approximation ( 16 ) and the analytic solution ( 11 ) for parameters characterizing silibinin ( Table 1 ) . Near the error in the log of the approximation is 5% and improves significantly with increasing ( see Fig . 3b ) . This improvement is not surprising: the approximations are for small and grows smaller with increasing . Therefore we can use the approximation to gain insight into the long-time behavior . We may also be able to use the approximation to gain some insight into the short-time behavior; although the errors near are not negligible , the approximation remains within the right order of magnitude , and away from the slope of the solutions appear similar with these parameters , see Fig . 3a . The approximation does not however capture the shoulder in the analytic solution near . The modified Bessel functions are infinite series and can be expressed as follows: For simplicity let with ( , , , and are the constant coefficients in equation ( 11 ) ) . Using the series expressions for Bessel functions we can re-write ( 11 ) as a series of exponential functions , ( 18 ) Since and the maximum drug efficacy , , is close to 1 , the exponents can be written as , where is the sum of the remaining terms in the Taylor series expansion , . We can re-write the series expansion for the exact solution ( 11 ) as ( 19 ) ( 20 ) since . As , as . Short term behavior is more difficult to discern as it depends on the magnitude of . We can use this series expansion to evaluate parameter regimes within which the approximation ( 16 ) is valid with regards to the parameter . The exact solution ( 19 ) depends on the exponential decay rates and where . The approximation ( 16 ) for small argument depends on the exponential decay rates , , , and ( the latter in the case only ) . For these to be the most slowly decaying rates of the exact solution ( 19 ) , is constrained ( recall ) : However , from Figs . 3 , 5a , 5c , and 6a , b , it is clear that in spite of the fact that does not satisfy the relevant condition , the approximations can be reasonably good . Direct examination of the numerical values of parameters reveals the source: the relative value of . A summary of how the approximations behave with is given in Table 2 .
Viral dynamic models of infection and treatment have frequently described the effect of therapy by a parameter , , the effectiveness of therapy , where . For example , if therapy blocks production of new virus from infected cells , then the rate of production under therapy is modeled as , so that when the drug is 100% effective , and no viral production occurs . This type of formulation has been used in modeling treatment for HIV [29] , [30] , HBV [31]–[33] , HCV [3] , [7] , and influenza [34] . However , the effectiveness of a drug frequently depends on its concentration and more complex models incorporating drug pharmacokinetics ( PK ) and drug pharmacodynamics ( PD ) have also made their way into viral dynamic modeling [13] , [14] , [17] , [35]–[37] . In many cases , drug concentrations are not measured and detailed PK/PD modeling cannot be performed . Nonetheless , it is clear that variations in time occur in drug concentration . Further , drug activity can also be time-dependent particular when the drug given is a “pro-drug” that needs to be metabolized into an active compound . For example , nucleoside or nucleotide reverse transcriptase inhibitors and polymerase inhibitors need to phosphorylated intracellularly to become active inhibitors [38] , [39] . One mechanism to account for time dependent changes in drug activity is to assume that the drug effectiveness , , rather than being constant is time dependent . Here we have studied in detail an HCV model in which the effectiveness increases with time to a maximum , assuming either or a more general form , where plays the role of . We showed that the HCV model with time-varying effectiveness , previously used in [6] , [11] , [12] , [16] , [17] , can be solved explicitly in terms of modified Bessel functions . One reason the model equations can be solved analytically is that the assumption = constant is made , linearizing the mass-action infection term . The assumption of constant has typically been made when short-term ( 2 week or less ) clinical trials are examined . However , the obtained solution may be more general , particularly for direct-acting antivirals . When therapy is very potent so the viral load rapidly decays many logs during the first days of therapy , as seen for example with daclatasvir , where decays 3 logs in the first 12 hrs of therapy [26] , the term no longer significantly influences the dynamics . Thus , after a very brief transient , whether is constant or not may have no practical effect on the underlying viral dynamics . Guedj et al [26] showed this to be the case for daclatasvir by finding an extremely accurate approximate solution to the viral dynamic model they used by assuming there were no new infections after therapy started , i . e . that = 0 . Plotting the solution for the viral load , , on a logscale we noticed that the virus appeared to decay with time on treatment in a biphasic manner for certain parameters of interest . Such biphasic declines have been observed in HCV patients treated with many different therapies and the lengths of each phase and the rates of decay during each phase are of biological interest [19] . We characterized the transition between phases as the point of maximum curvature in the solution , which can be computed from the solution . However , in order to ascertain the dominant decay rates during these two observable phases , we wanted to find approximations in terms of exponentials . While the model differential equations are sufficient to fit the data , the analysis that permits us to characterize the decay phases is only possible given the analytic solution . To this end , we examined classic approximations to Bessel functions as well as series expansions and showed that the long-time decay is dominated by the rate of loss of HCV-infected cells , , as had previously been shown in constant effectiveness models [3] . This is not surprising since at long times , , the drug effectiveness approaches a constant value , its maximum . At short times , the constant effectiveness model predicts the rate of viral decay is governed by the rate of viral clearance , . Here with the variable-effectiveness model we find that this need not be the case and more complex relationships between , and govern the short-term behavior . Using parameters estimated in previously published drug-treatment studies we showed how different combinations of parameters govern the short-term decay for different drug therapies . For example , when is large compared to and , as had been previously found for the HCV protease inhibitor danoprevir , the effectiveness rapidly approaches a constant and the first phase decline is essentially governed by as in the constant effectiveness model . However , when is comparable to or small than this is no longer the case and then plays a role in determining the first phase decay . We discovered for parameters governing the HCV protease inhibitor telaprevir , where that three distinct exponential phases appeared to govern the viral load decay , with rates of , , and . Viral decline under telaprevir treatment had been previously described as biphasic [6]; it is only through the approximations to the analytic solution that the middle , , phase was revealed . The model upon which we based our analysis , while derived for HCV , applies to a number of viral infections . For example , essentially the same model can be used for protease inhibitor treatment of HIV , since HIV protease inhibitors reduce the rate of production of infectious virus . Similarly , neuraminidase inhibitors used to treat influenza A virus infection also reduce the rate of production of infectious virus and again our results would apply . HIV reverse transcriptase inhibitors act to block infection . To analyze this situation would require a generalizationq of our current model in which the parameter rather than being constant was allowed to be time-varying . This remains an interesting problem for the future . | Fitting simple models of therapy for viral diseases , such as hepatitis C virus ( HCV ) or human immunodeficiency virus , to patient data has yielded significant insights into the underlying viral dynamics . In general , these models assume that , once therapy is started , the drug has a constant effectiveness . More realistic assumptions are that drug effectiveness either depends directly on the drug concentration or varies over time . Here a previously introduced varying-effectiveness ( VE ) differential equation model is studied in the context of HCV infection . We show that the previously-unsolved VE model can be transformed into a Bessel equation and derive an analytic solution in terms of modified Bessel functions with time-varying arguments . These analytic solutions can be more readily used to fit the model to patient data than the underlying differential equations . We also find approximate solutions and establish the asymptotic behavior of the system . Typically viral load measurements exhibit a biphasic decline after therapy initiation . We show that the rate of second phase decay is determined by the death rate of infected cells multiplied by the maximum effectiveness of therapy , whereas the rate of first phase decline may depend on multiple parameters , resulting in differing first phase declines across various HCV therapies . | [
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"co... | 2014 | A Hepatitis C Virus Infection Model with Time-Varying Drug Effectiveness: Solution and Analysis |
Understanding the principles by which agents interact with both complex environments and each other is a key goal of decision neuroscience . However , most previous studies have used experimental paradigms in which choices are discrete ( and few ) , play is static , and optimal solutions are known . Yet in natural environments , interactions between agents typically involve continuous action spaces , ongoing dynamics , and no known optimal solution . Here , we seek to bridge this divide by using a “penalty shot” task in which pairs of monkeys competed against each other in a competitive , real-time video game . We modeled monkeys’ strategies as driven by stochastically evolving goals , onscreen positions that served as set points for a control model that produced observed joystick movements . We fit this goal-based dynamical system model using approximate Bayesian inference methods , using neural networks to parameterize players’ goals as a dynamic mixture of Gaussian components . Our model is conceptually simple , constructed of interpretable components , and capable of generating synthetic data that capture the complexity of real player dynamics . We further characterized players’ strategies using the number of change points on each trial . We found that this complexity varied more across sessions than within sessions , and that more complex strategies benefited offensive players but not defensive players . Together , our experimental paradigm and model offer a powerful combination of tools for the study of realistic social dynamics in the laboratory setting .
Humans are a social species . Our most difficult and most crucial decisions—whom to trust , whom to double-cross , with whom to rear children—are most often decisions about other agents . Indeed , it has been conjectured that group living formed the primary driving force in the evolution of human cognition [1] . Yet social decisions are also among the most complex that agents face , since the relevant costs and benefits rely not only on one’s own preferences and options , but on others’ preferences , their options , and ( potentially ) their assessments of one’s intentions [2 , 3] . Over the last decade , much neuroscientific work has attempted to understand the physiological and cognitive basis for these social decisions [3–5] . Most of these studies have been performed in humans using non-invasive methods like functional MRI and EEG , though an important ( and growing ) strand of work has used animal models [6–14] , which allow for direct neural recording . In both cases , the behavioral tool of choice has been game theory . Game theory offers several important benefits for the study of social decisions: models of both cooperative and competitive interaction; a rich , well-developed theory; and a clear notion of optimality , Nash equilibrium , against which subjects’ behavior can be compared . In addition , there are vast parallel literatures studying empirical game play in real agents [2] , evolutions of strategies [15] , and games as models for ecological behavior [16] . Nonetheless , such paradigms are also limited as models of real-world social decisions . For example , in typical paradigms , the choice space is discretized and low-dimensional , whereas real social decisions are embodied , requiring movement in space from multiple effectors . Moreover , because laboratory tasks often involve ( repeated ) single decisions among small numbers of alternatives , the space of available strategies is highly constrained , often isomorphic to the space of choices . By contrast , interaction in social settings involves feedback from other agents in real-time , with a commensurately large space of potential strategies . Unfortunately , moving beyond small numbers of discrete choices drastically complicates behavioral analysis . Optimality may no longer be well-defined , and when it is , finding a solution may yet be computationally intractable . Moreover , when the potential behavioral repertoire on each bout of play is large , it may be difficult , if not impossible , to meaningfully average physiological signals like neural activity across bouts . Here we use insights from generative modeling , control theory , and inverse reinforcement learning [17 , 18] to show how it is nonetheless possible to characterize strategies in a dynamic , competitive decision at the single-trial level . Using data collected from a laboratory task in which pairs of rhesus macaques used joysticks to control onscreen avatars , we describe the avatars’ resulting trajectories in terms of time-varying latent “goals”—onscreen locations that function as set points for a control model that produces joystick movement . We use methods of scalable , approximate Bayesian inference to both infer goal trajectories at the single-trial level and generate entirely new gameplay that captures the richness of real player behavior . In the model , goals themselves are generated by a stochastic process analogous to the motion of a particle in a potential energy well , with the potential energy term capturing the interaction of each player’s goals with his opponent’s observed actions . This potential energy , flexibly parameterized by neural networks , allows us to succinctly describe player interactions apart from details of the control process , providing moment-by-moment measures of intention and decision complexity that can be correlated with neural signals . Just as importantly , though the model is developed for the case of a simple screen-based task with two players , it easily generalizes to other time series data ( including >2 real or artificial agents ) in which observations are the result of a control process and latent goals are the underlying variables of interest . In the sections below , we first describe the two-player dynamic decision task and its attendant data . We then outline our generative modeling assumptions , including the control and latent goal models . After that , we describe how to perform approximate Bayesian inference in the model using scalable Variational Bayes methods [19] . We assess the outputs of the model and compare with simpler alternatives , showing that the model not only captures the rich variation present in real game play but is capable of generating novel data of similar complexity . We then demonstrate that goals are better predictors of trial outcomes than either current velocity or players’ gaze positions and give an example of how our model can be used to perform controlled experiments via simulation of novel data . Finally , we use a simple measure of strategic complexity to consider the effect of this quantity on win rate . We conclude by discussing applications of our model to neural data analysis .
Let yt be the observed variables of the onscreen trajectory at time t . Similarly , let st be the collection of system variables . We typically consider the latter to be positions and velocities ( i . e . , s t = ( y t , y ˙ t ) ) , but they could additionally include accelerations , additional time points of lagged data , or variables encoding player identity or game condition . For the penalty shot task , yt evolves according to y t + 1 = y t + v max ⊙ υ t ( 2 ) with vmax a vector of maximum velocities in each dimension as determined from the data , υ ∈ [−1 , 1] a joystick position , and ⊙ the Hadamard ( elementwise ) product . We further assume that the joystick position υ is related to a latent control signal u via υ = tanh ( u ) . That is , the observed control , restricted by the finite joystick range , may be less than desired control . We assume that at each time t , ut is the output of a control model attempting to minimize an error et ≡ gt−yt with gt an instantaneous , state-dependent , set point for the controller that we will call a “goal” . These goals represent the instantaneous onscreen locations desired by each player , as evidenced by the fact that when yt = gt ( st ) ( next goal and position are equal ) , we have et = 0 and thus no need for control . When et ≠ 0 , however , we assume that changes in control are given by a proportional-integral-derivative ( PID ) controller: Δ u t ≡ u t - u t - 1 = L * e t = ∑ τ = 0 2 L τ e t - τ = κ [ ( 1 + Δ t T i + T d Δ t ) e t + ( - 1 - 2 T d Δ t ) e t - 1 + T d Δ t e t - 2 ] ( 3 ) with κ the proportional control constant and Ti and Td the integration and differentiation time constants , respectively . That is , L is a convolutional filter , determined by κ , Ti , and Td , learned separately for each player in each dimension . Changes in control are thus convolutions over the control error . Finally , to capture our uncertainty about this relationship , we model errors in control as normally distributed with variance ϵ2: u t ∼ N ( u t - 1 + L * ( g t - y t ) , ϵ 2 ) ( 4 ) where the equation should be read as implying a separate , uncorrelated , normal distribution for each coordinate in u ( with potentially distinct ϵi in each dimension ) . In practice , our onscreen observations have minimal noise , implying ϵ ≪ 1 . Unfortunately , Eq 4 is ambiguous , since for any sequence of controls ut and any L , the equations for g are linear and thus in general invertible . And while this symmetry is weakly broken by our model for g ( described below ) , which prefers some sequences of goals to others , we remove the ambiguity in practice by fixing control to be purely proportional early in training , consistent with the idea of a goal as a point toward which each player desires to move . In addition , a second issue arises from the fact that joystick positions are constrained to lie in [−1 , 1] along each dimension , while model-predicted control might be large . In our model , we achieve this by using a hyperbolic tangent function to link u ( desired control ) to υ ( joystick input ) . However , this means that small changes in υ can result in potentially large changes in inferred u and thus g . More concretely , when joystick input is near maximal ( υ ≈ ±1 ) , this will be consistent for any goal farther than a certain distance from the player’s current location . Ideally , one would consider υ a censored version of u , a possibility we leave to future work . Here , we remedy this by imposing a penalty during model training on any goals that far exceed the visible game area . For the goal time series , gt , we will assume a Markov process in which new goals are probabilistically selected at each time based on both the current goal and the current state of the system . That is , p ( u , g ) ∝ ∏ t p ( u t | u t - 1 , g t , y t ) p ( g t | g t - 1 , s t ) ( 5 ) More specifically , we will assume that at each time point , there exists a function V ( gt , st ) that captures the benefit in setting a particular goal based on the current state of the system . That is , we want to increase V as often as possible . ( Alternately , we could consider −V as an energy function that we wish to minimize . ) However , we also assume that the goal time series is relatively smooth , which we formalize by adding a regularization term for the rate of change between successive time points . More explicitly , let log p ( u , g ) = ∑ t [ - 1 2 ϵ 2 ∥ u t - u * t ( g t , y t ) ∥ 2 - 1 2 σ 2 ∥ g t - g t - 1 ∥ 2 + V ( g t , s t ) ] - log Z ( 6 ) Here , u*t ≡ ut−1 + L* ( gt−yt ) is the the predicted control and ϵ and σ govern the control noise and goal diffusion , respectively . In what follows , we will also find it useful to define β ≡ σ−2 and U ( g ) ≡ − σ2 V in order to write log p ( g ) = −βE ( g|s ) − log Z′ with E ( g | s ) = ∑ t [ 1 2 ∥ g t - g t - 1 ∥ 2 + U ( g t , s t ) ] ( 7 ) which results from marginalizing the ut out of Eq 6 . This formulation admits multiple interpretations: mostly simply , E ( g|s ) is the negative log probability of the goal time series: configurations that minimize this “energy” have higher probability . Along the same lines , the first term in Eq 7 is a penalty on large changes in g between time points , encouraging smoothness . In form , it is equivalent to a “kinetic energy” K ≡ g ˙ 2 / 2 . Likewise , the second term , U ( g ) , is a generator of changes in goal state . At each time point , goals are drawn toward regions of small U , making it analogous to a “potential energy . ” Together , the probabilistic model over goal trajectories in Eq 7 is equivalent to a path integral for a particle with position gt and energy K + U . In the limit of small V/small σ/large β , one is in either the low-temperature thermodynamic limit or the high-mass classical limit , and g is a spatially-varying perturbation of a Gaussian process . Alternately , in the limit of large σ , goals are simply chosen independently at each time point . In any case , we have made the strong assumption that dependence of gt on gt−1 occurs only through a momentum term , requiring that the “static” V term carries most of the weight of explanation . Unfortunately , for general V ( g|s ) , the distribution implied by Eq 7 is of the Boltzmann-Gibbs form and impossible to sample efficiently . If the goal of our inference is to model V itself , we will need a method for sampling from p ( g ) that still allows this partitioning . Let t = 1 be the time of the first observed data , y1 . In order to calculate y2 using Eq 2 , we need u1 , which from Eq 3 requires u0 , e1 , e0 , and e−1 . ( Recall that it is the joystick position υ1 that is observed; u1 is latent ) . We bootstrap this process by assuming the following: All future data points can then be calculated via the steps outlined above . Given the observed system trajectory yt , we would like to infer the underlying goal trajectory gt . In general , full Bayesian inference is intractable , but we employ a variational Bayes ( VB ) approach [19 , 21] that approximates this procedure . In brief , VB attempts to minimize the Kullback-Leibler divergence ( a measure of difference between distributions ) between a known generative model for which inference is intractable , p ( D , z ) , and an approximating family of posterior distributions , q ( z ) . This is equivalent to maximizing an evidence lower bound ( ELBO ) given by L = E q ( z ) [ log p ( D , z ) ] - H [ q ( z ) ] ( 11 ) with H [ q ( z ) ] the entropy of the approximating posterior . That is , inference is transformed into an optimization problem in the parameters of the approximate posterior q ( z ) , amenable to solution by gradient ascent . In our model , we make use of so-called “black box” methods [22–25] in which the gradients of the ELBO are replaced with stochastic approximations derived by sampling from q ( z ) , avoiding often difficult computations of the expectation in Eq 11 . Thus our only requirement for the recognition model is that we be able to sample z* ∼ q ( z ) and to compute log q ( z* ) . In our case , we begin with the generative model specified by Eq 6 . For the approximate posterior q ( g|y ) , we use the variational latent dynamical system ( VLDS ) model of [26 , 27] . ( We are not interested in the posterior over u and implicitly marginalize over it ) . The VLDS is a nonlinear generalization of the linear state space model , implying that inference in the model is a generalization of the Kalman filter . It uses neural networks to flexibly parameterize the mean and covariance of the underlying time series , which are assumed to change dynamically . As in [24 , 25] , samples from this posterior are then used to update both the parameters of the generative model ( L , ϵ , σ , μ , λ , w ) and the parameters of the approximate posterior q via gradient ascent . We used the number of change points in the puck trajectory as a rough measure of the each player’s strategic complexity . Specifically , a time point t is a change point for a player if sign ( υt ) ≠ sign ( υt+1 ) , υt , υt+1 ≠ 0 , and |υt − υt+1| ≥ 10−6 . That is , joystick input changes sign , is nonzero both before and after the change point , and the difference exceeds a minimal value . We studied the correlation between number of change points and game results at both the trial and session level . To avoid over-identification of change points due to frequent variation in control signals , all trials were smoothed by the same Gaussian filter described in Data section before this analysis . We also excluded the first and last five time points to minimize edge effects . For testing autocorrelation functions , we applied the Ljung-Box test to the autocorrelation function calculated on each session . For initial velocities , we discarded periods of non-movement at the start of the trial , selecting instead the velocity at the first time point where the norm of the velocity exceeded 0 . 001 . For analyzing whether outcomes of consecutive trials were correlated , we used Fisher’s Exact Test on game results ( summarized by a 2 × 2 contingency table of win/loss of tth and ( t + 1 ) th trials ) by session . We corrected for multiple comparisons via Bonferroni correction to ensure a family-wise false positive rate of 1% .
While players’ behavior was highly variable across trials and sessions ( Fig 2A ) , blockwise adjustments to players’ maximum velocity kept win rates similar across players ( Fig 2B ) . Likewise , the likelihood of a win was independent of whether the previous trial was a win or a loss ( N = 4/120 sessions significant for dependence; Fisher’s Exact Test for independence; α = 0 . 01 ) , suggesting little in the way of a “win-stay , lose-shift” or “hot hand” effect ( Fig 2C ) . Moreover , while players’ initial velocities tended to cluster , their final positions were more evently distributed ( Fig 2D and 2E ) . That is , players appeared to mix their play effectively . We found that initial velocities and final positions were only weakly autocorrelated ( v0x , shooter = 4/120 , v0y , shooter = 17/120 , yf , goalie = 21/120 , yf , shooter = 31/120 sessions significant; Ljung-Box test for autocorrelation at lag 1; α = 0 . 01 controlled for family-wise error rate ) ( Fig 2F ) , indicating that there was little strategic carryover across trials . In summary , while significant complexity and variation in play is apparent , this variation was not well captured by typical metrics derived from games with discrete action spaces . Because play in the penalty shot task is fundamentally dynamic and interactive , it is resistant to conventional models based on either discrete action spaces or simple heuristics . Instead , we opted to model player behavior as arising from two pieces: first , a dynamic goal model that encoded each player’s desired onscreen location as a function of both players’ instantaneous positions and velocities; and second , a control model that used these goals as its set points and produced joystick movement ( Fig 3 ) . Goals evolved dynamically with the game . This was captured in the model by a “value function” that at each moment drew each player’s desired onscreen location toward areas of high value and away from those of low energy . ( This is not the same as the value function in reinforcement learning , which sums all future rewards . Our V is more closely related to policy than expected value ) . For example , with the goalie at the upper edge of the screen , the energy function for the puck is expected to be low at the lower edge of the screen . However , it is important to note that energy functions for each player were separate . While both made use of explicit information from both players ( positions and velocities ) , each player’s goals were private ( i . e . , independent of one another given current game state ) . We trained this model using all behavioral sessions of the task as input . To account for variation in play across the experiment , we included a term that increased linearly as a function of session , as well as additional variables encoding experimental conditions ( see Methods ) . Fig 4 shows results from a restricted model using only the last ten sessions of data from a single shooter . Not only is the model able to produce accurate one-step-ahead predictions of the control signal ( Fig 4A ) and its derivative ( Fig 4B ) , it is able to generate synthetic data that capture the rich behavioral repertoire exhibited by real players ( Fig 4C and 4D ) . To assess the utility of our inferred goals as a construct , we next asked whether these goals contained predictive information about gross strategic behavior . To do so , we used a simple linear regression of final puck vertical position against goal position at each moment of each trial and calculated the coefficient of determination ( R2 ) as a function of time in trial . Fig 5 shows this function time-locked to both the beginning and end of trial . For comparison , we repeated the same analysis using two other predictors , the puck’s instantaneous velocity and the shooter’s eye position . We found that the shooter’s goal became more predictive than the puck’s velocity within the first 0 . 5s of the trial ( Fig 5A ) and remained the most predictive of the three variables until the last 0 . 3s of the trial , when it was overtaken by gaze ( Fig 5B ) . As might be expected , gaze becomes the most predictive single variable late in trial ( players attend to the location where the outcome will be determined ) , while variables like velocity and goal , which only influence position after a lag , become slightly less accurate . Moreover , while state , comprising both the players’ positions and velocities , is more predictive than any single variable , including gaze , adding goal information to state information does improve predictive performance ( Fig 5C and 5D ) . This rough analysis is thus consistent with the idea that our inferred goals provide information not contained in state and can be treated as a latent measure of momentary player intentions . Our assumption of a Gaussian mixture parameterized by neural networks in Eq 8 is a highly flexible one , raising the question of whether our model simply memorizes trajectories or whether a simpler set of assumptions might do . Fig 6 shows the results of comparing our model to two simpler variants on tests of trial generation and trial completion . The first comparison model assumes the same neural network structure but with a single Gaussian output distribution at each time t: e V ( g ) ∝ e - 1 2 σ 2 ( g - μ ) ⊤ · Λ · ( g - μ ) | Λ | 2 π σ 2 ; ( 12 ) The other comparison model uses a linear function in place of a neural network to model the dependency of goals on state: μ t = W 1 s t + b 1 λ t = softplus ( W 2 s t + b 2 ) w t = softmax ( W 3 s t + b 3 ) ( 13 ) where { ( W1 , b1 ) , ( W2 , b2 ) , ( W3 , b3 ) } are weights and biases , softplus ( x ) = log ( 1 + ex ) , and softmax ( x ) i = e x i / ∑ j e x j . We trained all three models on the same dataset with the same hyperparameters and stopping criteria specified above and then compared their performances on fitting control signals and derivatives , generating new data , and completing trials . Our model outperformed the two candidates in all three aspects: the approximate posterior for our model yields the overall smallest prediction error ( Fig 6A , 6D and 6G ) ; the generated trajectories resemble the real ones the most closely ( Fig 6B , 6E and 6H ) ; and trial completion for the neural network GMM demonstrates the most diversity ( Fig 6C , 6F and 6I ) . That is , the simpler models proved either insufficiently flexible to capture regularities in the data , resulted in quasi-deterministic behavioral policies , or both . By adopting a generative modeling approach along with a flexible class of parameterized functions ( neural networks ) , we have shown that our model can capture the variability present in real data ( Fig 4F ) . An additional benefit of such an approach is that our model can then be used as a simulator for purposes of running controlled experiments . These “counterfactual” simulations allow us to systematically vary task parameters and states to explore the responses of the trained model in configurations only sparsely covered by actual data . Here , to illustrate the potential of this approach , we performed a simple experiment to explore the effects of goal states on trial outcomes . If , as we claim , goals capture players’ latent targets as a function of changing game state , then altering goals should change subsequent game play . To check this , we fixed a time point midway through a specific trial and performed a series of trial completion experiments ( Fig 7 ) . In one set of experiments , the goalie’s goal is fixed to the lower corner of the screen ( Fig 7A , red X ) , while in the other , it is fixed in the upper corner of the screen ( Fig 7B , red X ) . In both cases , the shooter’s goals are allowed to evolve normally , and the goalie’s goals are allowed to move normally after the first time point of the trial completion . As Fig 7 illustrates , subsequent play is a product not only of initial goals , but of players’ co-evolving control strategies . As one expects , when the goalie’s initial goal is downward , the shooter compensates by moving the puck upward; when the goalie’s initial goal is upward , the shooter steers the puck down . However , what is more interesting to note is the subsequent trial outcomes: in Fig 7A , more extreme puck trajectories result in losses ( orange ) , since these more clearly signal the shooter’s intention and prompt the goalie to reverse direction more quickly . Likewise , in Fig 7B , earlier downturns in puck trajectory also result in losses . In both cases , the initial goal is rapidly reversed from its location at the start of the simulation , while less extreme or more ambiguous trajectories are likelier to result in wins ( blue ) . Thus , the results of our simple experiment suggest both that the model has done more than simply memorizing trajectories and that it can be used to answer “what if” questions via simulation . Since , as we have argued , players’ inferred goals constitute a moment-by-moment representation of strategic intention , and since the evolution of these goals is governed by each player’s value function V ( g , s ) ( Eq 7 ) , these components of our model are ultimately responsible for capturing the richness of players’ interactions . For instance , at a fixed time t in the trial , the structure of V ( gt , st ) determines whether a player’s trajectories fall into groups that will split or merge , how variable the trajectories are within each group , and thus how complex the shooter’s corresponding strategy is . Fig 8A depicts this strategic complexity at a moment midway through the trial . With the puck moving upward , the shooter’s value function ( blue ) is highest at a cluster of points spread vertically to the left of the goal line , indicating that the goals are pulled in that direction . Likewise , the value function for the goalie ( red ) is concentrated above the current vertical position of the puck , suggesting a tendency to follow its movement . As the form of these value distributions changes , so does the variability of the resulting trajectories . To quantify this strategic complexity , we considered the number of direction changes ( change points ) in each player’s trajectory on each trial . With this definition of complexity , we see that variation in strategic complexity exists at both the trial ( Fig 8B ) and session level ( Fig 8C ) . A one-way ANOVA shows significant differences across sessions for both shooters ( F119 , 59764 = 128 . 950 , p < 1 . 0 × 10−6 ) and goalies ( F119 , 59764 = 216 . 990 , p < 1 . 0 × 10−6 ) . Moreover , the average number of shooter change points shows a weakly increasing trend across sessions ( Fig 8D; β = 0 . 00735/session , Wald test p = 4 . 123 × 10−8 ) , indicating that players adopted more complex strategies over time . As one would expect , an increase in number of change points , corresponding to an increase in the variability of trajectories , translates to an increase in win rate for both shooters ( Fig 8E; β = 4 . 793%/change point , Wald test p = 3 . 381 × 10−3 ) and goalies ( Fig 8F; β = 3 . 695%/change point , Wald test p = 8 . 896 × 10−4 ) . Thus , players’ strategic complexity , as measured by the number of change points , can serve as a useful metric for evaluating player behavior .
The study of complex strategic decisions , especially social decisions , demands experimental paradigms that replicate this complexity . Here , we have introduced a two-player computerized task , Penalty Shot , that requires real-time behavioral adjustment and facilitates strategic variation . Not only do pairs of rhesus macaques readily learn the game , they exhibit individual playing styles and complex patterns of interactive play . By using a generative modeling approach that leverages approximate Bayesian inference and modern gradient-based training methods , we have shown that it is possible to capture this variability and produce moment-by-moment estimates of latent intentions and strategic complexity suitable for correlation with neural data . These results open new possibilities for the analysis of dynamic behavior and its associated neural data and have implications for the study of social behavior . | Most studies of strategic decision making make use of simple tasks in which agents choose among only a limited number of distinct options . But real-world behavior is complex , requiring ongoing adjustment of strategies . Here , we propose a new model that is capable of reproducing the rich behavior of monkeys playing against each other in a dynamic decision task . Our model quantifies players’ goals at each moment and offers a means of performing controlled experiments via simulation . This makes possible more realistic experimental paradigms for the study of strategic decision making and social interaction . | [
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] | [] | 2019 | Latent goal models for dynamic strategic interaction |
To understand the molecular processes underlying aging , we screened modENCODE ChIP-seq data to identify transcription factors that bind to age-regulated genes in C . elegans . The most significant hit was the GATA transcription factor encoded by elt-2 , which is responsible for inducing expression of intestinal genes during embryogenesis . Expression of ELT-2 decreases during aging , beginning in middle age . We identified genes regulated by ELT-2 in the intestine during embryogenesis , and then showed that these developmental genes markedly decrease in expression as worms grow old . Overexpression of elt-2 extends lifespan and slows the rate of gene expression changes that occur during normal aging . Thus , our results identify the developmental regulator ELT-2 as a major driver of normal aging in C . elegans .
Identifying the factors that govern the rate of aging could illuminate a way to reduce the incidence of many diseases at once . Therefore , it is critical to understand the molecular events that drive the transition from young to old . Yet , the underlying molecular mechanisms that drive the normal process of aging are poorly understood . The nematode worm Caenorhabditis elegans is an excellent model to study the normal aging process as it has a lifespan of approximately two weeks and shows signs of aging on many levels . Old worms move slowly and have less pharyngeal pumping [1] . At the tissue level in old age , the intestine loses microvilli and some of its nuclei [2] . The muscles show fragmented fibers indicative of sarcopenia [3] . At the sub-cellular level , old worms accumulate lipofuscin in the intestine , as well as lipids and yolk proteins throughout the body [1 , 4] . At the level of RNA changes , high-throughput technologies enable thousands of molecules to be profiled in parallel . Gene expression studies have identified over a thousand genes that show expression differences between young and old worms , referred to as the aging transcriptome [5–7] . The age-regulated genes tend to be expressed in the intestine , and have promoters that contain bindings sites for GATA transcription factors [5] . Several upstream regulators of the aging transcriptome have been identified and shown to drive of aging process . During aging , changes in the expression of transcriptional regulators such as ELT-3 , ETS-4 , UNC-62A , and PQM-1 cause changes in the expression of hundreds of their direct target genes , and modulate lifespan [5 , 8–10] . MicroRNAs also change expression during aging , thereby altering regulation of downstream targets and acting to both promote and antagonize longevity [11] . ChIP-seq data produced by the modENCODE Consortium has opened up new ways to search for regulators of the normal aging transcriptome in an unbiased manner [10 , 12 , 13] . One can screen the set of transcription factor binding sets generated by modENCODE to identify transcription factors that bind to age-regulated genes , thereby generating a candidate list of upstream drivers of gene expression changes in old age [10 , 13] . This is a powerful strategy because it offers a quantitative and objective way to screen for regulators with the largest impact on the aging transcriptome . Here , we identify elt-2 , which encodes a GATA transcription factor homologous to human GATA4 , as a direct regulator of the aging transcriptome . elt-2 is expressed exclusively in the intestine and plays a key role in inducing intestinal gene expression during embryonic development [14] . In adults , reduction of elt-2 activity by RNAi shortens the lifespan extension caused by mutations in daf-2 , eat-2 , and isp-1; ctb-1 , and also increases the toxicity of the pathogenic bacterium Pseudomonas aeruginosa [5 , 15–17] . To investigate the role of elt-2 during the normal aging process , we first examined the expression of elt-2 over time , and found that it decreases in old age . For the genes regulated by ELT-2 GATA , we found a striking pattern of transcriptional induction during early development followed by reduction during aging . Overexpression of elt-2 extends lifespan and reduces the magnitude of these age-related changes in gene expression . This transcriptional effect of elt-2 overexpression is seen not only in the intestine where it is expressed , but also in the muscle , hypodermis and neuronal tissues , indicating that the intestine communicates to other organs to slow down the aging process . These results show that a major aspect of the normal aging process in C . elegans involves loss of developmental control of the intestine .
One explanation for the overlap between ELT-2 GATA targets and age-regulated genes is that either the amount or activity of ELT-2 GATA changes with age , leading to changes in expression of its downstream genes . To test this possibility , we quantitated elt-2 RNA and ELT-2 protein levels in young and old animals . We used qRT-PCR to quantitate elt-2 mRNA levels in wild-type animals during aging . We first used qRT-PCR to compare elt-2 RNA levels in young ( L4 larval stage ) versus old ( day 13 of adulthood ) wild-type hermaphrodites . We used six reference genes ( let-70 , tbb-2 , htz-1 , pmp-3 , Y45F10D . 4 , cdc-42 ) as controls , and observed , on average , a two-fold reduction in elt-2 expression in day 13 adults ( S1 Fig ) . We then repeated the qRT-PCR experiment to measure elt-2 expression at L4 , day 3 , day 6 , day 9 , and day 12 of adulthood using only tbb-2 as a control , and observed a two-fold reduction in elt-2 levels by day 3 of adulthood ( Fig 2A ) . To quantitate ELT-2 protein levels during aging , we used a worm strain carrying an ELT-2:GFP translational reporter to measure expression during aging by fluorescent microscopy . We used the modENCODE strain that carries integrated copies of the full-length genomic elt-2 gene with GFP inserted in the 3’ end of the coding region . We observed a 50% decline in GFP fluorescence between Days 1 and 11 of adulthood ( Fig 2B ) . To control for the possibility that expression differences between young and old animals were due to silencing of the elt-2:gfp transgene in old age , we repeated this experiment using a strain that was homozygous for rde-1 ( ne300 ) . rde-1 encodes an Argonaute protein that is required for RNAi interference and the silencing of transgenes [20] . In the rde-1 ( ne300 ) mutant background , we observed a similar decline in ELT-2:GFP fluorescence during aging , indicating that elt-2:gfp decreases expression due to aging rather than transgene silencing ( S2 Fig ) . We also measured changes in ELT-2:GFP expression at the level of single cells using an automated C . elegans lineage analyzer [21] . For the cell lineage experiments , we first obtained high-resolution three-dimension confocal data stacks of individual worms , aged 1 and 12 days . For each image stack , the cell lineage analyzer first computationally straightened the worm and then identified each intestinal nucleus based on the invariant cell lineage . GFP fluorescence was measured from the entire volume of each nucleus and expression results are presented as a heat map where the color intensity corresponds to expression level in a single nucleus . We used the automated cell lineage analyzer to quantitate ELT-2:GFP levels in the 34 individual intestinal cells in Day 1 and Day 12 adult animals . While just 9 of the intestinal cells showed a statistically significant difference in ELT-2:GFP between Day 1 and Day 12 , 26 out of 34 intestinal cell nuclei had less GFP fluorescence in old age ( S3 Table ) . We combined the fluorescence measurements for all of the intestinal cells at each time point and found that ELT-2 GFP decreases by approximately 50% from Day 1 to Day 12 ( p < 10−9 ) ( Fig 2C ) . In summary , our results indicate that both elt-2 RNA and protein levels decrease during aging . Declining levels of elt-2 activity in old age might be detrimental and limit lifespan , might have no effect on worm physiology , or might be beneficial and help promote lifespan . In order to distinguish between these possibilities , we determined the lifespan of animals fed elt-2 RNAi or overexpressing elt-2 . The lifespan phenotypes we find from both the elt-2 RNAi and elt-2 overexpression strains are consistent with previous reports [16 , 18] . We reduced elt-2 activity using RNAi beginning at Day 1 of adulthood and measured lifespan in two independent experiments . To confirm knockdown of elt-2 , we performed qRT-PCR on worms that had been fed RNAi against elt-2 , and observed >90% reduction in elt-2 RNA levels ( S3A Fig ) . We observed a 25% reduction in the median lifespan and a 50% reduction in the maximum lifespan relative to animals fed a control vector ( log-rank p< 10−5 in each experiment ) ( Fig 2D ) . We also measured the lifespan of animals overexpressing elt-2 relative to transgenic controls . We generated two independent lines containing multiple copies of elt-2:gfp in an extrachromosomal array ( SD1963 and SD1964 , S10 Table ) . We performed RNA-seq on SD1963 and found the abundance of elt-2 mRNA , was about 4-fold higher at both a young and an old timepoint in the overexpressor strain relative to a control strain containing the co-transformation marker unc-119 ( + ) alone . We assayed the lifespan of these elt-2 overexpressor worms and found that median lifespan was extended 15–25% ( p-value = 0 . 003 ) ( Fig 2E , S4 Table ) . A previous study observed an increase in the median and maximum lifespan of an elt-2 overexpressor strain [18] . These results indicate that decreasing levels of ELT-2 expression in old age limit normal lifespan , and that increasing ELT-2 expression extends longevity . We wanted to investigate the functional implications of reduced expression of elt-2 in old age . Previous work has described several biological functions for ELT-2 GATA . During embryogenesis , elt-2 regulates differentiation of the intestine by activating the intestinal gene expression program [14] . During adulthood , elt-2 activity is required for maximal survival in response to pathogen infection , or for maximal longevity in a daf-2 mutant background [16 , 18 , 22] . One possibility is that , in the adult , ELT-2 has a distinct role in innate immunity or daf-2-mediated stress protection , or it could suggest that intestinal functions are indirectly required for immunity and stress protection . A key issue is whether the decline in ELT-2 expression in old age limits lifespan only due to a loss of innate immunity and stress protection or whether old age also involves a loss of general intestinal functions . This issue is important because there is ample precedent that loss of innate immunity and/or stress protection are involved in aging; for example , pha-4 and skn-1 are two transcription factor genes that have roles in stress protection and can extend lifespan [19 , 23] . By contrast , the idea that loss of general intestinal functions are involved in aging is more novel . In order to study the downstream functions of ELT-2 , we performed a genome-wide analysis of the transcriptional regulatory activity of ELT-2 during the L1 stage to define its functions in establishing and maintaining intestinal functions . We then performed an analogous genome-wide analysis of ELT-2 transcriptional activity in the L4 stage to define its functions in a mature intestine , such as innate immunity and stress protection . By comparing the transcriptional activities of ELT-2 in the L1 and L4 stages , we could determine whether ELT-2 GATA has similar or distinct gene regulatory functions in early development and adulthood . We could then ask how these transcriptional activities change in old age . To identify genes regulated by elt-2 , we first created an expression signature of genes that respond to elt-2 regulation at two timepoints: L1 larval stage and L4 larval stage . A transcriptional profile of elt-2 mutants animals at the L1 stage had been done previously [24] . We extended and confirmed the previous elt-2 expression signature in the L1 stage by using RNA-seq to identify genes that require wild-type levels of elt-2 for proper expression . Specifically , we cultured three biological replicates of L1 stage progeny from worms grown on either elt-2 RNAi bacteria or empty vector control bacteria , and sequenced 3’ end-enriched RNA-seq libraries . We confirmed knockdown of elt-2 RNA by using qRT-PCR and by observing that the L1 larvae arrested development ( S3B–S3D Fig ) . We used a rank product method with a false discovery rate of 10% , and identified 162 genes that are differentially-expressed in elt-2 RNAi worms compared to wild-type at the L1 stage ( S5 Table ) . The elt-2-regulated gene list consists of 153 genes with lower expression and 9 genes with higher expression in elt-2 RNAi worms ( S5 Table ) . These elt-2 regulated genes are strongly enriched for genes that are directly bound by ELT-2 , based on our ChIP-seq data ( 105/162 genes , p < 10−60 ) . We compared our results of expression changes in elt-2 mutants with the previous results . Of the 162 elt-2 regulated genes , 101 of these showed condordant two-fold changes expression in the previous study ( S4 Fig ) [24] . We identified elt-2 targets during the L4 larval stage in a similar manner . Synchronized L1 animals were fed either elt-2 RNAi or vector control , and RNA was isolated at the L4 larval stage . We identified 292 elt-2-regulated genes during the L4 stage . We compared the two sets of elt-2-regulated genes to each other to evaluate whether gene regulation by ELT-2 during the L1 stage is similar to its regulation in the L4 stage . Fig 3A and Fig 3B display the expression changes of the elt-2 regulated genes caused by elt-2 ( RNAi ) in each stage . In Fig 3B , we used a statistical approach to assign each gene according to whether it was regulated by ELT-2 GATA only in the L1 stage ( red dots , referred to as intestinal establishment genes ) , only in the L4 stage ( green dots , referred to as adult function genes ) or in both stages ( blue dots , referred to as general intestinal function genes ) ( see Methods and S6 Table for more detail ) . Out of a total of 223 genes , 103 genes are regulated by elt-2 at both the L1 and L4 larval stages ( general intestinal function class , 46% ) , 61 genes are regulated only at the L1 stage ( intestinal establishment , 27% ) and 59 genes are regulated only at the L4 stage ( adult intestinal function/innate immunity/stress response , 26% ) . Together , the general intestinal function and intestinal establishment categories define a set of elt-2 regulated genes that are activated by elt-2 in the intestine starting in early development . There is also a class of genes that are regulated by elt-2 just in the L4 stage that may have adult-specific functions , such as innate immunity and stress protection . By characterizing which class ( es ) of elt-2 genes change expression during normal aging , we will better understand which function ( s ) of ELT-2 are compromised in old animals . In addition to a genome-wide analysis , we used mCherry reporters to examine expression of three ChIP-seq targets of ELT-2 GATA from the L1 and L4 larval stages . We selected three genes ( ges-1 , gst-42 , and T28H10 . 3 ) that are directly bound by ELT-2 at the L1 stage and are known to be regulated through GATA sites during embryonic and the L1 stage during development ( S1 Table ) [14 , 26 , 27] . By analyzing expression data from DNA microarray experiments from Budovskaya et al . 2008 , we found that each of the three genes also decrease expression during aging ( Fig 3C ) [5] . Next , we examined expression of mCherry transcriptional reporters for these genes following elt-2 RNAi treatment of Day 1 adults . All three transcriptional reporters showed decreased levels of expression in elt-2 ( RNAi ) worms compared to controls , in agreement with the DNA microarray results ( p < . 05 , Student’s t-test ) . These results show that ELT-2 regulates these three genes during both adulthood and development ( Fig 3D ) . These results with reporter genes combined with the genome-wide expression results indicate that a significant portion of ELT-2 regulation is shared between early development and adulthood . We wanted to understand the impact of the age-related decline in ELT-2 expression . One possibility is that the decline in ELT-2 expression in old age only impacts the expression of the L4-specific genes ( adult function ) , which might suggest that the role of elt-2 in old age is limited to stress protection and innate immunity . Another possibility is that the age-related decline in ELT-2 expression affects the general intestinal genes , which would suggest that the loss of elt-2 expression in old age also leads to defects in general intestinal and intestinal establishment functions . To test these possibilities , we profiled gene expression following: 1 ) elt-2 ( RNAi ) , 2 ) during development ( using modENCODE RNA-seq data from seven timepoints during development ) and 3 ) during aging ( using data from a DNA microarray timecourse ) [5 , 28] . Fig 4 shows the results for the set of ELT-2 general intestinal function genes . As expected , this set shows a near-uniform pattern of activation during development; 93% of these genes increase expression between early embryos and Day 1 Adult worms ( Fig 4A ) . During aging , 93% of the general intestine genes shared between the L1 and L4 stages decrease expression; the average general intestinal function gene decreases expression by over 5-fold between Day 2 and Day 11 of adulthood ( Fig 4B ) . We accounted for the possibility that these results might be caused by changes in the mass of the intestine relative to the mass of the worm; specifically , if the relative size of the intestine either increased during development or decreased with age , then the results for this gene set may be a property for any intestinal-expressed gene . To exclude this possibility , we used a list of intestinal-expressed genes from Pauli et al . 2006 and found that they showed smaller changes in expression during development and aging compared to the general intestinal genes ( S5A and S5B Fig , K-S test p-value<10−16 in each case ) [26] . Next , we examined the age-related changes in expression for the adult function genes and the intestinal establishment genes and observed that expression of both sets of genes decline during normal aging ( Fig 4D–4I ) . While all three sets of ELT-2 genes decline during aging , the expression changes of the adult function genes are less extreme than those of the general intestinal function genes ( K-S test p-value = 3 . 7x10-3 ) . In summary , we used the transcriptional output of ELT-2 to define three sets of genes representing establishment of the intestine , general intestinal functions and adult function , and found that all three sets decline during aging . The changes in expression of the ELT-2 regulated genes in old age resemble their changes following elt-2 ( RNAi ) ( Fig 4B and 4C ) . Specifically , 81/88 of general intestinal genes show concordant expression changes following elt-2 ( RNAi ) and during aging ( 92%; p = 2 . 05x10-17 ) . This observation indicates that elt-2 ( RNAi ) recapitulates part of the normal aging signature by causing gene expression changes in young animals that resemble changes in old animals . Further , this observation indicates that part of the normal aging process involves loss of ELT-2 GATA transcriptional activity . To evaluate the effects of elt-2 overexpression , we compared the expression changes during aging in control animals to expression changes during aging in an elt-2 overexpressing strain . If elt-2 overexpression slows expression changes during aging , we should observe a reduced magnitude of expression changes in the elt-2 overexpression strain relative to the control strain . In our RNA-seq experiments , we obtained adequate read depth to calculate expression for about 7000 genes . We compared their changes in expression during aging in the control and elt-2 overexpressing strains . We performed a linear regression analysis of the expression changes and determined the slope of the resulting best-fit line , which provides a comparison of the age-regulated changes across the entire transcriptome for the two strains . We performed this analysis individually for each of the five biological replicates , and found that the elt-2 overexpressing line had smaller age-related expression changes than the control strain in all five cases ( p < 10−8 for each regression model ) ( S7 Table ) . When averaged together , the magnitude of age-related expression changes from the five replicates was lower in the elt-2 overexpressing line than in the control strain ( average = 0 . 87 , p = . 04 , 95% confidence interval 0 . 79–0 . 96 ) ( Fig 5A , S7 Table ) . This indicates that overexpression of elt-2 reduces the magnitude of the expression changes that normally occur over time , suggesting that elt-2 overexpression causes transcriptional changes that occur during normal aging to proceed more slowly . To understand the significance of this result , we performed the same linear regression analysis on transcriptome data from young and old worms from two other conditions that extend lifespan: daf-2 RNAi and B . subtilis diet ( Fig 5B and 5C ) [29 , 30] . For daf-2 we used data from a gene expression timecourse on both daf-2 RNAi and vector control [31] . For B . subtilis , we used data from young and old animals fed B . subtilis , compared to worms fed standard OP50 E . coli . Unlike elt-2 overexpression ( average R2 = 0 . 74 ) , we found that age-related transcriptional changes for each of these conditions showed little similarity ( daf-2 R2 = 0 . 08 , and B . subtilis R2 = 0 . 13 ) to those observed during normal aging , suggesting that the daf-2 mutation and B . subtilis feeding extend life by inducing different gene expression pathways rather than slowing down the expression changes that normally occur during aging . Next , we examined whether there was coordination between different tissues to slow gene expression changes in the elt-2 overexpressing strain . One possibility is that the effects of ELT-2 overexpression are limited to the intestine , which is the only tissue where it is expressed . Another possibility is that ELT-2 overexpression slows gene expression changes in the intestine , which in turn affects the rate of expression changes in other tissues as well . In order to distinguish between these possibilities , we analyzed age-related expression changes for genes specific to five tissues ( S8 Table ) [26 , 32–34] . As above , we generated scatterplots for tissue-specific genes showing the magnitude of their age-related changes in expression in the elt-2 overexpressing compared to the control strain . As might be expected , the average slope of the regression line for the intestine-specific genes was 0 . 85 , indicating an attenuation of age-related changes in this tissue ( p = 0 . 02 , S7 Table ) . For genes expressed specifically in the muscle , hypodermis , and neurons , the slopes of their best-fit linear regression were also less than one , in all five replicates ( S7 Table ) . Age-related changes in the germline were not significantly affected by elt-2 overexpression . These results suggest that overexpression of elt-2 in the intestine influences gene expression in the muscle , hypodermis and neuronal cells as well . It could be that other transcription factors that bind age-regulated genes from our initial screen also change activity or expression over time . If these factors also regulate elt-2 , their changing activities could account for the observed decrease in ELT-2 levels in old worms . In order to determine if these other factors regulate elt-2 , we fed RNAi against six of the other hits from our screen to animals on Day 1 and examined expression of ELT-2:GFP on Day 2 of adulthood and expression of elt-2 mRNA on Days 2 and 4 of adulthood . We performed fluorescent imaging and compared ELT-2:GFP intensity in these animals to animals fed an RNAi control . elt-2 mRNA levels were not affected by RNAi against any of the transcription factor genes , and only RNAi against unc-62 resulted in a change in ELT-2:GFP intensity . These results suggest that ELT-2 expression may be regulated by UNC-62 Hox , but not by the other five transcription factors ( S6 Fig ) . unc-62 encodes a Hox cofactor that is differentially spliced such that the unc-62A isoform appears exclusively in the intestine [9] . We found that UNC-62 binds to the upstream region of elt-2 by analyzing ChIP-seq data from the modENCODE consortium ( S7 Fig ) . Furthermore , elt-2 expression increases in unc-62 ( RNAi ) worms , indicating that UNC-62A normally functions to repress elt-2 expression in the intestine ( Table 1 ) . These results show that UNC-62A directly regulates elt-2 . However , as worms age , expression of UNC-62A in the intestine decreases , which should cause elt-2 expression to increase with age , opposite to what is observed [9] . Additionally , expression of ELT-2:GFP still decreases during aging when worms are fed RNAi targeting unc-62 ( S8 Fig ) . Hence , factors other than UNC-62A are likely to be responsible for the decrease in elt-2 expression in old age . We also investigated regulation of elt-2 expression during aging by ELT-7 , which is another intestinal GATA transcription factor . During embryogenesis , ELT-2 and ELT-7 are known to perform similar function in regulating intestinal gene expression , and ELT-7 is known to promote expression of elt-2 [25] . To test whether ELT-7 GATA regulates elt-2 expression in adult animals , we performed RNAi against elt-7 in Day 1 Adult animals followed by qRT-PCR of elt-2 . As a control , we found that elt-7 ( RNAi ) was effective in reducing the level of elt-7 mRNA ( S9 Fig ) . However , elt-7 ( RNAi ) did not alter the abundance of elt-2 mRNA in adult animals ( S9 Fig ) . We also compared the lifespan of elt-7 ( RNAi ) animals to control animals , and found no difference in lifespan ( S9 Fig ) . These results indicate that elt-7 is unlikely to be responsible for the declining expression of elt-2 , and that elt-7 is dispensable for normal lifespan . The results presented above show that low levels of elt-2 in normal old age is harmful and limits lifespan . Hence , in order for a worm to show extended longevity , it may either increase the expression of elt-2 or alter the worm’s physiology to protect it from the harmful effect of low elt-2 levels . We selected eight mutant strains that achieve longevity through different cellular mechanisms and compared levels of elt-2 expression in old animals in the long-lived mutant and controls ( S9 Table ) . For six longevity strains ( daf-2 , clk-1 . AMP Kinase , hsf-1 , lmp-2 , and Danio rerio sod-1 ) , we compared elt-2 mRNA levels by qRT-PCR in mutant animals to control animals of the same chronological age . For glp-1 ( e2141 ) and glp-4 ( bn2 ) mutants , we measured levels of ELT-2:GFP protein expression . We observed significantly increased levels of elt-2 expression during adulthood in seven of the eight strains ( daf-2 , clk-1 , glp-1 , hsf-1 OE , lmp-2 OE , sod-1 OE and unc-62A RNAi ) ( Table 1 ) . For the eighth strain ( expressing an activated form of AMP kinase ) , there was a four-fold increase in elt-2 expression that was borderline significant ( p = . 057 ) . These eight mutants affect diverse pathways and extend lifespan by different mechanisms , implying that most lifespan mutants result in increased expression of elt-2 . We also examined late-life elt-2 expression levels in dietary restriction conditions ( Table 1 ) . Dietary restriction extends worm lifespan but also directly affects the size and physiology of the intestine . We used two dietary restriction interventions known to extend lifespan: 1 ) eat-2 ( ad1116 ) mutants have low rates of pumping causing them to eat less , and 2 ) the solid dietary restriction method ( sDR ) , which involves feeding worms diluted levels of E . coli . We assayed elt-2 levels by qRT-PCR in old worms ( Day 13 of Adulthood ) under dietary restriction and ad libidum feeding conditions . We detected 23% less elt-2 mRNA in eat-2 animals compared to wild-type controls ( p = . 05 ) , but no significant difference between sDR and control animals . Under dietary restriction conditions , intestinal function is reduced from lack of feeding and may also lead to lower expression of elt-2 . Dietary restriction may activate an alternate pathway that allows animals to achieve extended life in spite of low elt-2 levels . In summary , lifespan extension for nearly all of the long-lived mutants examined involves increased levels of elt-2 expression in old age , and lifespan extension via dietary restriction involves inducing pathways that act independently of normal intestinal function .
Transcriptional profiling is a powerful way to study aging because it characterizes molecular changes during aging in a quantitative and unbiased way . We utilized the modENCODE ChIP-seq database to perform an unbiased screen for candidate regulators of the aging transcriptome . Using this approach , we found that the factor with the highest overlap was the well-studied developmental transcription factor elt-2 , implicating it as a transcriptional regulator of genes that change during aging . In this work , we found that declining levels of ELT-2 GATA in the intestine lead to reduced expression of intestinal genes during normal aging and a limited lifespan ( Fig 6 ) . ELT-2 is known to activate a large set of genes in the intestine that perform general functions in the gut . We defined the genes regulated by ELT-2 during gut development and found that they show a general decline in old age . This result indicates that part of the aging process involves a general decline in intestinal functions in C . elegans . Overexpression of ELT-2 extends lifespan , and lessens the magnitude of changes to the normal aging transcriptome as the worm transitions from young to old . For the ELT-2 overexpressing strain , worms with an old chronological age have transcriptional profiles that would be expected of worms of a younger chronological age , implying that ELT-2 overexpression slows down the rate of aging itself . By contrast , animals fed daf-2 RNAi or B . subtilis have an increased lifespan but dramatically altered gene expression profiles during aging . Changes in the transcriptome during normal aging bear little resemblance to the transcriptional changes in either of these conditions that extend lifespan . elt-2 is expressed exclusively in the intestine , yet the effects of elt-2 overexpression occur in other tissues , as well . This is evidence of coordination of aging between tissues , such that slowing aging in a single tissue ( i . e . the intestine ) can propagate the effect to other parts of the organism , either through signaling or through a different mechanism , such as better nutrition to the rest of the organism . The upstream factors responsible for the decline in ELT-2 GATA expression during aging are unknown . During embryonic development , expression of elt-2 is directly activated by several transcription factors including END-1 , END-3 , and ELT-7 [14 , 25 , 35 , 36] . END-1 and END-3 directly activate elt-2 expression [35 , 36] . However , end-1 and end-3 are expressed only for a short window of time during embryogenesis , and not at all during adulthood [35 , 37 , 38] . ELT-7 does not maintain expression levels of elt-2 in adult animals ( S9 Fig ) . Hence , none of these transcription factors appear to regulate elt-2 expression in adult animals . Once its expression is activated late in embryogenesis , ELT-2 maintains its own expression in an auto-activation loop through direct binding to its own promoter [14 , 39] . One possibility is that gradual loss of auto-activation during aging could explain the loss of elt-2 expression in old age . Another possibility is that changes in other pathways that affect lifespan are responsible for the loss of elt-2 expression during normal aging . We found that many long-lived strains display increased elt-2 levels in old age . One or more of these pathways could reduce the expression of elt-2 over time if the activities of these pathways were to change with age . For unc-62 , expression of the unc-62A isoform in the intestine declines with age rather than increases as would be expected if unc-62 were solely responsible for age-regulation of elt-2 [9] . For the other pathways , it is currently unclear how their activities change in old age . The eight long-lived strains examined in this work represent a small subset of the total number of genes known to affect lifespan in C . elegans . Many other lifespan mutants may also have increased elt-2 expression , and would then be candidates for upstream regulators responsible for low expression of elt-2 in old age . A third possibility is that the decline in elt-2 expression in old age might be a manifestation of damage accumulation . It is possible that the ELT-2 GATA protein may be sensitive to molecular damage , and that molecular damage of the ELT-2 protein could lead to reduced expression of the elt-2 gene by loss of auto-activation . However , ELT-2 expression begins to decline as early as Day 3 of adulthood , which is when there are few signs of overt damage or aging . Furthermore , not all transcription factors have reduced activity in old age , and thus it is not clear why oxidative damage would affect ELT-2 activity specifically and not other transcription factors . The low levels of elt-2 expression in old age limit lifespan . A variety of mutants and RNAi treatments with extended lifespan show increased levels of elt-2 expression in old age . Furthermore , loss of elt-2 activity shortens the lifespan of three long-lived mutants that have been tested: daf-2 ( insulin signaling ) , eat-2 ( caloric restriction ) , and isp-1; ctb-1 ( mitochondrial dysfunction ) [5 , 15] . Together , these results indicate that , for a wide variety of mutants and treatments , part of the strategy to increase lifespan involves increasing elt-2 expression in old age . The intestinal transcriptional network includes genes necessary for digestion , pathogen resistance , and epithelial cell polarity . Since the functions of these intestinal genes are essential for survival , global loss of their activities during the aging process limits lifespan . We found that the intestinal gene expression program controlled by the ELT-2 GATA transcription factor is lost during the aging process . This observation is surprising as aging is often thought to involve accumulation of damage rather than global changes in a developmental pathway . Understanding the upstream causes for changes in the ELT-2 transcriptional program might reshape how we think about the aging process . One model for aging is that damage accumulation serves as a molecular timer setting the pace of age-related changes [40] . According to this model , the age-related decrease in ELT-2 activity could be the result of an accumulation of damage of upstream factors needed to maintain ELT-2 expression . This possibility would reshape our thinking about damage accumulation because its effects would be centered on damage to key regulatory molecules , such as ELT-2 , rather than distributed across the entire cellular proteome . Another possibility is that changes in ELT-2 expression could be due to a loss of homeostatic control of expression , a molecular timing mechanism that might act independently of damage accumulation . Many transcription factors regulate the level of ELT-2 expression from embryogenesis to adulthood . For instance , three GATA factors ( END-1 , END-3 , and ELT-7 ) and autoregulation by ELT-2 control elt-2 expression during embryogenesis [14 , 25 , 35 , 36 , 39] . The Hox cofactor UNC-62A sets the level of elt-2 expression in the young adult . Just as the control of developmental ELT-2 expression is set by these regulatory factors independently of damage , changes to ELT-2 expression during aging could also occur independently of damage . There has been a considerable effort to understand whether the reproducible changes observed in old animals constitute a program for aging ( reviewed in [41] ) . During development , activation of the ELT-2 gene expression program is under strong evolutionary pressure to establish intestinal fate and function in the growing worm . In contrast to ELT-2’s role during development , changes in ELT-2 expression in old age are not likely to affect evolutionary fitness and are not likely to have evolved by natural selection . This is because the collapse of the ELT-2 transcriptional network that occurs after Day 3 of adulthood is after the time when most nematodes have died in the wild [42] . Hence , the consequent transcriptional changes seen in old age are rarely seen in nature , are probably outside the force of natural selection and are not a program with an adaptive function for aging ( reviewed in [41] ) . During aging , changes in expression of the genes regulated by ELT-2 appear ordered not because they evolved for age-related changes . Rather , they appear ordered because changes in expression of an upstream regulator such as ELT-2 ( even if not evolutionarily selected ) are passed along to its downstream genes . An emerging theme in aging research is the recognition that regulatory factors that initially act during development often change abundance during aging . Besides ELT-2 GATA in C . elegans , other developmental factors that change activity in old age include UNC-62 ( Homothorax ) , the ELT-3 , ELT-5 , and ELT-6 pathway of GATA transcription factors , and PQM-1 ( Zn Finger ) [5 , 9 , 10] . This type of aging misregulation is also described in mice . The Wnt signaling pathway and p16 ( a cell cycle repressor ) are developmental regulators that change with age and are partly responsible for age-related degeneration in many organ systems [43–46] . The general principle of aging promoted by drift of developmental pathways in old age is seen in multiple species , although the specific pathways are different in different organisms . These examples illustrate an emerging view of aging that involves alterations of regulatory programs in old age that act previously in young animals to establish developmental pattern and organ function .
ChIP seq data were acquired from modENCODE ( www . modencode . org ) . modENCODE produced ChIP seq data by immunoprecipitating GFP from a line expressing ELT-2:GFP that was stably integrated into the genome . The ELT-2 ChIP-seq data set included two biological replicates at the L1 larval stage , using controls and protocols previously described [47] . Significant peaks were called using the PeakSeq algorithm with a threshold of q < 10−5 [48] . Only peaks that were identified in both biological replicates were considered for analysis . Peaks were mapped to genes if the position of maximum read density was within the 5 kilobase pairs upstream of the gene’s annotated transcription start site , or contained within the gene body , as previously described [13] . The ELT-2 ChIP-seq dataset was not initially released by modENCODE because it did not meet one of the data quality thresholds–that the top 40% of peaks have a 70% correlation between replicates . The ELT-2 data were validated in this work by showing that intestinally-enriched genes are overrepresented among ELT-2’s ChIP-seq targets , consistent with its known function in intestinal development ( S10 Fig ) . modENCODE generated ChIP seq data for 58 transcription factors in 99 datasets ( some transcription factors were profiled at multiple developmental time points ) . All 99 ChIP-seq datasets produced by modENCODE were analyzed for binding to the list of age-regulated genes from Budovskaya et al 2008 . As in Van Nostrand et al . , 2013 , only low-complexity ChIP-seq peaks ( fewer than 8 other factors bound ) were considered for the analysis . Significance of overlap was calculated using a Chi-squared test . We accounted for multiple hypothesis testing using a Bonferroni-corrected significance threshold of p = . 05/99 = 5x10-4 . Lifespans and aging timecourses were performed on NGM plates containing 30 mM FUDR . Worms were moved to FUDR on Day 1 of adulthood ( when eggs were present ) . All experiments were carried out at 20 °C unless otherwise specified . Lifespans were initiated with at least 100 worms in each group . Timecourse experiments were initiated with at least 30 animals per group . Animals with burst vulvas were censored from the experiment . Data for all lifespan experiments can be found in S3 Table . Lifespan statistics were determined with a Log Rank test . Strains and associated details are listed in S10 Table . The ELT-2:GFP strain OP56 was produced by modENCODE for ChIP-seq analysis . Briefly , the strain was constructed by biolistic integration of the fosmid clone 915685980012804 H01 into the C . elegans genome [49] . To simplify image analysis , we constructed a strain ( SD1949 ) that is homozygous for the ELT-2:GFP transgene ( Is ( elt-2:gfp ) and also homozygous for glo-4 ( ok623 ) , which has less gut autofluorescence . The elt-2:gfp strain generated by modENCODE is unsuitable for lifespan analysis because it contains an integrated fosmid ( clone 915685980012804 H01 ) containing 3 other genes in addition to elt-2 ( C33D3 . 3 , C33D3 . 4 , C33D3 . 5 ) . A lifespan phenotype for this strain could be due to the effect of any one , or several , of the genes in the fosmid . For lifespan analysis , we generated new elt-2 overexpression lines: SD1964 and SD1965 . We used Gateway cloning ( Thermo-Fisher Scientific ) to generate a plasmid containing 5 kb of DNA directly upstream of elt-2 , the elt-2 coding region fused to GFP , and followed by the unc-54 3’UTR . This plasmid contains a functional copy of unc-119 to facilitate the selection of transgenic animals . A previous report showed that the expression of an elt-2 transgene is increased in an rde-1 mutant background [50] . We injected the above plasmid into the germline of unc-119 ( ed3 ) ;rde-1 ( ne300 ) animals . We isolated two lines of stably-transmitting unc-119 ( + ) animals which carry multiple copies the elt-2:gfp plasmid as an extrachromosomal array ( SD1964 and SD1965 ) . To create a control strain , we first generated a similar plasmid lacking the elt-2 promoter , coding sequence , or GFP , but containing the unc-119 ( + ) positive selection marker . We injected that plasmid into the germline of unc-119 ( ed3 ) ;rde-1 ( ne300 ) animals and isolated a line of stably-transmitting unc-119 ( + ) animals resulting in the control strain SD1965 . To generate strains expressing ELT-2:GFP that lack a germline , we crossed strain SD1949 to two strains of animals carrying temperature-sensitive mutations: glp-1 ( e2141 ) and glp-4 ( bn2 ) . The cross was carried out at 15°C to prevent sterility of the animals . F2 progeny were singled to individual plates . Worms were transferred after the first day of adulthood to new plates to produce 2 plates of F3 progeny from each F2 . For each F2 , one of the two plates of F3 animals was transferred to 25°C to screen for homozygosity of the germlineless allele . Homozygosity of the elt-2:gfp transgene was confirmed by microscopy . The transcriptional reporter strains SD1429 ( gst-42:Cherry ) , SD1960 ( ges-1:Cherry ) , and RW10819 ( T28H10 . 3:Cherry ) were previously generated . For gst-42 , ges-1 , and T28H10 . 3 , the promoter constructs from the C . elegans ‘Promoterome’ ( http://worfdb . dfci . harvard . edu/promoteromedb/ ) were inserted by Gateway cloning into an expression vector containing the Histone 1 coding region fused to Cherry and unc-119 ( + ) to facilitate screening for transgenic animals . These plasmids were integrated into the genome by biolistic bombardment . Strains SD1821 ( lmp-2 overexpression ) , SD1822 ( hsf-1 overexpression ) , SD1823 ( aakg-2 overexpression ) , and SD1829 ( sod-1 overexpression ) were described in [51] . qRT-PCR was used to measure transcript levels . At least 30 worms were used for each of at least three biological replicates . Total RNA was extracted from worms using TRIzol reagent ( Life Technologies ) and phenol-chloroform extraction . Single-stranded cDNA was prepared using oligo-dT primers . qRT-PCR primers for elt-2 were designed to span splice junctions to prevent amplification of genomic DNA . Before use in qRT-PCR experiments , each oligonucleotide pair was confirmed to amplify only cDNA , with no detectable amplification of genomic DNA . Three technical replicates were performed for each sample . Similarly designed primers against let-70 , tbb-2 , htz-1 , pmp-3 , Y45F10D . 4 or cdc-42 were used as references . qRT-PCR was performed using the Applied Biosciences SYBR Green PCR Master Mix and following the protocol described in Zimmerman et al . , 2014 [52] . We tested our reference primers to measure elt-2 levels in young ( L4 ) and old ( Day 13 ) worms . At the outset , we did not know of a specific control gene that did not change with age . If expression of the reference gene were to change with age , then calculation of the expression level of elt-2 would be affected . We initially used six different reference genes as controls ( let-70 , tbb-2 , htz-1 , pmp-3 , Y45F10D . 4 , cdc-42 ) , none of which were previously thought to change expression with age . We found that the change in the abundance of elt-2 from L4 to Day 13 was similar when each of the six references were used individually , suggesting that none of the reference genes are strongly age-regulated . For subsequent qRT-PCR analyses , we used tbb-2 as a reference gene . elt-2 was not identified as being age-regulated in a previous DNA microarray experiment [5] , possibly because the level of elt-2 RNA is relatively low or because DNA microarray experiments are relatively noisy . Worms were mounted on slides with a pad of 2% agarose . A solution of 100μg/mL levamisole was used to immobilize the worms . A coverslip was placed on the slide , and worms were immediately imaged on a Zeiss Axioplan compound microscope under the 20X objective . Exposure times were selected such that no image reached saturation intensity . For each imaging experiment , images for all timepoints/conditions were acquired on the same day using the same microscope and camera settings so data can be fairly compared . When possible , the entire worm was imaged in a single field , otherwise , it was imaged in two overlapping fields . ImageJ was used to quantitate integrated fluorescence intensity in the intestinal nuclei . For each worm , two background measurements adjacent to the intestinal nuclei were measured . These background measurements were averaged and subtracted from each integrated intensity measurement . Several strains carried the glo-4 ( ok623 ) mutation to reduce intestinal autofluorescence . See Strain List for details . Lineage analysis was performed as previously described on SD1949 [21] . Following computational straightening of image stacks and nuclear segmentation , intestinal nuclei were manually annotated with the VANO program . Background fluorescence was estimated as the average signal of 10 pseudonuclei drawn adjacent to the intestine . The fluorescent signal represents the sum of all GFP signal over every voxel of each nucleus . A previous study characterized the single-cell expression patterns from the automated cell lineage analyzer to the endogenous expression patterns assayed ( either by antibody staining against the endogenous transcription factor or in situ hybridization to the endogenous transcription factor mRNA ) for 53 low-copy number transgenic lines [21] . In 47/53 cases , there was strong agreement between the expression pattern from the cell lineage analyzer and the endogenous measurement . For all transcriptional profiling experiments , at least three biological replicates each of control and experimental were grown . Worms were grown on standard NGM supplemented with 30μM FUDR ( young vs . old transcriptome ) or 1mM IPTG and 100 μg/mL Ampicillin ( RNAi experiments ) . Culture conditions for each experiment are outlined below . 3’-end enriched RNA-sequencing ( 3SEQ ) libraries were prepared as previously described [9 , 53] . Briefly , barcoded sequencing adapters were ligated onto cDNA made from poly-A-enriched RNA . Libraries were pooled prior to sequencing . In all RNA-sequencing experiments , 110–165 million reads were obtained for each pooled sample , corresponding to 30–50 million reads per biological replicate . Significant gene expression changes were identified using the RankProd R Package to implement the analysis , which identifies differentially expressed genes at a 10% FDR cutoff , as in [9] . Because 3SEQ data only sample the 3’end of transcripts , the data are insensitive to gene size . Read counts were normalized for each gene as the fraction of reads obtained for that gene out of all total uniquely-mapped reads . Data have been deposited in GEO . Accession numbers can be found in S11 Table . The data from the microarray timecourse that produced the list of age-regulated genes are from Budovskaya et al . , 2008 . The authors provided the data as the log2 ratio of cy5-labeled sample to cy3-labeled reference . For our analyses , the expression of each gene across each stage was normalized to its expression at the initial Day 2 timepoint . Data for elt-2 null vs . wild-type L1 larvae used Serial Analysis of Gene Expression ( SAGE ) from McGhee et al . , 2009 . A single biological replicate was collected for each genotype . Results of SAGE were provided as Reads Per Million . Data for the modENCODE developmental timecourse used RNA-seq from developmentally-staged hermaphrodites and were obtained from Gerstein et al . , 2014 . Three biological replicates were collected for each of seven developmental timepoints . The authors provided the data as RPKM ( Reads Per Kilobase of transcript per Million mapped reads ) . For our analysis of this timecourse , the expression of each gene across each stage was averaged across biological replicates and normalized relative to its expression at the initial Early Embryo ( EE ) timepoint . Data for the gene expression profile of daf-2 RNAi and control animals during aging were obtained from Murphy et al . , 2003 . The linear regression analysis was performed as follows . First , the log2 ratio of old Day 8 to young Day 1 expression was calculated for each gene , in both the control and daf-2 RNAi conditions . A scatter plot was constructed containing the log2 ratios for every gene , and a linear regression analysis was performed to calculate a best fit a line to the data . | A central question in aging is to uncover the molecular drivers of the normal aging process . Using the roundworm C . elegans as a model organism , we found that the ELT-2 GATA transcription factor regulates many gene expression changes that occur during aging . During development , elt-2 functions as a key regulator of intestinal development . During aging , the levels of ELT-2 transcription factor decline , which causes expression of its target genes to decline and this limits lifespan . Reduction of elt-2 GATA activity in young worms induces gene expression changes resembling those that normally occur in old age . Overexpression of elt-2 slows down the rate of aging , and extends lifespan by about 25% . This work finds that worm lifespan is limited by the declining expression of a key developmental regulator . | [
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"development",... | 2016 | Deactivation of the GATA Transcription Factor ELT-2 Is a Major Driver of Normal Aging in C. elegans |
Patients with dengue fever and comorbidities seem to be at higher risk of developing complications and/or severe dengue compared to healthier individuals . This study systematically reviews the evidence related to comorbidities and dengue . A systematic literature review was performed in five databases ( EMBASE , PUBMED , Global Health , SciELO , Cochrane ) and grey literature for full-text articles since its inceptions until October 10 , 2015 . A total of 230 articles were retrieved . Sixteen studies were analysed after applying all inclusion and exclusion criteria . Seven case control studies and nine retrospective cohort studies showed that comorbidities may contribute to severe dengue , especially 1 ) cardiovascular disease , 2 ) stroke , 3 ) diabetes , 4 ) respiratory disease and 5 ) renal disease , as well as old age . However , due to heterogeneity in studies , the real estimate effect of comorbidities as modifiers of dengue severity could not be established . Further research in regions with high prevalence of dengue infection would contribute to a better understanding of the relevance of comorbidities in severe dengue , especially with a standardised protocol , for outcomes , specific comorbidities , study design—best using prospective designs—and sample sizes .
Dengue fever is an acute systemic infectious disease affecting mainly people in tropical and subtropical regions [1] . Dengue virus ( Flaviviridae family , Flavivirus genus ) has four different subtypes ( DENV-1 , DENV-2 , DENV-3 , DENV-4 ) and it is transmitted by infected Aedes spp mosquitoes ( Aedes aegypti and albopictus ) [2 , 3 , 4] According to estimates of Bhatt et al [5] , there are around 390 million dengue fever infections per year . There is no specific treatment for dengue fever [2 , 3 , 4 , 6] . The gold standard therapy of clinical management of severe cases of dengue—mostly related to plasma leakage or severe bleeding , but also to organ failure—is fluid replacement , both orally and intravenously ( crystalloids and/or colloids ) [7] . Intensive care monitoring and assessment of plasma leakage are vital . As the pathophysiology of dengue fever involves an increase in vascular permeability , rapid fluid replacement is needed—however , fluid replacement in excess might lead to hypervolaemia , pulmonary oedema and respiratory distress . This is an aspect to be carefully evaluated and observed especially in elderly patients , due to their reduction in cardiovascular output , pulmonary compliance and renal output , resulting in clinical complications [3 , 4 , 6 , 7] . During the 1950’s and 1960’s , the majority of dengue cases were described in children , in South East Asian countries [8] . Introduction of the transmitting vector in wild environments and urban areas , human migration and the presence of artificial egg reservoirs ( described for example in used tires ) have facilitated the spread the disease in many different regions , from the Caribbean islands to Brazil and from the Pacific islands to other South Asian countries causing major epidemics [2 , 6 , 9 , 10] . From a disease initially affecting children and young adults , dengue began to affect older people [2 , 7] . At the same time , many of these countries and regions where dengue is highly prevalent began to face the phenomenon of epidemiological transition [11] . The niche occupied by communicable diseases in the overall mortality rate has given space to non-communicable diseases , such as hypertension , diabetes and malignancies , with the ageing of population . As a result , dengue now affects older adults , an age group with inherently more comorbidities . Some authors have postulated that in adults , non-communicable comorbidities and other underlying medical conditions may have a role in predisposing individuals to the severe forms of dengue [7 , 12] . These comorbidities include cardiovascular diseases , endocrine diseases , allergies , haematological diseases , chronic hepatopathy , recipients of solid organ transplant , chronic renal insufficiency , autoimmune disorders , and also the condition old age [7] . Thus , the understanding of the relevance of comorbidities in the development of severe dengue is fundamental in order to better target clinical monitoring and interventions for improved clinical outcome . The objective of this study is to systematically review the existing literature on the relevance of non-communicable comorbidities , such as hypertension , diabetes mellitus , allergies , and also old age , for the development of severe dengue and to evaluate the association between these specific comorbidities and the severity of clinical dengue expression . The scope of this review is intentionally very broad , to highlight the importance of comorbidities in relation to dengue and to stimulate a discussion about further research and its directions .
The systematic review was performed in six databases ( Cochrane Library , EMBASE , Global Health , MEDLINE , SciELO and Google Scholar ) from their inceptions until October 10 , 2015 . Reference lists and grey literature were also searched for relevant articles . Using the PICO format ( acronym for “population or problem” , intervention or exposure of interest” , “comparison” and “outcome” ) [13] , the research question was framed as: “is the severity of dengue influenced by comorbidities ? ” . The categories for the search included: ( a ) Population: adults aged 15 years or older; ( b ) Intervention or Exposure: dengue and comorbidities; ( c ) Comparison: comparison of dengue severity in individuals with and without comorbidities ( this included the terms dengue fever ( DF ) , dengue haemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) of the 1997 WHO case classification and severe dengue ( SD ) , for the 2009 WHO dengue case classification ) ; ( d ) Outcomes: mortality and length of hospital stay . MeSH terms , Boolean operators AND and OR and truncation ( $ ) were inserted in the OvidSP platform ( EMBASE , MEDLINE and Global Health articles ) , the Cochrane Library and in the SciELO database ( Table 1 for the search terms ) . Because the SciELO database has a simplified search interface , the only keywords used with that database were DENGUE and COMORBIDIT* . The process to identify potentially eligible studies was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) flowchart [14] , comprising four steps: ( a ) identification , ( b ) screening , ( c ) eligibility and ( d ) inclusion of studies . The articles obtained from that search were exported to the software EndNote X6 . 0 . 1 ( Bld 8432 , Thomson Reuters ) . An additional search of records identified through grey literature was performed and included in the set . All duplicates were excluded through EndNote and manually as well . Then , articles that did not mention the word dengue or its variations in neither the title nor the abstract were excluded from the remaining articles . The remaining pool of articles was carefully reviewed to identify those that might be relevant to the question . Inclusion criteria for studies in this review were: clinical studies in humans , studies about dengue fever and non-communicable comorbidities , adults aged > 15 years old , any of the outcomes: death , fatality rate , mortality and length of hospital stay , cohort studies and case-control studies and studies in English , French , Portuguese and Spanish . Articles were excluded based on the following exclusion criteria: non clinical studies , non-communicable comorbidities not mentioned , dengue and other communicable diseases , studies restricted to pregnancy and children , review articles , case series and case reports , other languages than English , French , Portuguese or Spanish , and full text not available . An Excel spread sheet was developed to extract data from studies . Information collected were based on the PICO approach and included: general information about the article , studies designs and hypothesis , participants’ features , outcome data , results and main findings . The studies were analysed using a comparative approach of the extracted details . Because of the paucity and relatively low level of evidence of the identified studies , no quality assessment has been performed for further exclusion of articles , however the quality of the studies has been discussed in the discussion section .
A total of 238 potentially relevant articles were initially identified , 129 were excluded after screening the title and abstract . 109 articles were retrieved as full articles . A further 93 articles were excluded after applying all inclusion and exclusion criteria . 16 articles were included in the analysis ( Fig 1 for the flowchart of the selection process; Table 2 provides an overview of the studies ) . A meta-analysis was not performed , due to the heterogeneity of outcome measures .
The results presented highlight that comorbidities might influence the development of severe forms of dengue . Further research including standardised prospective cohort studies in regions with high prevalence of DENV infection would contribute to a better understanding of the relevance of comorbidities in severe dengue . | Dengue fever is a viral disease , transmitted by Aedes mosquitoes . Although for most cases of dengue fever the illness is self-limiting or asymptomatic , severe dengue can occur . Severe dengue is now classified by 1 ) plasma leakage , and/or 2 ) severe haemorrhage and/or 3 ) organ failure . Complications and deaths occur in this group of cases with severe dengue . Patients with dengue fever and comorbidities seem to be at higher risk of developing complications and/or severe dengue compared to healthier individuals . This study systematically reviews the evidence related to comorbidities and the severe forms of dengue fever . Sixteen studies were analysed after applying all inclusion and exclusion criteria . Seven case control studies and nine retrospective cohort studies assessed comorbidities and development of severe forms of dengue . The results showed that comorbidities are relevant to severe dengue , especially 1 ) cardiovascular disease , 2 ) stroke , 3 ) diabetes , 4 ) respiratory disease and 5 ) renal disease , as well as old age . The study of comorbidities in dengue fever is fundamental for improved patient outcome by differential case management of patients , reducing the burden of the disease . Further research in regions with high prevalence of dengue infection would contribute to a better understanding of the relevance of comorbidities with severe forms of dengue fever . An agreed protocol for such studies is urgently needed . | [
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As a universal energy generation pathway utilizing carbon metabolism , glycolysis plays an important housekeeping role in all organisms . Pollen tubes expand rapidly via a mechanism of polarized growth , known as tip growth , to deliver sperm for fertilization . Here , we report a novel and surprising role of glycolysis in the regulation of growth polarity in Arabidopsis pollen tubes via impingement of Rho GTPase-dependent signaling . We identified a cytosolic phosphoglycerate kinase ( pgkc-1 ) mutant with accelerated pollen germination and compromised pollen tube growth polarity . pgkc-1 mutation greatly diminished apical exocytic vesicular distribution of REN1 RopGAP ( Rop GTPase activating protein ) , leading to ROP1 hyper-activation at the apical plasma membrane . Consequently , pgkc-1 pollen tubes contained higher amounts of exocytic vesicles and actin microfilaments in the apical region , and showed reduced sensitivity to Brefeldin A and Latrunculin B , respectively . While inhibition of mitochondrial respiration could not explain the pgkc-1 phenotype , the glycolytic activity is indeed required for PGKc function in pollen tubes . Moreover , the pgkc-1 pollen tube phenotype was mimicked by the inhibition of another glycolytic enzyme . These findings highlight an unconventional regulatory function for a housekeeping metabolic pathway in the spatial control of a fundamental cellular process .
Glycolysis , which generates two ATP from each glucose molecule and produces two pyruvate molecules to fuel the mitochondrial tricarboxylic acid cycle , is a central enzymatic process in carbon metabolism . In addition , glycolysis also produces metabolic intermediates and reduced cofactors for secondary metabolism , as well as amino acid and fatty acid biosynthesis [1 , 2] . Recent studies have hinted at a role for energy in the regulation of cellular processes independent of the housekeeping function . For instance , aldolase , a glycolytic enzyme , acts as a sensor of glucose availability in mammalian cells , and represses the energy sensing AMP-dependent kinase ( AMPK ) pathway , which is known to coordinate cell growth , metabolism , and cell polarity [3–6] . Therefore , glycolysis may play a regulatory role in determining cell polarity regulation , although direct evidence for this role is lacking thus far . Polarized cell growth is a conserved cellular process shared by many diverse systems in eukaryotic species , such as the mating tubes in budding yeast , cell growth and morphogenesis in fission yeast and filamentous fungi , axon outgrowth in animals , and root hair and pollen tube formation in plants . Pollen tubes are a well-established and favorite model system for studying cell polarity formation and polar cell growth [7] . Pollen tubes are among the most rapidly extending polarized cells , growing at rates of up to 250 nm per second [8] . The rapid tip growth exhibited by pollen tubes is supported by cytoskeletal organization/dynamics and vesicular trafficking coordinated by a conserved signaling network dependent upon a plant Rho GTPase ( ROP1 ) [7–12] . ROP1 is activated in the apical region , where it orchestrates F-actin dynamics and calcium homeostasis to dynamically maintain apical growth in the pollen tube [11 , 13] . REN1 , a RhoGAP , acts as a global inhibitor to spatially restrict ROP1 activity to the apical plasma membrane at the pollen-tube tip region [14] . This self-organizing ROP signaling network is comprised of multiple coordinated pathways and feedback loops , providing a robust molecular linkage between the cytoskeleton , vesicular trafficking , and polarity formation [11–13 , 15–19] . It is conceivable that the rapid tip growth exhibited by pollen tubes is extremely energy-demanding . Overall elevations in energy metabolism in pollen tubes appears to rely on plastid-localized glycolysis and mitochondrial-localized respiration pathways [20–23] . As a result , respiration rates in pollen tubes are up to ten times greater than those in vegetative tissues [24] . In addition , the ethanol fermentation pathway is also active in support of pollen tube growth [25] . Apart from an overall increase in energy metabolism , rapid tip growth may also require a tight spatiotemporal regulation of energy production , given the tightly regulated spatiotemporal dynamic of the aforementioned processes . Phosphoglycerate kinase ( PGK ) is a key enzyme in the glycolytic pathway , responsible for catalyzing the reversible conversion of 1 , 3-biphosphoglycerate ( 1 , 3BPG ) to 3-phosphoglycerate ( 3PG ) . Here , we report that the Arabidopsis cytosolic phosphoglycerate kinase ( PGKc ) plays a regulatory role in the regulation of pollen tube polarity by modulating the apical distribution of the REN1 RhoGAP , and thus the activity of the apical ROP1 RhoGTPase as well . This action of PGKc is specific for the cytosolic glycolysis pathway and is independent of mitochondrial respiration . Our findings provide the first conclusive evidence that glycolysis plays an important and specific role in the regulation of cell polarity .
To discover new genes regulating pollen tube polarity , we performed a genetic screen for Arabidopsis thaliana mutants from the SALK collection of individually indexed homozygous T-DNA insertion lines presenting altered growth polarity . Among over 8000 individual lines screened , pollen tubes from SALK_066422C were identified to present defective polarized growth in in vitro germination medium . According to the annotation , SALK_066422C contains a T-DNA inserted into the 5th exon of AT1G79550 , which encodes a cytosolic phosphoglycerate kinase ( designated hereafter as PGKc ) ( S1A and S1B Fig ) . We therefore designated the mutant as pgkc-1 . The pgkc-1 mutant pollen germinated at a much faster rate than wild type ( WT ) plants ( Fig 1D ) . However , pgkc-1 mutant pollen tubes were significantly shorter than WT ones after 9 h ( Fig 1A , 1B and 1E ) . Moreover , the majority of mutant pollen tubes were swollen relative to WT , exhibiting irregular morphology and wider tube width ( Fig 1A , 1B and 1F ) . We also obtained an independent allele mutant with a T-DNA insertion in the 3rd intron of PGKc ( SALK_062377 , designated pgkc-2 ) , which showed similar pollen tube phenotypes ( S1A , S1B and S1G Fig ) . Quantitative reverse transcription polymerase chain reaction ( Q-RT-PCR ) showed that both pgkc-1 and pgkc-2 are knockout mutants for PGKc ( S1C Fig ) . We also performed a backcross of pgkc-1 with WT plants , where F2 progeny pgkc-1 homozygous plants showed defects in pollen tube polarity while WT progeny remained normal ( S1E and S1F Fig ) . This indicates the co-segregation of the pgkc-1 locus with the mutant phenotype . Finally , the pgkc-1 mutant was rescued by introducing PGKc genomic sequences , including the native promoter and terminator ( Fig 1C to 1F and S1D Fig ) . Taken together , our results confirm that loss of PGKc is indeed responsible for the pollen tube polarity phenotype . The vegetative growth and flowering of pgkc-1 plants were slightly delayed relative to WT , but mutant plant morphology was normal otherwise ( S2A–S2C Fig ) . The Arabidopsis genome contains three PGK genes , AT1G79550 ( PGKc ) , AT3G12780 , and AT1G56190 . Recent reports have shown that AT1G79550 encodes the sole cytosolic PGK , while AT1G79550 and AT3G12780 are plastid localized [26] . We also performed subcellular localization analysis using a GFP fusion protein . Consistent with the results of a previous study , we found PGKc to be localized to the cytoplasm and nuclei while the other 2 PGKs were localized to the chloroplasts ( plastids ) ( S3A–S3F Fig ) . Finally , both a previous study and publicly available microarray expression data showed that PGKc is expressed ubiquitously in most plant tissues , including pollen ( https://genevestigator . com/ ) [26] . Our surprising findings regarding PGKc knockouts prompted us to assess how a housekeeping glycolytic enzyme can regulate cell polarity . We first performed a series of assays to assess pgkc-1 mutant phenotype cellular mechanisms with known links to cell polarity defects . The spatiotemporal dynamics of apical actin microfilaments ( F-actin ) and vesicle trafficking is crucial for generation of cell polarity and pollen tube tip growth [7 , 13] . We observed F-actin organization in pgkc-1 pollen tubes by introducing a Lifeact-mEGFP marker via crossing [2 , 27] . In WT pollen tubes , highly dynamic fine F-actin structures were observed in the apical region , dense F-actin fringe structures were present in sub-apical regions , and parallel longitudinal F-actin bundles were found in shank regions ( Fig 2A ) . Dynamic apical F-actin has been shown to be disrupted by treatment with 1 . 5 nM Latrunculin B ( LatB ) , a chemical promoting actin depolymerization [9] [28] ( Fig 2A and 2B ) . In pgkc-1 pollen tubes , no significant difference was detected in the shank and sub-apical regions . However , fine F-actin filaments were significantly over-accumulated towards the apex of the apical tip region in pgkc-1 pollen tubes , even after LatB treatment ( Fig 2A and 2B ) . Indeed , treatment with 1 . 5 nM LatB had no significant effect on the germination , length , and morphology of pgkc-1 mutant pollen tubes , but greatly inhibited similar mechanisms in WT pollen tubes ( Fig 2C to 2G ) . Taken together , these results indicate that pgkc-1 mutation promotes the accumulation of F-actin in the apical tip region of the pollen tube . A previous study has shown that an increased level of apical F-actin leads to greater apical accumulation of exocytic vesicles [11] . Thus , we examined the distribution of exocytic vesicles in pgkc-1 mutant pollen tubes . The Rab GTPase RABA4D is a pollen-specific Arabidopsis homolog of animal Rab11 known to localize to post-Golgi compartments , including exocytic vesicles in pollen tube tips [14 , 29] . We introduced an EYFP-RABA4D marker into pgkc-1 pollen tubes via crossing . In WT pollen tubes , EYFP-RABA4D-labeled membrane compartments were punctuated and enriched in the tip region ( Fig 3A ) . In pgkc-1 pollen tubes , the apical distribution pattern of RABA4D was similar to that of WT pollen tubes ( Fig 3A ) , but quantification of EYFP-RABA4D signal showed that apical EYFP-RABA4D compartments were much more enriched in pgkc-1 pollen tube compared to WT despite lower signal intensity in the shank region ( Fig 3B ) , a similar pattern to that observed in pollen tubes with ROP1 over-activation [11] . We next examined whether pgkc-1 pollen tubes respond differently to Brefeldin A ( BFA ) , an inhibitor which interrupts vesicle trafficking by inhibiting vesicle formation from TGN and recycling endosomes [30–33] . Application of 0 . 4 μM BFA abolished the apical enrichment of RABA4D signal observed in WT pollen tubes , but had a markedly reduced effect on RABA4D localization in pgkc-1 pollen tubes ( Fig 3A and 3B ) . Moreover , BFA greatly inhibited WT pollen germination but only moderately affected pgkc-1 pollen germination ( Fig 3C to 3G ) . Interestingly , pgkc-1 pollen tubes exhibited enhanced growth depolarization when treated with BFA ( Fig 3C to 3G ) . Germinated pgkc-1 pollen tubes were shorter and wider , and multiple tips occasionally formed from a single pollen grain ( Fig 3D ) . These results suggested that the pgkc-1 mutation appears to enhance the production or accumulation of exocytic vesicles in pollen grains and tubes . This altered vesicular trafficking behavior in pollen tube tips is consistent with the aforementioned observed over-accumulation of apical F-actin [11] . Given the role of ROP1 GTPase signaling in regulating F-actin dynamics and vesicle trafficking [7 , 11 , 13] , we speculated that the F-actin dynamics and vesicle trafficking phenotype in the pgkc-1 mutant may be linked to altered ROP1 signaling . RIC4 binds active ROP1 via its CRIB4 domain . CRIB4-GFP localization to the plasma membrane indicates ROP1 activity in pollen tubes [14] . To evaluate whether ROP1 activity was altered in pgkc-1 mutants , we introduced CRIB4-GFP into the pgkc-1 mutant background . CRIB4-GFP localization to the apical plasma membrane was significantly broader in pgkc-1 mutant pollen tubes than in WT ( Fig 4A ) . Quantification revealed stronger CRIB4-GFP signal in pgkc-1 pollen tubes than in WT counterparts ( Fig 4B ) . This result suggested that active ROP1 levels were indeed excessive in pgkc-1 pollen tubes . A previous study has shown that the RhoGAP REN1 is an important regulator of ROP1 negative feedback loops . REN1 is localized to exocytic vesicles in the pollen tube tip [14] . A mutation in REN1 causes swollen pollen tubes and is correlated with hyper-activation of ROP1 [14] . We therefore introduced a GFP-REN1 reporter into pgkc-1 plants to observe the subcellular distribution of this negative feedback regulator of ROP signaling . Consistent with the previous study , GFP-REN1 was enriched in the apical region in an inverted-cone pattern , reminiscent of the distribution of RABA4D-labeled vesicles ( Figs 4C and 3A ) . Strikingly , in pgkc-1 pollen tubes , this apical localization of GFP-REN1 was abolished in stark contrast to the enhanced apical accumulation of RABA4D-labeled vesicles observed ( Fig 4C and 4D ) . These results indicate that pgkc-1 mutation disrupted apical localization of REN1 , which may be associated with ROP1 hyper-activation . To examine the functional interaction between PGKc and REN1 , we generated double mutants using pgkc-1 and ren1-3 mutant plants . ren1-3 plants contain a weak mutation consisting of a C terminus truncation which confers a mild polarization defect [14] ( S4 Fig ) . If PGKc functionally interacts with REN1 , the tip-targeting defect of REN1 present in pgkc-1 plants would have a synergistic effect with the phenotype observed in the ren1-3 mutant . We found that in standard medium , ren1-3 pollen tubes displayed near normal growth and morphology , while pgkc-1 pollen tubes exhibited reduced growth and moderate polarity defects ( Fig 4E and 4F ) . However , the pollen tubes of the pgkc-1/ren1-3 double mutant plants were much shorter and dramatically more swollen compared to either single mutant ( Fig 4G to 4I ) . These results indicate that a moderate REN1 defect in ren1-3 was synergistically enhanced by pgkc-1 , demonstrating the genetic interaction between PGKc and REN1 . We reasoned that PGKc , as a glycolytic enzyme , regulated pollen tube polarity through one or more of the following possible mechanisms: ( 1 ) pollen tube polarity may be linked to overall cellular ATP level , which is dependent on both glycolysis and mitochondrial respiration; ( 2 ) glycolysis may play a regulatory role in determining pollen tube polarity; and ( 3 ) PGKc may have evolved a new , so-called “moonlighting” function distinct from its role in glycolysis . We performed a series of assays to examine these possibilities . To assess a possible relationship between cellular ATP level and pollen tube polarity , we determined whether mitochondrial respiration , the downstream pathway of glycolysis and the main source of cellular ATP production , was involved in pollen tube polarity . The potent inhibitor oligomycin has been used to block mitochondrial respiration in pollen germination medium [34] . In our assay , 40 nM oligomycin significantly inhibited WT pollen tube growth ( Fig 5A and 5B ) . However , oligomycin-treated pollen tubes were uniformly short and thin , exhibiting a distinctly different phenotype than pgkc-1 pollen tubes ( Fig 5A to 5D ) . Therefore , we concluded that the pgkc-1 pollen tube phenotype was likely not caused by inhibition of respiration . To check if the role of PGKc in pollen tube polarity could be attributed to its glycolytic enzymatic activity , we generated a mutant version of PGKc termed mPGKc where an evolutionally conserved residue Glutamate179 was changed to Glutamine . This mutation has been shown to impair PGK catalytic activity but not binding kinetics in yeast [35 , 36] ( Fig 6A ) . We introduced native promoter-driven PGKc or mPGKc cDNA into pgkc-1 mutants , and found that WT PGKc cDNA transgene expression , while lower than native PGKc expression , was still able to complement the mutant phenotype ( Fig 6B to 6E ) . In contrast , mPGKc could not rescue the mutant phenotype despite similar levels of gene expression . These results indicated that glycolytic activity was required for PGKc function in pollen tube polarity ( Fig 6B to 6E , S5 Fig ) . If PGKc regulates pollen tube polarity through its glycolytic activity , we would anticipate that other glycolytic enzymes are also involved in this process . GAPDH is an enzyme which catalyzes the conversion of glyceraldehyde-3-phosphate to 1 , 3BPG ( Fig 7A ) . When we applied 40 μM of CGP 3466B maleate , a specific inhibitor of GAPDH [37] , WT pollen tubes exhibited a pgkc-1-like phenotype with depolarized morphology [38] ( Fig 7B , 7C and 7E ) . Furthermore , when either pgkc-1 or ren1-3 single mutants were treated with CGP , cell polarity defect magnitude was greatly enhanced , exhibiting significantly ballooned pollen tubes ( Fig 7B to 7I ) . To validate that the cellular mechanism underlying the GAPDH inhibition phenotype was similar to that underlying the pgkc-1 mutation phenotype , we observed the distribution of GFP-REN1 , CRIB4-GFP , EYFP-RABA4D and Lifeact-mEGFP in WT pollen tubes treated with 40 μM CGP 3466B . Similar to in pgkc-1 mutants , GFP-REN1 signal was diminished while CRIB4-GFP , EYFP-RABA4D and Lifeact-mEGFP signals were enhanced in the apical region after CGP 3466B treatment ( Fig 7J to 7M ) . Similarly , a double mutant of cytosolic GAPDHs , gapc1-1/gapc2-1 [30] , and the application of another GAPDH inhibitor , iodoacetate , also resulted in pollen tube phenotypes resembling that of pgkc-1 ( Fig 8A to 8D and S6 Fig ) . These results indicate that GAPDH activity is also involved in the regulation of pollen tube polarity . Taken together , we conclude that glycolysis plays an important role in the regulation of pollen tube polarity by affecting the association of the REN1 RopGAP with exocytic vesicles .
Our findings here clearly demonstrate that cytosolic glycolysis has a novel function in the regulation of cellular signaling , distinct from its conventional housekeeping role in carbon and energy metabolism . The global energy level is important for pollen development and pollen tube elongation [20–23 , 31 , 39] . In this study , we found that inhibition of mitochondrial respiration using oligomycin resulted in reduced pollen tube length and width . This phenotype is consistent with previous reports , while distinct from the reduced growth polarity induced by the pgkc-1 mutation or GAPDH inhibition ( Fig 5A to 5D ) . The ethanol fermentation pathway serves , concomitantly with oxidative respiration metabolism , as a bypass route to help maintain metabolic flux and energy supply in pollen tubes [25 , 40] . This pathway is also downstream of glycolysis and consists of two key enzymes , pyruvate decarboxylase ( PDC ) and alcohol dehydrogenase ( ADH ) [25] . In petunia , the mutation of a pollen-specific PDC2 gene was shown to cause reduced elongation of pollen tubes in the style , leading to a competitive disadvantage relative to WT pollen [41] . However , pollen tube polarity in pdc2 mutants appeared to be normal [41] . Therefore , we believe that pollen tube growth polarity is modulated by a specific regulatory aspect of cytosolic glycolysis rather than glycolysis-dependent respiration or fermentation ( Fig 9 ) . In animal cells , many glycolytic enzymes participate in moonlighting functions , including RNA binding , membrane fusion , cytoskeletal dynamics , autophagy , and cell death [42–46] . Similarly , cytosolic GAPDHs in plants demonstrate nuclear uracil-DNA-glycosylase activity and participate in plant immunity [47] . Here , we demonstrated that glycolytic activity is required for PGKc function in pollen tubes . Moreover , GAPDH , another enzyme in the cytosolic glycolysis pathway , plays a similar role as PGKc in pollen tube polarity . Based on these results , it is more likely that it is the glycolysis pathway which regulates pollen tube polarity , rather than a moonlighting function of a glycolytic enzyme ( Fig 9 ) . The pollen tube polarity defects present in the pgkc mutant have been associated with the over-activation of ROP1 , as well as the over-accumulation of F-actin and exocytic vesicles in the tip region . Previous findings have suggested that REN1-based negative feedback globally inhibited ROP1 , as ROP1 activity is dependent upon the association of REN1 with exocytic vesicles at the apical plasma membrane [14] . Both ren1 mutation and constitutively active ROP1 ( CA-ROP1 ) expression has been shown to cause ROP1 hyper-activation , leading to F-actin stabilization , apical cortex vesicle accumulation , and pollen tube depolarization [11 , 14] . In the pgkc mutant or during treatment with a GAPDH inhibitor , the association between REN1 and the exocytic vesicles is abolished , thus accounting for the observed over-activation of ROP1 . Accordingly , tip region over-accumulation of F-actin and exocytic vesicles appears to be attributed to ROP activation ( Fig 9 ) . The oscillation of apical ROP1 activity is regulated by positive and negative feedback via F-actin-mediated exocytosis [7] . Could the aberrant REN1 localization be the consequence of disrupted F-actin in the pgkc-1 mutant , rather than the cause ? According to previous studies , if the loss of PGKc activity simply enhance F-actin accumulation , then one may expect overall alteration of pollen tube elongation , rather than polarity . Mutations of F-actin severing factors RIC1 or MAP18 , also caused aberrant F-actin overaccumulation in the apical tip of pollen tubes [27 , 48] . However , ric1 mutant exhibited enhanced elongation and map18 is defective in growth direction of pollen tubes , while the pollen tube polarity was normal in both cases [27 , 48] . Therefore , we interpret disrupted RhoGTPase signaling in the pgkc pollen tubes as a reason rather than consequence of the aberrant cellular activities ( Fig 9 ) . Nevertheless , our study does not exclude the possibility that pgkc mutation might directly interrupt other unelucidated cellular processes , which simultaneously affect multiple steps in the feedback loops of RhoGTPase signaling , including REN1 distribution , F-actin dynamics , and exocytic vesicle trafficking . Several possible underlying mechanisms may link the glycolysis pathway with pollen tube polarity . Mitochondria provide most of the energy required by the cell . However , mitochondria are not evenly distributed in polarized cells , and may not meet the needs of all organelles [49 , 50] . In contrast , although net energy gain is low , glycolysis could produce ATP close to energy sinks , thus complementing mitochondrial function . For instance , in neurons , the vesicles in fast axonal transport are energized by on-board ATP provided by specifically localized glycolytic machinery rather than mitochondrial respiration [51] . Mitochondria are absent from the apical tip of pollen tubes , where PGKc is present [50 , 52] . Therefore , it is possible that cytosolic glycolysis may provide an ATP source in close proximity to some unclear vesicle activities , similar to the fast axonal transport , which are required for the targeting and/or trafficking of REN1 protein in pollen tube tips ( Fig 9 ) . Glycolysis is a fundamental energy metabolism pathway , but glycolytic enzymes and intermediates may also play important signaling roles in growth and development . One of the most important signaling hubs is the enzyme hexokinase ( HXK ) . As the first enzyme in glycolysis , HXK is able to phosphorylate glucose , producing glucose-6-phosphate [53] . Independent of its catalytic activity , plant HXK has also been proven as a glucose sensor for the regulation of sugar metabolism and signaling pathways [54 , 55] . Since PGK and GAPDH are downstream of HXK and aldolase , there is a possibility that PGKc or GAPDH inhibition might cause accumulation of glucose in pollen tube , resulting in a hyperactivation of HXK signaling in pollen tubes ( Fig 9 ) . It would be helpful to examine this possibility in the future by overexpressing HXK in pollen tubes . It is less likely but still possible that glycolysis may regulate pollen tube polarity through signaling by downstream intermediate metabolites , such as 3-phosphoglyceric acid ( 3PG ) , a product of PGKs . However , given that the plastidial glycolysis pathway remains intact in pgkc mutant pollen tubes , metabolic intermediates are unlikely to be deficient . Consistent with this , adding 3PG or pyruvate did not affect the pollen tube polarity phenotype of the pgkc mutant , even at concentrations inhibitory to WT pollen tubes ( S7 Fig and S8 Fig ) . Although we could not clarify whether exogenous metabolites could substitute for intracellular metabolic intermediates under our experimental conditions , this result indicates that these metabolites have no effect on pollen tube polarity . Nonetheless , future studies are needed to elucidate the mechanisms by which cytosolic glycolysis regulates the association of REN1 with apical vesicles and subsequent cell polarity modulation in pollen tubes .
Arabidopsis ( Columbia ecotype ) were used as WT specimens . All plants were grown under a 16 h photoperiod at 22°C . SALK collections of individually indexed homozygous T-DNA insertion lines were obtained from the ABRC ( http://signal . salk . edu/cgi-bin/homozygotes . cgi ) . For in vitro pollen germination screening , 5–10 seeds from each SALK line were grown in individual pots . Pollen grains from three plants for each line were collected and germinated in in vitro germination medium as previously described [14 , 56] . Lines with the pollen tube polarity phenotype were selected as mutant candidates for further verification . SALK_066422C ( pgkc-1 ) was identified during screening . Another allele , SALK_062377 ( pgkc-2 ) , was obtained from the ABRC . Genotyping was performed based on the protocol provided on the SALK website . gapc1-1/gapc2-1 double mutant seeds were gifts from Dr . Xueming Wang , and genotype was confirmed using primers as described [30] . All primers used are listed in Supplemental S1 Table Total RNA was extracted from indicated tissues using the E . Z . N . A . RNA extraction kit ( Omega ) according to manufacturer’s instructions . Oligo dT-primed cDNA was synthesized from 500 mg of total RNA using the PrimeScript RT reagent Kit with gDNA Eraser ( Takara ) . Quantitative PCR analysis was performed with the SYBR Premix Ex Taq II ROX plus kit ( Takara ) using a Mx3005 device ( Agilent ) . Relative levels of each transcript were calculated after being normalized to UBC21 endogenous control . All constructs were generated using Gateway technology ( Invitrogen ) . Primers used are listed in Supplemental S1 Table . All entry vectors were generated from the pDONR-zeo vector ( Invitrogen ) . LR reactions were conducted using LR Clonase II ( Invitrogen ) with corresponding entry vectors and destination vectors . To construct catalytic inactive mPGKc complementation vector , PGKc cDNA was cloned first . Then DpnI-mediated site-directed mutagenesis was performed to change the G535 to C [35 , 57] . pGWB604 vector was modified by inserting a 2 . 1 kb PGKc promoter with HindIII and SbfI . LR cloning were then performed to generate the proPGKc::PGKc-GFP or proPGKc::mPGKc-GFP constructs , respectively . Open flowers were collected and pollen grains were dusted onto standard agar-germination medium with 18% sucrose , 0 . 01% boric acid , 1 mM CaCl2 , 1 mM Ca ( NO3 ) 2 , 1 mM MgSO4 , pH 6 . 0 , and 0 . 5% Difc Noble agar ( BD Biosciences ) . Incubation times ranged from 2 to 9 h at 23°C , and pollen tubes were observed under an Imager M2 inverted microscope ( Olympus ) . Tube length and width were measured using ImageJ software . Since the morphology of pgkc-1 pollen tubes was non-uniform , pollen tube width was measured at the widest point . 50-100 pollen grains or pollen tubes were measured . For pollen tube chemical treatment , LatB ( Invitrogen ) , BFA ( Invitrogen ) , iodoacetate ( Sigma ) , oligomycin ( Sigma ) , CGP 3466B ( Tocris Bioscience ) at indicated concentrations were added to the above solid pollen germination medium . WT and mutant pollen grains were germinated using the same medium and compared side-by-side . For Lifeact-mEGFP , EYFP-RABA4D , REN1-GFP marker observation , previously reported marker lines were used for crossing with pgkc-1 mutants . Double homozygote F2 progeny were identified by genotyping for the presence of mutant pgkc-1 and observation of pollen GFP/YFP fluorescent signal , respectively . For the CRIB4-GFP marker , as pgkc-1 and CRIB4-GFP are linked on the same chromosome , a vector containing CRIB4-GFP was used to transform pgkc-1 . The chosen line was then backcrossed with WT , and CRIB4-GFP on a WT background was obtained as part of F2 progeny . Fluorescent microscopy was performed with a Spinning Disk Confocal Microscope Andor Revolution WD . To quantitatively measure GFP signal intensity , the ImageJ line profile tool was used according to user guidelines . Briefly , a five pixel block was drawn from the background toward the tip along the axis of a pollen tube . The signal intensity along the line was measured by the line profile tool . The apical tip was defined as the position where signal intensity was two-fold greater than the black background , and this position was designated as 0 μm . Fifteen to twenty pollen tubes were measured for each sample , with the data from each pollen tube aligned by tip position , and average intensities were calculated . | Glycolysis , which breaks down glucose to produce energy , has long been considered a “housekeeping” pathway in living cells , i . e . , it helps maintain basic cellular functions . Here , we found that the glycolysis pathway plays an unconventional regulatory role in cell polarity , i . e . , the intrinsic asymmetry in the shape , structure , and organization of cellular components . Mutation in the gene encoding the glycolytic enzyme cytosolic phosphoglycerate kinase ( PGKc ) leads to swollen and shorter pollen tubes in Arabidopsis thaliana , which is associated with the over-activation of Rho GTPase—a master regulator of cell polarity . Our results suggest that this phenomenon is caused by a specific regulatory role of cytosolic glycolysis rather than the global energy supply or moonlighting functions of glycolytic enzymes that modulate pollen tube growth polarity . Our findings shed light on the diverse biological roles of glycolysis in plants beyond simple “housekeeping” functions . | [
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... | 2018 | Glycolysis regulates pollen tube polarity via Rho GTPase signaling |
We develop a statistical mechanical model to analyze the competitive behavior of transitions to multiple alternate conformations in a negatively supercoiled DNA molecule of kilobase length and specified base sequence . Since DNA superhelicity topologically couples together the transition behaviors of all base pairs , a unified model is required to analyze all the transitions to which the DNA sequence is susceptible . Here we present a first model of this type . Our numerical approach generalizes the strategy of previously developed algorithms , which studied superhelical transitions to a single alternate conformation . We apply our multi-state model to study the competition between strand separation and B-Z transitions in superhelical DNA . We show this competition to be highly sensitive to temperature and to the imposed level of supercoiling . Comparison of our results with experimental data shows that , when the energetics appropriate to the experimental conditions are used , the competition between these two transitions is accurately captured by our algorithm . We analyze the superhelical competition between B-Z transitions and denaturation around the c-myc oncogene , where both transitions are known to occur when this gene is transcribing . We apply our model to explore the correlation between stress-induced transitions and transcriptional activity in various organisms . In higher eukaryotes we find a strong enhancement of Z-forming regions immediately 5′ to their transcription start sites ( TSS ) , and a depletion of strand separating sites in a broad region around the TSS . The opposite patterns occur around transcript end locations . We also show that susceptibility to each type of transition is different in eukaryotes and prokaryotes . By analyzing a set of untranscribed pseudogenes we show that the Z-susceptibility just downstream of the TSS is not preserved , suggesting it may be under selection pressure .
DNA structure has been known to be polymorphic since the earliest days of its investigation . Rosalind Franklin in her initial fiber diffraction studies found two distinct DNA structures , which she called the A-form and the B-form [1] . Transitions between these forms could be induced by changes of hydration state , with B-DNA being the hydrated form and hence presumably the biologically relevant structure . Yet the first determination of DNA structure at atomic resolution found that the sequence crystallizes into a left-handed helix , called Z-DNA [2] . Although the standard right-handed B-form helix is known to be the prevalent structure in vivo , DNA can assume many other conformations . Some , such as the A-form and the strand separated state , can occur in any DNA sequence , although the latter prefers A+T-rich sequences . Other conformations have either specific sequence requirements or strong preferences for certain sequence types . These include the Z-form , which prefers alternating purine-pyrimidine sequences , the cruciform , which requires a high degree of inverted repeat symmetry , the triple stranded H-form , which needs long , mirror symmetric homopurine or homopyrimidine runs , and the four stranded G-quadriplex structure , which requires four runs of G's in close proximity . Transitions from B-form to alternate DNA structures can be induced in susceptible sequences in a variety of ways , including changes of temperature , ionic conditions , hydration , or superhelical state . The first three of these conditions are approximately constant in vivo; only the level of imposed DNA superhelicity is subject to physiological changes that can affect the propensity of the molecule to transform to alternate conformations . Substantial levels of negative superhelicity are imposed on DNA in vivo by gyrase enzymes in prokaryotes , and by transcriptional activity in all organisms [3] . Although the superhelicity imposed by transcription in eukaryotes is transient , it is known to travel over kilobase distances and to persist long enough to drive DNA structural transitions [4] . Negative superhelicity imposes undertwisting torsional stresses on the DNA , which can induce transitions to alternate conformations that are less twisted in the right-handed sense than is B-DNA . Transitions to such states decrease the local helical twist , and thereby relieve some of the imposed superhelical stress . A transition will become favored at equilibrium when the amount of stress energy it relieves exceeds its energy cost . In vitro experiments have demonstrated superhelical transitions from the B-form to each of several types of alternate structures , including Z-DNA [5] , [6] , H-DNA [7] , locally strand separated DNA [8] , [9] , and cruciforms [10]–[12] . A structural transition also has been observed to occur in a superhelical plasmid that contains a quadriplex-susceptible region , although it was not verified that the alternate structure involved is the quadriplex [13] . It has been suggested that this region instead prefers to form H-DNA , to which it also is susceptible [14] . Since genomic DNA often has numerous sites whose sequences are susceptible to forming these alternate structures , in principle there are many different combinations of transitions that can occur in response to imposed negative superhelicity . Moreover , the transition behavior of each susceptible site is coupled to the behaviors of all other sites that experience the same superhelical stress . This coupling occurs because each transition relieves some of the imposed stress , which alters the probability of transition at all other sites throughout the region involved . In this way imposed superhelicity induces a global competition among all the sites that are susceptible to any type of transition . Non-linear and highly complex correlations occur among the transition behaviors of susceptible regions throughout the domain . To analyze this competition in its full complexity , it is necessary to develop methods that can treat the simultaneous occurrence of multiple competing transitions of different types . This paper presents the first computational method to analyze competing superhelical transitions in this way . Several theoretical models have been developed previously to analyze superhelical transitions in DNA . The earliest models were mechanical in nature , treating the transition as an “on-off” mechanism at a single susceptible site within a sequence that was otherwise unable to transform [15]–[18] . Subsequently , more detailed models were developed that used statistical mechanics to analyze transitions at a single susceptible site in a transition-resistant background [19]–[21] . This strategy is the basis for the Z-Hunt algorithm , which searches for individual Z-susceptible regions within a sequence by assessing the ability of each to undergo transition when placed alone in an otherwise non-transforming plasmid [22] . Competitions between two susceptible sites within a non-transforming background were also treated in this way . In some cases these involved two sites susceptible to the same type of transition [18] , [23] , [24] . In others idealized competitions between different types of transitions were examined , such as cruciform extrusion vs B-Z transitions [18] , [25] and denaturation vs B-Z transitions [16] , [26] . Although these models illuminated basic properties of superhelical transitions , they did not include the full competition among multiple sites that can occur in genomic sequences . It was soon recognized that a complete statistical mechanical treatment was required to accurately simulate the competitive behavior of conformational transitions in superhelical DNA sequences of kilobase lengths , which commonly contain numerous sites whose sequences render them imperfectly susceptible to transitions of several types . Several algorithms have been developed to analyze superhelical transitions to a single type of alternate structure in DNA sequences where every base pair is regarded as being able to assume that structure [27]–[31] . A conformational state is determined by specifying which base pairs are in the B-form state and which are in the alternate structure . These states are weighted according to the Boltzmann distribution , from which equilibrium properties of the system are determined under given environmental conditions and levels of supercoiling . This approach has been applied individually to each of several types of transitions , including strand separation and B-Z transitions , and a modified version has been used to treat cruciform extrusion [17] , [30]–[32] . Some of the techniques that have been developed are formally exact but computationally very slow [30] , while others are approximate . Among the approximate methods , the SIDD ( stress-induced duplex destabilization ) and the SIBZ ( stress-induced B-Z transition ) algorithms for treating strand separation and B-Z transitions , respectively , are based on a similar algorithmic strategy , which has proven to be both highly accurate and computationally efficient [31] , [32] . In order to develop quantitatively accurate statistical mechanical methods , it is necessary to have detailed knowledge of the alternate conformations being analyzed . One must know the geometry and flexibility of each alternate conformation , the energies of junctions between that structure and others ( most importantly the B-form ) , and the sequence-specific energetics of the transition from B-DNA to each conformation . Since strand separation and B-Z transitions have been implicated in biological functions , they have been widely studied . So the information regarding the energetics of these two transitions is available that enables their quantitative analysis . For this reason in this paper we focus on applying our multi-state approach to the competition between denaturation and B-Z transitions in superhelical molecules . Local separation of the two DNA strands at the correct times and locations is necessary for the initiation of transcription and replication , two key functions of DNA . Superhelical strand separation was the first DNA transition to be rigorously modeled in a way that enabled the analysis of sequences having arbitrary lengths [28] , [29] , [32] , [33] . The SIDD algorithm that was developed for this purpose has been applied to analyze a wide variety of DNA sequences , including complete genomes . Its results agree closely with experimental observations of the level of supercoiling required to drive stand separation and the locations of the melted regions within a sequence in all cases where experiments have been performed [29] , [32] , [34]–[36] . Since it costs less energy to melt an AT base pair than a GC base pair , local strand separation tends to occur in the A+T-rich regions of a sequence . Stress-induced duplex destabilization has been implicated in a variety of important biological processes , including the initiation of transcription from specific promoters , the functioning of replication origins in yeast and viruses , and scaffold attachment in eukaryotes [34]–[42] . Shortly after the discovery of Z-DNA it was theoretically predicted and experimentally verified that transitions to this structure could be driven by physiologically attainable levels of negative superhelicity [2] , [16] , [19] , [20] . Z-DNA has been experimentally detected at inserted Z-susceptible regions in torsionally stressed bacterial DNA , both in vitro and in vivo [21] , [43]–[47] . There is strong indirect evidence suggesting that Z-DNA also may occur in eukaryotic genomes in vivo [48]–[51] . At present , specific biological activities of Z-DNA have not been fully elucidated , although there is substantial indirect evidence that it may serve regulatory functions in several processes [48] . The repeat unit of Z-DNA is a dinucleotide , with one base pair in the anti and the other in the syn conformation . Although Z-DNA is known to prefer alternating purine-pyrimidine sequences , specifically or runs , it can occur in other base sequences at a higher energy cost [22]–[24] , [43] , [45] , [52]–[54] . The junctions energies and the free energies of the B-Z transition have been determined for all ten dinucleotides , including their dependence on their anti/syn character [21] , [22] , [25] , [52]–[54] . The first theories developed to study B-Z transitions treated highly simplified cases in which a transition could only occur at a single uniformly Z-susceptible site [16] , [18]–[20] , [25] . An extension of this approach has been developed , which uses a thermodynamic model to calculate the propensity of an individual segment , extracted from a genomic sequence , to form Z-DNA when placed in a Z-resistant background [22] , [55] . However , a base composition-dependent statistical mechanical model is required to calculate the competitive B-Z transition behavior of kilobase length DNA sequences . We have recently implemented the first algorithm , called SIBZ , that performs this type of analysis [31] . The SIBZ algorithm uses the same basic computational strategy as SIDD , but substantial modifications were needed to treat the B-Z transition . The results of SIBZ agree well with experimental measurements of the onset of transition as a function of superhelicity [5] , [52] , as well as experimental determinations of the locations where the superhelical B-Z transition occurs within genomic DNA sequences [49]–[51] . In this paper we develop the first algorithm that evaluates the statistical mechanical equilibrium behavior of a negatively supercoiled DNA molecule of kilobase length and specified sequence that is susceptible to multiple types of conformational transitions . Our method calculates separate transition profiles ( i . e . the probability of transition of each base pair in the sequence ) for each type of competing transformation . It also can calculate ensemble averages of other important parameters , including the number of transformed base pairs of each type , the number of regions experiencing each type of transition , the overall probability of transition to each type of secondary structure , and the probabilities of different types of transitions occurring simultaneously . The algorithm we develop to handle multiple competing transitions is based on and generalizes the numerical strategy used in SIDD for approximating the exact partition function . Although it necessarily makes some approximations due to the computational limitations of exactly evaluating the partition function , the SIDD-based approach has been demonstrated to provide accurate results in reasonable computational times . In principle the method we present can be used to analyze competitions among any number of different types of transitions . In practice , however , to make quantitative predictions one must know both the geometry of the relevant secondary structures and the base pair-specific energetics of transitions to those conformations . Here we implement the model for a three state system in which each base pair can occur in the B-form , which is regarded as the ground state , or in either of two alternate conformations . We explicitly analyze the competition between superhelical strand separation and B-Z transitions since their energy parameters are known at comparable temperatures and ionic conditions . Although the energetics governing these two transitions have the same orders of magnitude in the physiological temperature range , denaturation is more temperature dependent while B-Z transition causes greater relaxation . Our analysis shows that , because of these properties , the competition between these two types of transitions is quite complex , involving the interplay between base composition effects , imposed superhelical density , and environmental conditions . We call the new algorithm BDZtrans , for B-form to the Denatured and/or Z-form transitions .
Consider a topological domain consisting of base pairs that is susceptible to types of conformational transitions , . Suppose that a state of this domain contains specific base pairs in conformation . The total free energy of the state is given by ( 3 ) The first term in this expression is the total nucleation energy associated to this state . Suppose that there are junctions between the B-form and the -th alternate conformation , each of which has energy . Also assume that there are junctions between the alternate conformations and , , each with energy . Then the total nucleation energy associated with this arrangement is ( 4 ) The second term in Eq . ( 3 ) sums the transition energies of the -th base pair to be transformed to conformation over the regions of transition . This transition energy varies with the type of transition the base pair experiences , the identity of the base pair ( and sometimes also the identities of its neighbors ) , temperature , and ionic conditions . This term is summed over the number of base pairs involved in each transition . The third term in Eq . ( 3 ) is the Hooke's law torsional energy associated to the twisting of alternate conformation . The parameter is the torsional stiffness coefficient of conformation , and is its helical twist rate away from its relaxed conformation , measured in rad/bp . This term is required for strand separation , where the necessary parameter values have been determined [29] . However , it is not needed for transitions to other alternate conformations , such as the B-Z transition , whose twisting ( along with that of the B-form DNA ) is regarded as being incorporated into the residual superhelicity . The free energy for conformations such as cruciforms , in which the two strands are physically separated in space and do not twist around each other , also does not contain this term . More generally the helical twist could be considered to fluctuate independently for each transformed base pair . This has been done in a formally exact analysis of superhelical strand separation [30] . However , no significant difference was seen between the results of analyses that allowed independent twists , and those where all denatured base pairs were assumed to have the same twist , as in Eq . ( 3 ) . Therefore , in this analysis we choose the latter strategy . The last term in Eq . ( 3 ) is the quadratic energy associated with the residual superhelicity , as defined in Eq . ( 2 ) . The values of the constant and the other energy parameters are discussed below for the specific transitions modeled there . The expressions presented in this section can be applied to analyze molecules in which any number of superhelically driven transitions compete , provided the helical twist rates and transition energies of the alternate structures are known . Several computational strategies have been developed to analyze superhelically driven transitions in genomic DNA . Historically , the first fully developed algorithm focused on strand separation , since this is the only transition known to be required for essential biological processes such as the initiation of transcription and of replication . Although an exact theoretical method has been implemented that is capable of computing transition probabilities of individual base pairs in kilobase-scale genomic sequences , it proved too computationally cumbersome for widespread use [30] . However , an alternate strategy has been developed , called SIDD , that performs accurate and efficient approximate calculations . The initial SIDD algorithm focused on the superhelical strand separation transition [32] . This approach subsequently was modified into the SIBZ algorithm in order to treat superhelical B-Z transitions [31] . In this paper we further develop this computational strategy to enable efficient calculations of the equilibrium properties of superhelical molecules that are susceptible to multiple types of competing transitions . Although we focus specifically on the competition between denaturation and B-Z transitions in kilobase length superhelically constrained DNA sequences , in principle this approach can be applied to any number of different transitions . The basic strategy of the algorithm is first to determine the lowest energy state of the system . Then a threshold is set , and all states having energies less than are found and included in the analysis [28] , [32] . The number of states that are included , and hence the execution time , increases with , whose value must be chosen to suit the conditions assumed in the calculation and the level of accuracy desired . Extensive calculations using both SIDD and SIBZ have shown that this approach has an attractive combination of efficiency and accuracy . Comparisons of the SIDD results with those from an exact method show that this approximate approach has an accuracy of at least four significant digits in all calculated parameters at physiologically attained superhelicities when a threshold is chosen between 10 and 12 kcal/mol [32] . This accuracy is more than sufficient for comparison with experimental data . Although states having energies above the threshold are not explicitly included , a density of states technique has been developed that can approximately correct some calculated parameters for the cumulative effect of these high energy states [28] . Such corrections are beyond the fourth decimal of accuracy and hence are rarely needed in practice . Calculations on 5 kb segments under standard conditions take on average about 10 seconds on one Opteron processor , although some segments can require up to 5 minutes or even longer . The difference in execution times strongly depends on the base composition of the sequence . For example , when there is a dominant transition region with a low energy cost , transformed states that do not include this region have a much higher energy . Therefore , relatively few states are found in the energy range determined by the threshold , resulting in a quick execution time . However , if no dominant transition regions are present , many states will have comparable energies , requiring a longer run time . The free energy associated to each state of our competitive system , shown in Eq . ( 3 ) , is comprised of two parts . The first two terms in this equation assign nucleation and base-dependent transition energies to the set of transformed base pairs in each state . These energies only depend on which base pairs are transformed and the alternate structures that they assume . As their domains are discrete sets , they are collectively referred to as the discrete part of the energy expression . Once the secondary structures of all base pairs have been specified , it remains to partition the balance of the superhelical deformation between residual superhelicity and twisting of the denatured regions . Since this partitioning can be done in a continuous manner , this term is described as the continuous part of the energy . It contains the energy terms arising from the superhelical constraint and from the twist . This separation of the energy expression into discrete and continuous parts facilitates finding the minimum energy state , as well as the states that satisfy the threshold condition , in a computationally efficient way [32] . We first calculate the energy associated to the discrete states , the nucleation energy given in Eq . ( 4 ) and the base-dependent transition energies . We consider segments of length along the molecule . These segments are regarded as being susceptible to any type of transition , as long as they meet the sequence requirements for that alternate conformation . This is done for values of up to a limit . The value of is chosen so that all states with longer runs of transition of any type will have energies higher than the threshold at physically reasonable values of the superhelix density . In addition , some transition types may have a lower limit on the segment length . Consider a circular molecule base pairs long . ( We discuss linear molecules below . ) In this molecule there are different segments of each length , , one starting at each base location . For simplicity , each segment is assumed to border B-form DNA on both sides . The total transition energy of each segment is ( 5 ) The free energies found this way are sorted according to increasing energy into separate arrays for each transition type . The rows of these arrays are indexed by the length of the transformed segment . Each array has columns , equal to the length of the sequence being analyzed . In these arrays the first position of each transformed segment is stored along with its energy , as this information is required later in the calculation . Since the discrete components of the state energy are additive for multiple run states , these sorted arrays are also used to determine the discrete energies of states in which more than one run of transition is present . To consider the one-run states of the system , we add the appropriate quadratic free energy associated with the residual superhelicity to each entry in the -th row of all the arrays containing the discrete energies . This is done for each type of transition . ( The manner in which the torsional deformation energy is treated in the strand separation transition is described in the next section . ) The resulting energy values remain sorted within their rows , which enables an efficient search to be conducted for the lowest energy state among the untransformed or 1-run states . This lowest energy is taken as the initial value of . We next find all states whose energies satisfy , as described below . If in this process a multirun state is discovered whose energy is less than the current value of the minimum energy , then is assigned this lower value , which is used in the subsequent calculation . In practice this reassignment only occurs for a small fraction of sequences analyzed , and only when analyzed at extreme negative superhelicities . However , when it occurs more states are included than the final threshold cutoff condition requires , giving a correspondingly ( very slightly ) more accurate approximation . For multiple run states , the procedure followed is similar to that described above for one run states . For each number of runs , the algorithm considers all transition types , and the total number of runs , where is the maximum number of runs considered . In general , each transition type includes a high initiation cost for each additional run . When the number of runs becomes large enough , all such states will have energies that exceed the threshold , and hence will not be included in the analysis . For this reason it is appropriate to impose a limit on the total number of runs that are considered . This is done by calculating whether any state with a given number of runs could satisfy the energy threshold condition by assuming that all transforming base pairs have the lowest possible transition energies , so the transition becomes isoenergetic . If it is found that such a state could satisfy the threshold condition , then a search of states with that number of runs can be instituted . The Boltzmann factors associated with each state are accumulated into arrays for each transition type that are indexed by the lengths of their participating segments and their positions within the sequence . The contributions to the partition function for each type of conformation are collected separately in order to calculate their individual probability profiles . For multiple run states in which more than one type of transition occurs , the information for each run is placed in the appropriate array according to its length , position , and transition type . Details of these procedures may be found elsewhere [32] . A variety of equilibrium properties of the transition may be calculated from the information that is collected in these arrays . This includes the probability that each base pair in the sequence is in a particular alternate conformation , the expected number of runs of each transition type , the probability of the state with no transition , and other attributes of interest . We focus henceforth on analyzing the competition between denaturation and B-Z transitions in a superhelical plasmid base pairs long and having any specified sequence . We first examine the residual superhelicity associated with this competition , given in Eq . ( 2 ) , and then consider the state energy described in Eq . ( 3 ) . Strand separated DNA , being untwisted when unstressed , has turns/bp . It follows that the transition of base pairs from B-form to the unstressed strand separated state involves a twist decrease of turns . Since single-stranded DNA is highly flexible , the two separated strands in a melted region are able interwind in order to further relieve supercoiling stresses . The amount of helical interwinding that occurs is denoted by in radians/bp . The helicity of Z-DNA is turns/bp , the minus sign indicating that it is twisted in the left-handed sense . So the decrease of helicity for each base pair experiencing this transition is , which is approximately of untwisting per transformed base pair . Since the Z-form is torsionally stiff we do not consider its twist fluctuations separately , but rather regard them , together with those of the B-form regions , as part of the residual superhelicity . In addition , each B-Z junction requires an untwisting of turns [21] . Since the Z-form is favored in G+C-rich regions and strand separation in A+T-rich regions , in practice they are unlikely to both be competitive at the same regions . In particular , junctions where strand separated DNA directly abuts Z-form DNA are unlikely to occur in low energy states under the conditions assumed below . In this case the nucleation energy of Eq . ( 4 ) can be written as ( 6 ) where there are runs of conformation ( ) . ( A run is defined as a segment in which all base pairs are in the same alternate structure . ) Here the nucleation energy of a single run of type is , the cost of producing two junctions between B-DNA and that conformation . We consider a state in which there are denatured base pairs in runs , and Z-form base pairs in runs . Because the unit cell of Z-DNA is a dinucleotide , is an even number . Then the residual superhelicity whose general form is given in Eq . ( 2 ) becomes ( 7 ) The total free energy associated to this state is given by ( 8 ) The values of the various energy parameters found in this equation are discussed in the section below . In describing a particular state one first specifies the conformation of each base pair in the sequence being analyzed . Here they may be either B-form , Z-form , or melted . This determines the numbers and of transformed base pairs , and the numbers and of runs for each transition . This fixes all the factors in Eq . ( 7 ) except for the residual superhelicity and the twist of the denatured regions . There is a continuum of ways to partition the balance of the topological constraint between and of the single stranded regions . In previous papers we have developed and evaluated a number alternative ways of treating this partitioning [28] , [30] . We found that high accuracy can be achieved by minimizing the total free energy associated with these two quantities , which are the two terms on the right in Eq . ( 8 ) , subject to the condition that the sum remains constant . This minimum occurs when ( 9 ) Combining previously described terms and using this minimization condition in Eq . ( 8 ) , we obtain the following expression for the free energy of a state when denaturation and B-Z transitions compete: ( 10 ) In the present implementation we assume that strand separation is governed by copolymeric transition energies . That is , every or base pair is assigned the same separation free energy , while every or base pair is given separation free energy . Nearest neighbor energetics have been measured for strand separation under various environmental conditions [58]–[60] , and their use has been implemented as an option in the SIDD algorithm [61] . Although these can easily be incorporated into the present analysis , we choose to use the computationally slightly faster copolymeric energetics , since little practical difference has been seen between the results found using these two approaches . The free energy of strand separation depends on temperature according to the relationship ( 11 ) where for A or T bases and for C or G bases , respectively . The enthalpy of this transition has been measured to be kcal/mol and kcal/mol [29] . The entropy term in this equation is related to the transition temperature , which is the temperature at which the transition energy . In turn , varies with ionic strength according to ( 12 ) where is the salt concentration in molar units , the temperature is in degrees Kelvin , , and . The remaining energy parameters found in Eq . ( 10 ) that involve strand separation have been well evaluated at salt concentration 0 . 01 M and temperature 310 K , where the most sensitive experiments to analyze superhelical strand separation were conducted [9] . Analysis of these experimental results determined the torsional stiffness coefficient to be , and the nucleation energy to be kcal/mol [62] . We assume no temperature dependence for these parameters . The superhelical energy parameter is , where is the gas constant , is the temperature , and is the number of base pairs in the sequence being analyzed [63] , [64] . The unit cell of Z-DNA is a dinucleotide ( i . e . two neighboring base pairs ) , with one in the anti and the other in the syn configuration . Therefore , we henceforth regard the B-Z transition energy as referring to the energy of forming a unit cell of Z-DNA , hence associated to two base pairs . In addition , there is an energy cost required when two neighboring dinucleotide repeat units break the anti-syn alternation , as happens for example in the ( AS ) ( SA ) arrangement . This Z-Z junction energy is denoted by and is added to the total Z-forming free energy of states in which these junctions occur [31] . B-Z transition energetics have been determined for each of the ten possible dinucleotides . Most of these free energies were experimentally measured at room temperature and 0 . 1 M sodium concentration [21] , [52]–[54] , although some were estimated [22] , [25] . The B-Z transition energies of all ten dinucleotide pairs and the corresponding Z-Z junction energies at these environmental conditions are given in [22] , [31] . When the transition properties of uniformly Z-susceptible inserts within a pBR322-derived plasmid were determined from two-dimensional gel electrophoresis experiments , it was found that the B-Z transition behaved the same at C as at C [26] . This suggests that there is no significant temperature dependence of the B-Z transition , at least for the inserted sequence that was used . For this reason we assume that the dinucleotide , Z-Z junction , and the B-Z junction energies are all independent of temperature . If future measurements find differently , this assumption can easily be modified . There is an intricate interplay between the superhelical B-Z and local denaturation transitions that arises from the different energy dependencies of these reactions . Transition to Z-form involves a greater change of twist than does strand separation , so it relieves more superhelical stress . This suggests that the B-Z transition will occur at less extreme superhelicities than strand separation at physiological temperatures , other influences remaining fixed . However , the transition free energy of strand separation is highly temperature dependent , whereas the B-Z transition appears to be approximately independent of temperature . In consequence , as temperature rises one expects a change of behavior in a superhelical molecule that is susceptible to both types of transitions . At relatively low temperatures one expects B-Z transitions to dominate , with strand separation occurring only at more extreme superhelicities after the low energy Z-susceptible regions have transformed . However , as the temperature rises the free energy cost of strand separation diminishes , so one expects this transition to become more competitive . The calculations we report below suggest that these energy effects can render the competition between these two types of superhelical transitions quite complex in practice . In previous sections we summarized the general algorithmic strategy for treating multiple transition types . Here we describe how this algorithm is tailored specifically to model the competition between superhelical strand separation and Z-DNA formation . The state energy has been separated into the discrete ( and ) and continuous ( ) parts as described in Eq . ( 10 ) . We first consider the energy associated to the discrete states . We regard each segment of length along the molecule to be susceptible both to strand separation and , when is even , to Z-form . In a circular molecule of length and longest segment this produces a matrix of denaturation energies of dimension , and a matrix of B-Z transition energies of dimension . ( Here the square brackets denote the greatest integer function . ) We limit the minimum number of Z-forming dinucleotides in a single run to four , since shorter runs of Z-DNA have not been found experimentally [21] . We subtract three from the number of lengths considered because we do not include Z-runs comprised of 2 , 4 , or 6 dinucleotides . As described in the previous section , we assign copolymeric energies to denatured regions according to their base sequences . It follows that the discrete energy associated with the denaturation of specific base pairs is ( 13 ) where is the number of denatured or base pairs , so is the number of denatured or pairs , and is the number of denatured regions present . Next , we determine the energetics of transition to Z-form of runs that together contain transformed dinucleotides , hence base pairs . First , we find the most energetically favorable anti/syn conformation according to its base sequence , and then we calculate the total energy by summing the energies of each Z-DNA dinucleotide and all the occurring Z-Z junctions . Details of this procedure are provided elsewhere [31] . The discrete B-Z transition free energy is given by ( 14 ) where is the number of Z-Z junctions , and and are the dinucleotide and junction energies , respectively . The discrete energies are calculated from the above equations for single runs of transition of any length in the range . The free energies found this way are sorted according to increasing energy into arrays for denaturation and for the B-Z transition as described above . The rows of these arrays are indexed by the length of the transformed region , which is base pairs for denaturation and dinucleotides for the B-Z transition . In each execution of the algorithm one initially fixes the imposed linking difference and the temperature . This determines the parameters associated with the continuous component of the state free energy . This is the quadratic last term in Eq . ( 10 ) , which varies with the numbers of melted bases and of Z-form bases , and the number of Z-runs : . We note that does not depend upon the positions of the runs of transition within the sequence . If there are runs of transition in a state and transformed base pairs in the -th run , then ( 15 ) where {0 , 1} denote the denatured and the Z-form states , respectively . For one-run states of the system , we add to each entry in the -th row of the array containing discrete energies for strand separation , and to the corresponding entries of the array . Since the nucleation energy is significant for both denaturation and B-Z transitions ( kcal/mol in both cases ) , a limit on the total number of runs considered may be imposed . In SIDD analyses with threshold kcal/mol it was found that states with more than three runs were never found under reasonable conditions [32] . For the SIBZ algorithm it was determined that states with more than four runs would generally not occur [31] . Therefore we impose a limit of four runs in the present algorithm . For each number of runs , the algorithm is iterated for , where when a maximum of four simultaneous runs is allowed . This arrangement assures that there are three types of two-run states ( two melted regions , two Z-regions , or one melted and one Z-region ) . Similarly , there are four types of three-run states , and five types of four-run states . The energy associated with a state is calculated by adding the appropriate energies from the discrete arrays to the total superhelical energy for each number of runs of the appropriate types . For example , the energy of a two-run state where bases are strand separated and bases are in Z-form is , where and depend on the base sequence of the runs involved . Whenever the total energy associated to a state satisfies , its Boltzmann factor is calculated and added to the appropriate arrays as described above . We analyze linear molecules by connecting their ends with an inserted sequence , and then treating them as circular . To fully isolate one end from the other , the insert must be chosen so it is energetically highly disfavored to undergo any transition . For the B-Z and denaturation transitions this is achieved by inserting a segment between the ends . Since this run of G's is unlikely to either melt or form Z-DNA , its insertion prevents any artificial correlation between the two ends from arising , hence correctly simulates a linear sequence . The linking difference that corresponds to the specified superhelix density is imposed on the resulting molecule . The algorithm only reports the results for the actual sequence , and disregards those from the insert . In order to assess the computational accuracy of the BDZtrans algorithm we compare its results to those from an exact analysis of a simplified situation in which competition is limited to two homopolymeric sites . Specifically , we consider a 5 kb plasmid in which there is one uniformly Z-susceptible region and a second region , at a distance from the first , that is uniformly susceptible to denaturation . The A+T-rich , easily melted segment has length , while the Z-susceptible site is a dinucleotide repeat containing base pairs . By uniform susceptibility we mean that both transitions are homopolymeric: all dinucleotides in the Z-susceptible segment have the same transition energy , and all base pairs in the denaturation-susceptible region have the same transition energy . All other parts of the plasmid are regarded as being unable to undergo any form of transition , so the competition is exclusively between these two sites . In our simulations this is achieved by giving high transition energy values to all base pairs that are not in these regions . This example approximately corresponds to an experimental situation where a highly Z-susceptible sequence , such as , is inserted into a plasmid that contains a dominating SIDD site , such as the -lactamase terminator in the pBR322 plasmid . This case is analytically solvable by standard procedures that have been presented and applied elsewhere [16] , [26] . Here we also solve it using the BDZtrans algorithm , and compare the results . In the BDZtrans analysis we use an energy threshold of kcal/mol and consider only states with four or fewer runs of transition . Calculations were performed using both methods over a range of superhelical densities and temperatures for various combinations of segment lengths and . The analytic calculation allows the A+T-rich segment only to melt , and the segment only to assume the Z-form . However , the BDZtrans algorithm allows both regions to undergo either type of transition . In all situations where these two segments either are untransformed or experience their expected transitions , we find that the results from the two methods agree exactly up to the accuracy of double precision . ( Data not shown . ) Having established the high computational accuracy achieved by the BDZtrans algorithm , we now can use it to analyze other situations , where exact calculations are not possible .
We first analyze the competition between two regions in an otherwise transition-resistant background . The specific problems addressed here were chosen to eluciate the complexities that can arise in superhelical competitions between strand separation and B-Z transitions , even in simplified situations . The intricacies of these interactions result primarily from three factors . First , as shown in Eq . ( 11 ) and Eq . ( 12 ) , strand separation is strongly temperature dependent , with the transition temperatures of specific regions depending both on base composition and on ionic strength . In contrast , the B-Z transition appears to be essentially independent of temperature [26] . One anticipates that this will cause significant variations with temperature of the competition between these two types of superhelical transitions . Second , the B-Z transition relaxes substantially more superhelical stress per transforming base pair than does strand separation . Lastly , the relative lengths of susceptible regions also can strongly influence their competitions [18] . We first consider the case where the Z-susceptible insert is a sequence placed at position 1000 in a 5 kb plasmid , and the denaturation-susceptible region is at location 3000 . The temperature is set at 300 K , and the superhelix density is allowed to vary . We only consider negative superhelix densities , although the results are presented in graphs as a function of . Since 300 K is substantially lower than the transition temperature for poly-A ( see Eq . ( 12 ) ) , these conditions should favor the B-Z transition . However , as the A-rich region is long , its transition will produce more relaxation than will the short Z-forming region . We use BDZtrans to calculate the probability of transition of each base pair in each susceptible region . We then average these values over the lengths of the regions involved to find the average probability of melting of base pairs in the A-segment , and of Z-formation in the CG-region . The results of these calculations are shown in Fig . 1 ( a ) , which graphs the average probability of each type of transition in the corresponding segment as a function of superhelicity . When there is not enough superhelical stress to drive any transition . As the superhelix density becomes progressively more extreme , B-Z transition in the segment occurs first and dominates in the range . However , since the Z-susceptible region contains ten base pairs , it can only relax less than two superhelical turns . Although denaturation relaxes less stress per transformed base pair , in this sequence the A-rich segment is much longer than the Z-susceptible segment , and hence can relax substantially more superhelicity . In the range of there is a coordinated reversion of the Z-forming region back to B-form , coupled to denaturation of the A-rich segment . In this range it is energetically too costly for both transitions to occur , so transformation of the longer meltable region becomes favored because it provides more relaxation . When the region is essentially fully melted , and additional stress induces the B-Z transition . Around both segments are essentially completely transformed . Similar coupled transition-reversion events have been noted for other competitions , including those between two Z-susceptible regions [24] , [31] , between two cruciform extrusions , and between cruciform extrusion at one site and B-Z transitions at another [18] . In Fig . 1 ( b ) we show the results for the same system obtained when it is analyzed using the two-state algorithms , SIDD for denaturation and SIBZ for the B-Z transition . One sees that disregarding the competition between different types of transitions can result in an entirely incorrect representation of the transition behavior of the plasmid . First , the onset of the melting transition occurs at a lower value of in Fig . 1 ( b ) , since in SIDD denaturation is not competing with the B-Z transition , which is first to transform in the competitive situation , as shown in Fig . 1 ( a ) . Further , the reversion of the B-Z transition apparent in Fig . 1 ( a ) is not captured here , because the competition between that transition and melting is not considered . Using the individual algorithms separately wrongly predicts a B-Z transition with probability close to one for −0 . 055 , whereas including the competition with denaturation shows this probability actually to be below 0 . 2 . Next , we analyze the reverse situation , in which a short melting region competes with a longer Z-susceptible region . These calculations were performed at a temperature of = 340 K , which is higher than the transition temperature of = 322 K for A+T-rich DNA at 0 . 01 M salt concentration . The average transition probabilities calculated for this situation are plotted as functions of in Fig . 2 . Although the high temperature should favor denaturation , the high nucleation energy of denaturation keeps the region in the B-form state when the molecule is relaxed at this temperature . The onset of transition under these conditions occurs around . Since the Z-susceptible site is much longer and hence can relax more superhelicity , B-Z transition is the first to occur . The short AT-region can only relax about 1 . 5 turns of superhelicity while B-Z transition , although energetically more expensive , can relieve much more stress . This greater stress relief favors the latter transition , even at this high temperature . At the B-Z transition becomes essentially complete , and denaturation starts to occur as a second transition . In the range both segments are completely transformed . However , at extreme superhelicities of , the melting probability of the segment is seen to fall gradually back to zero . At this level of supercoiling BDZtrans finds that this segment transitions from the denatured state to the Z-form . This behavior can be understood by comparing the energies required for the AT-insert either to melt or to form Z-DNA , assuming that the entire segment is already in Z-form . By equating these two free energies , it is straightforward to obtain the critical superhelix density at which the insert begins to favor the B-Z transition over denaturation . For this sequence at T = 340 K we find . The value for agrees exactly with the result obtained by BDZtrans for where the two probabilities of the segment are equal , which occurs at the intersection of their two curves in Fig . 2 . This behavior also could not be predicted from separate analyses of each type of transition . These examples illustrate some of the complexities that can occur in multi-state superhelical transitions , even in artificially simple situations . In particular , the susceptibility of a region within a sequence to undergo a certain transition is not always a simple function of its base composition . Although at high temperatures it generally it takes less energy to melt an A+T-rich region than to transform it to the Z-helix , the results presented in Fig . 2 shows that under certain circumstances the opposite behavior can happen at equilibrium , even well above the melting transition temperature where one might imagine that denaturation would dominate . These examples also show the importance of competing together all transition-susceptible sites in the sequence , rather than simply analyzing the propensity of each individual region to transform independent of the rest of the domain . To date only one experimental investigation has been performed of the competition between melting and the B-Z transition in supercoiled DNA [26] . The pAT153 plasmid used in those experiments is a derivative of pBR322 that contains an A+T-rich , easily meltable 105 bp region at its -lactamase gene terminator . Two other plasmids were constructed by inserting a Z-susceptible sequence into pAT153 in order to observed the competition between strand separation and the B-Z transition in a situation where the regions involved do not abut . The pCG8/vec plasmid was constructed by inserting the highly Z-susceptible sequence , and the pTG12/vec plasmid was constructed by inserting . This insert is also susceptible to B-Z transition , although it requires approximately twice the energy per dinucleotide to transform as does . Each plasmid was subjected to two-dimensional gel electrophoresis to determine its transition behavior over a wide range of linking differences . The amount of residual superhelicity present at each linking difference can be measured directly from the 2-D gel data , as described elsewhere [65] . From this information the extent of transition-induced relaxation can be found as . We analyzed the transition behavior of each of these three plasmids directly from the original gel images , which were kindly provided by the experimental investigators [26] . To compare these experimental results with theory we used the BDZtrans algorithm to calculate the equilibrium transition behavior of each complete plasmid over the experimental range of linking differences . We consider superhelical competition between all base pairs in each plasmid , rather than isolating the competition between the transition susceptible sites . By inserting the condition from Eq . ( 9 ) into Eq . ( 7 ) , we obtain the following expression for the ensemble average value of the residual superhelicity when strand separation and B-Z transitions are competing: ( 16 ) Here the expressions in angled brackets are ensemble averages of the bracketed parameters , which are calculated using our numerical method . The relaxation experienced by a topoisomer due to transitions is given by . This quantity can be compared to the extent of relaxation experienced by the experimental plasmids , which is determined from the 2-D gels . A single transition was observed experimentally in the pAT153 plasmid , which contains the A+T-rich region found in pBR322 , but no Z-susceptible insert . This transition was reported to be highly temperature dependent , indicating that it is strand separation [26] . In contrast , two transitions were observed as negative superhelicity was increased in the pCG8/vec plasmid at = 305 K . The first transition , at the less extreme superhelicity , was suggested to be a B-Z transition both by chemical probing , and by the degree of unwinding it exhibited . This transition was found to be essentially independent of temperature , behaving the same at 281 K as at 305 K . This further confirmed its B-Z character , as strand separation is well known to be highly temperature dependent . The nature of the second transition in this plasmid was not determined experimentally . However , it was assumed to be denaturation , primarily because it behaved in a qualitatively similar manner to the transition observed in pAT153 . The pTG12/vec plasmid also showed two transitions , which were delayed in linking difference relative to those seen in pCG8/vec . The 2-D gel experiments were performed in TBE/2 buffer , which contained 45 mM tris borate and 0 . 5 mM EDTA at pH 8 . 3 . Unfortunately , the energetics of denaturation and of the B-Z transition have not previously been determined under these conditions . In particular , the energetics for strand separation described previously were determined at pH 7 . 0 [9] , and no correction for a higher pH is known . Since this difference in pH level constitutes a 20-fold decrease in the counterion concentration , it is reasonable to suppose that it could affect the energetics of melting , which are known to vary with the concentrations of larger monovalent counterions . When we ran the BDZtrans algorithm on the pAT153 , CG8/vec , and TG12/vec plasmids using the energy values described in the “Energy Parameters” section , we found that their qualitative experimental behaviors were correctly depicted by the numerical model . However , it was apparent that the transition energy parameter values used were too large , since the transition behavior predicted by BDZtrans was consistently shifted to more extreme superhelicities than were observed experimentally . Therefore , our first task in comparing numerical calculations with experiments was to determine the transition energetics appropriate to the experimental conditions . We did this in the following manner . First , we used the SIDD algorithm , which considers only strand separation , to fit the experimental data on the melting transition in pAT153 . To determine the relaxation as described above , we set the parameters and to zero in Eq . ( 16 ) , since the B-Z transforming insert is not present in this plasmid . When performing these fits to the data , we chose to vary only the transition energetics per base pair and to keep all other parameters fixed . The best fit with experiment was achieved when the sequence averaged transition energetics of the easily melted region are 0 . 45 kcal/mole per bp . This is about 0 . 3 kcal/mole/bp smaller than the value derived from the information in the “Energy Parameters” section , which pertain under other experimental conditions . It is well known that the melting energy of DNA decreases as salt concentration is lowered , as is shown in Eq . ( 12 ) . The present analysis suggests that a similar decrease may also occur when the counterion is . Next , we used the SIBZ algorithm , which considers only the B-Z transition , to analyze the first transition in the pCG8/vec plasmid . In this case we set in Eq . ( 16 ) , since there is no denaturation in this regime . We find that the best fitting B-Z transition energetics for the CG dinucleotide is approximately 0 . 1 kcal/mole/bp lower than the values found in [22] , [31] . The same analysis of the first transition in the pTG12/vec plasmid gives a similar result for the TG dinucleotide . We note that these results are in qualitative agreement with those found by the authors of the experimental paper [26] , who used a different and rather simpler method of analysis . Finally , we used the BDZtrans algorithm to analyze the full competition between strand separation and B-Z transitions in the pCG8/vec and pTG12/vec plasmid sequences . This was done at T = 305 K using the fitted energy parameter values found above for both transitions . The BDZtrans results are plotted as solid lines in Fig . 3 ( a ) and ( b ) , while the dots with error bars represent experimental data . The horizontal axis shows the imposed superhelicity , and the vertical axis plots the relaxation . We find close agreement in both cases between the results of BDZtrans and the experimental data . This accord shows that , given the correct energy parameters , the BDZtrans algorithm captures the competition between two alternate structural transitions in a quantitatively accurate manner . Both experiments and SIDD analysis have shown that the superhelical pBR322 plasmid contains a dominant melting region that is about 105 bp long and coincides with the -lactamase gene terminator [9] , [30] . Antibody binding experiments and SIBZ analysis of this plasmid has shown that it also contains several short Z-forming segments , the longest of which consists of 14 bp [31] , [66] . Here we use the BDZtrans algorithm to analyze how these two transitions compete under various conditions . Specifically , we calculate the probability of each transition occurring anywhere in the plasmid . This probability is defined as , where = , , and is the sum of all the Boltzmann factors for all states in which at least one region is in conformation . The probability that both types of transition occur in the same molecule also is calculated . We analyze the transition behavior of this plasmid at base pair resolution by also calculating the probabilities of each base being in either alternate conformation at equilibrium . The transition profiles show the graphs of these probabilities as functions of position . First , we used BDZtrans to analyze the pBR322 plasmid sequence at superhelix density and various temperatures . The results are shown in Fig . 4 ( a ) , which plots the probabilities of each transition as a function of temperature . It is apparent that the B-Z transition dominates at low temperatures , while strand separation prevails at high temperatures . The probability of both transitions occurring simultaneously also is shown . Although at the value of never exceeds 0 . 5 at any temperature , at more extreme superhelicities both transitions will occur simultaneously with high probability . We define the phenomenological competitive transition temperature to be the temperature at which both transitions are equally probable . At this superhelix density K . Figs . 4 ( b ) and 4 ( c ) plot the average numbers of transformed base pairs and runs of transition , respectively , for the pBR322 plasmid as functions of temperature at . Separate curves are shown for each transition type , and the total number of runs for both transitions is also given . The transition behavior seen in these graphs arises from the fact that the Z-susceptible regions in the pBR322 plasmid are several but short , while the region most susceptible to strand separation is about 105 bp long . At low temperatures and the B-Z transition is seen to dominate over strand separation . In this regime , although multiple sites are in Z-form , they together comprise only 25 to 30 base pairs on average . The difference in the numbers of base pairs undergoing each type of transition that is seen in Fig . 4 ( b ) is also a consequence of the fact that the B-Z transition relieves substantially more superhelical stress per base pair than does strand separation . As the temperature increases beyond 308 K strand separation comes to dominate , and the propensity to form Z-DNA falls back to zero . In this regime the number of denatured base pairs increases to large values . This behavior is a consequence of the strong temperature dependence of denaturation . With increasing temperature the energy required to denature a region goes down , so more of the imposed superhelicity is partitioned to this transition at equilibrium . Since the dominant destabilized site is long , all this melting can be accomodated within that site until the temperature reaches around 315 K , where the average number of runs start to exceed one , as shown in Fig . 4 ( c ) . At this point the first site is fully melted and a second site located near the promoter region of the -lactamase gene also starts to melt . Next , we compare the transition profiles calculated by BDZtrans for each type of transition with those calculated for denaturation alone by SIDD and for B-Z transitions alone by SIBZ . These three profiles are calculated at superhelix density and K , and are shown in Fig . 5 . Comparison of these profiles shows that the sites that transform when the two transitions are competing coincide with the dominant sites that are predicted when each transition is treated alone . However , the probabilities of transition at these sites are significantly smaller when the two types of transition are allowed to compete . The probability of the dominant melting region drops from when calculated by SIDD to when calculated by BDZtrans . Similarly , the transition probability of the dominant Z-forming region changes from when only the B-Z transition is allowed , to when both conformations compete . One sees that calculations in which only one alternate conformation is considered tend to overstate the transition probabilities relative to a more realistic analysis in which multiple transition types compete together . Table 1 shows sample numerical results and technical information regarding these computations . The execution times reported here are for calculations performed on a MacBook Pro with dual Intel processors . The SIBZ algorithm is slowest , as the B-Z transition occurs at multiple runs ( ) under these conditions . In consequence , SIBZ also includes the largest number of states . SIDD is fastest because melting occurs predominantly in single run states , so fewer states satisfy the threshold condition for this transition . When the full competition is analyzed using BDZtrans , the average number of runs of denaturation and of Z-formation are both smaller , so there are an average of 1 . 8 runs in this case . The total number of transformed base pairs also decreases for each transition . The execution time of BDZtrans is intermediate between those of SIDD and of SIBZ , as is the number of states it includes . These calculations show that the results from BDZtrans are qualitatively consistent with those from SIDD and SIBZ in that the competing transitions are largely limited to sites that dominate when each transition is considered alone . However , the probabilities of transition found by BDZtrans are not related in any simple way to those found by the other two algorithms . In particular , they are not weighted averages of the probabilities found by SIDD and SIBZ , and there is no direct way in which one could estimate the competitive behavior of these two transitions from the profiles found using the independent analyses . Rather , this behavior is determined by complex , globally coupled , non-linear interactions , and can only be assessed by a full analysis that simultaneously considers both types of competing transitions . Regulation of the c-myc oncogene has been intensively studied , in part because mutations involving this gene have been implicated in various cancers . Substantial evidence has been found suggesting that superhelical DNA structural transitions play roles in regulating c-myc . Its 5′ flank contains a SIDD site called FUSE , located 2 kb upstream from the promoters , three AluI fragments containing Z-forming sites , and a potentially either G-quadriplex forming or H-forming site called the CT element around 1 kb upstream from the promoters [14] . Experiments have shown that each of these sites can be driven into their alternate structure by the superhelicity that is induced by transcription [13] , [50] , [51] , [67] , [68] . Much is known about the mechanism by which superhelical denaturation of the FUSE element regulates transcription through binding of single strand-specific regulatory proteins . Less is known about the roles of the other two alternate structures , and nothing is known about the competition among them or how it modulates transcription [67] . Here we use BDZtrans to investigate how the strand separation and B-Z transitions compete in this region . Specifically , we analyze the transition behavior of a 5 kb region around the c-myc gene that includes the FUSE element and the three Z-susceptible sites . Fig . 6 ( a ) shows the transition profile of this region calculated using BDZtrans at T = 310 K and = −0 . 06 . The upper panel in the figure marks the locations of the FUSE element and the three AluI Z-sites , labeled Z1 , Z2 , and Z3 . The locations of the promoters also are shown . Under these conditions one sees a clear melting peak at the FUSE element . For Z-DNA there are two small peaks at Z2 and Z3 , and only an insignificant transition probability at Z1 . Fig . 6 ( b ) shows the overall probabilities and of each type of transition as a function of the superhelical density at T = 310 K . At low levels of negative superhelicity only the B-Z transition is present . As becomes more negative the probability of melting also increases , out-competing Z-DNA in some ranges . At −0 . 06 , both transitions have high probabilities of occurrence . However , as shown in Fig . 6 ( a ) denaturation is substantially confined to the FUSE element , while the propensity to form Z-DNA is distributed among several regions , each of which has only a low transition probability . One sees that Z-formation is predicted to occur at lower superhelical densities than does FUSE melting . This suggests that the presence of the Z-forming regions will delay the onset of FUSE melting to more extreme superhelicities than would be required in their absence . Moreover , when FUSE melting occurs , it is facilitated by the partial reversion of the Z-DNA back to B-form . In this way the B-Z transitions are predicted to have regulatory effects through their modulation of FUSE melting . Transcription in eukaryotes has been shown to produce enough negative superhelicity to drive structural transitions in regions upstream from active genes [4] . This suggests that superhelically driven transitions to alternate DNA structures could occur there in vivo , where they might serve transcriptional regulatory functions . SIBZ analysis has found an enrichment of regions with Z-forming potential around transcription start sites ( TSSs ) [31] . Other less rigorous methods , such as Z-Catcher and Z-Hunt , have found qualitatively similar patterns of Z-DNA enrichment around TSSs [31] , [55] , [69] , [70] , although they find different numbers of sites than we do . Here we examine how superhelical B-Z transitions compete with denaturation at these locations , as well as in the regions where transcription terminates . We compare the transition properties of the TSS regions in eukaryotes with those in a prokaryote , and with those from a class of pseudogenes that are not transcribed .
This paper develops the first computational method to analyze the statistical mechanical equilibrium behavior of a negatively supercoiled DNA with any base sequence that is subject to multiple , competing secondary structural transitions . This method calculates the probability of each base pair transforming into any of its available alternative secondary structures , as well as the ensemble average values of other parameters of interest . The analysis of multi-state competitions is required when sites susceptible to each type of transition are present in the same topological domain , as occurs in virtually all domains of kilobase length . The first implementation of this method is the BDZtrans algorithm , which analyzes the competition between superhelical strand separation and B-Z transitions . This competition was chosen for two reasons . First , complete information is available regarding the energetics of both of these transitions under comparable environmental conditions . Second , every base pair in DNA is susceptible to forming either alternate structure , at least in principle . So these two transitions compete in every DNA sequence . Using this algorithm we show that the competition between these transitions in superhelical DNA is highly intricate , depending in complex ways on base sequence , superhelix density , and temperature . Due to the temperature dependence of the energetics of strand separation , B-Z transitions dominate at low temperatures and denaturation becomes increasingly competitive as temperature increases . In the physiologically important temperature range 300–315 K , both types of transitions are reasonably competitive . Their interactions also depend in complex ways on the sequences and lengths of the transforming regions , and on the superhelix density . In an illustrative sample calculation we documented conditions in which B-Z transitions are preferred over denaturation at high superhelix densities , even when the temperature is above the melting temperature of A+T-rich DNA . To determine how strand separation and B-Z transitions interact in practice in superhelical domains , we used BDZtrans to analyze 12 , 841 mouse gene sequences at = 305 K and superhelix density = −0 . 06 . For each sequence in this set we assessed its equilibrium distribution , then determined the fraction of conformations in that distribution that had specific properties of interest . First , for every sequence in this set the probability of having no transition was essentially zero; virtually every conformation in the equilibrium distribution of every sequence was found to undergo some sort of transition under these conditions . Next , for each sequence we determined the frequency in its equilibrium distribution of conformations in which both denatured and Z-form sites were simultaneously present . We found that approximately half of these sequences have equilibrium distributions in which more than 10% of the molecules have coexisting Z-form and denatured regions . In 30% of the sequences these states dominate the equilibrium distribution . That is , more than half the molecules in the equilibrium distribution contain both Z-form and denatured regions . This shows the prevalence of states involving all three conformations in superhelically stressed genomic sequences , and indicates the importance of using computational methods that analyze their interactions . We have shown that one cannot develop an accurate analysis of multistate transitions by amalgamating results from two-state techniques . To this end we compared the results from BDZtrans with those from SIDD and SIBZ , two-state algorithms that treat strand separation and B-Z transitions , respectively . Although the dominant transition regions are often correctly identified by the individual algorithms , they substantially overestimate both the number of such regions and their relative propensities to experience transition . This happens because each transition type in fact competes with the other , transitions to which decrease the effective level of supercoiling . A variety of examples have shown that sequences susceptible to both types of transition can exhibit particularly complex behaviors that cannot be captured by combining the results from the two-state SIDD or SIBZ analyses . In essence , this is because one cannot get an accurate depiction of an equilibrium distribution that contains many conformations in which denatured and Z-form sites coexist by mixing one distribution in which only denatured states occur with a second distribution in which only Z-forming states are present . This is why a full multi-state analysis is required to accurately depict competitions involving multiple alternate conformations in superhelical DNA . Comparisons of the BDZtrans results with those from experiments investigating the superhelical competition between strand separation and B-Z transitions shows that , when the correct energetics are used , the BDZtrans algorithm accurately depicts the competitive transition behavior that was observed in these experiments [26] . We performed the first theoretical analysis of the competition between superhelical denaturation and B-Z transitions in the control regions of the c-myc oncogene , where both transitions are known to occur in vivo , and have been posited to serve regulatory functions [67] . Our results suggest that B-Z transitions near the c-myc promoters could modulate the known regulatory effects of strand separation at the upstream FUSE element . When the energetics of formation of the quadriplex that also can occur in this region become available , we will model the full three-way competitive interactions that can occur among these transitions in a quantitatively precise manner . We anticipate that this approach will illuminate the competitions among these three transitions , and thereby assist scientists to design experiments that assess their regulatory interactions . We used BDZtrans to analyze the competitive transition behaviors of collections of mouse and human gene sequences . Each sequence was 5000 bp long , aligned and centered on their annotated transcription start site ( TSS ) . We found a sharp increase of Z-forming sites that peaks just before the TSS , then continues a short distance into the transcribed region . This apparent enrichment suggests that B-Z transitions might be involved in the transcriptional regulation of some genes . Interestingly , the BDZtrans analysis of these mammalian gene sets also found that sites susceptible to superhelical denaturation are highly depleted over a broad region extending approximately one kilobase on either side of the TSS . The similarities of the patterns found for both transitions in human and mouse sequences suggests that these may be universal properties of mammalian genomes , and may also occur in other eukaryotes . This question will be investigated in future work . The depletion of stress-denaturable sites around mammalian TSSs may seem surprising , as strand separation is an essential step in the initiation of transcription from every gene . However , this process is stringently regulated by interactions between the DNA and a large number of other molecules . It is possible that the occurrence of superhelically denatured sites in 5′ gene flanks could disrupt this regulation in some manner . It has been shown that transcription can be initiated by the presence of single stranded regions of DNA alone , without requiring any other regulatory factors [76] . So if a site susceptible to superhelical denaturation occurred within the first kilobase 5′ of a gene , where its transcription would produce enough negative superhelicity to drive denaturation , the resulting open region could initiate unintended additional rounds of transcription . If this were a deleterious event , sites susceptible to superhelical strand opening would be expected to be disfavored near TSSs . We note , however , that superhelical destabilizations at more remote positions are known to serve specific regulatory functions . The FUSE element that is located 2 kb upstream from the major c-myc promoters regulates transcription of this gene in humans by processes involving superhelical destabilization [36] . The situation may be expected to be rather different in prokaryotes . These organisms are highly gene dense , so the intergenic transcriptional regulatory machinery must be positioned close to the genes or operons they control . Also , superhelicity is not transient in prokaryotes , but is maintained within domains by enzymatic as well as by transcriptional processes . The superhelix density in E . coli changes with environmental conditions and growth state , and is coupled directly to the expression levels of genes that are differentially expressed under these conditions . Our results suggest that superhelical denaturation would be highly competitive with B-Z transitions at temperatures characteristic of growth phase in a host , while B-Z transitions would dominate at the lower temperatures that occur outside of a host . However , the level of negative superhelicity imposed on the genome by gyrase also is higher during growth phase than in stationary phase . So a careful analysis of this situation requires that both effects be included . This matter also will be investigated in future work . BDZtrans analysis of a prokayotic gene set finds the opposite transition behavior in their 5′ flanks than was found for eukaryotes . A clear enrichment of denatured sites just upstream of the TSS ( i . e . the +1 position in prokaryotic genes ) has been found at = 310 K in E . coli , as well as in many other prokaryotic genomes that have been analyzed previously with the SIDD algorithm . Interestingly , at the temperature where the probabilities of strand separation and of B-Z transition are comparable in eukaryotes , in E . coli one finds that Z-DNA dominates away from the +1 gene positions . This result suggests that fundamental differences may exist in the process of transcription as it occurs in prokaryotes and in eukaryotes . When we compared the competitive transition behaviors around transcription start ( TSS ) and gene stop ( TES ) sites in humans , we found that opposite patterns prevail in the two regions . At the TSS the number of Z-susceptible sites is increased and the number of denaturation-susceptible sites decreased , relative to more distant regions . The opposite pattern occurs in the 3′ regions proximal to TESs . In these locations there is a clear and substantial enrichment of denaturing states , and a slight diminishing of Z-susceptible sites . This suggests that denatured DNA might play some role in processes occurring near gene 3′ flanks . The transition properties of transcribed mouse genes have been compared to those of a set of processed pseudogenes that do not transcribe . The results obtained by BDZtrans show no apparent pattern for either transition upstream of the pseudogene start sites . However , there is a substantial decrease in the number of sites susceptible to either type of transition just downstream of the pseudogene “start” positions . This result suggests that the maintenance of Z-susceptible sites just 3′ of the TSS in transcribed mouse genes may be under selection pressure , disappearing when that pressure is removed . To illustrate the practical utility of the BDZtrans algorithm , suppose that superhelical transition at a specific site is hypothesized to serves some regulatory function . To establish this hypothesis one must first show that the superhelical transition actually occurs at the site , and then prove that it exerts a regulatory effect . These questions are frequently investigated by inserting a segment containing the putatively regulatory susceptible site into a plasmid , perhaps along with a reporter gene . However , if superhelical transition at the test site is found not to occur in the plasmid it could be either because the hypothesis is false , or because in the plasmid that site competes with different alternatives than it does in its genomic context . Conversely , just because the transition occurs in the plasmid does not automatically mean that it also will occur in its genomic context . One can only draw inferences from the plasmid behavior regarding the genomic activity if the behaviors of the test site in the two contexts are comparable . The theoretical methods developed in this paper enable investigator to assess how the transition behavior of a site would be expected differ when it is placed in different contexts . Use of these methods will enable experimenters to design plasmids that most accurately address their questions . After transition at the test site has been shown to occur in the plasmid , it remains to establish that it is the superhelical transition itself serves the regulatory function , and not some other attribute of the site . To do this one must alter the transition properties of the test site without changing its other attributes - in particular the local base sequence of the region involved . One can insert at a remote position on the plasmid a different susceptible site that is designed to outcompete the transition at the test site . If transition at the insert site happens first , it will delay the transition at the test site to more extreme superhelicities . If the regulatory effect is delayed to the same degree , this is strong evidence that it is indeed the superhelical transition that exerts the regulatory effect . To design experiments of this sort one needs a way to assess how various inserts would compete with a given test site within a given plasmid . The multistate analytical methods developed here will enable experimenters to make these assessments . Since B-Z transitions relax the most superhelicity per base pair , under most conditions they transform at less extreme superhelicities than do other types of transitions . So the natural choice for a competitive insert would be a Z-susceptible site . If the transition whose putative regulatory properties are being tested is either denaturation or another BZ transition , then use of the BDZtrans method presented in this paper will enable experimenters to design the correct systems to rigorously test their hypotheses . These examples show how the techniques presented in this paper can be of immediate use to experimenters . Our precise quantitative method has the potential to enable the design of much more accurate and rigorous experiments than would otherwise be possible . The multistate methods developed here are capable of treating competitions involving all the possible secondary structures that can be driven by supercoiling . In addition to the Z-form and denatured conformations , this could include G-quadriplexes , H-form DNA , cruciforms , and possibly others . However , in order to make quantitative predictions of the superhelical competitive behavior of sequences containing sites that can form these structures , their transition energetics must be known under the assumed environmental conditions . This limits the present applicability of our method to treating competitions involving B-Z transitions and denaturation , as was done here . Information is available regarding the energetics of superhelical cruciform extrusion at perfect inverted repeat sequences , and the energy costs of some types of imperfections are known [65] . So analyses that include extrusion of cruciforms are being developed . Unfortunately , sufficiently complete information regarding the energetics of forming general G-quadriplexes and H-form triplexes is not available at present . The approach presented here will become applicable to more situations as our understanding of transition energetics improves . In particular , information regarding the energetics of formation of the quadriplex at the CT site in the c-myc 5′ flank is expected to be available soon . A website is available ( http://benham . genomecenter . ucdavis . edu ) where members of the scientific community may submit sequences of interest to them for analysis by the BDZtrans algorithm . The sequence must be either in FASTA format or in a file that contains sequence characters exclusively . Sequences of any length up to 10 kb may be submitted , although sequences of length around 5 kb are preferred . This site may also be used for SIDD and/or SIBZ analyses . | The stresses imposed on DNA within organisms can drive the molecule from its standard B-form double-helical structure into other conformations at susceptible sites within the sequence . We present a theoretical method to calculate this transition behavior due to stresses induced by supercoiling . We also develop a numerical algorithm that calculates the transformation probability of each base pair in a user-specified DNA sequence under stress . We apply this method to analyze the competition between transitions to strand separated and left-handed Z-form structures . We find that these two conformations are both competitive under physiological environmental conditions , and that this competition is especially sensitive to temperature . By comparing its results to experimental data we also show that the algorithm properly describes the competition between melting and Z-DNA formation . Analysis of large gene sets from various organisms shows a correlation between sites of stress-induced transitions and locations that are involved in regulating gene expression . | [
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] | 2012 | Theoretical Analysis of Competing Conformational Transitions in Superhelical DNA |
Three major forms of human disease , cutaneous leishmaniasis , visceral leishmaniasis and mucocutaneous leishmaniasis , are caused by several leishmanial species whose geographic distribution frequently overlaps . These Leishmania species have diverse reservoir hosts , sand fly vectors and transmission patterns . In the Old World , the main parasite species responsible for leishmaniasis are Leishmania infantum , L . donovani , L . tropica , L . aethiopica and L . major . Accurate , rapid and sensitive diagnostic and identification procedures are crucial for the detection of infection and characterization of the causative leishmanial species , in order to provide accurate treatment , precise prognosis and appropriate public health control measures . High resolution melt analysis of a real time PCR product from the Internal Transcribed Spacer-1 rRNA region was used to identify and quantify Old World Leishmania in 300 samples from human patients , reservoir hosts and sand flies . Different characteristic high resolution melt analysis patterns were exhibited by L . major , L . tropica , L . aethiopica , and L . infantum . Genotyping by high resolution melt analysis was verified by DNA sequencing or restriction fragment length polymorphism . This new assay was able to detect as little as 2-4 ITS1 gene copies in a 5 µl DNA sample , i . e . , less than a single parasite per reaction . This new technique is useful for rapid diagnosis of leishmaniasis and simultaneous identification and quantification of the infecting Leishmania species . It can be used for diagnostic purposes directly from clinical samples , as well as epidemiological studies , reservoir host investigations and vector surveys .
Molecular methods are increasingly employed for diagnostic and epidemiological studies on leishmaniasis in an effort to detect infection and categorize Leishmania at the genus , species or strain level [1] . Ideal assays should be easy to perform and interpret , rapid , sensitive , specific , and able to determine parasite loads accurately in hosts and vectors . Several techniques have been described for the identification and characterization of Leishmania at the molecular level . These include PCR- restriction fragment length polymorphism ( RFLP ) , sequence analysis of multicopy genes and intergenic spacer regions , DNA fingerprinting and randomly amplified polymorphic DNA , and PCR followed by reverse line blot hybridization [1]–[4] . Multilocus enzyme electrophoresis is an additional characterization technique that relies on variation in Leishmania enzymes electrophoretic mobility [5] . Several of these techniques , such as multilocus enzyme electrophoresis and randomly amplified polymorphic DNA , require isolation and culture of parasites , limiting their use in clinical situations where rapid diagnosis is required [1] . Three major forms of human disease are encountered: cutaneous leishmaniasis , visceral leishmaniasis and mucocutaneous leishmaniasis . In the Old World , the two major forms are cutaneous and visceral leishmaniasis , and the main parasite species responsible for these diseases are Leishmania infantum ( visceral and cutaneous leishmaniasis ) , L . donovani ( visceral leishmaniasis ) , L . tropica ( cutaneous leishmaniasis ) , L . aethiopica ( cutaneous and mucocutaneous leishmaniasis ) and L . major ( cutaneous leishmaniasis ) . Accurate and sensitive diagnostic and identification procedures are required to distinguish between these species , whose geographic distribution frequently overlaps , to enable adequate treatment and appropriate public health control measures . High resolution melt analysis ( HRM ) is an automated analytical molecular technique that measures the rate of double stranded DNA dissociation to single stranded DNA with increasing temperature . This dissociation is monitored by including a fluorescent dye in the PCR reaction that intercalates homogenously into DNA , and only fluoresces , when bound to dsDNA . The change in fluorescence measures the thermally-induced DNA dissociation by HRM and the observed melting behavior is characteristic of the particular DNA product as determined based on sequence length , GC content , complimentarity , and nearest neighbor thermodynamics . HRM has been used for the detection of single nucleotide polymorphism in genetic diseases . It was subsequently used for detection of internal tandem duplications , simultaneous mutation scanning and genotyping in bacteriology , cancer research and hematology [6] , [7] . It is a sensitive technique readily applied to pathogen detection , using DNA extracted directly from blood and other tissues , eliminating lengthy procedures such as parasite isolation and growth . Results are obtained without additional post-PCR processing in <2 . 5 hrs . We describe a new application of HRM for the rapid detection , quantification and speciation of Old World leishmanial species .
Samples were obtained from humans and domestic dogs as part of routine diagnosis of leishmaniasis , and from wild animals and sand flies during epidemiological studies . The study that concerned animals was conducted adhering to the Hebrew University's guidelines for animal husbandry and use of animals in research . The use of patient samples was approved by the Helsinki Committee for Human Research of the Hadassah Hospital , Ein Kerem , Jerusalem . Since the study was a part of routine diagnosis of suspected leishmaniasis and the diagnostic samples were submitted from several distant health care facilities , only oral informed consent was required and obtained . Informed consent was recorded in writing in the patient's file as required by the IRB committee . A total of 300 samples were examined , 159 from human leishmaniasis patients , 78 from naturally infected dogs , 38 from hyraxes , 15 from rodent species ( Psammomys obesus , Apodemus mystacinus , Gerbillus dasyurus , Acomys cahirinus , Meriones sacramenti and Eliomus melanurus ) and 10 from sand flies ( Ph . arabicus , Ph . paptasi , Ph . rossi and Ph . sergenti ) . A hundred and seventy one samples were from the Middle East ( Iran , Iraq , Israel , Jordan , the Palestinian Authority , Saudi Arabia and Turkey ) , 82 from Asia ( Afghanistan , Azerbaijan , China , Georgia , India , Turkmenistan , and Uzbekistan ) , 35 from Africa ( Algeria , Ethiopia , Kenya , Morocco , Namibia , Senegal , Sudan , and Tunisia ) and 12 from Europe ( Greece , Italy , Portugal and Spain ) . Of these , 131 DNA samples were purified from cultured promastigotes isolated from 98 humans , 21 dogs , 10 sand flies , 1 hyrax and 1 rodent . The remaining DNA samples were extracted directly from tissues including: human cutaneous lesions ( 66 ) , blood ( dogs-47 and hyraxes-12 ) , skin biopsies ( rodents-14 and hyraxes-16 ) , spleens ( hyraxes-4 and dogs-10 ) and lymph nodes ( dogs-10 ) . DNA was extracted from cultured parasites and directly from parasites in tissue smears made from cutaneous lesions , blood , skin or spleen biopsies by either the phenol-chloroform or guanidine thio-cyanate techniques [4] , [8] . A 265–288 bp fragment , depending on the leishmanial species , within the internal transcribed spacer 1 ( ITS1 ) region of the leishmanial ribosomal RNA operon was amplified by real-time PCR using the primers ITS-219F ( 5′- AGCTGGATCATTTTCCGATG- 3′ ) and ITS-219R ( 5′- ATCGCGACACGTTATGTGAG ) designed for this study and then examined by HRM analysis . Leishmania ITS1 DNA sequences were compared by multi-alignment ( ClustalW2; http://www . ebi . ac . uk/Tools/clustalw2/index . html ) . Genomic DNA from all Leishmania strains except for one taken from GenBank ( MHOM/ET/1972/L102 ) were verified by partial sequencing of the ITS1 at the Center for Genomic Technologies , Hebrew University of Jerusalem . The assay's specificity was evaluated with the following non-leishmanial trypnosomatids: Crithidia fasciculata ( ATCC 11745 ) , Leptomonas seymouri ( ATCC 30220 ) , Trypanosoma brucei ( BF 427 ) , T . evansi ( RoTot 1 . 2 ) , T . cruzi , and T . equinum . Trypanososma cruzi and T . equinum DNA were kindly supplied by Dr . P . Michels and Dr . V . Hannaert from the Université Catholique de Louvain , Belgium . DNA from control non-infected humans , dogs , hyraxes and rodents were also evaluated for possible response with the HRM PCR . The PCR reaction was performed in a total volume of 20 µl containing 5 µl DNA , 40 nM of each primer , 10 µl Thermo-start PCR Master Mix ( Thermo-start ABgene , Rochester New York USA ) , 0 . 6 µl 100-fold diluted SYTO9 ( Invitrogen , Carlsbad , CA ) , and sterile , DNase/RNase-free water ( Sigma , St . Louis , USA ) using a Rotor-Gene 6000 real-time PCR machine ( Corbett Life Science ) . Initial denaturation for 15 min at 95°C was followed by 40 cycles of denaturation at 5 sec at 95°C per cycle , annealing and extension for 30 sec at 57°C , and final extension for 1 sec at 76°C . This was followed by a conventional melting step from 60 to 95°C at 1°C/sec , after which the temperature was slowly decreased from 90 to 50°C ( 1°C/sec ) to allow re-annealing . In the final step , HRM analysis was carried out increasing the temperature from 75 to 90°C at 0 . 4°C/sec increments . All samples were examined in duplicate . A set of control DNA standards from cultured promastigotes were prepared and analyzed in parallel with the test samples to standardize the real-time PCR . Promastigotes of L . infantum were suspended in un-infected human blood counted in a counting chamber ( Z2 , Beckman Coulter , Inc ) to give a concentration of 5×107 parasites/ml . Ten microliters ( 5×105 parasites ) were suspended in 90 µl of un-infected human blood giving 5×103 parasites/µl . Samples ( 10 ul ) were then diluted tenfold six times and the DNA was extracted with guanidine thio-cyanate followed by silica beads [8] . Purified DNA was suspended in 100 µl of elution buffer and serially diluted from 5×102 to 5×10−2 parasites/µl . Five µl of DNA were used for each reaction . Restriction fragment length polymorphism ( RFLP ) was performed according to Schonian et al . , 2003 [9] . DNA sequences were compared for similarity to sequences in GenBank using the BLAST program hosted by NCBI , National Institutes of Health , USA ( http://www . ncbi . nlm . nih . gov ) .
DNA sequence multi-alignment analysis of nine strains from five Old World Leishmania species for the ITS1 regions amplified by the primers used in this study indicated ≥82% similarity between the sequences ( Figure S1 ) . The size for the product amplified ranged from 265 bp ( L . infantum and L . donovani ) to 288 bp ( L . aethiopica ) . Strains from different foci were used in order to account for potential variation in the ITS1 sequence among Leishmania species originating from a wide range of geographical locations . For example , L . donovani from India as well as an African strain were used , and L . tropica isolates from Tiberias and the Northern Sea of Galilee in Israel were used due to the known interspecies variation between them [10] . Altogether 30 different mismatch stretches spanning from 1 to 18 nucleotides each were observed . Longer sequence deletions , between nucleotides 172–177 and 236–253 , created gaps in alignment between several species . Insertion/deletion mismatches at positions 47–48; 62; 100–101 allowed the discrimination of L . tropica and L . aethiopica from L . major , L . infantum and L . donovani . Insertion/deletion mismatches at position 66–68; 162–163 , 184–185; 215; 242–250 and 267–268 positions separated L . infantum and L . donovani from the other three species . A deletion at position 142–143 and an insertion at position 162–164 separated L . major from the other species . Insertion of 6 bases between positions 172–177 and a deletion between positions 248–253 separated L . aethiopica from all other species . Other nucleotide mismatches such as transversions and transitions were also present , for example within L . tropica thus separating the strains of the North Sea of Galilee from the Tiberias and central Israel ones . Differences between L . tropica from these areas included deletions at the 228 and 241 positions , deletion/insertion at the 242–244 positions , three transitions and a number of transversions . The African L . donovani strain was different from the Indian strain by transversions at the 46 , 57 , and 80–82 positions . Leishmanial DNA was amplified and analyzed by HRM PCR from 300 samples . Species identification was confirmed by RFLP for 156 samples , by DNA sequencing of the ITS1 PCR products for165 samples , and by both techniques for 21 samples . The species identified in the samples were L . infantum ( n = 143 ) , L . tropica ( n = 86 ) , L . major ( n = 52 ) , L . aethiopica ( n = 7 ) and L . donovani ( n = 12 ) . The real-time PCR standard curve was linear ( R2 = 0 . 998 ) over a 5-log range of DNA concentrations and showed a 110% reaction efficiency as determined from the slope ( −3 . 1 ) of the curve ( http://www . stratagene . com/techtoolbox/calc/qpcr_slope_eff . aspx ) . No amplification was noted at the dilutions of 5×10−3 and 5×10−4 , and therefore 5×10−2 marked the lowest dilution where parasite DNA was detected . The lower limit of sensitivity from the standard curve was determined as 0 . 25 parasites per sample . The normalized HRM curves for the amplicons from the five leishmanial species are shown in Figure 1 . Each Leishmania species produced a unique melting plot that was easily distinguishable from other species and consistent with the observed nucleotide differences among them , except for L . infantum that shared a plot similar to L . donovani . HRM analysis showed very uniform patterns for every species compared to the corresponding conventional melting curves , highlighting the differences between the species and reducing misidentification . Melting curve patterns were consistent for each species whether the DNA sample was from cultured promastigotes , or amastigotes in blood and tissue samples . Non-template controls ( NTC ) showed no signal after 40 amplification cycles and primer-dimer formation was not noted . The normalization regions used for the analysis ranged from 75 . 23°C to 75 . 75°C in the leading range , and from 89 . 4°C to 89 . 8°C in the trailing range . Complete agreement was found between speciation by HRM analysis , RFLP and/or DNA sequencing . HRM PCR specificity was examined using DNA from other trypanosomatids . PCR products were observed with T . brucei , T . cruzi , T . equinum and C . fasciulata but not L . seymouri and T . evansi ( Figure 2 ) . However , the amplicons produced using the non-leishmanial DNAs were larger and the corresponding HRM plots were markedly different and did not overlap with any of the Leishmania spp . ( Figure 2 ) . Host DNA from non-infected humans , dogs , hyraxes and rodents was not amplified and no HRM plot was observed with this assay . Though unique identifying HRM patterns were discerned for each Old World Leishmania species , slight differences within a species were noticed among strains that coincided with specific leishmanial genotypes and/or geographic distribution , and could be correlated with nucleotide substitutions , insertions and/or deletions in the real-time PCR product . For instance , L . tropica strains from Israel originating just north of the Sea of Galilee ( microsatellite clade IV genotype ) and those from the vicinity of Tiberias , central and southern Israel ( microsatellite clade I genotype ) have been shown to be different by several molecular and biological criteria , including cell-surface antigens and DNA microsatellite analyses [10] , [11] . Strains from these adjacent regions were easily recognized since they gave slightly different HRM patterns due to differences in nucleotide sequence within the ITS1 fragment amplified in this assay ( Figures S1 and 3 ) .
This study describes a new technique for the rapid detection , quantification and species identification of Old World leishmanial species . HRM analysis is sensitive and can detect as little as 2–4 ITS1 gene copies in a 5 µl DNA sample , i . e . , less than a single parasite per reaction . It is a closed tube assay that does not employ additional fluorescent probes and simply utilizes a DNA melting assay and computerized analysis of the results to produce a graphic output , thus the risk of contamination of the samples is decreased . HRM analysis provides a distinct characteristic repeatable melting curve for each species that retains its general shape even when strains are from distant locations and have different hosts and vectors . It distinguished all the Old World leishmanial species causing human disease , except L . infantum from L . dononvani . These two species gave similar HRM curves . As the DNA sequence over the 265 bp region amplified by this PCR is almost identical for L . infantum and L . donovani , this finding is not surprising . Both species cause visceral leishmaniasis , and prognosis and treatment of this disease is similar . Development of an HRM assay that separates between L . infantum and L . donovani should be possible by choosing a short DNA region unique for each species , since this technique can potentially distinguish between DNA sequences that differ by only a single nucleotide . The technique was also able to discern inter-species variation as seen with strains of L . tropica isolates from the two Israeli foci . The appearance of complex HRM plots showing shoulders for the species giving larger PCR products such as L . tropica and L . aethiopica , is likely due to the presence of multiple melting domains with different Tm within the dsDNA amplicons . The non-leishmanial trypnosdomatids that were amplified by the reaction produced distinctly different curves that presented in an area of the plot situated away from the Leishmania spp . and not overlapping with them . Therefore , despite the fact that the assay amplifies the DNA of some other trypnosomatids , they can not be confused with the Leishmania spp . evaluated . Real-time PCR has been used for previous studies on different aspects of leishmaniasis including diagnosis , animal models , drug efficacy and vectorial capacity [12]–[17] . Different target genes and loci have been used including the Leishmania DNA polymerase gene [12] , kinetoplast DNA [13] , [15] , [16] and the SSU rRNA gene [14] , [17] . ITS1 HRM analysis further simplifies disease diagnosis by allowing rapid parasite identification and quantification , within less than 2 . 5 hrs , without need for species or genus specific probes . Furthermore , we have shown that HRM can be used for direct testing of patient samples such as skin smear , skin biopsy , visceral organ tissues and blood , and can be used for diagnostic purposes as well as epidemiological studies , reservoir host investigations and vector surveys . This technique could be especially valuable in regions where several leishmanial species exist causing disease with similar symptoms , but requiring different treatment regimens and having dissimilar prognosis . Leishmania major , L . tropica and L . infantum overlap in the Middle East and North Africa . In Israel , L . major and L . tropica cause a similar cutaneous disease whereas in Morocco , the situation is more complicated with cutaneous leishmaniasis caused by these two species and also by L . infantum [18] , [19] . This assay would also be useful in medical centers in non-endemic regions where infected patients require a rapid diagnosis at the Leishmania species level to receive correct therapy and prognostication . Its ability to quantify infection would make it useful in evaluating the success of therapy and new types of treatments in experimental animals and in tissue and cell culture systems . | Protozoal parasites of the genus Leishmania are transmitted by sand fly bites to humans and animals . Three major forms of disease are caused by these parasites: cutaneous leishmaniasis , responsible for disfiguring skin wounds; mucocutaneous leishmaniasis , causing non-healing ulceration around the mouth and nose; and the potentially fatal visceral leishmaniasis , involving internal organs such as the spleen and liver . More than 2 million new human infections are caused annually by leishmaniasis globally , it is endemic in more than 88 countries and prevalent also as an imported disease in non-endemic regions due to travel and tourism . Most species of Leishmania that infect humans are zoonotic and transmitted from animal reservoir hosts . As various leishmanial parasites cause disease with similar symptoms , but require different therapeutic regimens and have dissimilar prognoses , reliable , sensitive and rapid diagnostic assays are needed . This study focuses on the five main species that cause leishmaniasis in the Old World . It presents a new assay for rapid detection , species identification and quantification of leishmanial parasites in clinical samples , reservoir hosts and sand flies . This technique could be especially valuable in regions where several leishmanial species exist , in non-endemic regions where infected patients require a rapid diagnosis , and for epidemiological host and vector studies leading to prevention programs . | [
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] | 2010 | Detection and Identification of Old World Leishmania by High Resolution Melt Analysis |
The elimination of autoreactive T cells occurs via thymocyte apoptosis and removal by thymic phagocytes , but the sequence of events in vivo , and the relationship between thymocyte death and phagocytic clearance , are unknown . Here we address these questions by following a synchronized cohort of thymocytes undergoing negative selection within a three-dimensional thymic tissue environment , from the initial encounter with a negative selecting ligand to thymocyte death and clearance . Encounter with cognate peptide–MHC complexes results in rapid calcium flux and migratory arrest in auto-reactive thymocytes over a broad range of peptide concentrations , followed by a lag period in which gene expression changes occurred , but there was little sign of thymocyte death . Caspase 3 activation and thymocyte loss were first detectable at 2 and 3 hours , respectively , and entry of individual thymocytes into the death program occurred asynchronously over the next 10 hours . Two-photon time-lapse imaging revealed that thymocyte death and phagocytosis occurred simultaneously , often with thymocytes engulfed prior to changes in chromatin and membrane permeability . Our data provide a timeline for negative selection and reveal close coupling between cell death and clearance in the thymus .
As an important safeguard against autoimmunity , T cells bearing autoreactive T cell antigen receptors ( TCRs ) are eliminated during their development in the thymus , a process known as negative selection . Although much is known about the molecular events involved in negative selection [1] , surprisingly little is known about the dynamic aspects of the process . For example , what is the sequence of events from the first encounter with a negative selecting ligand until the death of the thymocyte ? How long does the process take ? Does a single encounter with self-peptide lead to migratory arrest , or do thymocytes remain motile and continue to sample the tissue for negative selecting ligands ? What is the temporal relationship between the death of the thymocyte and its clearance by phagocytes ? Pioneering studies carried out in the 1980s documented the absence of thymocyte populations bearing self-reactive TCRs in mice expressing the relevant self antigens [2]–[4] . However , these studies could not address the timing and mechanism of negative selection since self-antigens were present continuously and dying thymocytes were not detected . In subsequent experiments , rearranged TCR transgenes were expressed in mice without the relevant self-antigen , and then antigenic peptides were administered in vivo to induce negative selection [5]–[8] . In vitro co-cultures of transgenic thymocytes with cognate peptide-loaded antigen-presenting cells ( APCs ) have also been used to examine the process of negative selection [9]–[11] . These approaches have revealed extensive thymocyte death accompanied by nuclear condensation and other classic signs of programmed cell death , or apoptosis . However , the analyses were typically performed at a single time point , often a day or more after peptide addition , and thus it was unclear when the process of negative selection began and ended . In addition , the impact of systemic cytokines produced by mature T cell stimulation in the periphery was a confounding factor in many of the in vivo studies [12] , [13] . A recent study examining negative selection to endogenous self-antigen in vivo , reported a decrease in the number of autoreactive thymocytes becoming apparent 24–48 hours after the first signs of TCR triggering [14] . These delayed and asynchronous kinetics were attributed to the variable time for a thymocyte to encounter a thymic APC capable of providing a negative selecting signal . Thus , there remains considerable uncertainty regarding the timing of thymocyte death during negative selection . Another outstanding question is the relationship between thymocyte death and clearance by macrophages in vivo . In vitro studies of cultured cells undergoing apoptosis indicate that mitochondrial damage and caspase activation are followed by dismantling of cellular components accompanied by nuclear condensation , membrane blebbing , and exposure of phosphatidylserine ( PS ) on the outer face of the plasma membrane [15] . In vivo , PS exposure is thought to serve as an “eat-me” signal to phagocytes , promoting the removal of membrane blebs and apoptotic cells and preventing the release of cellular contents into extracellular space [16] . In the thymus , very few dying cells are observed at steady state , and apoptotic cells are found inside phagocytes , indicating efficient clearance mechanisms [17] . However , it is unclear whether thymocytes first undergo apoptosis and then are rapidly engulfed , or whether the engulfment by macrophages precedes thymocyte death . Determining whether apoptosis precedes phagocytosis or vice versa is important , both for understanding the mechanistic link between these events in vivo , and because the occurrence of cell death before phagocytosis may lead to greater potential inflammation due to the release of cellular contents . Studies of negative selection in vivo have largely focused on the end result of thymocyte self-reactivity , and we know little about the initial encounters between autoreactive thymocytes and thymic APCs presenting negative selecting ligands . For mature T cells in lymph nodes , the initial encounters with peptide–MHC-bearing dendritic cells can occur as transient , serial interactions prior to migratory arrest and stable conjugate formation , particularly under conditions of suboptimal stimulation [18]–[20] . An indication that autoreactive thymocytes may also engage in serial contacts with APC during negative selection comes from a steady-state model in which thymocytes undergo negative selection to a tissue-restricted antigen expressed in the medulla [21] . In this system a large number of autoreactive thymocytes persisted and remained motile in the thymic medulla , exhibiting a confined migration pattern that allowed for serial contact with multiple dendritic cells . However , because antigen was present continuously , it was unclear whether confined migration occurred during the initial contact with antigen , or reflected an adaptation of thymocytes to antigen exposure over time . Moreover , this model is based on a specialized form of negative selection in which medullary thymic epithelial cells exhibit stochastic and low-level expression of proteins that are otherwise restricted to peripheral tissues [22] , [23] . Much of the negative selection in the thymus is driven by ubiquitous , rather than tissue-restricted , self antigens , and these different forms of negative selection likely differ in terms of the abundance and spatial distribution of antigens , types of peptide-presenting cells , and molecular requirements [14] , [22]–[24] . Here we examine a cohort of thymocytes undergoing negative selection to a ubiquitous antigen within three-dimensional living thymic tissue . The initial encounter with negative selecting ligands leads to a rapid rise in intracellular calcium and migratory arrest over a broad range of peptide concentrations . Thymocytes with active caspase 3 are detectable starting at 2 hours after peptide addition , while other indicators of cell death , including changes in chromatin structure and membrane permeability , first become apparent at 3 h . In spite of the synchronous early response to negative selecting ligand , individual thymocytes undergo delayed and asynchronous entry into the death program from 2–12 hours after peptide addition . Time-lapse two-photon imaging revealed that thymocyte death and phagocytosis invariably occur together , with many thymocytes already engulfed by a macrophage before the changes in chromatin and membrane permeability are evident . These data provide a timeline of the major events during negative selection , and suggest close coupling between the thymocyte death and clearance by macrophages .
The majority of the studies of negative selection have utilized in vitro models that do not support thymocytes' normal motility , nor their dynamic interactions with cells in the three-dimensional tissue environment . To examine the impact of these factors on negative selection , we first compared the activation and death of thymocytes in response to negative selection signals in intact three-dimensional versus dissociated tissue . We incubated thymic slices containing F5 TCR transgenic thymocytes for 30 minutes with specific peptide ( NP366–374 derived from influenza nucleoprotein ) to mimic negative selection to a ubiquitous antigen and then continued the incubation for 10 hours either as an intact slice “in situ” or after dissociation of the tissue “in vitro” ( Fig . 1A ) . We then analyzed thymocytes by flow cytometry for up-regulation of the activation marker CD69 ( Fig . 1B ) and induction of active caspase 3 , an early marker of apoptosis ( Fig . 1C ) . We focused our analysis on CD4+CD8+ double positive ( DP ) thymocytes , since this population can be a target of deletion to ubiquitous self-antigens [14] . Because of the down-regulation of CD4 and CD8 following TCR stimulation , a phenomenon known as DP “dulling” , we adjusted the DP gate to include the CD4lowCD8low population [11] , [25] . Interestingly , CD4+CD8+ F5 thymocytes from intact slices showed greater CD69 up-regulation in response to specific peptide relative to dissociated slices ( Fig . 1B ) . Moreover , F5 thymocytes from intact thymic slices incubated with control peptide showed a lower level of non-specific cell death compared to thymocytes cultured in vitro . ( Fig . 1C ) . These results demonstrate that intact thymic slices are superior to dissociated thymic tissue for detection of specific cell death during negative selection , due to both more efficient responses to negative selecting ligands and lower levels of non-specific cell death . To establish a timeline of cell activation , apoptosis , and clearance during negative selection , we initiated a wave of negative selection by overlaying thymic slices containing F5 TCR transgenic thymocytes with specific peptide . The thymic slices were prepared from lethally-irradiated mice reconstituted with mixtures of wild type ( WT ) and F5 TCR transgenic bone marrow ( BM ) , allowing us to compare the response of polyclonal thymocytes as an internal control . Up-regulation of CD69 was first detected on CD4+CD8+ F5 thymocytes one hour after specific peptide addition ( data not shown ) and peaked between 2 to 4 hours , corresponding with the down-regulation of TCRβ ( Fig . 2A ) . Another activation marker , CD44 was also up-regulated in the experimental group , but with slightly delayed kinetics ( Fig . 2A ) . Finally , the down-regulation of CD4 and CD8 , a phenomenon known as DP “dulling” that is indicative of TCR signaling , could be detected starting 4 hours after specific peptide stimulation ( Fig . 2B ) [11] , [25] . No changes in these activation markers were observed on CD4+CD8+ F5 thymocytes from slices treated with a control peptide or on WT cells alongside activated F5 thymocytes , confirming that the changes were due to direct TCR engagement on F5 thymocytes rather than cytokine-driven bystander effects or non-specific signals . Altogether , these data show that F5 thymocytes are synchronously and specifically activated by the cognate peptide addition to thymic slices , leading to rapid changes in gene expression . To visualize the earliest response of thymocytes to their negative selecting ligand , we modified our system to accommodate time-lapse imaging . We used thymic slices from mixed BM chimeric mice in which a small proportion of the thymocytes express the F5 TCR and green fluorescent protein ( GFP ) . WT ( polyclonal TCR ) thymocytes labeled with cyan fluorescent protein ( CFP ) served as an internal control . Cortex and medulla were distinguished based on the proximity of the imaging volume to the capsule of the thymus and the characteristically lower position of the medulla ( Fig . S1 ) . We then performed two-photon microscopy of thymocytes within cortical regions ( corresponding to CD4+CD8+ thymocytes ) and midway though the imaging run , we added specific peptide to the perfusion medium . TCR transgenic thymocytes arrested their migration within minutes of peptide addition ( Video S1 and Fig . 3A and 3B ) . The WT thymocytes in the same imaging volume did not alter their migration pattern confirming that the stopping was induced directly by TCR triggering , rather than by alterations in the tissue microenvironment . The F5 thymocytes maintained low speed for hours after the encounter with their cognate pMHC ligand ( Fig . 3C ) . Migratory arrest was also observed when the experiment was performed with CD4+CD8+ F5 thymocytes purified by depletion of CD8SP and CD4−CD8− populations and overlaid onto thymic slices with OT I thymocytes serving as controls ( Video S2 ) [26] . To examine the relationship between thymocyte behavior and TCR signaling , we adapted the experimental system to allow for measurement of TCR induced calcium flux using the fluorescent Ca2+ indicator dye Indo-1 LR ( Fig . 3D ) . We isolated CD4+CD8+ thymocytes from F5 TCR transgenic mice , loaded them with Indo-1 LR , and overlaid them onto thymic slices . Labeled thymocytes were found in both the cortex and medulla of thymic slices ( Fig . S1 ) . This likely reflects the physiological trafficking of post-selection thymocytes in our samples , which are in the process of becoming CD8+CD4− single positive ( CD8SP ) and are beginning to express homing molecules , such as CCR7 , that give them access to the medulla [27] , [28] . In contrast , “pre-selection” CD4+CD8+ , isolated from non-selecting backgrounds localize overwhelmingly to the cortex [29] ( our unpublished observations ) consistent with their less mature phenotype . We performed all subsequent imaging studies in the medullary region due to the higher cell densities and superior image quality compared to the cortex . CD4+CD8+ thymocytes exhibited a relatively low intracellular calcium concentration and high motility in thymic slices prior to peptide addition , whereas addition of cognate peptide triggered a sharp increase in the intracellular Ca2+ concentration and a sudden drop in thymocyte motility . These effects could be observed in individual thymocytes ( Video S3 and Fig . 3E ) and at the population level ( Fig . 3F ) . The data confirm the inverse relationship between thymocyte motility and calcium signaling previously observed [26] and indicate that peptide diffusion in the tissue , loading onto MHC molecules , and encounter of thymocytes with peptide-MHC bearing cells occur rapidly in this system . The rapid migratory arrest observed for thymocytes encountering a negative selecting ligand resembles the behavior of mature T cells upon encountering an antigen-bearing dendritic cell ( DC ) under conditions of optimal priming [18] . On the other hand , less intense stimuli , including low peptide concentration , can lead to an initial phase of transient interactions of mature T cells with DC before forming stable contacts [19] . To determine whether limiting peptide concentration could also lead to transient interactions of thymocytes during negative selection , we first determined the minimal peptide concentrations that induce efficient negative selection in our system . We incubated thymic slices with different concentrations of NP peptide for 10 hours , and examined CD69 and active caspase 3 on F5 thymocytes by flow cytometry . Addition of 1 µM NP peptide induced strong CD69 and active caspase 3+ up-regulation , whereas 1 nM led to weaker but significant up-regulation ( Fig . 4A and B ) . Addition of 100 pM NP led to only slightly increased activation and apoptosis induction compared to control peptide , a difference that did not reach statistical significance . We then tested this range of peptide concentrations for their ability to induce calcium flux and migratory arrest of F5 thymocytes . Interestingly , all peptide concentrations tested induced Ca2+ flux and migratory arrest in the vast majority of thymocytes within 20 minutes of peptide addition ( Fig . 4C and Video S4 ) . Limiting peptide concentration affected the time required for the majority of thymocytes to respond: within a minute for the highest concentration tested ( 1 µM ) , 3–4 min with 1 nM and 15–20 min with 100 pM . In spite of this delay at the population level , individual thymocytes exposed to low peptide concentrations converted rapidly from non-signaling to signaling behavior ( Video S4 ) , suggesting that the delay in response at the population level was due to the lower probability of encountering APCs displaying sufficient number of pMHC to trigger a response . To confirm this , we aligned individual cell tracks based on the time point at which elevated calcium was first detected , and calculated the average calcium ratio and interval speed relative to the onset of signaling ( Fig . 4C ) . This analysis confirmed that low peptide concentrations induced an all-or-nothing response in thymocytes , with calcium influx and stopping occurring together , and reaching a maximum over a period of less than 30 sec ( Fig . 4C ) . These data indicate that thymocytes that encounter even a low concentration of negative selecting peptide undergo rapid TCR triggering and migratory arrest . Having defined the initial events following exposure of thymocytes to negative selecting stimuli in situ , we next turned our attention to the endpoints of negative selection , namely cell death and phagocytosis . To do so , we first determined more precisely the time required to complete negative selection using flow cytometric analysis of overlaid thymocytes at various times after peptide addition . To detect apoptotic cells , we used an antibody specific for active caspase 3 , an early marker for apoptosis induction , combined with the fixable live/dead dye Aqua , to identify cells that have lost membrane integrity ( Fig . 5A ) . Early apoptotic cells ( active caspase 3+ Aqua− ) were detectable above background starting at 2 hours and continuing until 12 hours after peptide addition ( Fig . 5B ) . A similar time course was observed for all apoptotic cells ( active caspase 3+ Aqua− and active caspase 3+ Aqua+ ) ( Fig . 5C ) . Because dying thymocytes are efficiently cleared by phagocytes , the number of active caspase 3+ thymocytes detectable at any given time point provides only a snapshot of negative selection . The cumulative loss of viable thymocytes should provide a more accurate read-out of the extent of negative selection over time , however this measurement is complicated by the variation in the number of thymocytes per slice and seeding of slices by F5 thymocytes . To get around these problems , we included a population of thymocytes bearing an irrelevant TCR ( OT I ) to serve as internal reference . We overlaid marked OT I and F5 thymocytes on WT thymic slices at a ratio of approximately 1∶1 , and determined the number of viable thymocyes of each donor type using flow cytometry at various times after peptide addition . We then used the ratio of F5 to OT I CD4+CD8+ thymocytes as a measurement of cell loss due to negative selection . With this approach , we were able to detect loss of viable cells as early as 3 hours after the addition of the specific peptide ( Fig . 5D ) , lagging 1 hour after the first appearance of active caspase 3+ Aqua− cells ( Fig . 5B ) . By 12 hours the number of non-apoptotic F5 thymocytes had decreased by ∼70% ( Fig . 5D ) . A similar time course was observed when total ( Aqua+ and Aqua− cells after exclusion of debris and doublets ) CD4+CD8+ thymocytes were quantified ( Fig . 5E ) . PS exposure , which serves as an “eat-me” signal to phagocytes , could be detected above background by 2 hours after peptide addition , corresponding to the first appearance of active caspase 3+ cells ( Fig . 5F ) . Importantly , the pan-caspase inhibitor zVAD-fmk ( zVAD ) abolished both PS exposure and cell loss indicating that caspases are essential for both processes ( Fig . 5D , 5E , and 5F ) . Together these data imply that thymocytes undergo caspase-dependent cell death asynchronously between 3 and 12 hours following synchronous encounter with negative selecting peptide . Having established a time window for negative selection in our system , we next set out to directly visualize thymocyte apoptosis and relate it to phagocyte clearance . To visualize cell death , we adapted a method in which cells are double-labeled with a cytosolic dye ( SNARF ) to detect the loss of membrane integrity and the nuclear dye Hoechst to detect apoptosis-induced changes in the chromatin [30] . Purified CD4+CD8+ F5 thymocytes were labeled with SNARF and Hoechst , seeded on thymic slices that were subsequently incubated with specific peptide , and analyzed by time-lapse two-photon microscopy in the medulla . Cell death could be detected by a sudden increase in the ratio of fluorescence in the blue to red channels due to a drop in SNARF and an increase in Hoechst signal ( blue to red or B/R ratio ) in individual thymocytes ( Fig . 6A , white arrow and green line ) while the B/R ratio of neighboring thymocytes remained unchanged ( Fig . 6A , brown line ) . While very few examples of cell death were seen during the first 3 hours , numerous examples were seen starting at 4 hours after addition of specific peptide ( Fig . 6B and Video S5 ) . Very little cell death was observed with control peptide or in the presence of the caspase inhibitor zVAD ( Fig . 6B ) . Cell death could be readily detected up to 12 hours after addition of specific peptide , the limit for maintaining adequate tissue viability under these conditions . These data are in good agreement with the time course for cell loss detected by flow cytometry ( Fig . 5E ) and confirm that there is considerable variation in the time to death of individual thymocytes following synchronous encounter with a negative selecting ligand . To relate thymocyte death to phagocytosis by macrophages , we seeded SNARF and Hoechst labeled CD4+CD8+ F5 thymocytes onto thymic slices from LysM-GFP reporter mice , in which GFP is expressed by phagocytes that are predominantly located in the medulla ( Fig . S1 ) [31] . In all cases , peptide-induced cell death occurred while the thymocyte was in intimate contact or enclosed by a LysM-GFP+ cell ( 31 out of 31 ) ( Fig . 6C and 6D and Video S5 ) . In contrast , only 54% of viable thymocytes from these same runs were in contact with phagocytes , and only 7% appeared to be in intimate contact or engulfed ( n = 1 , 461 ) . The time between the initial contact with the phagocyte and death varied considerably ( 2–56 min ) , however many thymocytes remained tightly associated with phagocytes for the duration of the imaging run ( >30 min ) while remaining viable ( Fig . 6D ) . The initial contact between the thymocyte and phagocyte could be observed in ∼45% of all phagocytosis examples ( 14 out of 31 ) . Surprisingly , in all cases ( 14 out of 14 ) , it was the slowly moving thymocyte that approached the phagocyte and not the other way around ( Fig . 6D and E and Video S6 ) . The majority of LysM-GFP+ phagocytes were stationary despite the abundance of dying cells around them , suggesting that , at least in this system , “find-me” signals and directed migration of phagocytes to apoptotic cells are not the prevalent clearance mechanism .
While many of the individual events during thymocyte negative selection have been identified , the relationship between them is unclear , in part because we lack information about the temporal sequence of events as they occur in vivo . Here we address this gap in our knowledge by following a synchronized cohort of thymocytes undergoing negative selection from their initial encounter with negative selecting ligands , to their eventual death and phagocytic clearance . We used two-photon time-lapse microscopy to directly visualize thymocyte cell death and phagocytosis , and observed that engulfment by macrophages precedes the permeabilization of the plasma membrane and chromatin condensation . Our work establishes the chronological sequence of events during negative selection , and reveals a surprising close coupling between apoptosis and phagocytosis in vivo . Our experimental setup allowed us to visualize the initial encounter between thymocytes and their negative selecting ligands in situ , revealing rapid migratory arrest accompanied by calcium flux . In contrast , autoreactive thymocytes in a steady-state , AIRE-dependent model of negative selection migrated relatively rapidly within confinement zones and no stopping phase was discerned [21] . While AIRE-dependent antigens are thought to be present at relatively low abundance in the thymus [32] , it seems unlikely that peptide abundance alone could account for the difference in stopping behavior in the two systems , since we show here that even very low peptide concentrations can induce an all or nothing stopping response in thymocytes . Moreover , a recent report of CD4+ SP thymocytes in the presence of an AIRE-dependent negative selecting antigen revealed examples of migratory arrest [33] . TCR signals and migration are interrelated , with signaling inducing migratory arrest , and migratory arrest in turn prolonging contact with a single APC and thus promoting sustained signaling . Thus , thymocytes may tune TCR signaling and migratory arrest to balance between sensitive antigen detection and efficient scanning of multiple thymic APCs . Altogether , these data are consistent with the view that thymocytes undergo calcium signaling and migratory arrest upon initial encounter with a negative selecting ligand , but the subsequent response depends on the intensity of the signal received . Encounter with a high abundance peptide and/or presentation by a more stimulatory APC would lead to continued migratory arrest and cell death within a few hours . On the other hand , in response to encounter with a low abundance peptide and/or presentation by a less stimulatory APC , a thymocyte may recover its motility and continue to sample the thymic environment for some time before either undergoing delayed negative selection , agonist selection [34] , or export from the medulla as a mature conventional T cell [35] , [36] . In many cases phagocytes migrate toward “find-me” signals released by dying cells [37] . On the other hand , a recent report in a zebra fish model revealed dying neurons actively migrating to regions of the brain containing phagocytes [38] . Here we observe that phagocytes are relatively abundant in the vicinity of dying thymocytes and approximately half of thymocytes are in contact with a phagocyte even in the absence of antigenic peptide . However , in the handful of examples in which we were able to observe the initial encounter between a dying thymocyte and the phagocyte prior to death and engulfment , it was the thymocyte that approached a sessile phagocyte . These observations suggest that a “find-your-phagocyte” model might operate in multiple in vivo settings . Through the use of time-lapse imaging and pharmacological interventions , we were able to elucidate the timing and sequence of events during the executionary phase of apoptosis resulting from negative selection in vivo . Caspase activation is the first marker of death induction that appears around 2 hours after pMHC stimulation . Phagocytosis of dying cells is dependent on caspases' enzymatic activity and follows within an hour , possibly mediated by the “eat-me” signal PS . Perhaps the most surprising result from our study is the observation that phagocytosis precedes typical features of apoptosis . The standard morphological description of apoptosis includes nuclear ( chromatin ) condensation and fragmentation , while the plasma membrane is still intact and cell disaggregation into apoptotic bodies or blebs that are ultimately engulfed by phagocytes [39] , [40] . Our in situ data suggest that the internal antigens of dying cells are guarded even more strictly than previously appreciated and the phagocytes may engulf the entire cell at the onset of apoptosis before blebbing or nuclear condensation has occurred . Moreover , the observation that chromatin changes are coincident with plasma membrane permeabilization is consistent with the possibility that phagocyte's lysosomal DNases could play a role in the nuclear breakdown . This raises the intriguing possibility that phagocytes may not be merely “undertakers” serving to remove corpses , but may also serve as “executioners” helping to deliver the final deathblow to the autoreactive thymocyte . A further intriguing possibility is that phagocytes could also serve as antigen presenting cells for negative selection . F4/80+ phagocytes can induce negative selection in thymic organ culture , although CD11c+ dendritic cells appear to do so more efficiently [41] , [42] . Moreover , in situ localization of thymocytes undergoing negative selection to a ubiquitous antigen in the cortex revealed a close association with CD11c+ DCs , but not with F4/80+ macrophages [14] . The LysM-GFP reporter that we use in this study to identify phagocytes , shows partial overlap with many of the markers used to identify macrophages and DCs , including CD11c ( data not shown ) . The notion that the same cell may participate in both the initiation and the clean up of negative selection awaits further experimental testing . Previous work has shown that negative selection requires new gene expression [9] , [43] and , specifically induction of the pro-apoptotic protein Bim [44] . Thus , the lag period between peptide addition and thymocyte death that we observe could reflect the need to accumulate Bim to levels sufficient to neutralize anti-apoptotic proteins of the Bcl-2 family , and thereby trigger mitochondrial membrane permeabilization , cytochrome C release , and caspase 3 activation . Interestingly , in spite of the fact that all thymocytes responded within minutes to peptide addition , the entry of cells into the death program occurred asynchronously over a period of several hours . Asynchronous cell death has also been reported for cultured cell lines after treatment with a uniform death inducing stimulus [45] , a phenomenon attributed to stochastic variation in expression of pro and anti-apoptotic proteins by individual cells [46] . TCR signaling in thymocytes can induce both pro-apoptotic factors such as Bim , as well as pro-survival factors such as Schnurri [47] . Moreover , non-TCR mediated factors such as cytokines may also provide survival signals to autoreactive thymocytes in vivo . Thus , it seems likely that autoreactive thymocytes assess the relative levels of various pro and anti-apoptotic factors in choosing when and if to die . Given emerging evidence that some autoreactive thymocytes may escape negative selection and give rise to agonist-selected or conventional peripheral T cells [34] , [36] , it is tempting to speculate that the prolonged waiting period after encounter with self antigen may allow autoreactive thymocytes to choose between these alternative fates .
C57BL/6J ( CD45 . 2 ) , B6 . SJL-Ptprca Pepcb/BoyJ ( CD45 . 1 ) , Ubi-GFP and Actin-CFP mice were from Jackson Labs . OT I RAG2−/− mice were from Taconic Farms . F5 RAG1−/− and LysM-GFP mice have been described [31] , [48] . All the mice were bred and maintained in specific pathogen-free conditions at the animal facility at University of California , Berkeley according to protocols approved by the Institutional Animal Care and Use Committee . For mixed BM chimera generation , T cell-depleted BM from donor mice ( F5 RAG1−/− CD45 . 2 and C57BL/6J CD45 . 1 , in equal proportions ) was injected into the recipients ( C57BL/6J CD45 . 1 ) irradiated with two doses of 550 rad 4 h apart from a 147Cs source . In some experiments , partial hematopoetic chimeras were generated by injecting neonatal C57BL/6J mice with BM from F5 RAG1−/− GFP and Actin-CFP at days 3 and 5 after birth . All chimeras were analyzed after >5 weeks post reconstitution . Peptide injection was carried out with 50 nanomoles of NP366–374 peptide ( Anaspec ) dissolved in PBS , intravenously . Thymic slices were prepared essentially as described [49] . Briefly , thymic lobes cleaned of connective tissue were embedded in 4% GTG-NuSieve Agarose ( Lonza ) in HBSS and cut with a 1000 Plus sectioning system ( Vibratome , Leica ) into 0 . 4 mm thick slices . The slices were laid on 0 . 4 µm Cell Culture Inserts ( BD Biosciences ) in 6-well plates ( BD Biosciences ) that contained 1 ml of complete RPMI ( cRPMI ) medium and incubated at 37°C in a plastic bag filled with 80% O2+15% N2+5% CO2 ( Blood Gas , Praxair ) . Different amounts of F5 specific , NP366–374 , and control peptides , Ova257–264 or VSV264–272 ( all from Anaspec ) were added in 1 ml of cRPMI and withdrawn after 30 min . In some experiments pan-caspase inhibitor I ( zVAD-fmk , EMD ) was added to 50 µM final concentration to the medium . In other experiments , the slices were dissociated in 5 ml cRPMI medium , the cell suspension spun down and resuspended in 200 µl cRPMI , and added to 96-well plate for further incubation . Thymocyte single cell suspension was prepared in PBS . CD4+CD8+ F5 thymocytes were depleted of non T cells and mature CD8 SP cells with anti-Biotin MicroBeads and LS columns ( Miltenyi Biotech ) following incubation with the following biotinylated antibody cocktail – CD11b , CD11c , CD19 , CD25 , MHC II , DX5 , Ter-119 , β7-integrin ( all from eBioscience or BioLegend ) . For labeling , 107 thymocytes were incubated with 2 µM Indo-1 LR ( Teflabs ) at 3 . 3×106 cells/ml for 90 min in cRPMI at 37°C . In other cases 2×107 thymocytes were labeled with 3 µM SNARF ( Invitrogen ) at 107 cells/ml for 15 min in pre-warmed PBS at 37°C , washed with cRPMI and further labeled with 5 µM Hoechst 33342 ( Invitrogen ) at 107 cells/ml for 15 min in pre-warmed cRPMI at 37°C . For overlaying on thymic slices , the cell suspensions were adjusted to 106 cells/20 µl , and 10–20 µl were gently overlaid on slices in Cell Culture Inserts . The cells were left to migrate into the slice for 2 h at 37°C/5% CO2 and then the cells that had failed to enter the slice were removed by gently washing with PBS . Single cell thymocyte suspensions were blocked with 24G2 supernatant for 10 min in ice and 2×106 cells were stained with antibodies for surface markers ( CD4 , CD8 , CD44 , CD45 . 1 , CD45 . 2 , CD69 , TCRβ in Pacific Blue , FITC , PerCP/cy5 . 5 . PE/cy7 , APC , and APC/eFluor780 , all from eBioscience or BioLegend ) in 0 . 5% BSA in PBS ( FACS buffer ) . For PS exposure , the cells were washed with DPBS and stained with Annexin V-AlexaFluor 647 ( Invitrogen ) in binding buffer for 15 min at room temperature in the dark followed immediately by fixation . For Live/Dead fixable Aqua ( Invitrogen ) staining ( or the equivalent fixable viability dye eFluor 506 from eBioscience ) the cells were washed with PBS and stained in 100 µl PBS with 1∶500 dilution from the dyes for 30 min in ice . The cells were washed with PBS and analyzed by flow cytometry or fixed with 2% paraformaldehyde ( Electron Microscope Sciences ) in PBS for 20 min in ice . For active caspase 3 intracellular staining , the fixed cells were permeabilized/blocked in 0 . 1% Saponin ( Sigma ) in FACS buffer+5% normal donkey serum ( Jackson Immunoresearch ) +10% 24G2 supernatant for 20 min in ice . Anti-active caspase 3 antibody ( Cell Signaling ) was applied for 40 min at 1∶400 and was detected with anti-rabbit PE ( Jackson Immunoresearch ) . Flow cytometry was performed on LSR II or Fortessa ( BD Biosciences ) and data analysis was carried out with FlowJo ( TreeStar ) . Thymic slices or intact thymic lobes were glued on coverslips and imaged by two-photon laser scanning microscopy with a custom-built up-right microscope or Zeiss 7 MP ( Zeiss ) , while being perfused with warmed ( 37°C ) , oxygenated phenol-free DMEM medium ( GIBCO ) at a rate 1 ml/min . Mode-locked Ti:sapphire laser Mai-Tai ( Spectra-Physics ) or Chameleon ( Coherent ) was tuned to 900 nm for CFP+GFP and Hoechst+SNARF excitation or 720 nm for Indo-1 LR excitation with appropriate filter sets . Imaging volumes of various sizes were scanned every 30 sec for 20–60 min and assigned to cortex or medulla based on distance from the capsule ( detected by second harmonic signal ) , density of LysM-GFP cells ( greater in the medulla ) and the characteristic lower position of the medullary region . Most of the imaging was done in the medulla , except where stated otherwise , because of the superior image quality . Perfusion with peptides was achieved by switching the perfusion medium to phenol-free DMEM containing various amounts of specific or control peptides . Due to the dead volume of the tubing it took 2–3 min for the peptide to reach the sample . Imaris 7 . 3 ( Bitplane ) was used to determine cell positions over time and tracking . The x , y , and z coordinates as well as the mean fluorescence intensities of the tracking spots for Ca2+-bound Indo-1 LR , Ca2+-free Indo-1 LR , Hoechst and SNARF were exported . Motility parameters were calculated in MATLAB ( Mathworks ) with a custom code that is available upon request . Interval speed is calculated by dividing displacement over time and the time interval is denoted . For example , when adjacent time points were used for the speed calculation it was denoted as “Interval speed ( 30 sec ) ” or when it was calculated over five time points it was denoted as “Interval speed ( 150 sec ) ” . Ca2+-ratio was calculated as a surrogate for Ca2+ intracellular concentration by dividing the mean fluorescence intensity of Ca2+-bound Indo-1 LR by the Ca2+-free Indo-1 LR with great care taken to avoid saturation of pixels . All the values were normalized so that the average Ca2+-ratio before peptide addition was zero ( corrected Ca2+-ratio ) . Blue/Red fluorescence ( B/R ) ratio as a measurement for the cell viability was calculated by dividing the mean fluorescence intensity of Hoechst ( blue ) by SNARF ( red ) . Prism 5 . 0 ( GraphPad ) was used for graphing and statistical analysis . Unpaired two-tailed t-test was used to determine significance when comparing between two groups , and one-way ANOVA with Tukey post-test was applied when more than two groups were compared . | As an important safeguard against autoimmunity , T cells bearing autoreactive T cell antigen receptors are eliminated during their development in the thymus , a process known as negative selection . Although much is known about the molecular events involved in negative selection , surprisingly little is known about the dynamic aspects of the process . Here we examine a synchronized population of developing T cells ( thymocytes ) undergoing negative selection within three-dimensional living thymic tissue . We show that the initial encounter with negative selecting ligands results in migratory arrest , but in spite of this synchronous early response , individual thymocytes then undergo delayed and asynchronous entry into the death program between 2 and 12 hours thereafter . Using time-lapse two-photon imaging , we reveal that thymocyte death and the clearance of the dead cells invariably occur together , with many thymocytes already engulfed by a macrophage before the cell death-related changes in chromatin and membrane permeability are evident . These data provide a timeline of the major events during negative selection , and suggest close coupling between thymocyte death and clearance by macrophages . | [
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] | 2013 | Elimination of Self-Reactive T Cells in the Thymus: A Timeline for Negative Selection |
The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks . Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1 , but they can also be stacked to learn increasingly abstract representations . Several computational neuroscience models of sensory areas , including Olshausen & Field’s Sparse Coding algorithm , can be seen as autoencoder variants , and autoencoders have seen extensive use in the machine learning community . Despite their power and versatility , autoencoders have been difficult to implement in a biologically realistic fashion . The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections . Here , we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas . Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections , and our results depend critically on this novel choice of plasticity rules . Specifically , we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity ( STDP ) , leading to a symmetric combined rule we call Mirrored STDP ( mSTDP ) . We show that with mSTDP , our network follows a learning rule that approximately minimizes an autoencoder loss function . When trained with whitened natural image patches , the learned synaptic weights resemble the receptive fields seen in V1 . Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks .
Neurons in the brain’s sensory areas need to form useful internal representations of the external world . Over the course of development , as these neurons create and modify their synaptic connections , they develop receptive fields which allow them to respond to characteristic stimulus features . The preferred features are relatively simple for neurons in primary areas such as primary visual cortex ( V1 ) and primary auditory cortex ( A1 ) , but increase in complexity , sparsity , abstractness , and size in higher brain areas . It is an intriguing possibility that the brain uses a similar mechanism to learn receptive fields in higher sensory areas as it does in the primary areas . If so , that mechanism must be flexible enough to work across the different regimes of sparsity , complexity , and abstraction . The mechanism must also be capable of producing representations which are potentially “stackable” , so that the output from one area can be represented in more abstract form in the subsequent area . For instance , if pairwise or higher order correlations in neuronal activity are present in one area , those correlations might be captured to form a more abstract representation in the next area . Finally , the mechanism must be implementable by biological neurons: all computations must be local , and synaptic weight changes should match experimentally observed synaptic plasticity . Here , we introduce a model for learning in a single area which we argue fulfills these requirements: it is biologically plausible while allowing varying levels of sparsity and producing representations that need not be uncorrelated . Many previous biologically plausible models of receptive field development learn local or “one-hot” representations , in which each stimulus causes approximately one neuron ( or one small neighborhood of neurons ) to respond; models in this class include Kohonen’s Self-Organizing Map [1] , LISSOM [2 , 3] , and Winner-Take-All models [4 , 5] . Learning in these models moves the winning neuron’s receptive field closer to the current stimulus using procedures which are simple , synaptically local , and do not require feedback connections . However , local representations have very limited capacity: they can represent O ( N ) distinct inputs with N neurons , thus requiring that the number of neurons is comparable to the number of features to be distinguished . Local models of low-level vision can succeed because natural image patches seem to exist in a space of low dimensionality [6] , and spatially localized features can be characterized using only a few parameters ( such as orientation , spatial frequency , and phase ) . However , in higher brain areas with larger and more complex receptive fields , the number of neurons required for a local model to be able to represent all possible stimuli would grow tremendously . By contrast , distributed models can represent many more potential inputs , from O ( ( nk ) ) for sparse models with k active units up to O ( 2 N ) for dense models [7–9] , and may therefore be better suited for modeling at all levels of the sensory hierarchy . Several biologically plausible models have been proposed for learning distributed representations in the special case where neuronal activity is very sparse and uncorrelated [7 , 10–12]; under these conditions , a simple learning rule similar to that seen in the local models can be used . However , a model which does not require neurons to be uncorrelated is desirable because neurons in real cortical networks respond to stimuli in highly correlated ways . This stimulus-dependent correlation should be distinguished from noise correlation , which measures the similarities of fluctuations in neuronal responses to identical stimuli . Noise correlation is frequently measured to be small , and so cortical firing is often described as “decorrelated” ( e . g . [13] ) . However , stimulus-dependent correlation is strong; in V1 , from 20–50% of neurons have been estimated to respond to each stimulus in their receptive field [14] . Many pairs of neurons have highly correlated responses when measured across multiple stimuli ( e . g . [15] ) . Importantly , in the context of a hierarchy , the correlations remaining in the neurons of one layer can be captured by neurons in subsequent layers . Perhaps the most well-known model for learning in V1 is Olshausen and Field’s Sparse Coding model [16] . Their algorithm attempts to find receptive fields which simultaneously preserve information while maintaining sparse neuronal activity , but it does not require neuronal activity to be uncorrelated in order to function . However , the algorithm thus far lacks a biological interpretation . A different spike-based matching pursuit model [17] uses different interactions to determine the neuronal activities but the same learning rule , and that learning rule similarly lacks a biological interpretation . Here , we introduce a novel biological mechanism for a well-known learning algorithm known as the autoencoder . Autoencoders are two-layer neural networks which attempt to learn distributed representations that can be used to accurately reconstruct their inputs . In an autoencoder , external stimuli induce activity in the lower-layer “visible” units . This activity , combined with feedforward connections , then creates a pattern of activity in the upper-layer “hidden” units . Finally , the network uses symmetric or “tied” feedback weights in order to create an attempted reconstruction in the visible layer . The objective of autoencoder learning is to find weights such that the reconstruction closely matches the original stimulus input , thus ensuring that the hidden unit representation is a good one; intuitively , reconstructions can only be accurate when the hidden layer retains sufficient information about the visible layer . An autoencoder can be made to find an efficient representation by adding a constraint on the activity or architecture of the hidden layer . This forces the network to find features which are useful for describing the particular types of stimuli seen during training . The constraint can take the form of a regularization term added to the loss function . Alternatively , it can be a hard limit , such as a limit on the number of hidden units , a requirement that hidden units be binary , or a requirement that hidden unit activity be sparse . Typically , autoencoders are trained using stochastic gradient descent on the squared reconstruction error ( or on the reconstruction error plus regularizer term ) ; for each stimulus presentation , synaptic weights are changed in the direction that would most decrease this loss function . In this work , networks are trained instead using the “autoencoder rule” , also known as Oja’s subspace rule [18] , which is an approximation to the full gradient descent expression . If the vector of input values is given by x → , hidden unit activities are given by y → , and the attempted reconstruction is the vector x → ^ , then the autoencoder rule states that for learning rate η , the change in synaptic weights wij between visible unit i and hidden unit j is given by Δ w i j = η ( x i - x ^ i ) y j ( Autoencoder learning rule ) ( 1 ) Autoencoders can be used to accurately model responses in early sensory areas; indeed , Olhausen & Field’s Sparse Coding network is an autoencoder with lateral interactions between the hidden units used to impose a sparsity constraint . But the autoencoder is a very general algorithm . With different neuronal activation functions and lateral interactions , autoencoders can also find the subspace spanned by Principal Component Analysis ( PCA ) eigenvectors [9 , 18] or perform an online implementation of K-means clustering [19] . ( See [9] for an extensive review of autoencoders and their relationship to other learning algorithms . ) These cases show that autoencoders can span the range between learning dense distributed models , as in PCA [20] , and local models , as in K-means . Sparse Coding , where several hidden units respond to each stimulus , falls in between these two extremes . Autoencoders have been used extensively in the machine learning community , where they have been stacked to form multi-layer representations of increasing abstraction [21–23] or used to pre-train deep neural networks that perform classification tasks [24] . There are two main difficulties regarding a biologically plausible implementation of the autoencoder . The first challenge arises from the fact that learning must depend on the difference of two neuronal activities: the original visible unit activity x → and the reconstructed activity x → ^ ( see Eq ( 1 ) ) . The second difficulty comes from the required symmetry of learning tied weights , where feedforward weights are equal to feedback weights . Preserving this symmetry over the course of learning dictates that any change to the feedforward synaptic strength between two neurons must be accompanied by an identical change to the feedback strength . If a feedforward synapse is weakened , the feedback synapse must also be weakened , and vice versa . In real neurons , feedforward and feedback synapses are physically distinct entities , and a biologically realistic model must account for how the two can experience identical ( or very similar ) plasticity . Previous implementations have addressed these two challenges by positing that hidden layer neurons are inhibitory and create negative reconstructions , so that the final activity in the visible layer is ϵ → = x → - x → ^ , and stipulating that learning then proceeds according to symmetric Hebbian rules Δwij = ϵi yj [25 , 26] . However , these implementations are biologically unrealistic in three important ways . First , they require visible unit activity levels ϵ → to become negative at times in order to create synaptic depression . Second , the inhibitory nature of the feedback connections is unrealistic , since it is known that most feedback connections between cortical areas arise from excitatory neurons , and most feature-selective neurons are excitatory [27] . Third , the learning rules themselves are unrealistic; experiments have shown that in real neurons , unlike those modeled in inhibitory feedback networks , synaptic plasticity is neither purely Hebbian nor symmetric . Instead , the sign of synaptic plasticity often depends on the relative timing of activity in pre- and post-synaptic neurons [28 , 29] , in a process known as spike-timing dependent plasticity ( STDP ) [29–32] . Here , we instead propose a spiking neural network in which feedback creates a weak , positive reconstruction . Unlike a previous proposal with a similar architecture [33] , our model uses a biologically realistic synaptic plasticity rule to implement learning . The required negative sign in the learning rule arises naturally from an additive version of STDP , while our proposed differences in the plasticity rules at feedforward versus feedback synapses [34] lead to effective symmetry in learning . We show analytically that the learning in our network approximates the autoencoder learning rule . To examine the behavior of our model in the sparse regime , we use a very simple , biologically plausible method for inducing individual hidden neurons to have high lifetime sparsity . Our method uses local homeostatic mechanisms within each neuron to drive the network to find sparse solutions , and is designed to mimic a biological process known as “synaptic scaling” [35–37] , in which neurons regulate their activity levels by modifying their susceptibility to synaptic inputs . The resulting sparsity is important because it is well known that algorithms which yield sparse representations of natural stimuli can learn synaptic weights which closely resemble the receptive field structures of simple cells in primary sensory cortices ( reviewed in [38] ) . The specific choice of algorithm seems to matter less than its basic ability to create a sparse representation [39]; while Sparse Coding was an early and famous example [16] , various well-known sparse algorithms give qualitatively and even quantitatively similar results on visual , auditory , and somatosensory stimuli . These include independent component analysis [40] , sparse autoencoders [41] , restricted Boltzmann machines [41] , and K-means clustering [42] ( all are reviewed in [43] and [39] ) . We use simulated networks of integrate-and-fire neurons in two experiments to show that our network is capable of minimizing reconstruction error in these example datasets . For the first experiment , in which we train the network using a dataset containing handwritten digits , we use model neurons that approximate the idealized units in a neural network by having synaptic weights that can become positive or negative and an additive form of synaptic scaling that resembles a neural network bias term . For the second experiment , we use a dataset containing whitened natural image patches and we verify that the learned receptive fields resemble those measured in primary visual cortex . Here , we more closely model biological excitatory neurons by restricting synaptic weights to be positive and by using a multiplicative form of synaptic scaling . In both experiments , dynamic parameters such as membrane time constants and synaptic transmission delays are set to biologically realistic values .
We begin by defining the general autoencoder problem in a two-layer neural network . Each neuron in the first , visible layer is connected reciprocally to each neuron in the second , hidden layer , and there are no lateral connections . During each training trial , the network is presented with stimulus x → in the visible layer . The network then computes a representation y → in the hidden layer , using feedforward weights W ( with the jth column vector denoted by w → j ) and other parameters θ according to the potentially non-linear function y j = f ( x → ; w → j , θ ) . ( 2 ) The network then computes an attempted reconstruction x → ^ in the visible layer using symmetrical or “tied” feedback weights W ⊺ and an activation function g , so that x i ^ = g ( ∑ j y j w i j ) . ( 3 ) A squared reconstruction error is defined as E = ‖ x → - x → ^ ‖ 2 = ∑ i ( x i - x ^ i ) 2 . ( 4 ) How should this network modify its weights so as to minimize this error , using stochastic gradient descent ? The derivative of E with respect to the weights wij between a visible neuron i and a hidden neuron j is ∂ E ∂ w i j = 2 ∑ i ′ ( x i ′ - x ^ i ′ ) ( ∂ x i ′ ∂ w i j - ∂ x ^ i ′ ∂ w i j ) . ( 5 ) The initial visible activities xi don’t depend on the weights , so ∂ x i ′ ∂ w i j = 0 for all i′ . Similarly , the hidden unit activity yj is independent of the weights to other hidden units , so ∂ y j ′ ∂ w i j = 0 for j ≠ j′ . If we define g i ′ ≡ d g d z | z = ∑ j y j w i j ( 6 ) then ∂ x ^ i ′ ∂ w i j = g i ′ ′ ∑ j ′ y j ′ ∂ w i ′ j ′ ∂ w i j + g i ′ ′ ∑ j ′ ∂ y j ′ ∂ w i j w i ′ j ′ = g i ′ ′ y j δ i , i ′ + g i ′ ′ ∂ y j ∂ w i j w i ′ j . ( 7 ) Therefore , - 1 2 ∂ E ∂ w i j = x i - x ^ i g i ′ y j + ∑ i ′ ( x i ′ - x ^ i ′ ) g i ′ ′ ∂ y j ∂ w i j w i ′ j . ( 8 ) There are two terms above because of the tied weights: changing wij modifies both feedforward and feedback connections , and these changes have two independent effects on the reconstruction error . The first term is simpler , and reflects the contribution from the changed feedback connections . Importantly , it depends only on the activities of the connected neurons i and j . We therefore say that it is a “local” computation , and one that might plausibly be computed by biological neurons . By contrast , the second term , which reflects the contribution from the changed feedforward connections , is non-local . It depends on the activities of every visible neuron; this information would not be available to a biological synapse . Previous authors have noted that the second term is often small [44] , so that an approximate gradient descent using only the first term works nearly as well as the full equation [44 , 45] . For linear reconstructions , where g′ is a constant , this becomes the autoencoder learning rule ( Eq 1 ) . This is the rule that we will implement biologically . We note that the designation of “local” or “non-local” depends upon the activity in the network . We could have written x ^ i in Eq ( 1 ) as g ( ∑j′ yj′ wij′ ) , and the learning rule would have appeared non-local due to its dependence on the yj′ terms . Indeed , it is this exact non-locality that has caused previous authors to argue that autoencoder learning is not biologically plausible ( e . g . [12] ) . Here , instead , information about all hidden-unit activities is incorporated into the reconstruction activations x ^ i of the visible units themselves . Any synaptic plasticity rule which incorporates x ^ i will allow synaptic changes to depend on the activity of all the hidden units and the initial activity of all the visible units—even though the learning rule is purely local . To implement the autoencoder learning rule with biologically realistic neurons , we propose a two-layer network of spiking neurons with Nvis neurons in the visible layer and Nhid neurons in the hidden layer ( Fig 1a ) . Every visible neuron is connected reciprocally with every hidden one , and there are no lateral connections within a layer . The matrix of feedforward connections is denoted W and the feedback connection matrix is Q; following sections will show how the weights become symmetric . Inputs to the network are pixel values of preprocessed training images , and they stimulate only the visible neurons ( Fig 1a ) . Plasticity in the system has two components: inter-layer synaptic weights evolve according to the mirrored STDP ( mSTDP ) rules , and hidden neurons homeostatically adjust synaptic scaling to maintain target average activity levels ( both are described below ) . In our simulations , we use the leaky-integrate-and-fire ( LIF ) model for the neurons . However , our main results only depend on an approximately linear relationship between input strength and neuronal firing rate , so other neuron models could work as well . The details of the model implementation and all parameters used in the simulations are summarized in S1–S7 Tables . Our input preprocessing begins with a mean-subtraction step . This leaves pixel values ν → e x t that can be either positive or negative , which allows for a parsimonious representation of input pixels that are above or below their average values . However , biological neurons cannot have negative firing rates . We accommodate this by using an “ON-OFF cell” strategy , which uses twice as many visible neurons as pixels in the stimulus image . The inputs for the first half of the visible neurons are ν → ON = max ( 0 , ν → e x t ) , while the inputs to the second half of visible neurons are ν → OFF = max ( 0 , - ν → e x t ) . This strategy closely resembles that used by subcortical cells in the mammalian visual system [46 , 47] , and it allows both the positive and negative areas of the mean-subtracted natural image patches to be represented with positive neuronal activities of similar magnitude . The network is trained through the sequential presentations of input stimuli . We choose parameters such that activity in the spiking network occurs in three rough bouts . Fig 1b shows activity for one visible and one hidden unit during a presentation . For each stimulus , visible neurons receive a brief pulse of excitatory synaptic input proportional to stimulus strength . This input causes the neurons to generate a series of spikes; the spike counts during this period are represented by the vector x → ∈ Z ≥ 0 N vis . Feedforward synaptic excitation causes some of the hidden units to spike; their spike counts are given by y → ∈ Z ≥ 0 N hid . These hidden-unit spikes occur at a delay with respect to the initial visible unit activity because of a short synaptic transmission delay and because excitation from many spikes is required before the neurons reach threshold . Finally , after a further delay , visible units may spike again due to feedback excitation . The total number of visible spikes occurring due to feedback is x ^ ∈ Z ≥ 0 N vis . In Fig 1b xi = 5 , yj = 3 , and x ^ i = 2; here , the three bouts are temporally separate , but in simulations there can be some overlap . To prevent reverberating activity from growing exponentially during the course of a trial , we can consider parameters that lead to weak feedback , such that the number of spikes in the attempted reconstructions x ^ is several times smaller than that in the the initial activities x → [34 , 48] . We denote the constant scaling factor α < 1 , and say that the network makes a successful reconstruction when x → ^ ≈ α x → . Tied weights along with weak feedback will be maintained when learning rules enforce the relationship Q = α W ⊺ . Because feedforward and feedback synapses occur at physically distinct locations ( Fig 1c ) , we will show separate , biologically plausible plasticity rules for both feedforward and feedback connections and describe how they can maintain this symmetrical relationship . We define a scaled spiking reconstruction error L spike = | | x → - 1 α x ^ | | 2 . To determine what synaptic weight changes will decrease this error , we first need to specify how the spike counts depend on the weights . If the neurons in the network behave like standard leaky integrate-and-fire neurons ( and time periods are short compared to the membrane time constant ) , their spike counts will be well approximated by rectified linear functions , so that y → ≈ max ( 0 , W x → ) and x → ^ ≈ max ( 0 , α W ⊺ y → ) . In this case , the first term of the gradient descent expression for wij becomes ( x ^ i > 0 ) × ( x i - 1 α x ^ i ) y j . In the common cases where xi and x ^ i are both zero or are both nonzero , this gives the approximate gradient descent rule which the network should follow: Δ w i j = ( x i - 1 α x ^ i ) y j ( Scaled autoencoder rule ) ( 9 ) Our goal will be to show that biologically realistic synaptic plasticity rules used by the neurons in our network can implement this scaled autoencoder rule both for feedforward and feedback connections . We numerically simulated a network of LIF neurons . Because of spiking neurons’ nonlinear responses to input , the variable time courses of activity in the network , and the exponential STDP rules , a LIF network does not exactly follow the scaled autoencoder learning rule given in Eq ( 9 ) . Moreover , the autoencoder learning rule itself performs only an approximate gradient descent on the reconstruction error . Our numerical simulations allowed us to investigate whether the LIF network could minimize the autoencoder loss function while still maintaining sparsity . ( S1–S7 Tables ) . The architecture of our simulated network was the same as that in Fig 1a , except that to control overall activity levels we included a pool of Ninh inhibitory neurons in each layer ( Fig 3 ) . The inhibitory neurons in each pool were connected reciprocally with every excitatory neuron in the layer . Connection weights to and from inhibitory neurons did not change during the simulations . Our model neurons were conductance-based leaky integrate-and-fire neurons with a spike frequency adaptation term , similar to those in [58] ( S6 Table ) . In our first experiment , where we trained the network with the MNIST dataset of handwritten numerals , synaptic weights from the visible and hidden units could take on positive or negative values . In our second experiment , where we trained the network with natural image patches , we imposed more biologically realistic constraints , and restricted weights from visible and hidden units to be positive only .
In this work , we propose a detailed and biologically realistic model for how spiking neurons could implement the commonly used unsupervised autoencoder learning algorithm . Our work provides a necessary first step in making biologically realistic models for any of the many unsupervised learning algorithms which include an autoencoder term , ranging from those inspired by machine learning to those inspired by biology , such as Sparse Coding [16] . We describe how strong feedforward and weak feedback excitation can drive a pattern of spiking activity that corresponds to the autoencoder’s visible unit input , hidden unit activity , and attempted reconstruction . Given this activity pattern , we show how STDP , a biological learning rule with strong experimental evidence , will cause changes in feedforward synaptic strength that approximate those dictated by the autoencoder learning rule . We argue that pure Hebbian STDP does not , however , cause the correct changes for the feedback synapses given this activity pattern . Instead , we draw upon recent experimental evidence to argue that those feedback synapses might learn according to a temporally reversed version of the learning rule , aSTDP , and show how STDP and aSTDP combine in the two-layer network context to form a symmetric learning rule we call mirrored STDP , or mSTDP . Finally , we show how mSTDP can allow both feedforward and feedback synapses to correctly implement the autoencoder learning rule . We further describe how the network can find sparse representations by requiring its hidden units to fire infrequently . We argue that biological neurons could accomplish this through the experimentally observed process known as synaptic scaling . This constraint was chosen here for its simplicity , but other forms of regularizers or sparsity constraint would also be compatible with our mirrored STDP model . For instance , in the Olshausen & Field Sparse Coding algorithm [16] , hidden units in each trial find an optimal sparse steady-state through inhibitory lateral interactions and a term that could be modeled as spike rate adaptation . Once this steady-state is achieved , synaptic plasticity proceeds according to the autoencoder learning rule and could therefore be implemented with a model similar to ours . Although we show here how networks could use mirrored STDP to implement autoencoder learning , we note that the basic principle can work independently of the specific plasticity mechanism . It only requires two factors . First , the network should have both a sensory-driven feedforward phase and feedback-driven attempted reconstruction phase . Second , during the feedforward phase , correlated firing should increase synaptic strength for both feedforward and feedback connections; in contrast , during the feedback phase , correlated firing should decrease synaptic strength . In our model , the decrease in synaptic strength during the feedback phase occurs because of the relative timing of activity in this phase . But similar results could be obtained , for instance , in a spike frequency model of plasticity in which weak firing due to feedback leaves neurons in a depressive regime ( e . g . [61] ) . Several previous spike-timing-based models of unsupervised feature learning have been successful at learning receptive fields that resemble those seen in V1; these include Rank Order Coding using SpikeNET , by Delorme , Perrinet and Thorpe [4] , and the SAILNet model of Zylberberg and colleagues [12] . Neither network can learn a dense distributed code: in Rank Order Coding , only a single hidden unit responds to each local stimulus , while in SAILNet , hidden units are encouraged to be uncorrelated and fire very infrequently . Non-distributed , biologically realistic models have even successfully been extended into mid-level visual areas; for instance , Masquelier and Thorpe have shown that a winner-take-all STDP model was capable of learning good features in the second level of a max-pooling hierarchy [5] . We argue that distributed representations are likely to be better models for yet higher visual areas because of their increased representational capacity . However , additional work will be required to elucidate the conditions under which distributed representations—such as those which can be learned by the autoencoder—are warranted , and when the simpler learning mechanisms used in winner-take-all networks will suffice . We test our model using two-layer networks of simulated integrate-and-fire neurons using two datasets: handwritten digits in the MNIST dataset and whitened natural image patches . In both cases , the network learns distributed hidden unit representations which are capable of reconstructions . However , the reconstruction performance is not as good for the natural image patches as for the MNIST dataset . This may in part be due to the fact that our current implementation only allowed us to explore the extremely sparse regime with low hidden unit activity , since parameters that led to less sparse solutions caused difficulties with runaway excitation during training . Indeed , when we increased the network activity after training by manually increasing synaptic scaling factors by 1 . 5 , reconstruction performance improved ( Fig 7 ) . Future work will be required to elucidate whether the training principles described here would continue to function in a more complicated network that is more robust to runaway excitation . In the case of the natural image patches , the learned feedforward weights resemble those observed in the early mammalian visual system . As such , the autoencoder may be a useful model to consider when studying the development of connections between pyramidal neurons in the lateral geniculate nucleus and primary visual cortex , or between primary and secondary visual cortex . Of course , early visual areas of the brain cannot learn different sets of receptive fields for different stimuli , as in the two datasets we used here . They must learn very general representations that can be used to build more specific representations further up in the cortical hierarchy . However , in those higher brain areas , if specific sets of neurons are activated in response to different types of stimuli ( such as faces ) , it is conceivable that an autoencoder-like algorithm could allow the development of more specialized receptive fields . By restricting our model neurons in the natural image patch experiments to have non-negative weights , we show that autoencoder learning can work when the neurons follow Dale’s law . However , a true understanding of how sparse response patterns can arise will require a model for the development of selective inhibition . Neurons with purely excitatory receptive fields can exhibit sparse firing when those receptive fields are very small or the excitation very weak . Indeed , the receptive fields learned by our model neurons with the natural image patches were localized to small regions . By contrast , when receptive fields can have inhibitive components , as in our MNIST experiment , neurons can fire in a sparse manner even when the receptive fields are large and complex . Future work is needed to explore how plasticity in inhibitory neurons might help develop these complex receptive fields . | In the brain areas responsible for sensory processing , neurons learn over time to respond to specific features in the external world . Here , we propose a new , biologically plausible model for how groups of neurons can learn which specific features to respond to . Our work connects theoretical arguments about the optimal forms of neuronal representations with experimental results showing how synaptic connections change in response to neuronal activity . Specifically , we show that biologically realistic neurons can implement an algorithm known as autoencoder learning , in which the neurons learn to form representations that can be used to reconstruct their inputs . Autoencoder networks can successfully model neuronal responses in early sensory areas , and they are also frequently used in machine learning for training deep neural networks . Despite their power and utility , autoencoder networks have not been previously implemented in a fully biological fashion . To perform the autoencoder algorithm , neurons must modify their incoming , feedforward synaptic connections as well as their outgoing , feedback synaptic connections—and the changes to both must depend on the errors the network makes when it tries to reconstruct its input . Here , we propose a model for activity in the network and show that the commonly used spike-timing-dependent plasticity paradigm will implement the desired changes to feedforward synaptic connection weights . Critically , we use recent experimental evidence to propose that feedback connections learn according to a temporally reversed plasticity rule . We show mathematically that the two rules combined can approximately implement autoencoder learning , and confirm our results using simulated networks of integrate-and-fire neurons . By showing that biological neurons can implement this powerful algorithm , our work opens the door for the modeling of many learning paradigms from both the fields of computational neuroscience and machine learning . | [
"Abstract",
"Introduction",
"Results",
"Discussion"
] | [] | 2015 | Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons |
Rhythmic voltage oscillations resulting from the summed activity of neuronal populations occur in many nervous systems . Contemporary observations suggest that coexistent oscillations interact and , in time , may switch in dominance . We recently reported an example of these interactions recorded from in vitro preparations of rat somatosensory cortex . We found that following an initial interval of coexistent gamma ( ∼25 ms period ) and beta2 ( ∼40 ms period ) rhythms in the superficial and deep cortical layers , respectively , a transition to a synchronous beta1 ( ∼65 ms period ) rhythm in all cortical layers occurred . We proposed that the switch to beta1 activity resulted from the novel mechanism of period concatenation of the faster rhythms: gamma period ( 25 ms ) +beta2 period ( 40 ms ) = beta1 period ( 65 ms ) . In this article , we investigate in greater detail the fundamental mechanisms of the beta1 rhythm . To do so we describe additional in vitro experiments that constrain a biologically realistic , yet simplified , computational model of the activity . We use the model to suggest that the dynamic building blocks ( or motifs ) of the gamma and beta2 rhythms combine to produce a beta1 oscillation that exhibits cross-frequency interactions . Through the combined approach of in vitro experiments and mathematical modeling we isolate the specific components that promote or destroy each rhythm . We propose that mechanisms vital to establishing the beta1 oscillation include strengthened connections between a population of deep layer intrinsically bursting cells and a transition from antidromic to orthodromic spike generation in these cells . We conclude that neural activity in the superficial and deep cortical layers may temporally combine to generate a slower oscillation .
The synchronous activity of neural populations results in voltage fluctuations observable in macroscopic ( e . g . , scalp electroencephalography ) and mesoscopic ( e . g . , local field potential or LFP ) recordings . Rhythmic voltage fluctuations—or oscillations—have been observed in the mammalian brain for over a century [1] . Although the purpose of these oscillations remains unknown , neural rhythms appear to temporally organize network activity patterns , and pathological changes in these rhythms often accompany disease [2] , [3] . What mechanisms produce these neural rhythms ? The complexity of the vertebrate brain—resulting not only from the sheer number of neurons ( approximately 109 ) and their connections ( approximately 1011 [4] ) , but also from the many different neuron classes ( e . g . , the diversity of inhibitory interneurons [5] ) —affords no simple answers . Yet , simple characteristic structural patterns appear fundamental to the brain's organization [5] , [6] . From these elementary network building blocks ( i . e . , structural and functional motifs ) more complicated structures may be generated in an efficient way [7]–[9] . Perhaps a similar strategy may be used to understand the rhythmic electrical activity of the brain . For example , the simplest ( yet biophysical ) model of the 40 Hz ( gamma ) rhythm involves only two interconnected cells ( one excitatory and the other inhibitory ) with reciprocal synaptic connections . In this “gamma –motif” the decay of inhibition paces the rhythm [10]–[12] . This motif , and its variations , may be used to construct more complicated gamma networks [13]–[15] . Here we consider how the elementary dynamic building blocks ( or dynamic motifs [16] ) of the gamma ( ∼40 Hz ) and beta2 ( ∼25 Hz ) rhythms may combine to generate a slower beta1 oscillation . To do so , we develop computational models motivated by a novel method of rhythm generation—period concatenation—as we now describe . We recently observed that application of 400 nM kainate to rat somatosensory cortex in vitro resulted , initially , in distinct rhythms in different cortical layers [17] . In the superficial cortical layers ( layers II and III , or LII and LIII ) we observed gamma frequency rhythms ( 38±3 Hz , n = 25 observations ) , and in the deep cortical layers ( layer V or LV ) nonsynaptic beta2 rhythms ( 24±2 Hz , n = 25 observations ) . The dominant mechanisms that regulate these two rhythms in secondary cortex are known; the decay time of GABAA IPSPs dictates the gamma period [12] , and an outward potassium current in LV pyramidal axons sets the beta2 period [18] . The initial interval of coexistent gamma and beta2 rhythms preceded the transition to a slower beta1 oscillation ( 15±2 Hz , n = 10 ) . This new rhythm appeared in both cortical layers upon reduction of glutamatergic excitation with 2 . 5 µM NBQX ( an AMPA receptor antagonist that also reduces kainate drive [19] ) . We note that the initial interval of kainate application—and its associated network activity at gamma and beta2 frequencies—was an essential prerequisite to the generation of the beta1 rhythm . If we bathed the slices in kainate and NBQX initially ( not kainate alone ) then we found no persistent rhythms . Both the population activity ( observed in the LFP ) and the spiking activity of individual neurons suggested that the beta1 rhythm resulted from period concatenation; the period of the slower beta1 rhythm ( ∼65 ms ) equaled the sum of the periods of the two faster rhythms ( gamma ∼25 ms and beta2 ∼40 ms ) . The ordering of the neural activity in each layer supported the period concatenation hypothesis: in the population and single unit activity of both layers we found delays consistent with individual cycles of the gamma and beta2 rhythms during a single beta1 oscillation . In particular , during the beta1 rhythm , the LII activity followed the LV activity by approximately one gamma cycle ( ≈1/ ( 40 Hz ) or 25 ms ) , and the LV activity followed the LII activity by approximately one beta2 cycle ( ≈1/ ( 25 Hz ) or 40 ms ) . To help illustrate these relationships between the cortical layers , we show a cartoon representation of the activity in Figure 1 . The in vitro observations suggest that concatenation of the faster gamma and beta2 rhythms ( through a summation of their periods ) may generate the slower beta1 oscillation . How might such a concatenation occur ? As a simple illustrative model we consider two excitable oscillators , one tuned to spike at gamma frequencies and the other to spike at beta2 frequencies . With enough excitation , both oscillators independently fire at their natural frequencies . If we reduce this excitation and connect the separate oscillators in a particular way , we can generate the slower beta1 rhythm as the concatenation of the gamma and beta2 periods . To do so , we connect the oscillators so that a spike in one resets and causes the other to fire one cycle later . For example , if the beta2 oscillator fires , it resets the gamma oscillator , which then fires 25 ms ( one gamma cycle ) later . Subsequently , the gamma oscillator spike resets the beta2 oscillator , which then fires 40 ms ( one beta2 cycle ) later . Connected in this way , both oscillators fire at beta1 frequency; the period ( 65 ms ) is the sum of the natural periods ( 25 ms and 40 ms ) of the excitable oscillators . Of course , biophysical cells are more complicated than simple excitable oscillators . In what follows , we examine the cell types , intrinsic currents , and synaptic connections that support the beta1 rhythm in vitro . In an ideal model of period concatenation , the mechanisms that support the faster rhythms combine to generate the slower oscillation . We will show that this scheme is nearly—but not quite—met in the computational model . The phenomenon of rhythm generation through period concatenation could be modeled in many different ways ( involving , for example , different cell types , ionic currents , and synaptic connections [20] . ) Here we implement the elementary dynamic building blocks of the gamma and beta2 oscillators ( i . e . , a gamma motif and beta2 motif ) and combine these to generate the beta1 rhythm . In doing so we describe how in vitro observations guide construction of the model and test its predictions . We begin by discussing the coexistent gamma and beta2 activity observed in vitro and simulated in the model . We then describe the transition to beta1 and propose that this transition requires a shift in the deep layer pyramidal cells from antidromic to orthodromic activity . Analysis of the cross-frequency interactions during the beta1 oscillation ( without access to the underlying mechanisms ) might simply suggest that the slower rhythm modulates the faster activity . As we will show , the combined approach of in vitro recordings , data analysis , and mathematical modeling reveals more detailed information about the intrinsic currents and synaptic connections that generate the beta1 rhythm through period concatenation of the faster oscillations .
During the high kainate condition in vitro , coexistent gamma and beta2 rhythms occur in the superficial and deep cortical layers , respectively [17] . To model the superficial layer activity , we implement a population of Pyramidal-Interneuron-Network-Gamma ( or PING ) type oscillators , each consisting of a basket and RS cell . We show the voltage dynamics of a single gamma oscillator ( i . e . , the gamma-motif ) in Figure 2A . The gamma activity results from the interactions between these two cell types , and the decay time of the inhibitory basket cell synapse determines the period of the gamma rhythm [12] . We also include a population of LTS interneurons in the superficial layer [21]–[23] . Under the high kainate conditions , these interneurons fire infrequently and have a disorganizing influence on the gamma oscillator , as we show in Figure 2B . But , following application of NBQX , the LTS interneurons play a vital role in establishing the beta1 motif , as we discuss below . We replicate the RS-basket-LTS circuit ( shown in Figure 2B ) twenty times and connect the RS neurons with electrical synapses to create a population of superficial layer cells , as described in the Methods section . To model the deep layer activity , we simulate a population of IB cells , each consisting of four compartments: an apical dendrite , basal dendrite , soma , and axon . We show a cartoon representation of a single IB cell model in Figure 3A . Recent experimental and modeling work has shown that the axonal M-current controls the period of the antidromic beta2 oscillation [18] . The same is true in the reduced model presented here; during beta2 activity , bursts of action potentials occur in an IB cell axon and travel antidromically to produce bursts of full action potential spikes and subthreshold “spikelets” in the soma ( Figure 3B ) . The dynamics of the M-current determine the interburst interval [18] . When the axon generates an action potential , the M-current increases . After enough sequential action potentials , the M-current becomes large enough that the outward , hyperpolarizing current prevents further spiking . The M-current then slowly decays and , after sufficient time , another burst of action potentials can begin ( Figure 3B ) . We may increase ( or decrease ) the interburst interval by decreasing ( or increasing ) the backward rate function of the M-current ( Figure 3C ) . A decrease in the backward rate function causes the M-current to decay more slowly and the interburst frequency decreases ( dotted curve ) . Conversely , an increase in the backward rate function increases the interburst frequency ( dashed curve ) . We model the dendrites of an individual deep layer IB cell with two compartments: a basal dendrite and an apical dendrite ( Figure 3A ) . This is , of course , a crude approximation to the true dendritic form . We utilize the two compartments to mimic changes in ionic currents and synaptic inputs that occur across the dendritic structure . In particular , we increase the conductance of hyperpolarization activated currents ( h-currents ) in the apical dendrite [24] , [25] , and will connect excitatory NMDA synapses from IB cell axons to IB cell basal dendrites [26] , [27] to generate the beta1 rhythm , as we describe below . Two types of inhibitory input also target the IB cell dendrites; the superficial layer LTS interneurons target the ascending apical dendrites , and a Poisson source of IPSPs target the basal dendrites ( we provide an interpretation of these random inhibitory inputs below . ) Although a crude approximation to the actual pyramidal morphology , we find that this simple model captures the essence of the observed dynamics , as we now describe . We show a cartoon representation of the entire reduced model in Figure 4A . We note that ascending excitatory synapses ( from the deep layer IB cells to the superficial inhibitory cells ) and descending inhibitory synapses ( from the LTS interneurons to the IB cell apical dendrites ) connect the two layers . In Figure 4B we show a typical simulation result for the 80 cells in the model ( twenty of each type ) . We plot the spiking activity of the three superficial layer cell types , and the spiking activity of the dendrites , somata , and axons of the IB cell population . We find that the individual IB cell dendrites generate action potentials infrequently and remain in a mostly inactive state . The IB cell axons generate bursts of action potentials at beta2 frequencies that are weakly synchronized across the population . The weakly organized beta2 activity results in a noisy synaptic input to the superficial layer inhibitory cells . The result is a disorganization of the dynamic motifs that define the gamma rhythm; the basket cells are perturbed directly by the deep layer excitatory inputs , while the the RS cells are perturbed indirectly through the deep layer excitation of the LTS interneurons . We illustrated the effects of these disorganizing deep layer inputs on the gamma rhythm in Figure 2B and will show below that removing the noisy synaptic inputs from the IB cells increases the superficial layer gamma power . We plot in Figure 4D the population average power spectra ( see Methods section ) of the RS cells ( green curve ) and IB cell axons ( red curve ) and find broad spectral peaks in the gamma range ( 40–50 Hz ) in the superficial layer and in the beta2 range ( 20–30 Hz ) in the deep layer . We also show the average cross-correlation ( see Methods section ) between the spike times of the IB cell axons and RS cells in Figure 4C . We find no obvious correlation between the activity of the two layers . Thus , although the layers interact through chemical synapses , these interactions are too weak to correlate the spike times of the two layers . In particular , the disorganizing beta2 frequency input from the IB cells to the superficial layer inhibitory cells does not phase lock ( and therefore correlate ) the rhythmic activity of the two layers . That the model generates coexistent gamma and beta2 rhythms is not surprising—we include in the model components necessary to produce these two rhythms and allow only weak interactions between the two layers . Having established that the reduced model can generate gamma and beta2 oscillations , we now determine how these model rhythms respond to a series of experimental manipulations performed in vitro . In each challenge the experimental results test and constrain the computational model . In addition , we use the model to suggest more detailed information not accessible in experiment . Our manipulations focus on the synaptic connections and intrinsic currents that support ( or disturb ) each rhythm . Analysis of in vitro slice preparations revealed a statistically significant increase in the gamma power of the superficial layers ( 215±29% , n = 6 , P<0 . 05 ) following a lesion through layer IV . We show example LFP recordings and power spectra for these in vitro results in Figure 5C . These observations suggested some functional connectivity between LV cells and superficial layer neurons involved in generating the gamma oscillation [17] . In the computational model , the superficial layer PING oscillators produce the gamma rhythm . We expect that ascending excitatory synapses from the IB cell axons perturb these superficial layer oscillators ( both directly and through activation of superficial LTS interneurons ) and therefore disturb the gamma rhythm . To test this expectation we separate the cortical layers in the model and observe the resulting activity . Specifically we remove the ascending excitatory synapses from the IB cells to the inhibitory cells , and we disconnect the apical dendrites from the IB cells ( we assume that a slice through layer IV severs the ascending dendrites of the IB cells . ) We suggest the associated parameter changes in Figure 5A . Following separation and elimination of the disorganizing inputs , the spiking activity of the superficial PING oscillators becomes more synchronized ( Figure 5B ) . This increased synchronization boosts the superficial gamma power ( Figure 5D ) , in agreement with the in vitro results . In experiment , separating the superficial and deep cortical layers requires a resection through the intervening layer IV . This physical separation necessarily destroys all interlaminar connections . In the model , we may study specific interlaminar connections to determine those that most disturb the superficial gamma rhythm . To do so we remove , one by one , the three types of interlaminar connections: ( i ) excitatory synapses from the IB axons to the basket cells , ( ii ) excitatory synapses from the IB axons to the LTS interneurons , and ( iii ) the apical dendritic compartments of the IB cells . We find that eliminating the first two connections ( but not the last ) increases the superficial gamma power ( data not illustrated ) . The excitatory input from the IB cells disturbs the gamma oscillators directly by depolarizing the basket cells , and indirectly by exciting the LTS interneurons ( which spike and hyperpolarize the RS cells . ) Without the disorganizing effects of the deep layer input , the RS cells drive the population of PING oscillators in synchrony . Inhibition plays a vital role in pacing the gamma and beta1 rhythms [12] , [17] . To determine the effect of inhibition on the beta2 oscillation , we applied 250 nM of gabazine to the in vitro slice preparation and found a statistically significant ( n = 5 observations , p<0 . 05 ) increase in the deep layer beta2 power ( Figure 6C ) consistent with previous results [18] . In the model , IPSPs ( from the superficial layer LTS interneurons and deep layer random sources ) target the IB cell dendrites . We think of the random inhibitory inputs as representing a noisy motif , perhaps important for other deep layer rhythms , but disruptive to the beta2 activity . Therefore , we expect that blocking IPSPs will eliminate these inputs and reduce the orthodromic , disruptive influence on the antidromic , beta2 rhythm . We determine the effect of blocking IPSPs in the model by setting the conductance of all inhibitory synapses to zero , as we indicate in Figure 6A . We find that elimination of IPSPs in the model reduces the orthodromic disruption of the beta2 rhythm . The IB cell dendrites now burst in response to the antidromic propagation of axonal activity . Without the disruptive inhibitory input to the IB cell dendrites , the IB cell axons burst with greater synchrony ( Figure 6B ) , and thus boost the beta2 power ( Figure 6D ) in agreement with the experimental results . We note that removing the basket cell IPSPs causes the electrically coupled RS cells to establish a positive feedback loop and fire rapidly ( Figure 6B ) . In the simple model of the gamma -motif implemented here , the RS cells spike on each cycle of the gamma rhythm . In vitro , individual RS cells spike much more sparsely during gamma activity [17] . We do not model this sparse activity here , which would require a much larger population of RS cells [12] , [13] . Therefore the model of superficial layer activity is not valid after removing the IPSPs that pace the gamma rhythm . We also use the model to investigate the type of inhibitory synapse most disruptive to the beta2 rhythm . To do so , we determine the individual effects of removing inhibitory synapses from: ( i ) the basket cells alone , ( ii ) the LTS cells alone , and ( iii ) the random sources ( to the IB dendrites ) alone . We find that , of the three , removing the latter two increases the beta2 power ( simulations not illustrated ) . Again we conclude that eliminating the disruptive activity of the IB cell dendrites ( here by removing disruptive inhibitory inputs to these compartments ) boosts the beta2 activity . The beta2 rhythm in the deep layer IB cell population originates in the axonal compartments ( in fact , the axonal M-current sets the period of this rhythm [18] ) . Input from the IB cell dendrites disturbs this rhythm , and we expect that silencing the dendritic compartments would boost the deep layer beta2 power . We test this hypothesis in the mathematical model by blocking a current important to both the dendrites and their superficial layer inputs ( the LTS interneurons ) : the h-current . We do so by setting the h-current conductance to zero in the RS cells , LTS interneurons , and IB cell dendrites . We shade the affected cells and compartments gray in Figure 7A and plot an example of the model spiking activity in Figure 7B . We find that blocking all h-currents eliminates the disruptive effect of the IB cell dendrites on the beta2 rhythm . The IB cell axons burst with increased synchrony and therefore the beta2 oscillations in the IB cell population increase in magnitude . The result is an increase in beta2 power , as we show in Figure 7D . We tested the effect of h-current block in vitro by applying 10 µM ZD-7288 to the slice preparation and found a dramatic increase in the beta2 power ( Figure 7C ) . This increase was statistically significant ( n = 5 observation , p<0 . 05 ) and verified the model prediction . The global blockade of h-current does not suggest which particular cell type ( if any ) is most important to this effect . Therefore , we consider in the model the effects of h-current block: ( i ) in the RS cells alone , ( ii ) in the LTS interneurons alone , ( iii ) in the IB cell basal dendrites alone , and iv ) in the IB cell apical dendrites alone . Of these three , only the middle two increase the beta2 power . By eliminating the h-current in the IB cell basal dendrites , we essentially silence these compartments and reduce the effects of the random inhibitory synaptic inputs to them . By eliminating the h-current in the LTS interneurons , we silence these cells and their disturbing inputs to the IB cells . We conclude that eliminating the disruptive inputs to the IB cells enhances the deep layer ( antidromic ) beta2 rhythm . In vitro , the initial interval of coexistent gamma and beta2 rhythms must precede the slower beta1 activity . Without this initial interval ( i . e . , with immediate application of NBQX to the slice preparation ) no beta1 activity occurs . We therefore expect that the coexistent fast rhythms change the network in a way that supports the slower beta1 oscillation . In addition , we know that NMDA receptor-mediated synaptic events support the beta1 activity—blocking NMDA before or after NBQX application prevents the beta1 oscillations ( as we discuss in detail below ) . To model the change in network structure that occurs during the fast rhythms , we assume a strengthening of all-to-all NMDA synapses from each IB cell axon to all IB cell basal dendrites [26] , [27] . We note that potentiation of NMDA synapses has been observed in LV pyramidal cells with bursting behavior [28] . We expect that these NMDA synapses between the IB cells strengthen gradually during the interval of coexistent gamma and beta2 activity . What effect does this gradual change have on the faster rhythms ? In vitro we observed that the patterns of field potentials remained constant from the onset of gamma and beta2 activity to immediately before application of NBQX . Whatever changes occurred in the network to support the beta1 activity had no impact on the faster rhythms . We test this in the model by strengthening the NMDA synapses between the IB cell population under the high kainate conditions . In agreement with the experimental observations , we find no change in the gamma and beta2 activity of each layer; the power spectra in the deep and superficial layers match those shown in Figure 4D ( results not shown . ) We conclude that the strengthening NMDA synapses between the IB cells do not impact the network dynamics until a reduction of excitation ( induced by NBQX ) occurs in the model , as we now describe . After an initial interval of coexistent gamma and beta2 rhythms in vitro , the transition to beta1 activity followed application of NBQX , resulting in a reduction of glutamatergic excitation via AMPA and kainate receptor subtypes . In both the deep and superficial layers , population activity ( as observed in the LFP ) oscillated at beta1 frequency and a consistent lead/lag relationship appeared between the two layers: LII activity preceded LV activity by ∼40 ms , and LV activity preceded LII activity by ∼25 ms ( see Figure 1 ) . We note that the timing of these lead / lag relationships suggests that the faster dynamics motifs ( i . e . , the gamma-motif [25 ms period] and beta2-motif [40 ms period] ) collaborate to generate the beta1 rhythm [17] . We now consider whether such collaborative interlaminar interactions can produce the beta1 oscillation in the model . We first assume that reduction of glutamatergic excitation decreases excitation in the network and inactivates the gamma and beta2 motifs [29] . We approximate this reduction by decreasing the depolarizing input current to the superficial layer cells , and the deep layer IB cell dendrites and axons . We also halt the Poisson distribution of IPSPs to the IB cell dendrites , thereby eliminating these disruptive inputs . We indicate these changes in Figure 8A by shading blue the affected cells and compartments . As described above , we also include NMDA synapses ( decay time constant 100 ms ) within the deep layer IB cell population that target the IB cell basal dendrites; we indicate these slowly decaying excitatory synapses with red lines and filled circles in Figure 8A . Producing the beta1 rhythm requires many mechanisms that support the superficial gamma and deep beta2 activity . To describe these mechanisms , we follow a cycle of the simulated oscillation as it propagates from the deep to superficial layer and back ( Figure 8B ) . We start at a burst of activity in the IB cell axons ( red dots , see Label 1 in Figure 8B ) . This burst delivers strong excitatory input to the superficial layer inhibitory cells . The basket cells spike ( blue dots , Label 2 in Figure 8B ) , thus inhibiting the RS cells and the LTS interneurons . We note that the basket cells fire and inhibit the LTS interneurons before the ascending NMDA synapses can depolarize these interneurons . This occurs in the model because we make the rise time of the NMDA synapse longer for the LTS interneurons than for the basket cells ( see Methods section ) . After receiving inhibitory input , the h-currents of the RS cells activate and depolarize these cells on a slow time scale . The RS cells recover from inhibition and spike ( green dots , Label 3 in Figure 8B ) , inducing the population of LTS interneurons to fire . Only a weak excitatory input from an RS cell is required to push an LTS interneuron past spike threshold; the slowly decaying excitatory input from the deep layer and the intrinsic h-current have steadily depolarized each LTS interneuron . Upon spiking ( purple dots , Label 4 in Figure 8B ) , the LTS interneurons inhibit the RS cells and the IB cell apical dendrites , temporarily halting the slow depolarization of the deep layer cells due to NMDA synaptic input . The slow depolarization of the dendrites continue—due to both h-currents and NMDA synapses—and the IB cells spike ( Label 5 in Figure 8B ) , inducing a synchronous burst in the deep layer population and restarting the beta1 cycle . Computing the population average power spectra of the deep and superficial layer cells , we find peaks near 13 Hz and higher order harmonics ( e . g . , 26 Hz , 39 Hz , 52 Hz , and so on; Figure 8D ) . For the RS cell , we note that the largest peak occurs near 26 Hz . The power of this peak includes two contributions: ( 1 ) harmonic power of the 13 Hz oscillation ( note 2×13 = 26 Hz ) , and ( 2 ) power resulting from the interval between an RS cell spike and the subsequent inhibitory input ( one beta2 cycle ) . The model of beta1 activity agrees with the observed rhythm in a fundamental way: this rhythm results from period concatenation . To show this we plot in Figure 8C the population average cross-correlation between the spiking activity of the RS cells and the IB cell axons . We find two intervals of increased correlation: between approximately −35 ms and −20 ms , and between approximately 45 ms and 60 ms . Thus , the deep layer population activity precedes the superficial layer activity by approximately 30 ms . The reason for this delay is that the bursting IB cell axons activate the superficial basket cells that inhibit the superficial RS cells . We also conclude that the superficial pyramidal activity precedes the deep layer activity by approximately 50 ms . The reason for this delay is that the RS cells activate the superficial LTS interneurons which inhibit the deep IB cell apical dendrites . These correlation results show that the temporal distributions of the superficial and deep layer activities in the model agree with those of the experiments . In both the model and experiments , during the beta1 rhythm the LII activity follows the LV activity by approximately one gamma cycle ( 30 ms or ≈33 Hz ) , and the LV activity follows the LII activity by approximately one beta2 cycle ( 50 ms or ≈20 Hz ) [17] . We note that the rhythmic activity of the model IB cells differ during the high kainate and low kainate drive conditions . During high kainate conditions , antidromic activity generates the beta2 rhythm . The M-current in the axons sets the period of the IB cell bursting [18] , and the dendritic activity interferes with this rhythm . During low kainate drive conditions , following potentiation of the NMDA synapses , orthodromic activity generates the beta1 rhythm . The h-current contributes to the dendritic depolarization following inhibitory input from the superficial layer and induces the axons to fire , thus continuing the beta1 oscillation . The intrinsic currents of , and synaptic inputs to , the IB cell dendrites play an important role in the beta1 activity . We illustrate the dynamics of these currents and inputs within the dendritic compartments of a single IB cell in Figure 9 . During a burst of activity in the IB cell axons ( e . g . , a strip of red dots in Figure 8B ) the slowly-decaying NMDA current ( red in Figure 9 ) activates and depolarizes the basal dendrite ( near t = 25 ms in Figure 9 ) . Approximately 30 ms later , inhibitory synaptic input from a superficial LTS interneuron hyperpolarizes the apical dendrite ( arrow , upper figure ) and activates the apical dendritic h-current ( blue ) . Both the h-current and NMDA current continue to depolarize the IB cell until a fast sodium current quickly activates ( gray ) and the neuron fires a burst of spikes near t = 100 ms . During spiking , dendritic calcium and potassium currents activate ( not shown ) , the h-current and NMDA current reset , and the beta1 cycle restarts . We note that the slowly decaying NMDA input depolarizes the basal dendrites , and that the population of dendrites enters a more active state than during the high kainate conditions ( compare the dendritic activity shown in Figures 4 and 8 ) . The synchronous bursts of activity strengthen the postsynaptic effect of the IB cells and effectively strengthen the ascending synapses from the deep to superficial layer . To illustrate the interplay of the M-current and h-current during beta1 , we plot in Figure 10 examples of the apical dendritic voltage ( yellow ) and axonal voltage ( orange ) of an IB cell , and the gating variables for the h-current ( dotted curve ) and M-current ( dashed curve ) . When the apical dendrite receives an IPSP ( from a superficial LTS interneuron ) , the h-current gating variable opens and depolarizes the dendrite , thus promoting spiking . When the IB cell generates an action potential , this gating variable closes and removes this depolarizing influence . The M-current in the axon behaves in an opposite way . When the axon bursts , this gating variable opens and hyperpolarizes the axon , thus preventing further spiking . The axon may spike again only after the M-current decays sufficiently . This example illustrates the complementary effects of the M-current and h-current in the IB cell model . The M-current acts to prevent spiking in the axon , while the h-current acts to promote spiking in the dendrite . In an ideal model of period concatenation , the mechanisms that generate the two fast rhythms would combine to produce the slow oscillation . This statement is nearly—but not quite—appropriate for the model proposed here . In the model of superficial layer activity , the inhibitory synapse from the basket cell to the RS cell paces the gamma rhythm . Increasing the decay time of this synapse slows the gamma rhythm . A population of these basket cell synapses participates in the beta1 rhythm , and if we increase the decay time of these synapses , then we also slow the beta1 oscillation . Thus , a fundamental mechanism pacing the fast gamma rhythm—namely the basket cell inhibitory synapses—also paces the beta1 rhythm . A more complicated relationship exists between the beta2 rhythm and its contribution to beta1 . During beta2 activity , M-currents in the IB cell axons pace the deep layer rhythm . But , during beta1 these M-currents have less influence on the frequency of the deep layer activity . More important are the h-currents , excitatory synaptic inputs , and inhibitory synaptic inputs to the IB cell dendrites . The transition from beta2 to beta1 involves a switch in the IB cell from antidromic to orthodromic activity . Therefore the beta2 component of the beta1 rhythm is dominated by mechanisms in the IB cell dendrites , not the IB cell axon . Thus , in the model , the concatenation depends on having two mechanisms ( an M-current and an h-current combined with excitatory and inhibitory synaptic inputs ) with the same time scale . In the model , the same cells generate the coexistent fast rhythms and combined slow oscillation , although the biophysical mechanisms important to each rhythm change in the deep layer IB cells . Simpler period concatenation models may be developed , but we do not believe that these models would agree in all ways with the in vitro data . In what follows , we compare the model with an additional set of experimental manipulations . We show that the two are consistent and verify a model prediction of the fundamental role for the h-current in vitro . We also use the model to suggest specific mechanisms important to the beta1 activity and its propagation between cortical layers . We begin with the observation that: Analysis of the in vitro data allowed us to constrain the computational model in many ways , but not completely . In the model of beta1 activity described above , we considered a strengthening of NMDA synapses between the population of deep layer IB cells . As a second model for the change in network connectivity that supports the beta1 rhythm , we consider NMDA synapses that descend from the superficial to deep layer pyramidal cells . In this case , we include slow excitatory synapses ( rise and decay times of 10 ms and 150 ms , respectively ) from each superficial layer RS cell to all IB cell apical dendrites [15] . Performing simulations identical to those described above , we find that the model produces beta1 activity with the characteristics of period concatenation . Moreover , we find that separating the cortical layers , blocking IPSPs , blocking NMDA synapses , or blocking the h-current destroys the beta1 activity . Thus , this second model—with potentiating NMDA synapses originating in the superficial layer RS cells and targeting the deep layer apical dendrites—also produces results consistent with the in vitro experiments and modeling results described above . The essential ideas of the previous model can be captured in a simpler model , but at the expense of becoming more abstract . To construct a simpler model , we do not replicate the cells and dynamic motifs to create neural populations . Instead , we employ single copies of the gamma-motif , the beta2 -motif ( a single IB cell ) , and the LTS interneuron . In addition , we represent the IB cell dendrite as a single compartment ( and do not distinguish between the apical and basal dendrites . ) In each cell and compartment we implement the same currents and synaptic connections utilized in the more detailed model ( e . g . , an M-current in the IB cell axon and ascending excitatory synaptic connections from the IB cell to the superficial inhibitory cells . ) The goal of creating such a simple model is to understand the fundamental mechanisms that support the beta1 oscillation . We show a cartoon representation of the simple model in Figure 12A . In the superficial layer , the RS and basket cells form a single ( PING ) oscillator that generates gamma activity , and in the deep layer a single IB cell axon generates the beta2 rhythm . The mechanisms that support the gamma- and beta2-motifs are identical to those in the more detailed model . We simulate the transition to beta1 activity in two ways . First , we decrease the excitability of the network by reducing the depolarizing input currents to all cells and compartments except the IB cell dendrite and soma; we indicate these hyperpolarizations by shading the affected cells and compartment blue in Figure 12B . Second , we depolarize the IB cell dendrite ( yellow ) and strengthen the ascending synapses from the IB cell to superficial inhibitory cells ( thick black lines , Figure 12B ) . Both changes mimic effects in the more detailed model . For the population of cells described above we included excitatory NMDA synapses between the IB cells . These synapses act to depolarize the IB cell basal dendrites and help synchronize the IB cell bursting , thus effectively strengthening the ascending synapses . In the simple model , we approximate the effect of increased IB cell synchronization as increased ascending synaptic input . The result is stronger synapses from excitatory to inhibitory cells , which we might also interpret as potentiation of these synapses between the two cell populations . We find that the simple model can reproduce all of the in vitro observations ( e . g . , blocking the h-current boosts beta2 activity and eliminates beta1 activity; data not shown ) . We suggest that this greatly reduced model reveals the fundamental mechanisms of the rhythms and period concatenation with four ( rather than 80 ) cells .
We have constructed a computational model , consisting of four cell types , and compared it with in vitro observations from rat somatosensory cortex [17] . In those recordings , we observed a transition from coexistent gamma and beta2 rhythms in the superficial and deep cortical layers , respectively , to a common beta1 oscillation in both cortical layers . We proposed that the slower rhythm resulted from the concatenation of periods of the two faster rhythms: gamma period ( 25 ms ) +beta2 period ( 40 ms ) = beta1 period ( 65 ms ) . Both the fast and slow rhythms were sensitive to numerous experimental manipulations ( e . g . , separating the cortical layers boosted the gamma power under high kainate conditions and eliminated the beta1 power under conditions of low kainate drive . ) In this manuscript , we showed that a biologically realistic , yet simplified , computational model could reproduce the coexistent gamma and beta2 rhythms , the transition to beta1 , and numerous experimental manipulations . Moreover , we used the model to suggest the specific mechanisms important for the generation and modification of each rhythm . In the superficial layer , reciprocal synaptic connections between an RS and basket cell created the inhibition-based gamma rhythm; the decay time of the basket cell inhibitory synapse determined the period of this oscillation . In the deep layer , the M-current in the IB cell axons paced the beta2 rhythm . We proposed in the model that these dynamic motifs combined to create the slower beta1 oscillation . During beta1 activity , the LTS interneurons ( initially disruptive to the coexistent gamma and beta2 oscillations ) served a vital role , and the rhythm propagated between the cortical layers . Thus , unlike the coexistent gamma and beta2 rhythms , the beta1 rhythm represented a state in which activity in both deep and superficial layers of neocortex temporally combined to produce oscillations in which interlaminar interactions were vital . We considered four manipulations of the model and in vitro preparation during beta1 activity . The outcome of each manipulation—separating the cortical layers , blocking the h-current , blocking IPSPs , or blocking the NMDA synapses between IB cells—was the same: elimination of the beta1 rhythm . We noted that the loss of beta1 activity occurred when the rhythm could not propagate between the cortical layers; thus , connections between layers were essential to the beta1 rhythm . We also noted that the beta1 rhythm is not quite a concatenation of the gamma and beta2 rhythms . Instead , we found that two different mechanisms support the beta2 and beta1 oscillations . In the high kainate condition , the beta2 rhythm resulted from antidromic activity in the IB cell axons . In the subsequent low kainate drive condition , the beta1 rhythm resulted from orthodromic activity in the IB cell dendrites . We noted that the initial interval of coexistent gamma and beta2 activity must precede the beta1 rhythm . If we start the in vitro slice preparation in the low kainate drive condition ( i . e . , with kainate+NBQX ) we find no oscillatory activity . Therefore , the initial interval of fast rhythms must change the network to support the slower beta1 oscillation . In the model , we assumed that the coexistent fast rhythms facilitated a potentiation of NMDA synapses between the population of IB cells [28] . In developing the computational model , we also proposed two additional scenarios for the change in network connectivity that supports the beta1 rhythm . First , we suggested strengthening the descending synapses from the superficial RS cells to the deep layer IB cells . Second , for the reduced model , we strengthened ascending synapses from the deep layer IB cells to the superficial inhibitory neurons . Each model was sufficient to generate the beta1 activity through period concatenation and agreed with the observational data . In vitro the transition to beta1 may perhaps incorporate aspects from all three scenarios , and future studies may suggest more general conditions for period concatenation incorporating all three models . In the population models proposed in this work , we considered the activity generated in a single cortical column of somatosensory cortex . An improved model would describe the activity of multiple , interacting cortical columns . Including interactions between columns would permit synaptic connections not present in the single-column model ( for example , excitatory synapses from deep layer IB cells to superficial RS cells observed in rat motor cortex [30] ) . Multi-column models incorporating adjacent cortices ( and their region-specific oscillatory circuits and agonists [31] ) may reveal even richer phenomena . What computational roles might the separate gamma and beta2 rhythms , and the transition to a slower beta1 oscillation , serve ? In the in vitro observations and computational models considered here , the initial faster rhythms coexisted in different cortical layers . The transition to the slower beta1 oscillation established a common rhythm that propagated between both layers . We might therefore interpret the transition to the beta1 oscillation as binding the superficial and deep layers of a cortical column . This binding organizes subnetworks of neurons within the input ( superficial ) and output ( deep ) layers of a cortical column [32] . Transitions between gamma , beta2 , and beta1 oscillations are also observed in vivo [33] , [34] although the role of rhythms in these cognitive functions remains unknown . However , beta oscillations ( 12–29 Hz ) have been associated with long-range synchronization [35] , which may be relevant to the interlaminar beta1 oscillation analyzed here . Recent observations suggest that rhythms within distinct frequency intervals interact , and that these interactions may occur in different ways [36]–[45] . Given the increasing amount of evidence supporting cross-frequency interactions , the mechanisms involved in generating multi-frequency oscillatory states at the cellular and network level become fundamental to understanding brain rhythms . In this work , we described these mechanisms for a particular example of rhythm generation through period concatenation . Because the slow rhythm ( beta1 ) resulted from the concatenation of two faster rhythms ( beta2 and gamma ) , the fast activity was necessarily locked to a specific phase of the beta1 rhythm . For example , the interval between the basket cell firing and the RS ( or LTS ) cell firing defines ( approximately ) one gamma cycle . This fast activity is locked to a particular phase of the IB cell population bursts; this locking is apparent in Figure 8B . Analysis of these data alone suggests that the slow rhythm ( beta1 ) modulates the faster rhythm ( gamma ) . One might therefore conclude that the slow oscillation drives the faster rhythms . This conclusion is only partially correct; in the model the faster rhythms combine to create the slower oscillation . Data analysis alone cannot distinguish the mechanisms producing each rhythm . Instead , a combined approach of data analysis and biophysical modeling is required . Using this combined approach we suggest that faster rhythms , of different coexistent frequencies in different cortical laminae , may be present to maintain independent temporal processing of cortical input in each lamina . If synaptic potentiation occurs then these faster rhythms may concatenate , producing a slower rhythm representing the temporally correlated activity patterns in both deep and superficial laminae—thus uniting previously independent , lamina-specific activity patterns into a single cortical dynamic process .
Slices of parietal neocortex ( 450 µm thick ) , were prepared from adult male Wistar rats and maintained in an interface chamber . Details of artificial cerebrospinal fluid composition and recording techniques are in [18] . Spectra were all calculated from 60 s epochs of LFP data using MATLAB . All drugs were applied directly to the slice perfusate . All procedures were performed in accordance with the UK animals ( scientific Procedures ) act . The model consists of four cell types in two cortical layers . We describe the cells , synapses , and analysis methods in this section ( detailed equations may be found in the Text S1 . The model cells are reductions [46] of detailed multi-compartment representations of rat cortical neurons [15] . To construct the reduced model , we first include explicitly the mechanisms essential for gamma and beta2 rhythm generation observed in the superficial and deep cortical layers , respectively . We then include an additional cell type observed in vitro and chemical synapses to connect the two cortical layers . We begin with a description of the superficial layer model . A simple model of the superficial layer gamma rhythm consists of two neurons—one excitatory and one inhibitory—interacting through reciprocal synapses ( the so-called Pyramidal-Interneuron-Network-Gamma or PING rhythm [12] . ) For the excitatory neuron , we implement a reduction of a superficial regular spiking ( RS ) pyramidal cell [15] . The reduced model consists of one compartment with four intrinsic membrane currents: a leak current , a transient inactivating sodium current ( NaF current ) , a delayed rectifier potassium current ( KDR current ) , and a hyperpolarization activated ( or anomalous rectifier ) current ( h-current ) . The first three currents facilitate the generation of action potentials ( or spiking ) in the model [47] . The last current slowly depolarizes the RS cell following inhibitory input [48] and serves a vital role in the beta1 oscillation , as we describe in the Results section . The form of these intrinsic membrane currents , the reversal potentials , and the dynamics of the gating variables follow [49] . For the inhibitory neuron , we implement a superficial basket cell [15] . The reduced model consists of one compartment with three intrinsic membrane currents: a NaF current , a KDR current , and a leak current . The dynamics and reversal potentials for these currents follow the intrinsic interneuron properties stated in [50] . We note that the traditional spiking currents ( the NaF , KDR , and leak currents ) are similar ( but not identical ) for the RS cell and basket cell models . We connect the RS and basket cells with reciprocal synapses . The equations governing the synaptic dynamics are similar to those described in [35] and [51] . The rise and decay times of the fast excitatory ( AMPA ) synapse are 0 . 25 ms and 1 ms , respectively . The rise and decay times of the fast inhibitory ( GABAA ) synapse are 0 . 5 ms and 5 ms , respectively . We also include a fast inhibitory autapse on the basket cell with the same GABAA dynamics . We did not include fast rhythmic bursting ( FRB ) neurons in the reduced gamma model . Recent experimental and modeling results suggest that these cells provide excitation via axonal plexus activity to drive neocortical gamma rhythms [50] . To avoid the complexity of such a system ( which requires a large cell population with axonally interconnected principal cells ) while maintaining sufficient phasic drive to generate gamma activity , we include tonic input currents injected to both cells . Each cell also receives a weak stochastic input with normal distribution that results in fluctuation of ±0 . 25 mV . The final cell type we include in the superficial layer is a low threshold spiking ( LTS ) interneuron [21]–[23] . Our reduced model of this cell consists of a single compartment with four currents: a NaF current , a KDR current , a leak current , and an h-current . The dynamics and reversal potentials for these currents follow the intrinsic interneuron properties stated in [50] . We form reciprocal synaptic connections between the LTS interneuron and RS cell . The rise and decay times of the excitatory synapse are 2 . 5 ms and 1 . 0 ms , respectively , and the rise and decay times of the inhibitory synapse are 0 . 5 ms and 20 ms , respectively . We also include an inhibitory synapse from the basket cell to the LTS interneuron ( rise time 0 . 5 ms and decay time 6 . 0 ms ) and an inhibitory autapse on the LTS interneuron ( rise time 0 . 5 ms and decay time 20 ms ) . A weak stochastic input ( normally distributed ) perturbs the voltage and results in fluctuation of ±0 . 25 mV . To establish a population model of the superficial layer gamma activity , we create twenty replications of the RS-basket-LTS cell circuit described above and shown in Figure 2B . We make all parameters identical for each cell type of the population except for the depolarizing input currents which we independently vary for each cell of each triad . This heterogeneity causes the twenty RS cells to spike at different frequencies , ranging from 30 Hz and 50 Hz . We connect the RS-basket-LTS cell triads with a single type of connection: all-to-all electrical coupling between the RS cells [15] . To model the deep layer beta2 rhythm , we implement a reduction of the LV tufted intrinsically bursting ( IB ) pyramidal cell [15] . This cell type appears to dominate the beta2 rhythm in vitro [18] . Our reduced model of the single IB cell consists of four compartments: an axon , soma , apical dendrite , and basal dendrite . Each compartment contains the intrinsic membrane spiking currents: a NaF current , a KDR current , and a leak current . In addition , we include in the axon a muscarinic receptor suppressed potassium current ( M-current [52] , [53] ) , and in the dendrites we include a h-current , a M-current , and a high-threshold noninactivating calcium current ( CaH current ) . The maximum conductance of the h-current in the apical dendrites exceeds that in the basal dendrites ( to crudely mimic the increase in h-current observed with distance from the soma in apical dendrites [24] , [25] ) . Both dendritic compartments also receive inhibitory synaptic input—the basal dendrite from a Poisson source of IPSPs and the apical dendrite from the superficial layer , as we describe below . We connect the dendritic and axonal compartments to the soma through electrotonic coupling . The coupling conductances between the axon and soma were identical for both compartments . We set the coupling conductance from the dendrites to soma to exceed the coupling conductance from the soma to dendrites ( so that the soma voltage has a weaker effect on the dendritic compartment's dynamics [46] , [54] . ) Each compartment receives a tonic drive , and the axon and dendrites receive weak stochastic inputs ( normal distribution , ±0 . 1 mV fluctuations ) . To establish a population model of the deep layer activity we create twenty replications of the IB cell . Each member of the IB cell population consists of the same ( four ) compartments and currents described above . We make all parameters the same in each cell , except for the depolarizing currents to each compartment . We vary these inputs from cell to cell to establish heterogeneous bursting activity in the cell population; the interburst frequency of the twenty IB cells ranges from 20 Hz to 30 Hz . In the model , consisting of only twenty IB cells , we cannot represent a sparsely connected axon plexus which involves a large number of cells [15] . Therefore , we simply connect the IB cell population with all-to-all axonal gap junctions . From the superficial to deep layer we include a synapse from a single LTS interneuron to a unique apical dendrite of an IB cell ( we note that the axons of superficial LTS interneurons extend up to layer I—a lamina rich in LV pyramidal cell apical dendrites [22] . ) Explicitly , if we number the population of LTS interneurons and IB cell apical dendrites 1 through 20 , then LTS interneuron 1 forms a synapse on IB cell 1 , LTS interneuron 2 forms a synapse on IB cell 2 , and so on . The rise time and decay time of these synapses are 0 . 5 ms and 20 ms , respectively [15] . From the deep to superficial layer , we include synapses from each IB cell axon to all basket cells and LTS interneurons [55] , [56] . For the basket cell postsynaptic target , the rise and decay times are 0 . 25 ms and 1 . 0 ms , respectively , and for the LTS interneuron postsynaptic target the rise and decay times are 2 . 5 ms and 50 ms , respectively . We note that the differences in the synaptic rise times may result from differences in axonal conduction times or dendritic delays ( resulting perhaps from differences in dendritic morphologies or differences in locations of synaptic contacts [27] , [57] . ) We do not include ascending synapses from deep layer IB cells to superficial pyramidal cells because anatomical studies indicate few synapses between these populations within a cortical column [15] , [56] , [58] . In addition to these three types of permanent synapses , we include a fourth potentiating synapse: a slow ( NMDA ) excitatory synapse extending from each IB cell axon to all IB cell basal dendrites [26] , [27] . The rise and decay times for this synapse are 0 . 5 ms and 100 ms , respectively . We employ the latter synapse to generate the beta1 rhythm , as we show in the Results section . We use the Interactive Data Language ( IDL ) to compute numerical solutions to the model equations . We implement a second-order difference method with a time step of 0 . 01 ms , and follow the Euler-Maruyama algorithm to include stochastic inputs . Readers may obtain the simulation code by contacting the authors . To analyze the simulation results we compute two measures: the power spectrum and the cross-correlation . We briefly describe each measure here ( detailed discussions of these measure may be found in the literature , for example [59] ) . To compute a power spectrum , we first compute ten realizations of the model , each with a different set of random tonic input currents to the cells and compartments , and each lasting 500 ms . Then for each realization we compute the population average voltage of the cell type of interest by summing the voltages of all ( twenty ) cells and dividing by the total number of cells . We then compute the power spectra of the population average voltages , and average these spectra across the ten realizations . To compute the cross-correlation between the spike times of the RS cells and IB cell axons , we first simulate the model to create 1 s of data . We then locate the spike times and create new “binary” time series for each ( of the twenty ) RS cell and IB cell axon . We make the binary times series 0 everywhere except at the spike times where we set the time series to 1 . We then compute the cross-correlation between the binary time series of each IB cell axon and the corresponding RS cell . By corresponding , we mean the RS cell in the unique triad whose LTS interneuron forms a synapse on a particular IB cell dendrite . We compute the cross-correlation in this way to avoid the effects of correlations in the subthreshold membrane potentials ( e . g . , correlations in hyperpolarizations of two cells . ) We compute these cross-correlations for all twenty pairs of IB and RS cells , and average the results over the twenty cell pairs . | Since the late 19th century , rhythmic electrical activity has been observed in the mammalian brain . Although subject to intense scrutiny , only a handful of these rhythms are understood in terms of the biophysical elements that produce the oscillations . Even less understood are the mechanisms that underlie interactions between rhythms; how do rhythms of different frequencies coexist and affect one another in the dynamic environment of the brain ? In this article , we consider a recent proposal for a novel mechanism of cortical rhythm generation: period concatenation , in which the periods of faster rhythms sum to produce a slower oscillation . To model this phenomenon , we implement simple—yet biophysical—computational models of the individual neurons that produce the brain's voltage activity . We utilize established models for the faster rhythms , and unite these in a particular way to generate a slower oscillation . Through the combined approach of experimental recordings ( from thin sections of rat cortex ) and mathematical modeling , we identify the cell types , synaptic connections , and ionic currents involved in rhythm generation through period concatenation . In this way the brain may generate new activity through the combination of preexisting elements . | [
"Abstract",
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] | 2008 | Rhythm Generation through Period Concatenation in Rat Somatosensory Cortex |
Bistability has important implications in signaling pathways , since it indicates a potential cell decision between alternative outcomes . We present two approaches developed in the framework of the Chemical Reaction Network Theory for easy and efficient search of multiple steady state behavior in signaling networks ( both with and without mass conservation ) , and apply them to search for sources of bistability at different levels of the interferon signaling pathway . Different type I interferon subtypes and/or doses are known to elicit differential bioactivities ( ranging from antiviral , antiproliferative to immunomodulatory activities ) . How different signaling outcomes can be generated through the same receptor and activating the same JAK/STAT pathway is still an open question . Here , we detect bistability at the level of early STAT signaling , showing how two different cell outcomes are achieved under or above a threshold in ligand dose or ligand-receptor affinity . This finding could contribute to explain the differential signaling ( antiviral vs apoptotic ) depending on interferon dose and subtype ( α vs β ) observed in type I interferons .
Molecular switches play an important role in cell signaling . It is known that transcriptional switches control differentiation decisions [1] and major developmental signaling pathways use different mechanisms to switch from transcriptional repression to activation of target genes [2] . A system operating as a switch responds in an all-or-none fashion to a graded stimulus . Instead of a mere ultrasensitive response ( sigmoid input-output relationship steeper than the Michaelis–Menten type [3] ) , switch-like systems undergo a transition between two discrete outcomes , often accompanied by hysteresis . In the context of mathematical models of ordinary differential equations ( ODEs ) , switch-like behavior is captured by the nonlinear phenomenon of bistability where two stable steady states coexist for a certain range of the model parameters . A bistable system can switch between two different stable steady states in a threshold dependent manner , producing a sharp change in the output as a response to a gradual change in a stimulus or control parameter . Reaction networks may also have more than two different steady states , as it is the case for multi-site phosphorylation systems [4] . Paradigmatic examples of bistable switches in signaling include the cyclin dependent kinase network that controls the cell cycle [5–8] , the transcriptional switch responsible for stem cell fate decision ( self-renewal or differentiation ) [9] , the pheromone sensing MAPK pathway in S . cerevisiae [10] and the lysis/lysogeny switch in the λ-phage [11] . Recent studies also suggest important roles of bistability in several malfunctions and diseases . To name a number of examples , Kafsack et al . [1] discovered a transcriptional switch controlling a differentiation decision in protozoan malaria parasites; Rieger et al . [12] explain through bistability the threshold phenomena in protein aggregation ( neurodegenerative disease can originate from the misfolding and aggregation of proteins ) ; Alam and Gorska [13] speculate that ERK1/2 bistability serves as a signaling memory for epithelial priming of the immune system in chronic asthma , and Shiraishi et al . [14] propose a large-scale analysis of network bistability for various human cancers to identify genes that can potentially serve as drug targets or diagnostic biomarkers . In the process of developing models of cell signaling pathways the question frequently arises whether a signal transduction mechanism ( either the complete pathway or a submodule ) is capable of multiple steady states . Bifurcation diagrams are useful to study the qualitative and quantitative behavior of equilibria when appropriate parameter and steady state values to start the analysis are provided . However , standard continuation/bifurcation tools are not suitable in practice if , as in the case of real modeling problems in cell signaling , the systems are high dimensional and there is inadequate information a priori about parameter values . Chemical Reaction Network Theory ( CRNT ) has been postulated as a promising framework for the qualitative understanding of complex biological systems from structural properties [15–17] and , in particular , for linking structural properties of reaction networks with the capacity for multiple steady states [18–20] . The two main bodies of classical CRNT theory ( so called deficiency-oriented and injectivity oriented , see [21 , 22] and [18 , 23 , 24] for examples of each ) , as well as recently developed methods [25–28] , rely on properties of the underlying network structure and provide useful results without any specification of the kinetic constants or steady state values . A theory based on analysis of subnetworks and atoms of multistationarity has been developed in [29 , 30] . Algebraic approaches such as Gröbner basis have been also successfully applied to chemical reaction systems [31–34] . In case we have experimental evidence of bistability ( for example , in the form of hysteretic dose response curves ) we can exploit CRNT results to get insight into the network’s potential qualitative behavior and obtain quantitative information regarding the parameters [35] . However , CRNT results are usually illustrated by theoretic examples ad hoc and the application to the modeling process of biological pathways is still scarce ( a recent work using CRNT in Wnt signaling by MacLean et al . [36] is a notable exception ) . We aim to provide methods that exploit the structure of chemical reaction networks to allow , in combination with bifurcation analysis , for easy and effective detection of multistationary behavior in signaling pathways . To this purpose we develop , within the framework of chemical reaction networks , sufficient conditions for the existence of a saddle-node , taking into account appropriate assumptions in cell signaling settings . Optimization is used to search efficiently through the state-parameter space providing ( in case a saddle-node is found ) the state and parameter values from which to start a bifurcation diagram using standard continuation/bifurcation tools . If the system is multistationary ( i . e . the saddle-node is a saddle-node bifurcation ) , two equilibrium branches are automatically computed . In presence of mass conservation we exploit previous ( deficiency-oriented ) work by Otero-Muras et al . [37] . For open systems without mass conservation our approach is based on ( injectivity-oriented ) results originally developed in the context of continuous flow stirred tank reactors by Craciun and Feinberg [23] . Both approaches cope with common characteristics of signaling networks , and together cover the majority of examples in signal transduction pathways , modeled with mass action kinetics . Type I interferons are a family of highly related proteins that regulate many different cellular functions ( showing , among others , antiviral , antiproliferative and immunomodulatory activities ) . All Type I interferon subtypes ( including IFNα2 and IFNβ ) interact with the same pair of receptor subunits at the cell membrane and induce the activation of the JAK/STAT pathway [38] . The different cellular responses ( outputs ) after IFN treatment depend on the IFN dose , receptor binding affinity ( through a different stability of the ternary complex [39] ) and the cell specific context ( namely receptor density ) [40] . However , the detailed cellular mechanisms translating these input and cell context differentials into specific biological response patterns are still unknown . In this work we explore ( sub ) networks potentially playing a key role in the pathway dynamics , where the capacity for multiple steady states would reflect the outcome of a cell decision process . To this aim we search for bistability in different subnetwork structures compatible with current biological knowledge about the interferon signaling pathway . Specifically , we search at three different levels: ligand-receptor interactions at the cell membrane , early STAT signaling and STAT signaling including the expression of key proteins .
Firstly , we introduce the necessary concepts from the Chemical Reaction Network Theory formalism , the mathematical notation used throughout the paper , as well as the fundamental assumptions of the methods presented next . In this section we present a deficiency oriented approach to efficiently detect multi-steady state behavior in signaling pathways with mass conservation ( this includes closed networks and open networks with mass conservation ) . Based on a previous work [37] we here develop sufficient conditions for the existence of a saddle-node in the specific context of signaling pathways . We formulate the search as an optimization problem that provides , in case it is detected , the coordinates of a saddle-node . From this point , a continuation of equilibrium is started by means of a standard continuation algorithm , to verify whether the saddle-node is indeed a saddle-node bifurcation ( which leads to two different equilibrium branches ) . In this section we present an injectivity-oriented approach to efficiently detect multistationary behavior in signaling pathways in absence of mass conservation relations . The approach is based on previous work by Craciun and Feinberg [23] in the context of Continuous Flow Stirred Tank Reactors ( CFSTR ) . There , the authors propose sufficient conditions for multistationarity in fully diffusive networks ( where all the species are present in the feed and outflow streams ) . Here , we first define the class of semi-diffusive networks , which complies with the natural assumptions for signaling pathways , and we develop sufficient conditions for a saddle-node to occur . We propose an algorithm to efficiently search for parameters that satisfy the saddle-node conditions in terms of reaction fluxes , and show how to retrieve the exact coordinates ( in terms of kinetic rate constants and steady state concentrations ) of the saddle-node , starting from the set of optimal fluxes . Finally , a continuation of equilibrium is started in forward and backward directions providing , in case that the saddle-node is a saddle-node bifurcation , two branches of equilibria .
The formation of the ternary interferon-receptor complex is among the best studied components of the interferon pathway [38] . The model we consider comprises the receptor-ligand interactions occurring on the cell membrane and , more precisely , the binding of extracellular interferon to one of the interferon receptor subunits ( IFNAR1 or IFNAR2 ) and the subsequent recruitment of the other receptor subunit to compose the IFN-IFNAR1-IFNAR2 ternary complex ( see Fig 4A ) . The stability of the ternary complex has been experimentally shown to trigger differential biological responses [39] , and we therefore investigate whether bistability could be present already at this initial stage of interferon signalling . To model signalling processes downstream of the activated receptor-ligand complex , we consider the mechanism depicted in Fig 5A . Taken with mass action kinetics , this mechanism is compatible with the experimental data obtained for the first two hours upon IFN stimulation ( included in S1 Appendix ) and with current biological knowledge reporting a pivotal role of STAT2 in type I IFN signaling [57] . The JAK/STAT pathway transduces a multitude of signals from various receptors to trigger appropriate gene transcription [58] . Thus , as a shared module , we decided to investigate whether its topological structure could host bistable dynamics . The model incorporates STAT2 activation through binding to the ternary interferon-receptor complex and a subsequent recruitment and activation of STAT1 [59] . Phosphorylated STATs can further dissociate from the activating complex and form homodimers ( STAT1-STAT1 ) or heterodimers ( STAT1-STAT2 ) that act as transcription factors and modulate biological responses . The existence of bistability in a pathway submodule does not necessarily imply that a bigger network containing this submodule is bistable [19] . Next we aim to elucidate whether the bistability is preserved when the STAT pathway is embedded in a broader network including transcriptional feedback . Multiple feedbacks have been uncovered in JAK/STAT signalling , including inhibition by suppressor of cytokine signaling ( SOCS ) [60] , USP18 negative feedback control [61] or STAT1 expression . We consider here the expression of STAT1 via interferon regulatory factor IRF1 and CREB-binding protein ( CBP ) , as postulated by Smieja et al . [55] . We examine the mechanism depicted in Fig 5B , considering basal expression of STAT1 , STAT2 , IRF1 , CBP and a constant inflow of activated receptor , as well as the degradation of all species . We found a set of fluxes fulfilling the sufficient conditions for a saddle-node based on the injectivity criterion . Starting a continuation from this point we find that in fact , the STAT network extended to include the STAT1 feedback , preserves its bistability . Bistability of the network in Fig 5B also follows from Theorem 5 . 1 in [62] . Finally , we analyze the early STAT signaling network coupled with the receptor complex formation network upon IFN induction . The complete module is depicted in Fig 6A . Using the deficiency-oriented approach the network is found to be bistable . This example shows how our method is complementary to existing theory: bistability of the network also follows from Theorem 4 . 2 in [29] , and our method provides parameter sets leading to bistable behaviour . We take one set of parameters found by our algorithm which fulfills the saddle-node condition , and perform a bifurcation analysis using standard tools [53] . We observe that , by varying a parameter related to the affinity of the receptor towards interferon ( k29 ) , we obtain a discontinuous jump in the steady state levels of the proteins involved , showing hysteretic behavior and a rather broad range of bistability . Below a threshold in the parameter value , the system is in a steady state with high level of the STAT1-STAT1 transcription factor and low level of STAT1-STAT2 , while above the threshold the situation is reversed ( low STAT1-STAT1 , high STAT1-STAT2 ) . We observe also a threshold behavior with respect to the initial levels of STATs and IFN receptors ( bifurcation diagrams are included in S1 Appendix ) .
Reaction networks , ranging from chemical species interactions in stirred tank reactors to molecular pathways within cells , can be classified according to the exchange of matter with the environment through the ( real or imaginary ) boundaries of the volume where the reactions are taking place . In cell signaling , these fluxes of matter can be of the form of protein basal formation , degradation or translocation from/to a different compartment . One fundamental difference with respect to the Continuous Flow Stirred Tank Reactor ( CFSTR ) paradigm is the fact that , due to the presence of protein complexes , not all the species can be in the inlet/input flow . To overcome this , we define the class of semi-diffusive networks to which the majority of signaling pathways without mass conservation can be easily adapted . In order to provide adequate methods for detecting sources of bistability in signaling , we take into account the particularities of signaling models ( both with and without mass conservation ) . We formulate the search for multistationarity as an algorithm , in which first global optimization methods efficiently search for saddle-node points in a signaling pathway of interest , identifying decision variables that cause a saddle-node ( and potentially multiple steady states ) to occur . These decision variables contain the kinetic rate constants for systems with mass conservation , and the reaction fluxes for systems without mass conservation ( in this case a compatible set of kinetic constants and protein steady state values is retrieved from the reaction fluxes ) . If a saddle-node is found , in a second step we start a continuation of equilibrium in forward and backward directions using standard bifurcation tools [53] such that , provided that the saddle-node is a saddle-node bifurcation , two branches of equilibria are automatically computed . One interesting aspect of the approaches presented is that we can incorporate all the quantitative information available regarding experimental conditions , kinetic constants , etc . by fixing known kinetic parameters , protein concentrations or reaction fluxes at the steady state . This is particularly useful in the context of integrated experimental-computational modeling approaches when we are interested in detecting sources of bistability under specific biological/experimental conditions . Global optimization solvers provide us both with computational scalability to handle high dimensional state and parameter spaces typical in the context of modeling signaling pathways , and computational speed to find solutions in the order of seconds . As a drawback , if the algorithm does not find a zero of the objective function we cannot formally preclude the existence of saddle-nodes and therefore multiple steady states . Non-heuristic search strategies like interval methods will provide conclusive results [37] but they become computationally untractable in realistic signaling scenarios . Our approach was motivated by the need of evaluating bistability sources at different levels during the modeling process of the interferon signaling pathway . Although Chemical Reaction Network Theory appeared to be a powerful framework , to the best of our knowledge , there was no unified method for multi-stationarity detection based on CRNT that provides parameter sets leading to multiple steady states and that could also be applied to the majority of signaling pathways . As mentioned in the introduction , other methods for detection of complex nonlinear behavior in biochemical reaction networks based on different network graphs have been developed recently , including the software GraTelPy by Walther et al . [25] which uses graph-theoretic analysis of the bipartite graph to find sources of multistability , oscillations or Turing instabilities , and the toolbox CoNtRol by Donnell et al . based on the DSR graph [26] . Importantly , the two methods presented here can be fully automated in an algorithmic manner , combining numerical with simple symbolic computations ( basically simple symbolic derivatives ) . Moreover , the formulation of the saddle node detection as an optimization problem allows to exploit the efficiency of global optimization solvers . In terms of network size , this ensures good scalability to large networks . The implementation of a Matlab toolbox is subject of ongoing work . Our approach has broad applicability , since , as stated in the Methods section , the assumptions that we need ( namely mass action kinetics , the existence of a positive steady state , a uniterminal graph in case of networks with mass conservation , and semi-diffusive regime in case of no mass conservation ) are mild in the context of cell signaling . Regarding the biology of type I interferons , the following open questions have attracted the attention of the community: why are there many different interferon subtypes ( for example , 16 subtypes in the IFN I family in humans ) and how can different signaling outcomes be generated through the same receptor . Depending on the type/dose of interferon and the cell context , the cell outcome might vary from antiviral to apoptotic activity . It has been shown that differences in affinity to the receptor subunits , through a different stability of the ternary complex , dictate differential biological activities [39] but the underlying signaling mechanisms are not understood . Our analysis of network topologies shows that bistability appears already at the level of early STAT signaling , and that varying parameters related to the interferon input and affinity , the system can decide ( in a threshold fashion ) between two different outcomes , characterized by different levels of active transcription factors coding for two families of genes ( ISRE and GAS ) . We also observe threshold behavior with respect to the initial levels of proteins within the cell . In this way , the STAT signaling pathway could be translating input/cell context differentials into different response patterns , contributing to explain the observed differential signaling . The analysis of the interferon pathway demonstrates that CRNT-based methods can help understanding realistic signaling pathway representations; it opens a promising line for further investigation concerning both STAT and IFN signaling by combining in-silico and in-vitro approaches . | Type I interferons ( IFNs ) regulate a variety of cell functions , exhibiting , amongst others , antiviral , antiproliferative and immunomodulatory activities . Due to their anticancer effects , type I IFNs have a long record of applications in clinical oncology . It is still an open question how type I IFNs generate so diverse signaling outcomes by activating the same receptor at the cell membrane and triggering the same JAK/STAT pathway . It has been experimentally shown that differences in ligand affinity towards the receptor , IFN dose and receptor density are translated into different activities , but the underlying mechanisms of differential responses remain elusive . Looking for potential cell decision processes that could help answering this question , we explore the capacity for bistability at different levels of the IFN pathway . The search for bistability sources in interferon signaling is performed within the framework of Chemical Reaction Network Theory , by adapting previous results to the specific context of signaling pathways . Surprisingly , we find a source of bistability already at the early STAT signaling level . As a result , we show that the pathway has the capacity to translate a difference in affinity or IFN dose into a binary decision between High/Low or Low/High activation profiles of two IFN transcription factors ( ISGF3 and STAT1-STAT1 homodimers ) responsible for the upregulation of two different families of interferon stimulated genes: ISRE and GAS . | [
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"chemistry",... | 2017 | Chemical Reaction Network Theory elucidates sources of multistability in interferon signaling |
HIV-1 frequently escapes from CD8 T cell responses via HLA-I restricted adaptation , leading to the accumulation of adapted epitopes ( AE ) . We previously demonstrated that AE compromise CD8 T cell responses during acute infection and are associated with poor clinical outcomes . Here , we examined the impact of AE on CD8 T cell responses and their biological relevance in chronic HIV infection ( CHI ) . In contrast to acute infection , the majority of AE are immunogenic in CHI . Longitudinal analyses from acute to CHI showed an increased frequency and magnitude of AE-specific IFNγ responses compared to NAE-specific ones . These AE-specific CD8 T cells also were more cytotoxic to CD4 T cells . In addition , AE-specific CD8 T cells expressed lower levels of PD1 and CD57 , as well as higher levels of CD28 , suggesting a more activated and less exhausted phenotype . During CHI , viral sequencing identified AE-encoding strains as the dominant quasispecies . Despite increased CD4 T cell cytotoxicity , CD8 T cells responding to AE promoted dendritic cell ( DC ) maturation and CD4 T cell trans-infection perhaps explaining why AE are predominant in CHI . Taken together , our data suggests that the emergence of AE-specific CD8 T cell responses in CHI confers a selective advantage to the virus by promoting DC-mediated CD4 T cell trans-infection .
During natural SIV/HIV infection , CD8 T cells have been shown to be important in viral control [1–6] . CD8 T cells were first shown to play a critical role in maintaining viral suppression in the SIV/non-human primate ( NHP ) model [1–3] . In the hyper-acute phase of HIV-1 infection , the kinetics of CD8 T cell activation , as well as the magnitude of response , directly correlated with a lower viral load set point [4] . Given that CD8 T cells play an important role in viral control during natural HIV-1 infection , a deeper understanding of how CD8 T cell function is influenced by the virus could greatly inform the development of optimal vaccination and functional cure strategies . One major obstacle to inducing effective CD8 T cell responses is the rapid rate of viral mutation , promoting the selection of escape mutations which in turn increase viral fitness by diminishing the cytotoxic CD8 T cell response [7–10] . Our group and others have used population level analyses to define HIV-1 adaptation through HLA-I associated polymorphisms [11–14] . We use the term adaptation rather than escape to be more inclusive of mutations that afford a benefit to the virus but that do not necessarily result in decreased immune recognition . We have termed epitopes containing these HLA-I associated polymorphisms as adapted epitopes ( AE ) , and those epitopes lacking any evidence of HLA-I associated changes as non-adapted epitopes ( NAE ) . Using this approach , we previously demonstrated that individuals infected with a transmitted founder virus ( TFV ) , highly adapted to their HLA-I alleles , were found to have accelerated CD4 T cell decline and an increased viral load ( VL ) set point [14] . Furthermore , during acute infection , we found AE were poorly immunogenic , suggesting that HIV-1 adaptation is primarily associated with early escape from CD8 T cell responses [14] . However , it remains unclear how AE affect CD8 T cell responses in chronic HIV infection ( CHI ) . Not all adapted epitopes result in complete ( classical ) escape from the CD8 T cell response . In fact , in spite of decreased recognition in acute infection , the overall predicted HLA-I binding affinity is comparable for many non-adapted and adapted epitopes , suggesting that AE can be presented to CD8 T cells [14] . And , it was previously shown that de novo CD8 T cell responses can be generated in response to emerging escape mutations in chronic HIV-1 infection [15] . Our group and others have previously demonstrated that CD8 T cell cross-reactivity broadens from acute to chronic infection [16–18] . Interestingly , there has also been increasing evidence that HIV-1 adaptations may confer several viral benefits other than just classical escape , such as increasing viral fitness [19 , 20] , compensating for fitness costly mutations [21 , 22] , and acting as a “decoy” by drawing CD8 T cell responses away from other epitopes [23] . Another intriguing viral advantage non-classical adaptation was put forth by Mailliard et al . where some variant epitopes elicited a “helper-like” CD8 T cell phenotype , which contributed to viral trans-infection by promoting monocyte derived DC maturation and inducing a pro-inflammatory response [24] . Along the same lines , another recent study showed that resistance of monocyte derived macrophages to HIV-specific cytotoxic CD8 T cell killing promoted inflammation whereas CD8 T cells rapidly killed CD4 T cell targets , suggesting that CD8 effectors may yield different outcomes depending on the type of target cell [25] . Our present study aimed to assess the immunogenicity of AE in chronic HIV-1 infection and to determine how these HLA-I restricted adaptations might alter the host immune response and benefit the virus . We found that contrary to acute infection , CD8 T cells increasingly target AE with higher in vitro cytotoxicity in chronic infection . In spite of this apparent increase in immune pressure , AE remained the dominant epitope encoded by viral quasispecies in chronic infection . We further found that AE-specific CD8 T cells promoted viral trans-infection from mature DCs to CD4 T cells , suggesting a mechanism by which HIV-1 adaptation confers a viral advantage other than direct immune evasion .
In order to determine the immunogenicity of AE in chronic infection , we assayed PBMC samples from 65 HIV-1 chronically infected patients ( Table 1 ) . Each sample was tested for IFNγ-producing CD8 T cell responses for predicted NAE and AE pairs that were restricted by their HLA-I alleles using an ELISpot assay . Contrary to what we have seen in acute infection [14] , the frequency and magnitude of responses towards NAE and AE were similar in chronic infection regardless of protein specificity ( Fig 1A and 1B ) . We also compared the CD8 T-cell responses restricted by protective HLA alleles ( B*27 , B*57 , and B*5801 ) with CD8 responses restricted by neutral alleles ( all other HLA-I alleles ) , and did not see any significant differences between these 2 allelic groups . More than half of AE ( 56 . 76% ) were immunogenic in chronic infection; however , it is clear that the more often an AE was tested , the higher the chance we saw a response in at least one individual ( Fig 1C ) . In fact , when we looked at unique AE that were tested in at least five different individuals , we found that 80% of these AE elicited CD8 T cell IFN-γ response in at least one patient . Interestingly , only 12 . 7% ( 14/110 ) of the adaptations studied significantly impaired the predicted HLA binding changing the epitopes from strong to weak binder or from weak to no binder as defined by the NetMHC ( S1 Table ) . Additionally , only 33 . 6% ( 37/110 ) of adaptations were located at anchor positions , defined here as the P2 or C-terminal residue . Taken together , our current results indicate that a significant proportion of adapted epitopes are immunogenic in chronic infection , suggesting these mutations are non-classical adaptation . Next , we evaluated the development of these responses longitudinally . We tested transmitted NAE and AE encoded by the TFV in 13 individuals ( Table 2 ) sampled at both acute and chronic infection time points for IFNγ responses by ELISpot assay . TFV encoded HIV pre-adaptation to CD8 T cells was very common . Overall , 36 unique NAE and 37 unique AE were tested in these 13 individuals , the majority ( 61/73 ) of which were tested in one patient and the others in two or three patients ( S2 Table ) . Although some TFVs were enriched with either NAE or AE , we observed overall half of HLA restricted epitopes in TFVs were pre-adapted in this cohort ( S2 Table , Fig 2A ) . The AE-specific CD8 T cells demonstrated a higher response rate ( p = 0 . 01 ) and magnitude ( p = 0 . 02 ) during chronic infection compared to acute infection , while the NAE-specific CD8 T cell responses remained similar ( Fig 2B , 2C and 2D ) . Collectively , these data indicate that CD8 T cell IFNγ responses targeting transmitted AE increase significantly in frequency and magnitude from acute into chronic infection . Prior work by our group showed a broadening of CD8 T cell cross-reactivity from acute to chronic infection [16] and our IFNγ ELISpot data showed an increase in AE responses in chronic infection . We , therefore , determined whether the same population of CD8 T cells in chronic infection would respond to both the NAE and AE forms of an epitope . A higher proportion of patient samples responded to both the NAE and AE ( dual positive response ) as compared to only the NAE or AE form ( single positive response , p<0 . 0001 and p = 0 . 0004 respectively , Fig 3A ) . These dual positive responses were also greater in magnitude as compared to single positive responses ( combination of single NAE and AE responses , p = 0 . 003 , Fig 3B ) . Next , we stained these cells with four pairs of NAE and AE specific HLA-I tetramers conjugated with different fluorochromes ( NAE-APC or AE-PE ) . In all six dual responding individuals tested , we consistently observed a single population of CD8 T cells labeled by both the NAE and AE tetramers as shown in a representative example in Fig 3C and cumulatively in Fig 3D , indicating a dominance of cross-reactive CD8 T cells responding to both NAE and AE during chronic infection . Since we observed increased AE-specific IFNγ responses in chronic infection and these responses could often be attributed to cross-reactive CD8 T cells , we evaluated the cytotoxicity of CD8 T cells recognizing AE versus NAE pulsed targets . We expanded antigen-specific cells in vitro by co-culturing the isolated CD8 T cells with peptide pulsed autologous monocytes . Using these peptide-specific CD8 T cell lines generated against NAE or AE , we assessed cytotoxicity with a 7AAD killing assay , in which we quantified the percentage of 7AAD+ CD4 T cell targets at various effector to target ratios as an output of CD8 T cell cytotoxicity , as described previously [14 , 16] . Overall , cytotoxicity was assessed for six different NAE/AE pairs in seven CHI patients ( S3 Table ) . A representative example of flow cytometry based gating and normalized data from CHI-6 is shown in S1A Fig , Fig 4A and 4B . Cumulative data analysis showed that the CD8 T cell lines generated against AE consistently elicited stronger cytotoxic responses to peptide-pulsed CD4 T cells ( p = 0 . 02 ) than their corresponding NAE counterparts ( Fig 4C ) even though their ex vivo IFNγ ELISpot response magnitude were not significantly different ( S2A Fig ) . Whenever cell number was not limited , we also tested these CD8 T cell lines for cytokine/effector molecules production , including IFNγ , TNFα , CD107a , perforin , and granzyme A/B production , which have been shown to be relevant to CD8 T cell cytotoxicity [26–28] . We did not detect any significant differences in the frequency of their production ( either mono or polyfunctional responses ) between NAE and AE specific CD8 T cell lines ( S2B–S2F Fig ) . Since we saw differences with cytotoxicity but not with cytokine/effector function , we next asked if these CD8 T cells respond differently to stimulation by NAE versus AE . Multiple surface markers , including PD1 , TIM3 , LAG3 , TIGIT , CD160 , CD27 , CD28 , CD38 , CD57 and CD69 , have been shown to play an important role in regulating CD8 T cell function and impacting disease progression during HIV-1 infection [29–37] . Thus , we assessed the expression level of these surface makers . Because we had previously observed that a single CD8 T cell population was responsible for dual NAE and AE responses , we assessed the expression of these markers on IFNγ+ CD8 T cells following NAE or AE peptide stimulation ( S1B Fig , Fig 5A ) . CD8 T cells stimulated with AE expressed significantly lower levels of PD1 and CD57 and higher levels of CD28 ( Fig 5B ) , suggesting a more activated and less exhausted/senescent phenotype consistent with the enhanced cytotoxicity data seen in Fig 4C . We also observed a trend towards lower expression of the other two exhaustion markers , LAG3 and TIGIT , on CD8 T cells responding to AE ( Fig 5B ) . While increased expression of TIM3 was seen on AE-responding CD8 T cells ( Fig 5B ) , a recent study found no evidence that TIM3 truly marks exhausted CD8 T cells [38] . Overall , these data indicate that cross-reactive CD8 T cell responses against NAE and AE are associated with a differential expression of molecules involved in CD8 T cell function and that AE-stimulated CD8 T cells have a less exhausted and less senescent phenotype . Since AE were increasingly targeted and induced a higher cytotoxic response during chronic infection , we next determined whether the virus evolved by mutating away from this increased immune pressure during chronic infection . We sequenced the viral quasispecies present in chronic infection in six of the seven individuals that were also evaluated for cytotoxicity ( Fig 4 ) . The seventh individual ( CHI-3 ) had undetectable viral load at the time point of interest preventing successful sequencing attempts . Phylogenetic analyses showed significant viral heterogeneity at the quasispecies level among all six individuals . For each individual , the frequency of NAE and AE of interest was assessed with representative data shown in Fig 6A . A majority of HIV quasispecies encoded AE in five of the six individuals sequenced ( Fig 6B ) . The exception is individual CHI-2 , whose sequence revealed a higher frequency of the NAE-FRL9 ( FPVRPQVPL ) epitope ( 99 . 11% ) as compared to its counterpart AE-FKL9 ( FPVKPQVPL ) epitope ( 0 . 89% ) ( Fig 6B ) . We next determined whether viruses encoding AE are maintained over time . The chronic time point sequences were compared with the TFV sequences in the longitudinal cohort that we tested for TFV encoded epitope specific CD8 IFNγ response . Although AE responses were enriched over time as shown in Fig 2B and 2C , in the five patients in whom we sequenced and examined ten different AE , we only saw one case of mutation from AE to NAE ( AE-FKL9 to NAE-FRL9 ) . In the same group of patients , we saw four different cases out of eleven where NAE mutated to AE ( 36 . 36% , Fig 6C ) . Taken together , these data suggest that even though AE are increasingly recognized by CD8 T cells in chronic infection , they persist in circulating viral sequences and that AE may confer some yet to be described advantage to HIV-1 . Our findings were puzzling since despite AE-specific CD8 T cells demonstrating a less exhausted phenotype and enhanced cytotoxicity , AE were the predominant epitope type in chronic infection . Indeed , due to their evolution in CHI , viral adaptations are defined by using chronic HIV sequences [11 , 39] . A prior study by Mailliard et al . described impaired killing of dendritic cells by a variant epitope induced cross-reactive CD8 T cells [24] . DCs that came in contact with these cross-reactive CD8 T cells matured into a pro-inflammatory phenotype with an efficient viral trans-infection capacity . We thus hypothesized that during HIV-1 chronic infection , cross-reactive CD8 T cells responding to AE might promote DC maturation and facilitate HIV-1 trans-infection from DCs to CD4 T cells . To test this hypothesis , we modified previously described DC maturation and trans-infection assays [24] . For validation experiments we used a cross-reactive SL9 ( SLYNTVATL ) CD8 T cell clone , derived from a healthy donor , that was able to cross recognize and respond to several natural variants including SFL9 ( SLFNTVATL ) and SVL9 ( SLYNTVVTL ) ( S3A Fig ) [40–42] . We observed a higher frequency of mature DCs ( CD83+ CD86+ ) in the context of cross-reactive CD8 T cell responses to SFL9 and SVL9 as compared to the cognate response to SL9 ( S1C and S3B Figs ) . We then cultured activated CD4 T cell with an R5-tropic virus at multiplicity of infection ( MOI ) of 10−1 and 10−4 . Consistent with prior findings [43] , an MOI of 10−4 was not sufficient to directly infect CD4 T cells ( S3C Fig ) . Thus , matured DCs were incubated with virus at MOI of 10−4 for all subsequent viral trans-infection assays . When CD4 T cells were cultured with mature DCs co-cultivated with SFL9 and SVL9 pulsed CD8 T cells , we observed a higher frequency of Gag-p24 stained trans-infected CD4 T cells ( S1D and S3D Figs ) , including T cells that had downregulated CD4 following infection , as has been previously described [44 , 45] . Moreover , consistent with prior work [24 , 43] , we also observed more efficient viral infection , as measured by Gag-p24 expression , by DC-to-CD4 T cell trans-infection than from infection of CD4 T cells directly by free virus present in a supernatant ( cis-infection ) ( p = 0 . 04 , S3E Fig ) . We then used this optimized assay to test DC maturation in the presence of NAE and AE-generated CD8 T cell lines from PBMCs obtained from CHI patients with positive responses to both epitope forms . Additional responses to three NAE/AE groups in four patients , i . e . Gag A*0301-RLRPGGKKKYK ( RKK11 ) / RLRPGGKKRYK ( RRK11 ) / RLRPGGKKQYK ( RQK11 ) , Env B*07-IPRRIRQGL ( IL9 ) / IPRRIRQGF ( IF9 ) and Nef A*3002- GYFPDWQNY ( GYY9 ) / GFFPDWQNY ( GFY9 ) , were tested . We first confirmed the functionality of CD8 T cell lines following epitope-specific expansion by testing for IFNγ production ( Fig 7A–7D ) . Next , DC maturation assays were performed for each cell line to compare the DC phenotype in co-culture with NAE or AE stimulated CD8 T cells . When co-cultured with NAE or AE stimulated CD8 T cells , peptide pulsed DCs show no difference in cell death ( Fig 7E ) . We also found that all CD8 T cell lines , regardless of the epitope used for expansion , resulted in a high frequency of mature DCs when stimulated with AE ( p<0 . 005 , Fig 7F and 7G ) , and these DCs demonstrated an enhanced ability to trans-infect virus to CD4 T cells ( p = 0 . 04 , Fig 7H and 7I ) . Taken altogether , our findings suggest that while there is a broadening of AE responses during chronic infection , these adaptations may contribute to viral pathogenesis by altering CD8 T cell function to facilitate DC-mediated viral trans-infection . A lower antigen sensitivity , which is also often referred to functional avidity , was previously associated with the shift from cytotoxic to “helper-like” CD8 T cell phenotype which facilitated viral dissemination [24] . Thus , we hypothesized that CD8 T cells might respond to AE with a lower antigen sensitivity than NAE . PBMCs with paired NAE/AE responses on IFN-γ ELISpot were cultured with the relevant peptides at 10-fold serially diluted concentrations . In total , 26 NAE/AE pairs from sixteen CHI individuals were tested ex vivo . The CD8 T cell responses to AE showed a 9-fold higher EC50 ( median = 773 . 6 ) than NAE ( median = 84 . 42 ) . These data showed that AE-specific responses had a lower antigen sensitivity or needed a higher antigen concentration than NAE ones ( p = 0 . 007 , Fig 8A and 8B ) , in spite of comparable ex vivo IFNγ ELISpot responses ( S4 Fig ) . Because our DC maturation and CD4 trans-infection assays were performed using CD8 T cells in vitro , we also performed antigen sensitivity testing using expanded CD8 T cells and demonstrated that AE-specific CD8 T cells consistently needed higher antigen concentration for stimulation ( lower antigen sensitivity ) than their NAE counterparts ( Fig 8C–8E ) .
Earlier studies have mainly focused on HIV adaptation in the context of a lack of immune recognition through abrogated HLA-I binding and/or impaired CD8 T cell receptor ( TCR ) recognition [46–49] or classical escape . These studies were also often limited to certain immunodominant epitopes restricted by HLA-I alleles that are linked to delayed disease progression [9 , 50–52] . It is worth noting that infection with viruses encoding classical escape mutations to these protective alleles results in a loss of viral control [13 , 50 , 53] . Indeed , we previously showed that the effect of protective HLA-I alleles , such as HLA-B*57 , was abrogated by infection with a virus pre-adapted to these alleles [14 , 54] . We also found that AE were poorly immunogenic in acute HIV-1 infection , indicating classical escape and providing a possible explanation as to why infection with a virus pre-adapted to a host’s HLA-I alleles predicts disease progression . However , a growing body of literature suggests there are non-classical forms of HIV adaptation where immune recognition is preserved but some other advantage is conferred to the virus [2 , 23] . Our current study illustrates one such mechanism where , in spite of persistent immune recognition induced by HLA-I associated adapted epitopes , the resulting CD8 T cell response appears to aid viral trans-infection of CD4 T cells by promoting dendritic cell maturation . Although a population based study on HIV-1 subtype C estimated that roughly half of HLA-associated adaptations impact peptide binding or HLA processing [55] , we do not observe a significant difference in predicted HLA-I binding affinity between our predicted NAE and AE [14 , 56] . Additionally , the majority of HLA-I associated adaptations in our studies were not located in HLA-I anchor sites ( S1 Table ) . This would suggest that most predicted HLA-I adaptations could potentially be recognized by CD8 T cells , a prediction that we confirmed in our studies . Our current study shows that AE-specific responses increased in magnitude and frequency from acute to chronic infection , suggesting that CD8 T cell responses to AE take longer to develop , perhaps due to a lower sensitivity of CD8 T cells to AE . Besides , in contrast to our prior finding where AE specific CD8 T cells harbor a poor cytotoxicity during acute infection , we demonstrated a greater cytotoxicity activity of AE specific CD8 T cells in chronic infection . This difference might be explained by differing levels of exhaustion/senescence and/or other contributing factors , like viral load , epitope frequency and CD8 T cell clonal profile . However , our understanding of what factors influence the kinetics of the CD8 T cell response , as well as how such kinetics are connected to HIV-1 adaptation , remains incomplete . Past studies focusing on the expansion of CD8 T cell responses in HIV infection illustrated the dynamics of antigen-specific TCR repertoires [57 , 58] , and future work should delve into how changes in TCR repertoires may influence the development of responses to AE versus NAE as well as the evolution of viral adaptation . Another intriguing finding in this study was that many of the NAE and AE responses detected were due to cross-reactive CD8 T cells . While some mono-specific CD8 T cell responses were detected , they were consistently of a lower magnitude as compared to the cross-reactive ones . Although this is in contrast to a previous study indicating that CD8 T cell responses to escape variant resulted from de novo CD8 T cell populations [15] , our findings agree with our prior study showing that HIV-specific CD8 T cell cross-reactivity is enhanced during chronic infection [16] . Moreover , several other studies have demonstrated the presence of cross-reactive CD8 T cell responses in chronic HIV infection [16 , 24 , 59–63] . In addition , in SIV infection , cross-reactive CD8 T cell responses to variant epitopes arise over time but fail to control escaped viral quasispecies [18] . Indeed , our sequencing analysis indicated that a majority of viral quasispecies in chronic infection encode AE despite our observation of enhanced cross-reactive responses and higher in vitro cytotoxicity of AE-specific CD8 T cells , suggesting that these CD8 T cell responses may be unable to effectively control virus in vivo . Furthermore , a recent study found that AE within TFV Gag sequences are unlikely to revert to NAE or mutate to another variant [64] . CD8 T cells can play a “helper” role that impacts the overall immune response and anti-viral immunity [65–67] . For example , besides killing virally infected cells , CD8 T cells can also induce lysis of antigen presenting immature dendritic cells ( iDCs ) [65] and promote DCs maturation in viral infection [67 , 68] . In the context of HIV , we showed that cross-reactive CD8 T cells from chronically infected individuals , who responded to AE more efficiently , induced greater DC maturation than the same CD8 T cell population responding to NAE . These mature dendritic cells more efficiently trans-disseminated HIV to activated CD4 T cells . Thus a positive feedback loop is established between CD8 T cells and DC as more AE specific “helper” CD8 T cells are primed by preferentially matured AE-expressing DC . As a consequence , an “epitope spreading” phenomenon aiding in pathogenesis could be exploited by HIV-1 . The persistence of AE-encoded viral quasispecies and increasing AE specific CD8 recognition in chronic infection could be explained , at least partially , by this “helper” role of CD8 T cells . The persistence of AE-encoded viral quasispecies suggest the “helper” rather than the “killer” effect as a better predictor of CD8 T cell potency in vivo . However , it remains unclear why AE-specific CD8 T cells exhibited greater cytotoxicity toward target CD4 T cells while promoting DC maturation to fuel viral infection . A recent study showed resistance of monocyte-derived macrophages to CD8 T cell killing was associated with prolonged cell-to-cell contact that subsequently led to a pro-inflammatory environment suboptimal for effector cell function . Meanwhile , the same CD8 T cells rapidly killed CD4 T cell targets [25] , suggesting that differential pathways associated with susceptibility or resistance to effector cell killing can occur when interacting with different target cells . Another past study showed an association between the optimal quantity ( antigen concentration ) and quality ( peptide-MHC stability ) of antigen and a faster transition into “phase two” CD8 T cell-DC interaction which is known to be necessary in vitro for full commitment to T cell activation [69] . It has also been shown that CD8 T cell derived Granulocyte-Macrophage Colony-Stimulating Factor ( GM-CSF ) plays a critical role in facilitating DC maturation and production of pro-inflammatory cytokines [68] . It is possible that an analogous phenomenon is occurring in the context of AE-specific CD8 T cell responses . Our findings indicate that AE induce CD8 T cell responses with a higher antigen threshold than those induced by NAE , which may result in a prolonged synapse time between effector CD8 T cells and targets such as DCs , ultimately leading to a detrimental pro-inflammatory environment that fuels infection [24 , 25] . It is worth investigating in future studies how this process might tip the balance in favor of DC-mediated viral trans-infection over killing of CD4 T cells . In summary , we show the presence of enriched AE-specific CD8 T cell responses in chronic HIV infection and demonstrated that these responses contributed to enhanced viral trans-infection rather than viral containment . This study expands our current understanding of how HIV exploits host immune responses in chronic infection and highlights the importance of understanding AE-specific CD8 responses in the context of vaccine and therapeutic strategies . For instance , future vaccine strategies , especially those aiming at inducing broader CD8 T cell responses by targeting multiple variants , should be designed with caution . Additionally , many HIV-1 infected individuals are not diagnosed until chronic infection , and recent studies have shown that the latent reservoir in chronically infected individuals likely encodes CD8 T cell escape mutations [70] . While our studies are hopeful in that the majority of AE are immunogenic in chronic infection , they also indicate that these CD8 T cells may be continuing to drive the infection of CD4 T cells . As such , future studies may need to focus on a better understanding and improvement of AE-specific CD8 T cells as part of a comprehensive strategy towards HIV cure .
All patients included in this study were adults and recruited from the University of Alabama at Birmingham Adult AIDS 1917 clinic after obtaining written , informed consent and approval from the IRB ( X981027004 , X160125005 and X140612002 ) at UAB . All patients were typed for their HLA class I alleles . Peripheral blood mononuclear cell ( PBMC ) and plasma samples were collected . Samples from acutely HIV-1 infected ( AHI ) patients naïve to antiretroviral therapy ( ART ) at an average of 37 days post-infection ( DPI ) ( n = 13 ) were tested . Transmitted founder virus ( TFV ) sequences were inferred from the plasma of these 13 AHI patients using a single genome amplification method , as described previously [71] . Longitudinal chronic infection samples from these patients at an average of 511 DPI were also tested . An additional cross-sectional cohort of chronically HIV-1 infected ( CHI ) patients infected at least one year ( n = 65 ) , were studied . We predicted the optimal sequences ( 8-11mer ) of HLA-I restricted non-adapted ( NAE ) and adapted epitopes ( AE ) using Microsoft Research’s EPIPRED software ( S1 Table ) [14] . Autologous peptides were designed for each AHI patient based on HLA-I alleles and TFV sequence . For each CHI patient , both NAE and the corresponding AE peptides were determined based on HLA-I alleles . Overall , 77 NAE/AE groups ( 31 restricted by HLA-A , 41 restricted to HLA-B and the 5 pairs restricted by HLA-C alleles ) were tested in this study . All peptides were synthesized in a 96 well array format ( New England Peptide ) . Each peptide was reconstituted at 40 mM in 100% DMSO and stored at -70°C [14 , 16] . Nitrocellulose plates ( Millipore ) were coated overnight with anti-IFNγ antibody and were subsequently blocked with R-10 media ( RPMI + 10% human AB serum ) for 2h . PBMCs were thawed and rested overnight at 37°C/5% CO2 . PBMCs ( 105 cells/well ) were cultured in duplicate ( when cell number was limited ) or triplicate with the peptide of interest at 10 μM in R-10 media for 22-24h . Cells cultured in media without peptide and in media with PHA were used as negative and positive controls , respectively . Following incubation , the plates were washed and treated with biotinylated anti-IFNγ antibody for two hours followed by streptavidin-alkaline phosphatase for one hour , and finally developed with the NBT/BCIP substrate for 5–10 minutes . Plates were read and counts were determined by CTL ImmunoSpot analyzer ( version 5 ) . Number of spots was averaged and normalized to SFU ( spot forming units ) per 106 cells ( SFU/106 ) . A positive response was defined as ≥55 SFU/106 and ≥ four times background ( media only wells ) [6 , 14 , 16] . Serial 10-fold dilutions ( from 106 to 100 nM ) of peptides were used to stimulate PBMCs in an IFNγ ELISpot assay as described above ( done in triplicate ) . Antigen sensitivity or functional avidity was then quantified as an EC50 value [61 , 62 , 72] , which is the peptide concentration that elicited 50% of maximal IFNγ response for any given epitope . This value was calculated by plotting a dose-response curve in GraphPad Prism ( version 7 . 0 ) . Four paired NAE/AE based HLA class I tetramers were synthesized by NIH Tetramer Core Facility as follows: A*23:01-RYF10 ( RYPLTFGWCF ) / RFF10 ( RFPLTFGWCF ) , B*07:02-NRI10 ( NPRISSEVHI ) / HKI10 ( HPKISSEVHI ) , B*35:01-TIY8 ( TPGPGIRY ) / TVY8 ( TPGPGVRY ) and B*44:02-AIW11 ( AEIQKQGQGQW ) / ALW11 ( AELQKQGQGQW ) . All NAE and AE tetramers were conjugated to APC and PE , respectively . Each tetramer was validated in an individual with a positive IFNγ ELISpot response to the epitope of interest and HIV+ HLA-I mismatched and HIV- HLA-I matched PBMC were used as negative controls . Tetramer titrations were performed using two-fold dilutions to ascertain the optimal concentration , which was then used in all subsequent assays . Of note , certain HLA subtypes , e . g . B*0702 , B*3501 , and B*4402 , were enriched since responses directed by these HLA-restrictions were used for tetramer analysis . A positive tetramer population is defined as > 3 fold than the negative control and ≥ 0 . 05% above the background . Besides , the reactive tetramer staining should be significantly higher than the negative control based on Fisher’s exact as adapted from prior study [73] . PBMCs with dual NAE and AE specific CD8 T cell responses were labeled with tetramers at room temperature for 30 min and then were stained at 4°C for 30 min with dead cell dye ( Invitrogen ) , anti-CD3-Alexa 780 ( eBioscience ) , anti-CD4-Qdot655 ( Invitrogen ) , and anti-CD8-V500 ( BD Pharmingen ) . At least 106 total events were acquired on an LSR II flow cytometer ( BD Immunocytometry Systems ) , and data were analyzed using FlowJo ( version 9 . 6 . 4; TreeStar Inc . ) . PBMCs responding to both NAE and AE in an IFNγ ELISPOT assay were pulsed with the peptides at 10μM in the presence of anti-CD28 and anti-CD49d . Monensin and brefeldin A were added 1 hour after peptide stimulation . The cells were incubated for an additional 11h . Following incubation , cells were surface stained for 30min at 4°C with dead cell dye ( Invitrogen ) , anti-CD3-Alexa 780 ( eBioscience ) , anti-CD4-Qdot655 ( Invitrogen ) , and anti-CD8-V500 ( BD Pharmingen ) in the following panels: ( 1 ) anti-TIGIT-Percp/CY5 . 5 ( Biolegend ) , anti-CD160-Alexa488 ( eBioscience ) , anti-PD1-Alexa700 ( Biolegend ) , anti-TIM3-BV421 ( Biolegend ) and anti-LAG3-PECy7 ( Biolegend ) and ( 2 ) anti-CD28-FITC ( BD Pharmingen ) , anti-CD27-PECy7 , anti-CD38-v450 ( eBioscience ) , anti-CD57- Percp/CY5 . 5 ( Biolegend ) , and anti-CD69-Alexa700 ( Biolegend ) . The cells were then permeabilized and stained with anti-IFNγ-PE at 4°C for 30 min . At least 106 total events were acquired on an LSR II flow cytometer ( BD Immunocytometry Systems ) , and analyzed using FlowJo ( version 9 . 6 . 4; TreeStar Inc . ) . The criteria of positivity is the same as defined above for tetramer staining [73] . Epitope-specific CD8 T-cell lines were expanded in vitro as previously described [14] . Briefly , cryopreserved PBMCs ( obtained from chronically HIV-1 infected patients ) were thawed and plated in a 48-well plate at 1 . 2×106 cells/ml in serum free RPMI media . Plates were incubated at 37C/5% CO2 for two hours , after which media containing non-adherent cells was removed . Adherent cells were irradiated at 3 , 000 rad and pulsed with the appropriate peptide at 10 μM for 2 h . Autologous CD8 T cells were isolated from the same PBMC sample using the CD8 untouched isolation kit ( MACS Miltenyi Biotec ) . CD8 T cells were then plated at 0 . 5×106 cells/well onto the peptide-pulsed monocytes in the presence of complete media ( RPMI+10% FBS ) containing IL-7 ( 25 ng/ml ) . IL-2 ( 50U/ml ) was added to the culture on the second day . The culture was then maintained by replacing half the media with freshly made media containing IL-2 ( 50U/ml ) every three days , and CD8 T cells were re-stimulated on day seven ( and weekly thereafter ) with peptide-pulsed monocytes . CD8 T cell clone ( SL9 ) was a gift from Dr . June Kan-Mitchell . Cytokine and effector molecule production was measured using flow cytometry as described previously [16] . Briefly , 0 . 5×106 epitope-specific CD8 T cell lines were stimulated with cognate peptide at 10 μM in the presence of anti-CD28 and anti-CD49d as well as anti-CD107a-FITC ( BD Biosciences ) antibodies . Monensin and Brefeldin-A ( BD Biosciences ) were added one hour after peptide stimulation , and the cells were incubated for an additional 5 hours ( CD8 T cell lines ) at 37°C/5% CO2 . Following incubation , cells were stained with dead cell dye ( Invitrogen ) , anti-CD3-Alexa 780 ( eBioscience ) , anti-CD4-Qdot655 ( Invitrogen ) , anti-CD8-V500 ( BD Pharmingen ) , anti-CD14-Percp/CY5 . 5 ( BD Pharmingen ) and anti-CD19-Percp/CY5 . 5 ( BD Pharmingen ) at 4 °C for 30 min . Cells were then permeabilized and intracellularly stained with anti-IFNγ-Alexa 700 ( BD Biosciences ) , anti-TNFα-PECy7 ( BD Biosciences ) , anti-Perforin-PE ( eBioscience ) and anti-Granzyme A/B-V450 ( BD Biosciences ) at 4°C for 30 min . At least 300 , 000 total events were acquired on an LSR II flow cytometer ( BD Immunocytometry Systems ) , and data were analyzed using FlowJo ( version 9 . 6 . 4 , TreeStar Inc . ) . The criteria of positivity is the same as defined above for tetramer staining [73] . Polyfunctionality analysis was performed using boolean gating and polyfunctionality index was calculated using the method as described previously [74] . Briefly , we used algorithm ( 1 ) Polyfunctionalityindex=∑i=05Fi* ( i5 ) q ( 1 ) Where 5 is the number of functions studied as described in this assay , Fi is the frequency of cells displaying i functions and q is the parameter that modulates the weight of each Fi . In this study , we used q = 1 ( the most conservative value ) . Epitope-specific CD8 T cells were expanded as described above and rested in R-10 media without IL-2 for 24 hours at 37°C/5%CO2 . Target CD4 T cells were isolated from HIV-1 seronegative individual matched for the HLA-I allele of interest and were activated with PHA ( 5μg/ml ) and IL2 ( 50U/ml ) for two days . Activated target cells were cultured with or without cognate NAE or AE peptide for one hour . Next , NAE or AE specific CD8 T cell lines were co-cultured with CD4 T cell targets in duplicate at 0:1 , 1:1 , and 3:1 effector to target ( E/T ) ratios for 24 hours . After incubation , the co-cultured cells were surface stained with anti-CD3-Pacific Blue and anti-CD4-Qdot655 ( all Invitrogen ) at 4°C for 30 min . The cells were then labeled with 7-aminoactinomycin D ( 7AAD , BD Biosciences ) at 5pg/μl for 30 min at 4°C . Events were acquired on an LSR II flow cytometer to detect the apoptosis of CD4 T cells ( 7AAD+ CD4 T cells ) as an indication of CD8 T cell mediated killing . Data for each E:T ratio was normalized to corresponding negative control at the same E:T ratio and then normalized to E:T at 0:1 for each line . The area under curve ( AUC ) value was calculated for the 7-AAD expression using GraphPad Prism ( version 7 . 0 ) and was used for statistical analysis . Monocytes were isolated from PBMCs ( obtained from chronically HIV-1 infected patients ) using the human CD14 MicroBeads ( MACS Miltenyi Biotec ) and cultured for 7 days in IMDM ( Invitrogen ) media containing 10% FBS in the presence of GM-CSF and IL-4 ( both at 1000 IU/ml; R&D Systems ) to generate immature DC ( iDC ) . Half of the media was replaced every two days with freshly made media containing 1000 IU/ml of GM-CSF and IL-4 to maintain the DC culture . Epitope-specific CD8 T cells were added directly to autologous iDC at 3:1 effector to target ( E/T ) ratio in the presence or absence of peptide of interest ( 10 μM ) and co-cultured for 48 hours . Immature dendritic cells cultured in the presence of a maturation cocktail containing TNFα ( 50ng/ml ) , IFNα ( 3000U/ml ) , IFNγ ( 1000U/ml ) , IL-1B ( 25ng/ml ) , and pI:C ( 20ug/ml ) were used as a positive control . After two days , cells were stained with dead cell dye ( Invitrogen ) , anti-CD3-Pacific Blue , anti-CD8-V500 , anti-CD14-alexa700 , anti-CD83-PE , and anti-CD86-FITC ( all from BD Pharmingen ) at 4°C for 30 min . Cells were then washed and events were acquired on an LSR II flow cytometer . An HIV-1 infectious molecular clone ( IMC ) was generated using the sequence of a transmitted founder virus ( HIV-TRJO ) as a viral backbone ( provided by Dr . Christina Ochsenbauer ) . DC were co-cultured with CD8 T cells in the presence of peptide of interest or maturation cocktail ( see above ) for 48 hours . CD8 T cells were then removed from the culture using CD8 Dynabeads ( Invitrogen ) , and the DC were loaded with a low MOI of virus ( 10−4 ) for two hours at 37°C and 5% CO2 . The cells were then washed three times with fresh media to remove excess virus . CD4 T cell targets isolated from PBMC ( obtained from HIV-1 seronegative donors ) were activated with IL2 and PHA as described above and were added into the DC culture at a DC to CD4 ratio of 1:10 . After four days , cells were labeled with dead cell dye ( Invitrogen ) , anti-CD3-Pacific Blue ( BD Pharmingen ) , and anti-CD4-alexa 780 ( BD Pharmingen ) at 4°C for 30 min . The cells were permeabilized and labeled with anti-gag p24-PE ( BD Pharmingen ) . Events were acquired on an LSR II flow cytometer and Gag-p24 expression was quantified in CD3+/CD4+ cells . Viral RNA was extracted from plasma using QIAamp RNA mini kits ( Qiagen , Valencia , CA ) and cDNA synthesis was carried out using Superscript IV ( Invitrogen ) [75] . cDNA synthesis was initiated by outer reverse primer: 5’- TAA CCC TGC GGG ATG TGG TAT TCC -3’ for segment 1 ( HXB2 position 691–2348 ) ; cDNA synthesis was initiated by outer reverse primer: 5’- CCC CTA GTG GGA TGT GTA CTT CTG -3’ for segment 2 ( HXB2 position 2042–5187 ) ; cDNA synthesis was initiated by outer reverse primer: 5’—GCA CTC AAG GCA AGC TTT ATT GAG GC -3’ for segment 3 ( HXB2 position 4954–9557 ) . The nested PCR reactions were carried out by using Q5 Hot Start High-Fidelity DNA Polymerase ( NEB ) . The segment 1 first round PCR primers were: sense primer 623F+ 5’- AAA TCT CTA GCA GTG GCG CCC GAA CAG—3’; anti sense primer 2CRX- 5’- TAA CCC TGC GGG ATG TGG TAT TCC—3’; the second round primers were: sense primer G1+ 5’- GCA GGA CTC GGC TTG CTG AAG CGC—3’; anti sense primer G10- 5’- TAC TGT ATC ATC TGC TCC TGT ATC—3’ . The segment 2 first round PCR primers were: sense primer P1+ 5’- GAA AAA GGG CTG TTG GAA ATG TGG—3’; anti sense primer P17- 5’- CCC CTA GTG GGA TGT GTA CTT CTG-3’; second round primers were: sense primer P2+ 5’- AGG AAG GAC ACC AAA TGA AAG-3’; anti sense primer P16- 5’- GGA TGA GTG CTT TTC ATA GTG A-3’ . The segment 3 first round PCR primers were: sense primer FB6+ 5’- GCA TTC CCT ACA ATC CCC AAA G-3’; anti sense primer FB12- 5’- GCA CTC AAG GCA AGC TTT ATT GAG GC-3’; second round primers were: sense primer FB7+ 5’- TCT GGA AAG GTG AAG GGG CAG TAG-3’; anti sense primer FB13- 5’- GGT CTA ACC AGA GAG ACC CAG TAC AG-3’ [76] . PCR products were electrophoresed on an agarose gel to confirm the presence of the target DNA and further purified by Qiaquick PCR purification kit ( Qiagen ) . The PCR products are sequenced by making 6 SMRTbellTM barcoded libraries which contains multiple HIV-1 PCR amplicons from multiple patients ( PacBio Template prep kit ) . Each library was constructed by pulling same segment multiple PCR amplicons from multiple patients in equimolar amounts and based on the length of the amplicons . Libraries were sent to University of Delaware DNA Sequencing & Genotyping Center for PacBio sequencing . Sequence data was derived from MDPseq work flow . Sequences were analyzed phylogenetically using Geneious software ( Biomatters , Auckland , NZ ) . Data were analyzed using Fisher’s exact t test; Mann-Whitney test for unpaired comparison; nonparametric Wilcoxon ranked test ( two-tailed ) for paired comparison and Pearson correlation analysis . Specifically , for the data in Figs 1B and 8B , we applied a mixed effects model to account for structural variability of our data . GraphPad Prism ( version 7 . 0 ) was used to perform these analyses . Significance was determined as p value < 0 . 05 . | HIV-1 infection remains a critical public health threat across the world . Over the past two decades , CD8 T cells have been clearly shown to exert immune pressure on HIV and drive viral adaptation . Previously , our group reported that such HLA-I associated adaptations can predict clinical outcomes and are beneficial to HIV-1 as CD8 T cells are unable to recognize epitopes with adaptation in acute HIV infection . However , it is still unclear how HIV-1 adaptation impacts CD8 T cells during chronic HIV infection . In this study , we observed an enhancement of CD8 T cell responses targeting adapted epitopes in chronic infection . Although these responses were cytotoxic , they also exhibited a “helper” effect by promoting viral infection of CD4 T cells via interaction with dendritic cells . This phenomenon may contribute to the persistence of adapted viruses . In summary , these findings present a novel mechanism of CD8 T cell driven HIV-1 adaptation . | [
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"retrov... | 2019 | CD8 T cells targeting adapted epitopes in chronic HIV infection promote dendritic cell maturation and CD4 T cell trans-infection |
Following the recognition of pathogen-encoded effectors , plant TIR-NB-LRR immune receptors induce defense signaling by a largely unknown mechanism . We identify a novel and conserved role for the SQUAMOSA PROMOTER BINDING PROTEIN ( SBP ) -domain transcription factor SPL6 in enabling the activation of the defense transcriptome following its association with a nuclear-localized immune receptor . During an active immune response , the Nicotiana TIR-NB-LRR N immune receptor associates with NbSPL6 within distinct nuclear compartments . NbSPL6 is essential for the N-mediated resistance to Tobacco mosaic virus . Similarly , the presumed Arabidopsis ortholog AtSPL6 is required for the resistance mediated by the TIR-NB-LRR RPS4 against Pseudomonas syringae carrying the avrRps4 effector . Transcriptome analysis indicates that AtSPL6 positively regulates a subset of defense genes . A pathogen-activated nuclear-localized TIR-NB-LRR like N can therefore regulate defense genes through SPL6 in a mechanism analogous to the induction of MHC genes by mammalian immune receptors like CIITA and NLRC5 .
Plants employ the Nucleotide Binding-Leucine Rich Repeat ( NB-LRR ) family of intracellular receptors to detect pathogens and initiate defense signaling [1] , [2] . NB-LRRs have structural similarity with the mammalian NOD-like receptors ( NLRs ) , but unlike NLRs that recognize conserved Pathogen Associated Molecular Patterns ( PAMPs ) , each plant NB-LRR recognizes a unique pathogen-encoded effector protein . NB-LRR association with an effector and subsequent receptor activation leads to a number of cellular responses that includes massive transcriptional reprogramming [3] . Ultimately , these responses often culminate in a specialized form of programmed cell death ( PCD ) - the hypersensitive response ( HR ) that restricts pathogen to the infection site thereby protecting the rest of the plant from disease [4] . Several plant NB-LRRs have been shown to localize to the nucleus , which suggests that they may participate in defense transcriptome reprogramming ( reviewed in [5] ) . Barley CC-NB-LRR MLA10 associates with HvWRKY1 and HvWRKY2 transcriptional repressors in the presence of the AVRA10 effector [6] . Arabidopsis TIR-NB-LRR SNC1 associates with the transcriptional repressor TOPLESS-RELATED 1 ( TPR1 ) to negatively regulate expression of known defense suppressors [7] . Arabidopsis RRS1-R is an atypical immune receptor that has the TIR-NB-LRR domains fused to a C-terminal WRKY domain which is characteristic of WRKY-type plant transcription factors [8] . RRS1-R recognizes the Pop2 effector from Ralstonia solanacearum and was observed in the nucleus only during an active immune response [9] . Interestingly , mammalian NLR proteins CIITA and NLRC5 are present in the nucleus and interact with transcription factors to promote the transcription of major histocompatibility complex ( MHC ) class II and class I genes [10] , [11] . However , plant NB-LRR interaction with a positive regulator of defense gene transcription has not been described . The Nicotiana TIR-NB-LRR immune receptor N , provides immunity against all strains of Tobacco mosaic virus ( TMV ) [12] except the TMV-Ob strain [13] . N specifically recognizes the 50 kD helicase domain ( herein referred to as p50-U1 ) within the 126 kD replicase of TMV-U1 [14] , [15] . Recognition of p50-U1 is specific because N-mediated responses are not activated by p50 from the TMV-Ob replicase ( herein referred to as p50-Ob ) . N recognizes p50-U1 indirectly by detecting a change in the localization of an intermediary interacting protein - the chloroplast-localized N Receptor Interacting Protein 1 ( NRIP1 ) [16] . While viral effector recognition occurs in the cytoplasm , the nuclear localization of N is required for defense signaling [17] . Here we show that the N immune receptor associates with the SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 6 ( SPL6 ) transcription factor during an active immune response . SPLs are defined by the presence of the conserved DNA-binding SQUAMOSA PROMOTER BINDING PROTEIN ( SBP ) domain [18] . SBP-domain containing proteins are ubiquitously found in the plant kingdom , from algae to higher plants . A subset of SPLs are regulated by the microRNA ( miR ) 156/157 [19]–[21] . Many of the characterized SPLs have been found to regulate flowering time , leaf development , transition from juvenile to adult phase , and pollen development ( reviewed in [21] , [22] ) . SPLs role in immunity however , has not been described . We provide genetic and molecular evidence that the SPL6 transcription factor is required for N-mediated resistance to TMV . N and SPL6 associate in planta only in the presence of p50-U1 effector from the defense eliciting TMV-U1 strain and not in the presence of non-eliciting p50-Ob . These results indicate that only p50-U1-activated N associates with SPL6 . Consistent with these observations , a mutation in the P-loop within the NB domain of N that prevents its activation also abolishes N's association with SPL6 . We show that Arabidopsis SPL6 is required for the function of TIR-NB-LRR RPS4 but not for CC-NB-LRRs RPS2 and RPM1 . Using Arabidopsis whole genome microarray analysis , we show that SPL6 can potentially positively regulate RPS4-mediated defense gene expression . These results point to a conserved role for SPL6 in TIR-NB-LRR-mediated immunity . Our findings support a model in which an effector-activated immune receptor associates with a positive transcriptional regulator like SPL6 to induce successful innate immune responses .
We identified 14 clones representing an SPL family member that interacted with N in a yeast two-hybrid screen . Full-length amino acid sequence of the N . benthamiana SPL that interacts with N indicated that it is most similar to Arabidopsis SPL6 ( AtSPL6 - At1g69170 ) . The two proteins share 83% identity within the SBP domain and 35% identity and 48% similarity within the full protein ( Figure S1 ) . Further yeast two-hybrid analysis indicated that NbSPL6 interacted with the full-length N or its TIR and LRR domains ( Figure 1A ) . To study the in planta dynamics of N and NbSPL6 association , we first determined the subcellular localization of these proteins . NbSPL6 contains a bipartite nuclear localization sequence ( Figure S1 ) . Transient expression of NbSPL6 fused to citrine under the control of a constitutive 35S promoter in N . benthamiana leaves confirmed that it localizes to the nucleus ( Figure 1B and C ) . We further confirmed these results by biochemical fractionation . NbSPL6 fused to an HA tag was expressed in N . benthamiana leaves . NbSPL6-HA was detected exclusively in the nuclear-enriched ( NE ) fraction ( Figure 1D ) . Similar biochemical fractionation experiments using previously characterized genomic N fused to a TAP tag ( gN-TAP ) [16] , [17] indicated that N is present in both the cytoplasm ( nuclear depleted , ND ) and the nuclear ( NE ) fractions in the presence and absence of the p50-U1 viral effector ( Figure 1E ) . We next tested the association of NbSPL6 with N in planta . As a control , in these experiments , we used p50 from the TMV-Ob strain that does not elicit an N immune response . Previous attempts to localize p50-Ob described in [14] produced aberrant chloroplast localization [17] . However , extension of p50-Ob by six amino acids at the N-terminus produced nuclear and cytoplasmic localization , which is identical to that seen with tCFP-p50-U1 ( Figure 1F ) . In agreement with previous reports [14] , [23] , p50-Ob-tCFP did not induce HR-PCD in N-containing Nicotiana plants while tCFP-p50-U1 induced HR ( Figure 1G ) . The expression levels of the two p50 proteins were comparable ( Figure 1H ) . For all further experiments , we used tCFP-p50-U1 as the elicitor of N-mediated immune response and p50-Ob-tCFP as the non-elicitor . Low expression levels of NbSPL6-HA made it a challenge to detect the protein in total protein extracts . To overcome this problem , researchers working with Arabidopsis SPLs use miR156/157 resistant version of SPLs [20] , [24] . We therefore created a miR resistant version of NbSPL6-HA ( rNbSPL6-HA ) that contains silent substitutions in seven nucleotides within the miRNA target site . rNbSPL6 is 100% identical to NbSPL6 at the amino acid level but resistant to miR156/157 . When rNbSPL6-HA was transiently expressed in N . benthamiana leaves it accumulated to detectable levels in the total protein extracts ( Figure 1I and Figure S2 ) . For in planta association experiments , we co-expressed gN-Myc and rNbSPL6-HA and 8 or 12 hours later tCFP-p50-U1 or p50-Ob-tCFP was infiltrated . The leaf samples were collected between 44 and 50 hours post-infiltration ( hpi ) of N and SPL6 . As a control , gN-Myc was coinfiltrated with NLS-GUS-HA followed by infiltration with tCFP-p50-U1 . Our results indicate that gN-Myc co-immunoprecipitates with rNbSPL6-HA only in the presence of defense eliciting tCFP-p50-U1 but not in the presence of the non-eliciting p50-Ob-tCFP ( Figure 1I and Figure S2 ) . gN-Myc failed to associate with NLS-GUS-HA even in the presence of tCFP-p50-U1 ( Figure 1I and Figure S2 ) . These results indicate that in planta , only p50-U1 activated N associates with NbSPL6 . To further confirm N and NbSPL6 association during an immune response , we utilized the non-invasive Bimolecular Fluorescence Complementation ( BiFC ) assay [25] . We co-expressed genomic N fused to the N-terminal 155 amino acid residues of citrine ( gN-Yn ) and NbSPL6 fused to the C-terminal of citrine ( NbSPL6-Yc ) . tCFP-p50-U1 , p50-Ob-tCFP or tCFP was infiltrated 8–12 hrs after the initial infiltration . Expression of the full-length fusion proteins was confirmed by immunoblots ( Figure 2A–C ) . Co-expression of gN-Yn and NbSPL6-Yc with the tCFP-p50-U1 effector reconstituted citrine fluorescence , indicating that following activation by p50-U1 , N associates with NbSPL6 ( Figure 2D , Columns 2 and 3 ) . Interestingly , the reconstituted citrine fluorescence was localized to subnuclear bodies . In contrast , in the presence of the non-eliciting p50-Ob-tCFP , gN-Yn and NbSPL6-Yc failed to reconstitute citrine fluorescence in 87% of the cells examined ( Figure 2D , column 4 ) . Very weak citrine fluorescence was observed in the remaining 13% of the cells ( based on the ratio of cells expressing fluorescence in the presence of p50-Ob-tCFP to that observed in the presence of tCFP-p50-U1 ) . Similarly , co-expression of gN-Yn and NbSPL6-Yc with tCFP alone did not reconstitute citrine fluorescence in 90% of the cells examined ( Figure 2D , Column 1 ) . In 10% of the cells , we observed very weak citrine fluorescence . These results suggest that N predominantly associates with NbSPL6 within subnuclear bodies in the presence of the defense eliciting p50-U1 effector . Since p50 is a part of the 126 kD TMV replicase , we tested for N and NbSPL6 association in the presence of the full-length 126 kD replicase . Consistent with previous data [26] , p126-U1-cerulean localized to cytoplasmic bodies ( Figure 2E , column1 ) . Similar localization pattern was observed for p126-Ob-tCFP ( Figure 2E , column 2 ) . Expression of both the 126 kD proteins was confirmed by immunoblotting ( Figure 2F and G ) . Co-expression of gN-Yn and NbSPL6-Yc in the presence of p126-U1-Cerulean reconstituted citrine fluorescence within subnuclear bodies ( Figure 2E , column 1 ) but this was not observed in the presence of p126-Ob-tCFP ( Figure 2E , column 2 ) . These results confirm that N associates with NbSPL6 following its activation by the TMV-U1-replicase . We examined the function of NbSPL6 in the N-mediated resistance to TMV using a well-established Tobacco rattle virus ( TRV ) -based Virus Induced Gene Silencing ( VIGS ) approach [27] . This system has been successfully used to identify and characterize genes required for N-mediated resistance to TMV [16] , [27] , [28] . To test the function of NbSPL6 in N-mediated defense , we targeted the unique 3′ region of NbSPL6 that includes the 3′UTR . Transgenic N-containing N . benthamiana plants [27] were inoculated with Agrobacterium-containing the recombinant TRV-NbSPL6 and empty TRV-vector constructs . In addition , we also inoculated plants with the positive control , TRV-N that is designed to silence the N gene [27] . Twelve days post-silencing , the plants were infected with TMV-U1 and monitored for the induction of HR-PCD and resistance response . In the VIGS-vector control plants , TMV was restricted to the infection site and the upper uninoculated leaves remained healthy ( Figure 3A , top panels; Figure S3 ) . However , the NbSPL6-silenced plants exhibited a loss-of-resistance phenotype ( Figure 3A , third panels; Figure S3 ) . This is characterized by collapse of the inoculated leaf and movement of TMV into the systemic tissue eventually leading to spreading HR-PCD and death of the whole plant ( Figure 3A , third panels; Figure S3 ) . The N silenced plants showed a similar phenotype to the NbSPL6-silenced plants following inoculation with TMV ( Figure 3A , second panels; Figure S3 ) . SPL family contains multiple members [21] , [22] . The 70 amino acid SBP domain is conserved among different members while the region flanking the SBP domain is quite variable . To determine if loss of N-mediated defense to TMV is specific to NbSPL6 , we silenced the NbSPL6Like gene . When compared to NbSPL6 , NbSPL6Like shares 91% amino acid similarity within the SBP domain and 31% similarity at the full-length protein level ( Figure S1 ) . The phenotype observed for the NbSPL6Like silenced plants was similar to the vector control with the virus mainly being contained to the inoculated leaves ( Figure 3A , bottom panels ) . These experiments were repeated 3 times . We observed loss-of-resistance in 100% of the plants silenced for N and in 54% of plants silenced for NbSPL6 ( Figure S3 ) . Quantitative real time RT-PCR ( qRT-PCR ) results showed that NbSPL6 transcript levels reduced significantly in the VIGS-NbSPL6 plants compared to the VIGS-vector control plants ( Figure 3B ) . We did not observe a significant difference in the NbSPL6Like transcript levels between the VIGS-vector control and VIGS-NbSPL6 silenced plants ( Figure 3B ) . Similarly , in the VIGS-NbSPL6Like plants , NbSPL6Like transcript was downregulated but the levels of NbSPL6 remained unchanged ( Figure 3B ) . This indicates that the NbSPL6 silencing effect is specific . To confirm that TMV spreads systemically into the upper uninoculated leaves in the NbSPL6-silenced plants , we tested for the presence of the TMV transcripts in the upper un-inoculated leaves . A significant amount of TMV replicase RNA or coat protein RNA was detected in the NbSPL6 and N silenced plants but not in the VIGS-vector control plants or VIGS-NbSPL6Like plants ( Figure 3C; Figure S3 ) . These results indicate that NbSPL6 is required for N-mediated resistance to restrict TMV to the infection site . In a number of NB-LRRs including N , mutations within the P-loop of the NB domain have been shown to abolish functionality [29] , [30] . It has been hypothesized that following effector recognition , the ATP binding/hydrolysis at the NB domain promotes a conformational change in the immune receptor , which shifts it into an active , signaling-competent state [30] , [31] . Previously it was shown that a mutation in the lysine222 ( gNK222A ) or glycine221-lysine222 ( gNGK221-222AA ) residues led to a loss-of-function N protein [29] , [32] . Since only activated N can associate with NbSPL6 ( Figure 1 and 2 ) , we tested the effect of P-loop mutations on this association . Biochemical fractionation experiments showed that gNGK221-222AA-TAP has a localization pattern similar to gN with the protein being observed in both the cytoplasm and nucleus ( Figure 4A ) . BiFC assays were carried out to test for the association between the P-loop mutant gNGK221-222AA-Yn and p50-U1-Yc or p50-Ob-Yc . Expression of the proteins was confirmed by immunoblotting ( Figure 4B and C ) . Interestingly , gNGK221-222AA-Yn accumulated to significantly higher levels compared to gN-Yn ( Figure 4B ) , which is consistent with a previous observation [29] , [32] . We observed reconstitution of citrine fluorescence when gN-Yn and gNGK221-222AA-Yn was co-expressed with p50-U1-Yc or p50-Ob-Yc ( Figure 4D ) . To further confirm the BiFC results , we performed co-immunoprecipitation assays . We transiently co-expressed gN-Myc or gNGK221-222AA-Myc with p50-U1-HA-Yc or p50-Ob-HA-Yc in N . benthamiana leaves . Pseudomonas syringae effector avrRps4-HA that is not recognized by N was used as a control . Both N and NGK221-222AA associated with p50-U1 and p50-Ob though the association with p50-Ob was weaker ( Figure 4E ) . N and NGK221-222AA did not associate with avrRps4-HA ( Figure 4E ) . Since the NGK221-222AA mutant fails to initiate the defense response in the presence of p50-U1 , we analyzed the association of the mutant with NbSPL6 . For this , gNGK221-222AA-Yn and NbSPL6-Yc were co-expressed in the presence of tCFP-p50-U1 using conditions similar to those used for gN . Under these conditions , we were unable to observe reconstituted citrine fluorescence ( Figure 5A ) , indicating that gNGK221-222AA does not associate with NbSPL6 . These results were further confirmed by co-immunoprecipitation assays . gNGK221-222AA-Myc failed to associate with rNbSPL6-HA in the presence of tCFP-p50-U1 ( Figure 5B ) . Collectively , these results indicate that a functional P-loop is not required for N's association with the defense-eliciting p50-U1 but is crucial for its association with NbSPL6 . The P-loop activity may directly enable association with NbSPL6 and/or it activates N which temporally precedes NbSPL6 association . Characterization of SPLs in Arabidopsis , rice and Antirrhinum revealed that SPLs have conserved function in development among different species ( reviewed in [21] , [22] ) . We therefore tested the role of Arabidopsis SPL6 ( the presumed ortholog of NbSPL6 ) in innate immunity . For this , first we analyzed SAIL_18b_C07 line ( http://signal . salk . edu/cgi-bin/tdnaexpress ) in which the T-DNA insertion is in the 3′UTR of AtSPL6 . RT-PCR analysis revealed that AtSPL6 transcript levels are similar in the insertion line and the wild type Col-0 plants ( data not shown ) . We therefore generated AtSPL6 RNAi lines . After characterization of RNAi lines , we selected two independent lines ( #3 and #9 ) that showed significant reduction in AtSPL6 RNA levels ( Figure 6A; Figure S4A ) . In Arabidopsis Col-0 plants , the TIR-NB-LRR RPS4-mediates defense against Pseudomonas syringae pv tomato ( Pst ) expressing the avrRps4 effector ( Pst::avrRps4 ) . In agreement with previously published report [33] , an rps4 knockout line ( rps4-2 ) shows significant susceptibility to Pst::avrRps4 , ( Figure S4B ) . We observed a 10 fold increase in Pst::avrRps4 titer in two independent AtSPL6-RNAi lines compared to Col-0 infected plants ( Figure 6B , Figure S4B ) . We also tested , if AtSPL6 function is required for CC-NB-LRRs RPM1 and RPS2 in Col-0 that provide resistance against Pst::avrRpm1 and Pst::avrRpt2 respectively . In contrast to Pst::avrRps4 , there was no difference in the growth of Pst::avrRpm1 and Pst::avrRpt2 between AtSPL6-RNAi and Col-0 plants ( Figure 6B ) . Similarly growth of the virulent pathogen Pst DC3000 , that evokes only the basal immune response , was found to be similar in the AtSPL6-RNAi lines and Col-0 ( Figure S4C ) . These results indicate that AtSPL6 is required for the TIR-NB-LRR RPS4-mediated immunity but not for CC-NB-LRR RPM1 , RPS2 function or basal immunity . In Arabidopsis , 11 SPL genes including AtSPL6 are regulated by miR156 [20] . In miR156 overexpression ( miR156-OX ) plants , whole genome microarray experiments revealed that the transcript levels of all targeted SPLs including those of AtSPL6 are down-regulated [19] . RPS4 expression level remained unaltered in these plants . Interestingly , Pst::avrRps4 grew to ∼20 fold higher titer in miR156-OX plants compared to Col-0 ( Figure 6C ) . However , there was no effect on the RPS2- and RPM1-mediated defense response ( Figure 6C ) . These pathogenicity assays confirm that AtSPL6 is required for RPS4-mediated defense response against Pst::avrRps4 . Since our results indicated that SPL6 is required for the function of two nuclear-localized TIR-NB-LRRs from two different plant species , we reasoned that it might participate in transcriptional reprogramming during an immune response . Whole transcriptome microarray analysis is well established in Arabidopsis , so we performed microarray analysis of AtSPL6-RNAi plants using Affymetrix ATH1 Arabidopsis GeneChips . Col-0 and AtSPL6-RNAi plants were either mock-inoculated with 10 mM MgCl2 or inoculated with Pst::avrRps4 ( 107 cfu/ml ) and tissue was collected at 3 h and 6 h post-infection . These time points and conditions were chosen based on similar whole genome microarray analysis carried out on Pst::avrRps4 infected Arabidopsis [34] , [35] . When compared to Col-0 , our analyses identified 312 and 387 genes that were expressed at a lower level ( 2 fold or more ) at 3 hpi and 6 hpi respectively in the AtSPL6-RNAi plants . Moreover , of the 2678 genes that were activated during RPS4-mediated response in Col-0 , a total of 322 genes remained unresponsive in the AtSPL6-RNAi plants ( Table S1 ) . Biological Networks Gene Ontology ( BINGO ) [36] analysis of AtSPL6 regulated genes revealed a strong enrichment of defense genes ( GO defense response genes , Cor P value = 5 . 14E-11 ) . Some of these genes include previously characterized defense responsive genes such as PR1 , ALD1 , AIG1 , NUDT6 , PAD4 , FMO1 , and LURP1 [34] , [37]–[39] ( See Table S1 ) . We picked a small subset of candidate genes from our microarray data set and carried out quantitative real-time PCR to confirm their responsiveness to Pst::avrRps4 infection in Col-0 and AtSPL6-RNAi plants . This set included genes that have previously been shown to be responsive during RPS4-mediated resistance [34] . qRT-PCR confirmed that in AtSPL6-RNAi plants , the 9 selected genes were less responsive to Pst::avrRps4 ( Figure 6D ) . Together , these results indicate that SPL6 transcription factor functions as a positive regulator of defense gene induction during innate immunity . Future experiments will be directed towards identifying the direct targets of SPL6 during innate immunity .
SBP-box containing genes are ubiquitously found in the plant kingdom and a number of Arabidopsis SPLs ( SPL3 , SPL4 , SPL5 , SPL9 and SPL15 ) have been found to have overlapping functions especially in regulating flowering time , leaf development , and transition from juvenile to adult phase [21] . While the role of SPLs in development has been extensively studied , their role in defense has not been described . Our report on SPL6 is the first to show a transcriptional regulatory role for the SPL family in innate immunity . Future studies should determine how SPL6 participates in defense transcriptome induction . In addition , possible role ( s ) for other SPLs in plant innate immunity should be investigated . N provides resistance against all strains of TMV except TMV-Ob , hence at temperatures above 20°C , TMV-Ob can systemically infect N-containing plants [13] . Initial attempts to characterize p50-Ob were complicated by the fact that the protein mislocalized to the chloroplast [17] . We therefore used a p50-U1-Ob chimera , which had a localization pattern similar to p50-U1 ( cytoplasm and nuclear localization ) [16] , [17] . While we could not detect an association between the p50 chimera and N , we observed that it could still associate with , and alter the localization of NRIP1 [16] . Here , we have used p50-Ob with six additional amino acids at the N-terminus . The localization pattern of this p50-Ob is similar to that of N eliciting p50-U1 . N can associate with p50-Ob though the association is weaker than that seen with p50-U1 . However , this association is not sufficient to trigger N-mediated HR-PCD and defense . Our results indicate that this could partly be because of Ns failure to associate with SPL6 in the presence of p50-Ob . Therefore , immune receptor association with the pathogen effector alone is not sufficient to induce an immune response . We hypothesize that in the case of N and p50-U1 , following association , the N-NRIP1-p50-U1 complex promotes a crucial conformational change in N that enables it to perform the subsequent steps necessary for defense signaling . N may be unable to undergo such a conformational change in the presence of p50-Ob , making the association unproductive . We envision that N activation is dependent on the structural features of p50-U1 that are different in p50-Ob . This is in agreement with previous studies using p50-U1-Ob chimeras and mutational analysis that have indicated that the three dimensional structure of TMV p50 is more important in HR-PCD induction than the primary sequence [14] . [23] showed that a single P1089L point mutation in the p50 domain of TMV-Ob ( p50-Ob-NL-1 ) was sufficient to restore N recognition , and proposed that this mutation might alter the structural conformation of the p50 domain to enable N activation . In agreement with this , a preliminary structural analysis predicted that the leucine at position 1089 results in a protein containing a single long α helix in the place of two α helices [23] . Detailed structural analysis of p50-U1 , p50-Ob and p50-ObNL-1 is necessary to gain insights into the importance of effector structure and its role in N activation and defense signaling . NRIP1 localizes to the nucleus following association with p50 but it is unclear if it associates with SPL6 or is a part of a complex with N and SPL6 . We also observed a consistent enhancement in N protein accumulation in tissue specifically co-expressing N and p50-U1 . This is in agreement with previous observations [29] , [32] . Interestingly the levels of N protein appear to increase mainly in the nuclear-depleted tissue . It is possible that cytoplasmic N protein may be stabilized during an active immune response and further experiments are needed to address this hypothesis . The P-loop within the NB domain of plant NB-LRRs is the site of ATP binding [30] . Mutations in the P-loop of N are predicted to abolish its ATP binding ability . In agreement with this , P-loop mutants of N lose resistance to TMV [29] , [32] . Our biochemical fractionation experiments indicate that ATP binding is not the major factor that determines nuclear localization since gNGK221-222AA has a localization that is similar to gN . These results are similar to the observations made with RPS4 [33] but different from CC-NB-LRR Rx in which a P-loop mutation significantly reduced its nuclear accumulation [40] . ATP binding is also not necessary for N association with the p50 effector since NGK221-222AA could associate with p50 . Similarly the P-loop mutant of Arabidopsis TIR-NB-LRR RPP1 can associate with its cognate effector ATR1 from Hyaloperenospora arabidopsidis [41] . However , our results show that a functional P-loop is necessary for Ns association with SPL6 in the nucleus . It is possible that N may undergo an ATP binding/hydrolysis-dependent conformational change that switches inactive N into an activated , signaling competent state . It is only this activated N that can associate with SPL6 to induce a successful immune response . The results presented here point to N activation prior to its association with SPL6 in the nucleus . What events lead to N activation ? It has previously been reported that N undergoes TIR domain-mediated oligomerization only in the presence of defense eliciting p50-U1 effector and that this process requires a functional P-loop [32] . It is possible that oligomerization is the crucial step that leads to N activation and that this must occur prior to SPL6 association . While the P-loop mutant NGK221-222AA cannot oligomerize in the presence of p50-U1 [32] , it is as yet unknown if p50-Ob can induce oligomerization of wild-type N . Future studies should test if the p50-Ob structural constraints discussed above limit N's ability to undergo oligomerization . It is interesting that N associates with SPL6 within distinct subnuclear bodies . Certain plant MADS box transcription factors also associate in distinct subnuclear bodies [42] . The authors hypothesize that the subnuclear regions represent sites in the chromatin to which transcription factors are recruited . Localization of certain mammalian and nematode transcriptional co-regulators to nuclear bodies has also been documented [43] , [44] . Thus it is possible that subnuclear bodies where N and SPL6 are associating may correspond to regions of active defense gene transcription . Silencing NbSPL6 in Nicotiana plants compromises N-mediated defense against TMV . Similarly , AtSPL6-RNAi plants are compromised in RPS4-mediated resistance to Pst::avrRps4 . These results suggest that SPL6 positively regulates immune signaling mediated by two different TIR-NB-LRRs from two different plant species . Our microarray analysis revealed that a significant number of RPS4-mediated defense responsive genes might be regulated , either directly or indirectly , by SPL6 . Our data suggest that N and possibly RPS4 function as positive regulators of defense genes by recruiting transcription factors like SPL6 . This is similar to the mechanism used by the mammalian NLRs CIITA and NLRC5 , which recruit transcription factors to induce the expression of MHCII and MHCI genes [10] , [11] . The recruitment and modulation of SPL6 by N , WRKYs by MLA10 and TPR1 by SNC1 highlights not only the diversity of transcription factors that are regulated by immune receptors but also shows the different strategies used by immune receptors to activate defense gene expression . The role , if any , of nuclear-localized immune modulator Enhanced Disease Susceptibility ( EDS1 ) in N-SPL6 association needs to be investigated . EDS1 is required for basal immunity and for the function of TIR-NB-LRRs reviewed in [45] . EDS1 resides in cytoplasmic and nuclear pools and nuclear EDS1 is required for immune receptor-mediated induction of transcriptional reprogramming [35] . Activation of RPS4 in the presence of bacterial avrRps4 has been shown to enhance accumulation of EDS1 in the nucleus [35] . Recent evidence indicates that EDS1 associates with three TIR-NB-LRRs - RPS4 , SNC1 , and RPS6 in the cytoplasm and nucleus [46] , [47] . Future research will be directed towards testing for possible requirement of EDS1 in modulation of SPL6 activity . Given these data , we propose the following model that details the molecular events from pathogen recognition to transcriptional reprogramming ( Figure 7 ) . In uninfected cells , N is in its resting state and found in the nucleus and in the cytoplasm . For several immune receptors such as Rx , Bs2 , Mi , I2 , and RPS5 , extensive intra-molecular interactions keep the protein in an auto-inhibited state ( reviewed in [31] ) . However , similar interactions have not been shown to occur with N in planta [32]; Dinesh-Kumar , unpublished ) . Alternatively , unknown host factor ( s ) may associate with N to keep it in an inhibitory state . In uninfected tissue , NRIP1 is solely localized to the chloroplast [16] , and nuclear N and SPL6 do not associate . SPL6 may associate with the cis-acting elements of defense responsive genes , however , they are not transcriptionally active . During TMV infection , the presence of the viral p126 replicase or the p50 effector induces NRIP1 relocalization from the chloroplast to the cytoplasm and nucleus ( not shown in the model ) . In the cytoplasm , NRIP1 associates with p50/p126 and this complex is recognized by cytoplasmic N ( Figure 7 , phase I ) . The initial events in effector association do not seem to depend on functional P-loop because the NGK221-222AA mutant can still associate with p50 ( Figure 7 , phase I ) . However , following effector association , we hypothesize that p50-U1 alters the structure of N to induce a conformational change that would require ATP binding and/or hydrolysis . Alternatively , there may be a secondary interaction between the LRR domain and p50-U1 that may release the TIR-NB interface to facilitate nucleotide binding [31] , [48] . Even though N is not fully activated , this step ‘potentiates’ N for further interaction/signaling events ( Figure 7 , phase II ) . The P-loop mutation , which abolishes ATP binding , would preclude the conformational change and the protein would remain inactive ( Figure 7 phase I ) . Although p50-Ob can associate with N , it may be that p50-Ob does not induce the crucial conformational change , ATP binding/hydrolysis , and/or oligomerization necessary for subsequent defense-signaling steps ( Figure 7 , phase I ) . As a result , p50-Ob bound N is unable to switch into an activated state or associate with SPL6 in the nucleus ( Figure 7 , phase I ) . It is as yet unclear as to whether the conformational change induced in N is sufficient for it to bind to nuclear SPL6 . If this were the case , then potentiated N would directly translocate into the nucleus to bind with SPL6 and enhance the transcriptional activation of defense responsive genes ( Figure 7 , phase II-pathway A ) . Alternatively , additional steps may be required before N can associate with SPL6 . For example , following the potentiation step , the TIR domain of N may mediate oligomerization . ATP binding is crucial to this step since the P-loop mutant is unable to undergo oligomerization [32] . However , oligomerzation is not sufficient to make N signaling-competent since some TIR and NB domain mutants that can oligomerize still fail to elicit HR-PCD [32] . Thus the oligomerization step may lead to the recruitment of additional host factor ( s ) that then assist N into attaining its final signaling competent state ( Figure 7 , phase II ) . The oligomerized and activated N translocates into the nucleus to associate with SPL6 ( Figure 7 , phase II pathway B ) . To distinguish between these two pathways , it must be determined which form of N ( activated monomeric N or oligomerized N ) is capable of binding SPL6 . Within the nucleus , activated N associates with SPL6 to either enhance its DNA binding abilities or to recruit the transcriptional machinery to the SPL6 bound promoters . In either event , N and SPL6 association is the key step towards transcription of defense genes ( Figure 7 , phase III ) . In conclusion , results presented here lend support to the emerging concept that nuclear-localized plant immune receptors directly regulate defense genes by controlling the activity of key transcription factors . It highlights the remarkable ability of immune receptors to recognize pathogens as well as to regulate nuclear activities .
Nuclear fractionation was performed using a modified protocol described by [49] . Plant tissue was gently ground in modified Honda buffer ( 2 . 5% Ficoll 400 , 5% Dextran T40 , 0 . 4M Sucrose , 25 mM Tris-HCl , pH 7 . 4 , 10 mM MgCl2 ) and complete protease inhibitor cocktail ( Roche ) in a mortar and pestle . The ground tissue was filtered through 70-µm nylon mesh . Triton X-100 was added to a final concentration of 0 . 5% and the tissue was incubated on ice for 15 minutes . The lysate was centrifuged at 100 g for 5 minutes to remove cellular debris followed by centrifugation at 1500 g to precipitate the nuclei . An aliquot of the supernatant was collected for the Nuclei Depleted fraction . The nuclei enriched pellet was washed 3 times in Honda buffer containing 0 . 1% Triton X-100 . The pellet was resuspended in an appropriate volume of Nuclei sonication buffer ( 1 mM EDTA pH 8 . 0 , 10%v/v glycerol , 75 mM NaCl , 0 . 05% w/v SDS , 100 mM Tris HCl , pH 7 . 4 , 0 . 1% Triton X-100 ) with complete protease inhibitor ( Roche ) ) and sonicated 4 times ( 10 s at 20% capacity ) . The sonicated samples were centrifuged at 12 , 000 g for 30 min at 4°C and the supernatant was collected as the Nuclei Enriched fraction . Agrobacterium tumefacians strain GV2260 containing different expression constructs were infiltrated into N . benthamiana leaves as described previously [16] . N and NGK221-222AA containing cultures were adjusted to OD600 = 2 . 1; NbSPL6 to OD600 = 1 . 5; TMV-126 kD to OD600 = 1 . 2; and p50 to OD600 = 1 . For co-infiltration assays , N and NbSPL6 cultures were mixed in a 1∶1 proportion and infiltrated into 4-week old N . benthamiana leaves . 8 to 12 hrs post infiltration , p50 or TMV-126 kD cultures were infiltrated into the same leaf sectors . Plant tissue expressing the protein ( s ) of interest was collected and ground in liquid nitrogen . Total protein extracts were prepared and immunoblots were probed and processed as previously described [16] . Antibodies used include mouse anti-cMyc ( Santa Cruz ) or mouse anti-cMyc-peroxidase ( Roche ) , mouse anti-GFP ( Covance ) , rabbit anti-tCFP ( Evrogen ) , rat anti-HA ( Roche ) or rat anti-HA peroxidase ( Roche ) , rabbit anti-PEPC ( Rockland ) , rabbit anti-Histone H3 ( Abcam ) and anti-mouse , anti-rat or anti-rabbit peroxidase ( Sigma ) . In the blots that were probed with anti-Myc or anti-HA peroxidase , the PVDF membrane section containing the protein markers was probed separately with anti-rabbit peroxidase . For co-immunoprecipitation assays with N and NbSPL6 , Agrobacterium containing the different expression constructs were infiltrated into N . benthamiana leaves as described previously [16] , [17] . N and NGK221-222AAcontaining cultures were adjusted to OD600 = 2 . 1; SPL6 to OD600 = 1 . 5; NLS-GUS-HA and p50 ( U1 and Ob ) to OD 1 . 0 . Plant tissue was ground in liquid nitrogen and the proteins were extracted using the co-immunoprecipitation buffer ( 100 mM NaCl , 20 mM Tris pH 7 . 5 , 1 mM EDTA , pH 8 . 0 , 0 . 1% Triton , 10% Glycerol , 5 mM DTT , 2 mM NaF , 1 mM PMSF ) and Complete protease inhibitor cocktail ( Roche ) . The extracts were centrifuged at 3000 g for 5 minutes and the supernatant was passed through a Qiashredder column ( QIAGEN ) to remove residual cell debris . The filtrate was pre-cleared with protein G sepharose beads ( Amersham Bioscience ) with a 30 min incubation at 4°C . The samples were centrifuged at 3000 g for 2 minutes and anti-HA agarose beads ( Sigma-Aldrich ) were added to the supernatant . The samples were rotated for 2 hrs at 4°C and washed 3 times with co-immunoprecipitation buffer containing 200 mM NaCl . The beads were boiled with 2× loading buffer and samples were separated on an SDS-PAGE gel followed by western blotting . For Immunoprecipitation assays with N and p50 , Agrobacteria containing the different expression constructs were infiltrated into N . benthamiana leaves as described above . avrRps4 containing cultures were infiltrated at an OD600 = 1 . 0 . The ground plant tissue was extracted with co-immunoprecipitation buffer containing 150 mM NaCl . The samples were centrifuged at 20 , 817 g for 10 minutes . The supernatant was centrifuged at 20 , 817 g for 5 min to remove residual cell debris . The samples were processed as mentioned above , the only difference being that the wash buffer contained 300 mM NaCl and 0 . 2% Triton . The samples were washed 4 times . Agrobacterium containing the different constructs were infiltrated into N . benthamiana leaves at the ODs indicated above . Live plant tissue imaging was performed using a Zeiss LSM510 META confocal microscope ( Carl Zeiss ) using 40× or 63× apochromatic water immersion objectives . For tissues expressing N , SPL6 , p50 and p126 , samples were visualized for protein expression between 44 to 50 hrs post N and SPL6 infiltration . All other tissue samples were visualized 44 hrs post infiltration . The 458 nm and 514 nm excitation laser lines of a 25 mW Argon laser ( Coherent ) with appropriate bandpass emission filters were used to image citrine , tCFP , and cerulean . The 458 nm laser line of a 25 mW Argon laser and a META detector were used for imaging chloroplast autofluorescence . For BiFC assays , percentage of cells expressing citrine fluorescence was determined in 5 mm sq tissue sectors . VIGS assays were carried out on transgenic N-containing N . benthamiana plants as described previously [27] . 12 days post silencing , two leaves from each plant were mechanically inoculated with diluted TMV-U1-infected leaf extract . The plants were monitored for the development of HR-PCD and systemic infection up to 14 days post TMV infection . VIGS assay was repeated three times using up to a total of 30 plants per VIGS construct . SAIL_18b_C07 seeds were obtained from ABRC and confirmed for the presence of the T-DNA insertion using the LB primer AGA TGA AGA CGA CCA CCG TAC and RB primer TGT TGC AGA AAA TGA TGT TGC along with LB1 T-DNA primer GCC TTT TCA GAA ATG GAT AAA TAG CCT TGC TTC C . Total RNA from homozygous insertion plants and Col-0 was isolated using RNeasy Mini kit ( QIAGEN ) . 3 µg of RNA was used for the synthesis of cDNA using SuperScript II reverse transcriptase ( Invitrogen ) . Semi quantitative PCR was performed as described previously [27] using AtSPL6 and EF1α specific primers . The primer pair 5′CGG CTG GGT ACC GTT TCA TTT CCT CTC AGA GTT 3′ and 5′TGC CGC AGG CCT TTA GGA GCC AGG GAA ATA AAG 3′ containing the restriction sites Kpn1 and Stu1 was used to amplify 708 bp cDNA fragment of AtSPL6 . The primer pair 5′GGC CTC GGT ACC GTT TTA TTC TTT CTC CTC TCA 3′ and 5′CGC TCC GAG CTC TTA GGA GCC AGG GAA ATA AAG 3′ containing restrictions sites for Kpn1 and Sac1 was used to amplify a 908 bp genomic fragment of AtSPL6 . These PCR products were cloned into pYL400 vector in an anti-sense orientation to each other and downstream of a constitutive 35S promoter . The orientation of the two inserts was such that when transcribed , it would result in an RNA transcript with a double hairpin loop and stem structure . GV3101 Agrobacterium-containing AtSPL6-RNAi was transformed into Col-0 via the floral dip method [50] . Transformants were selected on Gentamycin ( 100 µg/mL ) containing MS plates . Total RNA was isolated from 4-week old Col-0 and AtSPL6-RNAi plants using RNeasy Mini kit ( QIAGEN ) . 3 µg of RNA was used for the synthesis of cDNA using SuperScript II reverse transcriptase ( Invitrogen ) . Semi quantitative PCR was performed as described previously [27] using AtSPL6 and EF1α specific primers . Two independent lines ( #3 and #9 ) that showed significant downregulation of AtSPL6 transcript were chosen for pathogen assays . Line #9 was used for microarray analysis . Total RNA from VIGS plants was extracted using Plant RNeasy mini kit ( QIAGEN ) . First strand cDNA was prepared from 1 µg total RNA using SuperScript II reverse transcriptase ( Invitrogen ) . qPCR was performed using SYBR green ( Applied Biosystems ) in the ABI 7900 qPCR machine ( Applied Biosystems ) . The fold change in mRNA levels was determined using the comparative Ct method after the data was normalized using EF1α as an internal control . Pst::avrRpm1 , Pst::avrRpt2 and Pst::avrRps4 were grown on KM plates with appropriate antibiotics . The cells were harvested between 40–46 hrs; resuspended in 10 mM MgCl2 , adjusted to 1×104 cfu/mL and infiltrated onto 6 to 8 four-week old Col-0 and AtSPL6-RNAi plants . Pst DC3000 was infiltrated at a concentration of 1×106 cfu/mL . Three leaves per plant were infiltrated for each line . Leaves of comparable age and at similar positions on the shoot were used for bacterial infiltration . The trays were covered with a humidity dome during the duration of the experiment . Bacterial growth curves were determined as described [51] . Each experiment was repeated three times . 12 plants from 4-week old Col-0 and AtSPL6-RNAi were mock-infiltrated with 10 mM MgCl2 or with a high titer ( 1×107 CFU/ml ) of Pst::avrRps4 . Total RNA from leaf samples harvested at 3 hpi and 6 hpi was extracted using Plant RNeasy mini kit ( QIAGEN ) . cRNA preparation , hybridization and slide scanning was performed according to manufacturer's instructions ( http://media . affymetrix . com/support/downloads/manuals/expression_analysis_technical_manual . pdf ) at the WM . Keck Biotechnology Resource Laboratory , Yale University . A single array was run for the analysis . Gene expression intensities were calculated using the GC-RMA software [52] and normalized between slides via quartile normalization . Fold change values were calculated from the resulting signal intensities . For real time PCR , first strand cDNA was prepared from 1 µg total RNA isolated from Pst::avrRps4 infected Col-0 and SPL6-RNAi plants using SuperScript II reverse transcriptase ( Invitrogen ) . qPCR was performed using the iQ SyBR Green Supermix ( Bio-Rad ) in the Bio-Rad iCycler iQ multicolor real-time PCR system . Primary data analysis was performed with Bio-Rad iCycler iQ software . Relative RNA levels were calculated using the 2ΔΔCt method after normalizing to the internal control Ubiquitin [53] . | Pathogen infection causes significant economic loss of crops worldwide . To fend off pathogens , plants use the Nucleotide-Binding domain and Leucine Rich Repeat ( NB-LRR ) class of immune receptors . Although we have some insight into how plant NB-LRRs recognizes pathogens , we know little about NB-LRR spatial distribution and dynamics during the immune response . Some plant NB-LRRs are present in the nuclear compartment of the cell suggesting that they may directly control defense gene expression . The tobacco N immune receptor that provides immunity against Tobacco mosaic virus ( TMV ) infection is present in the nucleus and associates with the SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 6 ( SPL6 ) transcription factor . This association is detected only when the TMV effector , p50 , is present in the cell . This suggests that N associates with SPL6 only during an active defense response . SPL6 function is required for defense against TMV . SPL6 from Arabidopsis functions in resistance against the bacterial pathogen Pseudomonas syringae expressing the AvrRps4 effector and positively modulates defense gene expression . These findings define a novel conserved function for SPL6 transcription factor from different plants species in defense against pathogens . This is the first evidence for the function of SPL-type transcription factors in defense . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
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] | 2013 | Novel Positive Regulatory Role for the SPL6 Transcription Factor in the N TIR-NB-LRR Receptor-Mediated Plant Innate Immunity |
The current treatment of eumycetoma utilizing ketoconazole is unsatisfactory because of high recurrence rates , which often leads to complications and unnecessary amputations , and its comparatively high cost in endemic areas . Hence , an effective and affordable drug is required to improve therapeutic outcome . E1224 is a potent orally available , broad-spectrum triazole currently being developed for the treatment of Chagas disease . E1224 is a prodrug that is rapidly converted to ravuconazole . Plasma levels of E1224 are low and transient , and its therapeutically active moiety , ravuconazole is therapeutically active . In the present study , the in vitro activity of ravuconazole against Madurella mycetomatis , the most common etiologic agent of eumycetoma , was evaluated and compared to that of ketoconazole and itraconazole . Ravuconazole showed excellent activity with MICs ranging between ≤0 . 002 and 0 . 031 µg/ml , which were significantly lower than the MICs reported for ketoconazole and itraconazole . On the basis of our findings , E1224 with its resultant active moiety , ravuconazole , could be an effective and affordable therapeutic option for the treatment of eumycetoma .
Mycetoma is a serious health problem with high morbidity . It is endemic in subtropical areas and often leads to severe deformity and disability [1] . The disease has long been disregarded by international health organizations but was recently recognized by WHO as a neglected tropical condition ( http://www . who . int/neglected_diseases/diseases/en/ ) . One of the main problems of eumycetoma is its recalcitrant nature , which necessitates prolonged antifungal therapy combined with massive and repeated surgical debridement . In severe cases , amputation of the affected part may be the only remaining treatment option [2] . Madurella mycetomatis is the most common fungal pathogen causing eumycetoma in arid climate zones , particularly in northeastern Africa . The infection by M . mycetomatis is characterized by the presence of black grains in tissue [3] . Previous reports showed that this fungus was most susceptible to the azole class of antifungal agents [4] , [5] , [6] . Ketoconazole and itraconazole are the most frequently used drugs for the treatment of mycetoma . However , therapy failure is common and high recurrence and amputation rates are reported [7] . Another concern is that both the Food and Drug Administration ( FDA ) and the European Medicines Agency ( EMEA ) recently restricted the use of ketoconazole due to its toxic side effects ( http://www . fda . gov/Drugs/DrugSafety/ucm362415 . htm ) [8] , making the need for an alternative treatment for eumycetoma even more urgent . Since M . mycetomatis appeared to be most susceptible to the azole class of antifungal agents , a new azole probably has the best chance of meeting that need . A new azole currently under development is ravuconazole . Ravuconazole is a broad-spectrum triazole that showed activity against a wide array of fungal species including Aspergillus spp . , Candida spp . , and Cryptococcus neoformans [9] , [10] . Studies have shown that the efficacy of this new triazole was comparable to that of posaconazole and voriconazole [9] , [10] , [11] . In addition to antifungal activity , ravuconazole also showed in vitro activity against the parasite Trypanosoma cruzi , the causative agent of Chagas disease , another neglected tropical disease on the WHO list [12] . Eisai developed a prodrug of ravuconazole ( E1224 ) which has a simpler chemical structure , is safe , and has a long half-life in humans [13] . These attributes will reduce the costs of ravuconazole treatment and will make it an affordable drug for people in endemic countries . In the present study , we investigated the antifungal activity of ravuconazole ( the active moiety of E1224 ) against 23 isolates of Madurella mycetomatis .
The 23 isolates were obtained from 23 patients seen at the Mycetoma Research Centre , University of Khartoum , Sudan , and preserved in the collection of Erasmus Medical Centre , Rotterdam , and CBS ( Fungal Biodiversity Centre ) , Utrecht , The Netherlands . All the strains were previously collected and were taken from the above mentioned collections for the study . The identity of the strains was confirmed with a multi-locus analysis of rDNA internal transcribed spacer and partial large subunit and compared with M . mycetomatis type strain CBS 109801 [14] . Prior to susceptibility testing , fresh cultures of the strains were made on Sabouraud's dextrose agar ( SDA ) plates which were incubated for three weeks at 37 °C . The in vitro activity of ravuconazole was determined using the 2 , 3-bis ( 2-methoxy-4-nitro-5-sulfophenyl ) -5-[ ( phenylamino ) carbonyl]-2H-tetrazolium hydroxide ( XTT ) broth micro-dilution assay to estimate the minimum inhibitory concentration ( MIC ) for the strains [15] . The method was described and validated by Ahmed et al . for susceptibility testing of M . mycetomatis using a standardized hyphal inoculum [15] . For the assay , about 2 cm of fungal colonies grown on SDA plates were scraped off and inoculated into tubes with 10 ml RPMI 1640 medium containing 0 . 35 g/liter L-glutamine and 1 . 98 mM 4-morpholinepropanesulfonic acid ( MOPS ) . Prior to incubation , the fungal mass was sonicated for 5 s at the maximum power of a sonicator ( Beun de Ronde , The Netherlands ) . Tubes were incubated for 7 days at 37 °C . After incubation , mycelia were washed once with RPMI and sonicated again for 5 s at the maximum power . The final inocula were adjusted spectrophotometrically ( 660 nm; Novaspec II , Pharmacia Biotech , Cambridge , U . K . ) to obtain transmissions in the range of 69–71% . Ravuconazole was kindly provided by Eisai Co . , Ltd . , as reagent-grade powder and used in concentrations ranging from 0 . 002 to 2 µg/ml . In addition to ravuconazole , MICs were also determined for ketoconazole ( Janssen Pharmaceuticals , Belgium ) and itraconazole ( Janssen ) in concentrations ranging from 0 . 016 to 16 µg/ml . The assay was carried out in round-bottom microtitre plate where 100 µl of the inoculum were added to 2 µl of drug concentrations . For each isolate drug free control and negative control were included to define the end point reading . Endpoint reading was done after 7 days of incubation at 37 °C using XTT; MICs were defined as the lowest concentration with a minimum of 80% growth reduction . With the XTT assay , 100% reduction in viable fungal mass could not be used as an end-point , since a number of strains had pigments that influenced the color intensity [15] . The 80% boundary was found to correspond with the MICs obtained visually for the fungistatic drug amphotericin B [15] . All experiments were performed in duplicate on different days . Association between MICs obtained for ravuconazole and the comparator azoles were done using the Mann-Whitney test and Wilcoxon's signed rank test .
As shown in Table 1 , fifty percent of the strains were inhibited by a concentration of 0 . 063 µg/ml ( MIC50 ) for both ketoconazole and itraconazole , while a concentration of 0 . 25 µg/ml ( MIC90 ) was required to inhibit 90% of the strains . Significantly lower MICs were obtained with ravuconazole in comparison to ketoconazole and itraconazole ( Mann-Whitney , p<0 . 0001 for both comparisons ) , with MICs ranging from ≤0 . 002 to 0 . 031 µg/ml ( Fig . 1 ) . Same results were obtained when using Wilcoxon's signed rank test [Z-value: -4 . 1973 , p-value is 0 . 00 for both drugs] . Moreover , there is no cross susceptibility among strains showed low MICs for ravuconazole and those of ketoconazole and itraconazole . A concentration of 0 . 004 µg/ml ravuconazole was needed to inhibit 50% of the strains , whereas 0 . 016 µg/ml was required to inhibit 90% of them .
In this study , we demonstrated that Madurella mycetomatis , the most common etiologic pathogen for mycetoma , was highly susceptible to ravuconazole with MICs ranging from ≤0 . 002 to 0 . 031 µg/ml . These MICs were not only considerably lower than those found for ketoconazole and itraconazole in the present study , but they were also lower than those reported for voriconazole ( 0 . 016–1 µg/ml ) , posaconazole ( 0 . 03–0 . 125 µg/ml ) , and isavuconazole ( 0 . 016–0 . 125 µg/ml ) [4] , [5] , [6] . Only a few reports are available regarding the susceptibility of other eumycetoma causative agents towards ravuconazole . Studies have shown that ravuconazole has inhibitory activity against the black-grain eumycetoma species Exophiala jeanselmei and to the saprobe Curvularia lunata that occasionally has been observed in eumycetoma [10] , [16] . In contrast , resistance was reported for the white-grain eumycetoma causative pathogens Pseudallescheria boydii and Fusarium species [10] , [16] , [17] . Good inhibitory activity of ravuconazole was reported for members of Chaetomium , a genus that was found to be phylogenetically close to the genus Madurella [14] , [18] . Low MICs were reported for Chaetomium species ranging from 0 . 06 to 1 µg/ml , but these values were higher than the results reported in this communication [18] . Studies of the in vitro activity of ravuconazole against the more common pathogenic fungi , including Cryptococcus neoformans , Candida species , Aspergillus species , and the dermatophytes , showed that the drug has activity comparable to that of other triazoles [10] , [16] , [19] , [20] . Moreover , ravuconazole showed potent in vitro activity against the parasite Trypanosoma cruzi [12] . Several studies have been conducted to evaluate the in vivo efficacy of ravuconazole and E1224 using animal models of aspergillosis , candidiasis , and cryptococcosis , with each demonstrating encouraging activity of the drug [21] , [22] , [23] . In addition , phase 1/2 clinical trials have shown that ravuconazole and E1224 were well tolerated . Ravuconazole had a relatively long half-life of 4–8 days and the peak plasma concentrations of the drug ranged from 1 . 20 to 6 . 02 µg/ml when 50–400 mg/day was administrated orally for 14 days [24] . E1224 provides the advantage of more favorable pharmacokinetics with a half-life of ravuconazole ( resulting from conversion of E1224 to ravuconazole ) of 7 . 7 to 10 . 5 days and peak plasma levels of 3 . 7–379 µg/ml when 200–400 mg/day was administrated orally for 14 days [25] . This serum level of the drug is much higher than the concentration needed to inhibit 90% of the M . mycetomatis strains in the present study ( MIC90 of 0 . 016 µg/ml ) . Furthermore , in rabbits it was demonstrated that ravuconazole concentrations in the liver , adipose tissue , marrow , kidney , lung , brain and spleen exceeded concurrent plasma concentrations [26] . Moreover , high concentrations were also detected in lung and uterus of rat [27] . Due to these high levels of the drug in tissue , good therapeutic efficacy was obtained in animal models with pulmonary and disseminated aspergillosis , candidiasis , histoplasmosis , intracranial and disseminated cryptococcosis [21] , [23] , [28] , [29] . Based on the in vitro susceptibility generated in this study , the next step will be to study the efficacy of ravuconazole in an animal model of mycetoma . We conclude that ravuconazole has potent in vitro activity against M . mycetomatis . Compared to other infectious fungi , Madurella is exceptionally susceptible to this drug . With its favorable pharmacokinetic properties and low toxicity , E1224 with its resultant active moiety , ravuconazole , could be a promising antifungal agent for treatment of eumycetoma . A clinical trial is now required for an in vitro-in vivo correlation of the activity of the drug . | Madurella mycetomatis is the most common etiologic agent of eumycetoma worldwide . Treatment of this infection is very difficult and associated with high recurrence rates and low cure rates . Currently the treatment consists of a combination of surgery and antifungal therapy . Antifungal therapy is usually given for at least one year . However , the commonly used antifungal ketoconazole is too expensive for many patients in endemic countries and has many side effects . In the present study we evaluated the in vitro activity of the new antifungal agent ravuconazole against M . mycetomatis . The drug showed excellent in vitro activity against all tested strains and its prodrug , E1224 , might be a potential new therapeutic option for eumycetoma caused by M . mycetomatis . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"biology",
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] | 2014 | Madurella mycetomatis Is Highly Susceptible to Ravuconazole |
The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell . Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes . Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale , computational methods for predicting the consequences of mutations on binding affinity are highly desirable . We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation . As a standalone feature , the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials , despite being two orders of magnitude faster once the profile has been constructed . Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation , the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches . By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score , a composite model was constructed through the random forest training , which generates a Pearson correlation coefficient >0 . 8 between the predicted and observed binding free-energy changes upon mutation . This accuracy is comparable to , or outperforms in most cases , the current best methods , but does not require high-resolution full-atomic models of the mutant structures . The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies .
The formation of protein-protein complexes plays an essential role in the regulation of various biological processes . Mutations play fundamental roles in evolution by introducing diversity into genomes that can either be selectively advantageous or cause a change in protein affinity that can result in malfunction of the protein interaction network [1 , 2] . The Human Genome Project has yielded a wealth of data concerning natural human genetic variation that remains to be fully utilized . For example , it is well known that different people with the same condition often respond differently to the same treatment . A treatment that is effective in one population may have no effect or even be deleterious in another population . Knowledge of how individual subpopulations respond to drugs therefore remains a major bottleneck within the drug discovery process . Understanding how this natural variation within the human genome impacts the protein interaction network is expected to yield insight into this process , provided that the impact of a mutation on the formation of a protein complex can be reliably predicted . The rational design or modification of the affinity and specificity of protein-protein interactions is another challenging issue that has stimulated considerable efforts , as it presents many promising applications , notably for both industrial and therapeutic purposes [3] . Most of these efforts involve the prediction of the effect of a mutation upon the Gibbs free energy change of protein-protein binding ( ΔΔG ) on a large scale . Quantitatively , ΔΔG values for protein interactions may be measured experimentally by a variety of biophysical techniques [4 , 5] . However , these methods are , with few exceptions , inherently low-throughput due to the need to express and purify each individual mutant protein before measurement . Alternatively , deep mutational scanning can be coupled with functional selection to analyze the effect of a large number of mutations on protein binding at specific sites within a protein [6 , 7] . Deep sequencing is a very powerful method that has generated impressive insights into residue-specific contributions to protein binding . However , this method is still in its infancy and routine application is still difficult . As a result , scientists have increasingly turned to computational methods to predict ΔΔG values . For a rigid protein , the ΔΔG of folding or protein binding can be determined relatively accurately from a full-atomic description of the protein structure or complex , using either potentials based on molecular mechanics that attempt to quantify the interactions in physically meaningful terms [8] , statistical potentials based on the likelihood of similar interactions and local conformations occurring in the PDB [9] , or some combination of the two . However , this approach ignores the structural changes that can occur upon mutation , which can alleviate clashes and position residues in conformations more favorable for interaction . Accordingly , much research has gone into the incorporation of flexibility into energetic calculations [8 , 10–12] . However , the method is computationally expensive for large datasets to the extent that it becomes prohibitive for genome-wide studies or even scanning mutations on a single protein . In addition , in many cases , a more exact physical representation of the molecular structure and interactions have proved to be less accurate than simpler models due to the inherent inaccuracy of each term in the force-field . As such , alternative methods have been proposed that either use reduced representations of the protein structure or simplified interaction schemes ( for example , the use of Cβ and contact potentials ) [13 , 14] or omit the atomic details of the structure entirely and use machine-learning to predict ΔΔG values from sequence conservation or from gross structural features such as solvent accessibility and secondary structure . The accuracy of a machine learning method ultimately depends on the quality of the feature set and the experimental data available to train the method . If the training set is representative , completely covering all relevant types of interactions and not significantly biased towards specific interactions , it is possible to use machine learning to accurately predict the effect of a mutation using features that are only weakly predictive on their own . If the training set is not representative , then a model formed from only weak predictors is usually not generalizable [15] . The effect of mutations on protein stability has been heavily studied experimentally and non-redundant datasets have been constructed that are believed to be representative of all classes of possible interactions . By contrast , information on the effects of mutations on protein complex formation is much more limited with the data heavily focused on only a few interaction types [16] . For this reason , constructing a machine learning method for the prediction of ΔΔG values for protein complex formation is more difficult than constructing a machine learning method for stability predictions [9] . As a result , the resulting methods generally have a lower accuracy compared to protein stability predictions [9] . Furthermore , the models are usually less generalizable and often show large drops in accuracy when tested on new data not in the training set . This limitation can be overcome if new and more accurate predictors are available for ΔΔG prediction . Because physics based features often share the same limitations , attempts have been made to predict ΔΔG using alternate scoring methods . Based on their success in the prediction of ΔΔG values for protein stability [17 , 18] , sequence based features have been suggested as predictors of protein-protein interaction ΔΔG values [19] . Protein binding affinity is under evolutionary pressure and we expect residues that contribute strongly to binding energetics to be more strongly conserved than residues which have minimal impact on binding . The conservation of binding residues plays an important role in many “hot spot” prediction programs which seek to identify sites on the interface which strongly influence binding [20] . Taking this approach further , it is likely that the observed distribution of amino acids at a site within the interface reflects at some level the amino acid energetic preferences for binding . Other things being equal , the probability of finding an amino acid which unfavorably impacts binding at an interface site will be less than finding a more favorable amino acid—provided that affinity , and not some other property , is the driving force for selection . However , in many cases there are other driving forces for selection besides protein-protein binding affinity such as binding specificity [6 , 21 , 22] , foldability [23] , or protection against aggregation [24] . In addition , closely related sequences bear the imprint of their evolutionary relationship independent of any functional relationship [25] . The limited time of divergence from a common evolutionary ancestor creates a phylogenetic signal that can complicate analysis as not all possible mutations are effectively sampled during the divergence time [26] . Both effects can be reduced by considering structurally similar interfaces rather than closely evolutionarily related proteins . Structurally similar interfaces are expected to serve similar roles regardless of their evolutionary relationship; an effect that can be seen by the existence of highly similar interfaces in proteins that are otherwise structurally dissimilar and evolutionarily distant [27] . Using this approach , we show an interface binding profile score , called BindProf , formed from an aligned ensemble of structurally similar interfaces has accuracy as a standalone feature similar to , or in most cases , better than many composite all-atom potentials . Unlike the all-atom energies , it can be calculated very rapidly once the profile is constructed . The on-line server of the BindProf program is freely available at http://zhanglab . ccmb . med . umich . edu/BindProf .
Since the most distinctive feature of our approach is the use of structurally similar interfaces of protein complexes in the PDB to score the effect of a mutation , we first consider the most accurate way to predict ΔΔG of protein binding using only this information . The amino acid distribution of structurally similar complexes can be analyzed quantitatively by the use of structural profile scores . Similar to a position specific scoring matrix , a structural profile score F ( p , a ) reflects the log odds likelihood of an amino acid ( A ) being found at a particular position ( p ) in an aligned ensemble of structurally similar proteins [30] F ( p , A ) =∑a=120g ( p , a ) M ( A , a ) ( 1 ) where g ( p , a ) is the Henikoff weighted frequency of the amino acid a appearing at the pth position in a multiple sequence alignment ( MSA ) with exactly redundant interface sequences removed; M ( A , a ) is the BLOSUM substitution matrix with a varying for 20 amino acids , which is used to account for missing structures in the PDB . Experimental ΔΔG values are therefore hypothesized to be proportional to the mutant profile score defined as the difference between the profile scores of the wild type ( WT ) and mutant ( Mut ) amino acids at position p in the interface: ΔΔGcalc=∑a=120g ( p , a ) M ( AWT , a ) −∑a=120g ( p , a ) M ( AMut , a ) ( 2 ) The profile therefore depends on both the cutoff level for defining a similar complex and the measure of similarity used . The definition of “similar” is less straightforward in regards to interfaces than it is with overall protein structure . Similarity of protein structures can be defined by a normalized , length independent measure of structural difference , TM-score , which has been shown to have a close relationship with fold classification [31 , 32] . For interfaces , a straightforward definition is to use the normal procedure for the structural comparison of proteins but to only consider interface residues in the comparison [33] . A similar interface in this case is defined as having a high TM-score when only residues at a given cutoff distance ( 4 Å ) from the other chain are considered for alignment and scoring ( iTM-score , see definition in Methods ) [33] . A more stringent comparison ( Iscore ) can be made by considering not only backbone alignment but also contact patterns at the interface to more clearly distinguish closely related proteins [33] . Finally , even close structural matches can result in significantly different binding energetics if there is a mismatch of interaction types at the interface . For example , the mutation of hydrophobic to a charged residue can result in a severe loss of affinity if the mutation is located within a hydrophobic pocket . Accordingly , the alignment can be modified to take into account physicochemical similarity during alignment using a pharmacophore classification of residues to identify residue similarity ( PCscore ) [34] . In Fig 2 we show the correlation between ΔΔG values calculated by the mutant interface profile scores ( Eq 2 ) and experimental ΔΔG values of protein-protein interactions from the SKEMPI database [16] as a function of the alignment methods and cutoff values . Each method shows the expected general rise and fall in the accuracy at extreme values as the cutoff is made either too strict or too loose . Too loose cutoffs degrade the accuracy of the profile score as structurally unrelated complexes are included in the profile and the specific information from structurally related complexes is lost . Too strict cutoffs , on the other hand , include too few sequences to construct an accurate profile that reflects all the actual allowable possibilities at the interface . While all similarity measures show low accuracy asymptotically at very high and low cutoff values , a simple unimodal distribution of accuracy with the cutoff value is only observed for the profile score formed from structural alignment of the monomeric protein . In this case , the accuracy of the profile score reflects the underlying bimodal distribution of the TM-Score , which has a sharp division near TM-Score cutoff values of 0 . 5 separating similar folds from unrelated structures [32] . Since TM-Scores of 0 . 5 and above correspond with high probability to similar folds while a TM-Score below this value indicates essentially no relationship between structures [32] , the monomeric profile score is only accurate above a TM-Score 0 . 5 . However , the actual correlation with the experimental ΔΔG values is modest and the profile scores from all interface alignment methods yield a significantly better correlation for nearly the entire range of cutoff values . The relationship between cutoff value and ΔΔG prediction for the interface alignment methods ( iTM-score , Iscore , and PCscore ) is more complex reflecting a more complex underlying distribution . In each case , the accuracy of ΔΔG prediction is at least bimodal with the cutoff value . Like the monomeric structure profile , the accuracy rises at strict cutoff values . As the cutoff is reduced it levels off as an adequate representation of closely related complexes is built . However , unlike the monomeric structure profile , the accuracy rises again at lower cutoff values , eventually reaching a higher accuracy than can be achieved by profiles constructed from closely related complexes . Closer inspection of the actual origin of this effect is the inclusion of sequences at lower cutoff values that can be aligned accurately to a region within the interface but with relatively poor overall global alignment . From the viewpoint of applications which rely on global properties like the recognition of convergently evolved similar interfaces for function annotation [35–38] , these sequences are less useful as they reflect similarity in only a small region of the interface . However , on a physical level , binding interactions are fundamentally local properties . In the interior of a protein , amino acids are tightly packed and a mutation at one site can cause a rearrangement of the protein core [39] . At the interface , however , packing is less tight and a considerable fraction is exposed to solvent even in the protein complex [40] . The difference in packing gives a conformational freedom at the interface that is not present in the interior which can retard the propagation of packing defects throughout the interface after a mutation [41] . With this in mind , the relative inaccuracy of profiles based on PCscore alignment at predicting ΔΔG values can be explained , despite the fact that PCscore is the only method that attempts to incorporate physicochemical similarity into the alignment procedure . Because PCscore penalizes amino acid mismatches more severely , more sequences with good local matches but poor global similarity are missed . Taken individually , sequences with higher interface similarity should be more predictive of ΔΔG then sequences with lower interface similarity . However , the accuracy of the interface profile score is highly dependent on the number of sequences that can be aligned at the site of the mutation . A representative example is shown in Fig 3A . At a high interface similarity cutoff ( IScore = 0 . 25 ) , the accuracy of the profile score rises steeply until about 15 sequences can be aligned at the position , mirroring a similar result for protein stability [28 , 29] . At low interface similarity ( IScore = 0 . 2 ) , the number of sequences is less predictive of the accuracy of the profile score , likely because a sufficient number of sequences can be found for all positions except those at the extreme edge of the interface ( see below ) . We therefore considered an adaptive procedure to form a more accurate profile . The sequences are first sorted by descending interface similarity . All sequences with an interface similarity above a strict cutoff are added to the profile and up to n sequences are added until the second , looser cutoff is reached . Fig 3C shows the improvement in ΔΔG prediction from Iscore alignment as a function of n for the optimal high and low interface similarity cutoff values ( IScore = 0 . 25 and 0 . 19 ) . n reaches a shallow maximum around 80 sequences . The adaptive profile shows a significant improvement over the profile formed from a high similarity cutoff and a smaller improvement over the profile formed from a high similarity cutoff . To assess the potential of interface profile scores for either standalone ΔΔG prediction or as a feature in machine learning based score combinations , we compared the accuracy of interface profile scores formed from high , low , and adaptive profiles by Iscore alignment to a diverse set of multi-scale potential terms . Although iTM-score profiles are slightly more accurate than Iscore profiles at predicting ΔΔG ( Fig 1 ) , we chose Iscore profiles for comparison because an additional feature calculated from the profile , the fraction of conserved contacts , can be used to predict the accuracy of the profile score for machine learning . The tested set of potentials includes: the all-atom empirical potential FoldX [42 , 43] , a composite statistical and physics based potential from Rosetta ( Talaris 2013 ) [44] , residue and all-atom docking potentials ( PIE [45] and PISA [46] , respectively ) , all atom and Cβ based statistical potentials ( DCOMPLEX [47] and RF_CB [48] , respectively ) , a shape complementarity score [49] , changes in the total , polar , and hydrophobic solvent accessible surface area ( SASA ) , the difference in hydrogen bond counts across the interface in the structures of the WT and mutant complexes , the volume difference between WT and mutant residues , and pharmacophore count differences of hydrophobic , and aromatic and hydrogen bonding forming residues between the WT and mutant complexes [50] . The Pearson’s correlation coefficient c between predicted and experimental ΔΔG values is shown in Fig 4 for the adaptive interface profile score and the multi-scale potentials described above . When all mutations are considered , the adaptive interface profile score is more accurate at predicting ΔΔG than all the other potentials considered except for FoldX . However , the difference in c between FoldX and the adaptive interface profile score is not statistically significant when using a two-tailed Fischer r-to-z transformation ( p-value = 0 . 32 ) . The difference in c between the adaptive interface profile score and the all-atomic docking potential PISA is also statistically insignificant ( p-value = 0 . 2 ) . The adaptive interface profile score is superior in accuracy to all other potentials tested at high statistical significance ( p-value<0 . 001 ) . From Fig 4 , FoldX appears the most accurate single method in terms of Pearson correlation coefficient c although it is statistically indistinguishable with BindProf and PISA . However , this value could be biased somewhat by the fact that the side-chains of the mutant have been reconstructed using the FoldX force field . A mismatch between the force field used to optimize the side-chain rotamers and the scoring potential can result in a degradation of the performance . In our early trials , the Talaris2013 Rosetta force field generally showed similar performance to FoldX values if the side-chains were reconstructed using the Talaris2013 forcefield . We note that although the BindProf score compares favorably with other individual potentials , the Pearson correlation coefficient c is still relatively low ( below 0 . 5 ) . However , one of the key features of BindProf is that it works on a fundamentally different basis then the other methods that are currently in use . This complementarity should be of important help for improving the overall recognition accuracy of multiscale potentials when combined with other sources of potentials as demonstrated below . In many applications it is desirable to know the accuracy of ΔΔG prediction across different categories of experimental ΔΔG values . For example , the accuracy of predicting destabilizing mutations is significantly less important in protein design than the accuracy of predicting favorable mutations , as strongly destabilizing mutants are rejected during the design process . Any inaccuracy in prediction therefore only matters to the extent they are misclassified as favorable or neutral mutations . On the other hand , favorable mutations should be enriched during the design process and accurate ΔΔG prediction is essential for these mutations . We therefore recalculated the Pearson’s correlation coefficient c between experimental and calculated ΔΔG values restricting the dataset to the entries with experimental ΔΔG values within the appropriate range . Interface profile scores show exceptional performance relative to other predictors ( c = 0 . 5 ) at predicting favorable mutations ( ΔΔG values ≤0 kcal/mol , 27% of the total , see Fig 5B ) . This is an important result as finding favorable mutations is a very important target for many applications , such as protein design to build more tightly binding interfaces , which have so far proven difficult to predict by physics based methods [12 , 51] . The most predictive feature in most categories , FoldX , performs poorly here ( c = 0 . 28 compared to c = 0 . 46 for destabilizing mutations ) , similar to previous observations which also included a degree of backbone flexibility by incorporating a short relaxation before the calculation of FoldX energies [51] . Likewise , other features like shape complementarity and the statistical potentials DCOMPLEX and RF_CB that normally perform well also perform poorly in this category . This effect is even more magnified when only strongly favorable mutations ( ΔΔG values ≤ -1 kcal/mol , 8% of the total ) are considered ( Fig 5D ) . Interface profiles are less accurate in predictions of unfavorable mutations ( ΔΔG values ≥0 kcal/mol , 75% of the total in Fig 5E ) , likely because the statistics of unfavorable mutations are based on a lower number of frequency counts within the profile [17] . Full atomic physical potentials ( FoldX and the Rosetta’s Talaris2013 score function ) and docking potentials ( PISA and PIE ) do well in this category . Shape complementarity is also predictive of unfavorable mutations ( c = 0 . 31 ) while it is not predictive of favorable mutations ( c = -0 . 13 ) . All methods were inaccurate in determining the subtle differences between neutral mutations ( ΔΔG values between 1 and -1 kcal/mol , 46% of the total , Fig 5G ) . Fortunately , inaccuracies within this range are usually of less consequence since a mutation with a ΔΔG value between 1 and -1 is often tolerated with little impact on a protein’s function . However , the cumulative impact can be significant when multiple mutations are considered such as in protein design applications . Since all the methods are inaccurate within this range and only a small fraction of mutations are actually favorable , reverting mutations with predicted ΔΔG values >1 kcal/mol back to WT may be a successful strategy for loss of affinity in design proteins through the accumulation of many small errors . We next sought to see if the accuracy of interface profile scores could be predicted from the characteristics of the profile . Interface residues play different roles in protein-protein interactions and display both different conservation patterns and different types of interactions depending on their relative position within the interface [40] . Since the accuracy of both the interface profile scores and the sequence and physics based scores are expected to be sensitive to these changes , it is of interest to compare the accuracy of different methods based on the different types of interface residues . This requires that a standard classification of the roles that different residues play in binding be made , which is difficult if only their geometric position within the interface is considered . Instead , one of the most natural classification of interface residues for binding energetics is determined by comparing the relative solvent accessible area of the residue in the monomeric protein ( rASA ) to the relative solvent accessible area in the protein complex ( rASAc ) ( Fig 6 ) . Following Levy [40] , the “core” residues are defined as residues which are exposed in the monomeric protein ( rASA>25% ) but buried in the protein complex ( rASAc <25% ) . Core residues are typically hydrophobic with a composition strongly divergent from the composition of the remainder of the protein surface [52] . Core residues supply the bulk of the energy driving association by hydrophobic interactions [53] . The hydrophobic interactions within the complex cause the core region to become tightly packed upon complex association with little room for conformational variability . For these reasons , the core residues are strongly conserved during evolution [53 , 54] , and mutations in this region are usually more strongly unfavorable when compared to mutations at the periphery of the interface ( see Figs 7 and S1 ) . “Rim” residues surround the core residues and are also exposed in the monomeric protein . But unlike the core residues , the rim residues become only partially ( 0–25% rASAc ) buried upon complex formation . The rim residues have a composition more similar to the surface of the protein away from the interface [52] . Rim residues are frequently charged and often engage in hydrogen bonding or salt bridges with the binding partner [53] . The rim residues help to alleviate protein aggregation by charge repulsion and can contribute to binding specificity by forming specific polar contacts with the binding partner . In some cases , the rim residues also tune the strength of binding , stopping the formation of an excessively stable complex which prevents the formation of other complexes within the interaction network . Most of the favorable mutations are found within this region , with the most common favorable mutation being a charge reversal which alleviates an unfavorable electrostatic interaction within the complex . Rim residues show much less sequence conservation than the core residues . Because of their role in the fine tuning of protein interactions and because the rim of the interface is less tightly packed [41] than the core residues , these residues are much less evolutionarily conserved . “Support” residues are partially buried in the monomeric protein , and fully buried in the complex . As such , they are usually hydrophobic and located in the center of the interface near the core residues . However , because the change in surface area upon complex formation for support residues is less than core residues they are less important energetically and are subject to more sequence variation than the core residues . The final two categories of “surface” ( rASAc >25% and rASA <25% ) and “interior” ( rASAc <25% and rASA <25% ) consist of residues that make no contacts with the binding partner . Mutations within these regions only influence complex formation indirectly by influencing conformational changes , by destabilizing protein folding [23 , 55] , or by long-range electrostatic interactions and alteration of the hydrogen-bonding network [56] . Consequently , they generally have a minimal impact on the energetics of complex formation ( Fig 7 ) . Since this classification by changes in rASA upon complex formation also indirectly reports on the position of the mutation within the interface , it is expected that the performance of the interface profile score will vary as well . The interface profile score is most accurate for the core residues ( Fig 9 ) which are generally located at the center of the interface ( Fig 6 ) . The alignment is significantly more accurate in this region compared to the rest of the interface , especially when the cutoff is restricted to only highly similar complexes ( Fig 8 ) . The relative advantage of the interface profile score over methods is decreased when non-core residues are considered . The all-atom physics based potentials Talaris2013 and FoldX were also less accurate in predicting the ΔΔG of mutations outside the core residues , most likely because electrostatic and hydrogen bonding interactions are significantly more difficult to predict by physics-based methods than interactions primarily based on hydrophobic contacts [57] . Instead , the docking potentials PIE and PISA are the most accurate methods for the RIM regions . PIE and PISA are statistical potentials based on the difference in distance distributions between native and incorrectly docked complexes at the residue ( PIE ) or atomic level ( PISA ) . By contrast , some of the sequence-based features increased in accuracy in the Rim relative to the Core region such as the change in the count of the number of hydrogen-bond donors and acceptors and the number of aromatic residues . Finally , ΔΔG within the interior and surface regions is correlated with the change in hydrophobic and polar interfacial SASA after mutation . Although the correlation is modest here ( Fig 9E and 9F ) , this is an important result as other features performed poorly for these regions . The results above suggest: These features motivated us to combine the interface profile score with other scoring functions which the profile score is complementary to increase the mutation residue recognition . One common approach of the automated feature combination is machine-learning techniques which use features that are weakly predicting on their own but can be combined to give an optimal prediction of ΔΔG . We first examined whether a technique can be constructed using only the information within the interface profiles . We constructed a 13 feature set by considering 3 interface profile scores using profiles made from high and low interface similarity cutoffs ( Iscore = 0 . 19 and Iscore = 0 . 25 ) and the adaptive interface profile along with 10 additional features reflecting the quality of the high and low interface similarity profiles . These cutoff levels were selected on the basis of validation on a separate testing dataset comprised of 20% of the data not used in validating the final result . For the high and low interface similarity profiles we calculated additional features , including The first two features report on the relative quality of the alignment of the structural profile; whether the ensemble of aligned structures actually resembles the protein complex under question or not . The last three features measure the information content within the profile and reflect whether the profile is sufficiently diverse to fully reconstruct the mutational landscape of the interaction . A random forest algorithm was then used to predict ΔΔG with these features using repeated 10 fold cross-validation ( Fig 10A ) . Using only the features derived from the interface profile scores , it was possible to get a correlation coefficient of c = 0 . 71±0 . 07 ( Fig 10A ) on the 10 fold cross-validated set . This level of accuracy compares favorably to the accuracy of other state-of-the-art methods [8 , 14 , 50 , 51] , despite being two orders of magnitude faster than the molecular dynamics based energy minimization methods [8 , 51] and having far fewer terms than other machine learning based models [14 , 50] . A true direct comparison , however , is difficult because of the different datasets used in training and different methods of cross-validation for various methods . In particular , our dataset considers both single and multiple site mutations but is only trained on dimeric complexes . A true test at the statistical significance level would require retraining each method with the specific dataset used here . Furthermore , small differences in accuracy in machine learning based methods using large amounts of features may not translate to real differences in accuracy outside of the SKEMPI dataset [16] . Nevertheless , it is possible to conclude that the structural interface profile-based method by itself can give an accuracy comparable to state of the art methods . Among the top performing methods , the Beatmusic method [9] using a combination of 13 statistical potentials weighed by solvent accessibility achieves a correlation coefficient of 0 . 4 on a non-redundant , single mutation set of the SKEMPI database and 0 . 68 after the removal of outliers . The residue level contact potential of Moal and Fernandez-Recio [14] achieves a similar performance of c = 0 . 68 when tested against the SKEMPI subset used here . The interface profile scores and profile-based features can be incorporated with the other potentials to give an even more accurate method . We consider two additional methods , using tenfold cross-validation to confirm the results . The first method uses all the 13 profile features above and the Cβ potentials PIE and RF_CB ( Fig 10B ) . This method has the advantage that the side-chains do not need to be calculated for each position which is the most time-consuming part of the calculation . This method has even greater accuracy than the profile only method ( c = 0 . 80±0 . 04 ) . Although the dominant feature in terms of determining relative error is the Cβ statistical potential RF_CB , the most important term in terms of node purity is the low interface similarity profile score and the other profile based features are also important features in the approach both in terms of relative error and node purity ( Fig 10B right side ) . If all the terms are considered , the accuracy increases only slightly ( c = 0 . 83±0 . 05 ) above the residue-level potential model ( Fig 10C left side ) . In this model , the interface profile scores are still dominant terms ( Fig 10C right side ) . The standard cross-validation normally used to validate the accuracy of machine learning assumes the validation set is a non-biased subset that is representative of the actual population . In reality , the SKEMPI database is a non-representative sample of the actual protein-protein complexes . To test this bias , we performed an additional , stricter cross-validation by holding out all mutants of the proteins being tested during training [50] . This leave one out approach to cross-validation is more realistic than the standard validation process as information on mutants for the specific protein being tested is normally not available and therefore should not be included in the validation procedure . This procedure also has the effect of testing the influence of protein specific information on the model procedure and therefore serves as an indication of the overall generalizability of the model . The results of this procedure performed for the potential including all terms ( Fig 10C ) is shown in Fig 11 for the 24 proteins that have more than 10 mutants . The standard error of ΔΔG prediction is reported here rather than the correlation coefficient c as the range of ΔΔG values varies substantially among different proteins . For example , the experimental ΔΔG values for three of the proteins ( 1GC1 , 1E22 , and 1A22 , left side of Fig 11 ) are mostly near zero ( mean |ΔΔG|<0 . 5 ) , indicating neutral mutations that have little effect on protein binding . The standard error of prediction is therefore more informative in this case as c becomes less meaningful when the values are distributed only within a narrow range . As can be seen from Fig 11 , the impact of leaving out the tested protein during training does not have a substantial impact on prediction—the mean standard error across the set increases only slightly from 1 . 11 kcal/mol to 1 . 33 kcal/mol . Such minor decrease in accuracy is smaller than the decrease seen with many other machine learning methods . For example , the accuracy of the mCSM method drops from an original cross validated standard error of 1 . 02 kcal/mol to 1 . 55 kcal/mol using a similar leave one protein out approach [50] . Overall , this accuracy is still comparable to or higher than most of the much more computationally intensive molecular dynamics based methods explicitly considering conformational flexibility [51 , 58 , 59] . Like all mutation prediction models , the final machine-learning model has limitations . Many of the limitations are general and apply to any method that attempts to predict ΔΔG values for affinity changes by a structure-based approach . First , the model is trained only to predict ΔΔG values for dimeric complexes where mutations occur only on the side of the interface for individual complexes . While the method can be extended relatively easily to predict mutations for trimers and other types of oligomeric complexes , removing the restriction to search for linked mutations on both sides of the interface simultaneously is more difficult . Many of the terms such as the profile scores , the associated confidence measures of the profile scores , and the pharmacophore counts are strictly linearly additive with respect to the number of mutations . This assumption , which is generally not true for mutations affecting protein stability , is backed by large-scale binding selection mutagenesis experiments showing that the enrichment ratio of double mutants is strongly predicted by the enrichment ratios of the respective single mutations [60] . In these experiments , only one protein is mutated at a time corresponding to mutations on one side of the interface only . When both sides of the interface are mutated , specific interactions such as the formation of a salt-bridge across the interface can cause strong non-linearity when double mutations are compared to the sum of the respective single mutations [61] . However , for most applications one-sided mutations are of the most interest since the binding partner can be assumed to have the WT sequence since mutations are generally rare . Finally , training and testing was performed on the SKEMPI database [16] . This database includes entries for all complexes for which a ΔΔG value and structure are available . The database does not evenly represent the universe of actual protein complexes and some protein complexes and mutation types are heavily represented while others are underrepresented . Exploring other more comprehensive datasets should help further improve BindProf .
Protein-protein interactions are critical for nearly every process in the cell and deleterious mutations hindering these interactions can have severe consequences for the associated cellular function . A variety of efforts from personalized medicine to understand viral evolution require knowing how specific mutations effect the protein-protein interactions . Conversely , designing proteins with improved binding or altered specificity requires that the impact of mutations on the native interface be understood . Currently this information is not available experimentally on the proteome-wide scale necessary for these tasks . Towards this end , considerable effort has been devoted towards developing methods to predict the impact of mutations on binding affinity . Most of these approaches rely on physics based methods that attempt to faithfully model on the atomic level the interactions determining protein-protein binding affinity . However , a major obstacle of such approaches is the need for the reconstruction of the full-atomic model for every mutant complex , which limits the accuracy of the approach ( since the position of the side-chains is difficult to model ) and reduces the computational speed and the range of applications ( since rebuilding the full-atomic model is generally the most time-consuming step ) . In this work , we developed a novel approach , BindProf , aiming to overcome some of these limitations by introducing an interface structure profile based scoring function built on the multiple sequence alignments of analogous protein-protein interactions collected from the PDB . Interface profile scores constructed in this manner can be used as either as a predictor of the Gibbs free energy change of protein-protein binding ( ΔΔG ) in their own right or combined with other features in a machine learning approach . Considered as a standalone feature , the adaptive interface profile score created by BindProf has an accuracy similar to the best all-atom potentials ( Fig 4 ) . However , unlike physics based potentials , the profile scores can be used to score thousands of mutations across a protein-protein interface very quickly ( approximately 20 msec per mutation as opposed to an average of 115 seconds , for instance , for building and scoring a full atom complex by FoldX ) as once the profiles are constructed the scoring of individual mutants is reduced to a very fast table lookup . In addition , the accuracy of the interface profile score can be inferred from the location of the mutation within the interface and from the characteristics of the structures used to create the profile ( Fig 9 ) . This is an advantage over current physics-based methods in which the accuracy is difficult to infer ahead of time . As such , profile scores play prominent roles in composite scoring approaches where they are combined with other features predictive of their accuracy such as the average RMSD for the aligned residues and the sequence entropy within the profile at the mutation position ( Fig 10 ) . We therefore expect that interface profiles may play important roles in future composite scoring approaches . The effectiveness of interface profile scoring in predicting binding affinity changes has implications beyond the prediction of ΔΔG values for protein affinity changes . First , the fact that such a method can be constructed at all is independent confirmation of the results of Gao and Skolnick [62] that the existing PDB library is densely connected and approaching completeness with respect to the interface structural space , even if it is not yet complete with respect to the fold space of all possible quaternary structures . If the interface structural space of the PDB library was sparsely connected with few known structural neighbors for each complex , the profile would consist of only a few sequences and the structural profile would not be predictive of ΔΔG values . This effect can be inferred from Fig 2 when only high cutoff values are considered . Second , the degree of correlation between ΔΔG and the interface profile score bears some relationship to the degree that evolution has selected for protein binding affinity at the interface rather than other factors , although the exact relationship is obscured by the limited amount of experimental data available . As more experimental ΔΔG values are measured , profile scoring may help establish the exact role of binding affinity in evolutionary fitness . Overall , the creation of a novel evolutionary based approach with specific characteristics ( including high complementarity with physics based scores , high accuracy in finding favorable mutations , low computational cost on a per mutant basis , and a relative insensitivity to side-chain conformation ) should find an important application in many biomedical studies including protein design and disease-associated mutation analyses .
Experimental ΔΔG values were derived from the SKEMPI database that consists of experimental protein affinity changes upon mutation for protein-protein complexes in which a crystal structure of the WT complex are available [16] . A subset of the database was used for testing of the interface profile scoring and multi-level machine learning . First , the selection was restricted to mutations occurring at one side of the interface to match the normal biological situation in which mutations are relatively rare and it is expected that at least one chain in the complex is WT . Since the interface profile score is fundamentally a property between two protein pairs , only dimeric complexes were selected for analysis from this set , although the method can be extended for the analysis of higher oligomeric complexes . Finally , the SKEMPI database contains multiple entries for a single mutation for 186 entries in this set . These redundant entries were averaged with outlier replicants with ΔΔG values one standard deviation above the mean disregarded . The final dataset contains 1725 entries for 130 complexes . Both single site point mutations and multiple point mutations are considered . For random forest machine learning , three separate training , testing , and validation datasets were constructed . The training set ( 60% of the data ) was used to construct the model , while the testing set ( 15% of the data ) was used to tune the number of variables attempted in each split . The final model was evaluated by 10 fold cross-validation repeated three times on the validation set ( 25% of the data ) . Crystal structures were first downloaded from the PDB and stripped of water and all non-protein ligands . A short optimization of the structure of the WT protein complex was then performed to eliminate small clashes and other undesirable features by the RepairPDB function within FoldX [43] . Structures of the mutant complex were then generated from the optimized WT structures by the BuildModel function within FoldX . The temperature for FoldX model building and energy scoring is set to the experimental temperature when known , otherwise it is set to 298 K [16] . For all the sequence and physics based energies except the docking functions PIE [45] , PISA [46] , and DCOMPLEX [47] and the all atomic energy functions Talaris 2013 [44] and FoldX [42 , 43 , 63] energies were calculated separately for the mutant and WT complex structures and for both monomeric structures . The predicted ΔΔG values are then equal to: ΔΔGWT→Mut=[EWT ( complex ) −EWT ( monomers ) ]−[EMut ( complex ) −EMut ( monomers ) ] ( 3 ) where E is the relevant energy function . For the docking functions PIE , PISA , DCOMPLEX , FoldX and the Rosetta Energy function Talaris2013 , this calculation is performed internally and ΔΔG is directly proportional to the difference between the energies of the two complexes: ΔΔGWT→Mut=EWT ( complex ) −EMut ( complex ) ( 4 ) Changes in SASA upon mutation and number of hydrogen bonds across the interface were calculated by the Interface Analyzer in Rosetta [64] . Interface structural alignment was performed using the COTH complex library of non-redundant dimeric structures . To create this library , higher order complexes in DOCKGROUND [65] are first split into all possible combinations of pairwise dimers . This is repeated for all the alternative binding modes contained within the pdb file . All dimers with either chain having less than ten interface residues are removed . The remaining structures are then filtered based on sequence and structure similarity of the complete complex to other complexes in the library . If a dimer shares at least 70% sequence identity and a TM-score at least 0 . 8 obtained from MM-align [66] to another structure in the complex library , it is removed from the database . The current library contains ~55000 protein-protein complexes . Interface alignment was performed by either Ialign [33] or PCalign [34] program . The iTM-score and Iscore values are calculated by Ialign and PCscore returned by PCalign . The equation for the interface similarity metric iTM-score is a direct analogue of the scoring matrix for TM-score [31] except that only residues within a cutoff depth of 4 Å are considered for the alignment , i . e . iTM-score=1LQ∑i=1Na11+di2/d02 ( 5 ) where LQ is the total number of residues in the interface , Na is the number of aligned residues , di is the distance between the Cα atoms of residues at ith aligned residue pair , and d0 is an empirical scaling factor dependent on LQ to ensure the length invariance of the final score [31] . The Iscore is defined similarly except for the addition of a contact overlap factor fi reflecting the fraction of conserved contacts , i . e . Iscore=1LQ∑i=1Nafi1+di2/d02 . ( 6 ) Here fi = ( ci/ai + ci/bi ) /2 , where ai and bi are the numbers of interfacial contacts of ith aligned residue pair for the template and query complex , respectively , and ci is the number of overlapped contacts . A contact is defined as being overlapped if the residues forming these contacts are aligned in the two pairs of chains . The PCscore is defined analogously to the Iscore with the addition of chemical similarity measure Ii of ith residue pair: PCscore=fcLQ∑i=1Na11+0 . 25 ( 1−Ii ) +di2/42 ( 7 ) where fc is the ratio of common contacts between two sets of aligned interfacial residues . Ii equals to 1 if the ith pair of aligned residues are in the same chemical type , or 0 otherwise . To define the chemical equivalency , the amino acids are split into non-overlapping groups of positively charged ( K , R ) , negatively charged ( E , D ) , mixed hydrogen bond donor/acceptors ( N , Q , S , T ) , aromatic ( F , W ) , hydrophobic ( C , A , I , L , M , P , V , G ) and mixed donor/acceptor or aromatic ( H , Y ) . | Few proteins carry out their tasks in isolation . Instead , proteins combine with each other in complicated ways that can be affected by either the natural genetic variation that occurs among people or by disease causing mutations such as those that occur in cancer or in genetic disorders . To understand how these mutations affect our health , it is necessary to understand how mutations can affect the strength of the interactions that bind proteins together . This is a difficult task to do in a laboratory on a large scale and scientists are increasingly turning to computational methods to predict these effects in advance . We show that by looking at the multiple alignments of similar protein-protein complex structures at the interface regions , new constraints based on the evolution of the three dimensional structures of proteins can be made to predict which mutations are compatible with two proteins interacting and which are not . | [
"Abstract",
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] | [] | 2015 | Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles |
The HIV/AIDS pandemic is a major global health threat and understanding the detailed molecular mechanisms of HIV replication is critical for the development of novel therapeutics . To replicate , HIV-1 must access the nucleus of infected cells and integrate into host chromosomes , however little is known about the events occurring post-nuclear entry but before integration . Here we show that the karyopherin Transportin 3 ( Tnp3 ) promotes HIV-1 integration in different cell types . Furthermore Tnp3 binds the viral capsid proteins and tRNAs incorporated into viral particles . Interaction between Tnp3 , capsid and tRNAs is stronger in the presence of RanGTP , consistent with the possibility that Tnp3 is an export factor for these substrates . In agreement with this interpretation , we found that Tnp3 exports from the nuclei viral tRNAs in a RanGTP-dependent way . Tnp3 also binds and exports from the nuclei some species of cellular tRNAs with a defective 3′CCA end . Depletion of Tnp3 results in a re-distribution of HIV-1 capsid proteins between nucleus and cytoplasm however HIV-1 bearing the N74D mutation in capsid , which is insensitive to Tnp3 depletion , does not show nucleocytoplasmic redistribution of capsid proteins . We propose that Tnp3 promotes HIV-1 infection by displacing any capsid and tRNA that remain bound to the pre-integration complex after nuclear entry to facilitate integration . The results also provide evidence for a novel tRNA nucleocytoplasmic trafficking pathway in human cells .
Akin to other viruses [1] , after cell-receptor mediated entry into the cell , HIV-1 undergoes an uncoating step by shedding its capsid core [2] , [3] , which is constituted by approximately 1 , 500 capsid proteins ( CA ) arranged in a hexameric lattice [4] . This step is incompletely understood yet it is important to maintain optimal infectivity [3] , [5] , [6] . If uncoating of the viral core takes place too early , infectivity is impaired as observed with viral mutants having unstable capsid cores or in the presence of certain members of the TRIM protein family [5] , [7] , [8] . If the virus uncoats too late or incompletely , infectivity is also impaired [5] , [9] . Interestingly , proper uncoating of the viral core , which is thought to take place in the cytoplasm of infected cells during reverse transcription [6] , [10] , can also influence later events such as nuclear entry and integration [9] , [11] . Changes in CA have been shown to impact on HIV-1 nuclear import and infection of non-dividing cells in several ways . Substitution of HIV-1 CA with MLV CA impairs HIV-1 ability to infect non-dividing cells [12] . This CA substitution makes HIV-1 phenotypically similar to the murine leukemia virus ( MLV ) , which cannot efficiently infect non-dividing cells and maintains relatively large amounts of CA associated with its reverse transcription complex ( RTC ) [13] . HIV-1 CA may determine which components of the nuclear pore complex ( NPC ) are preferentially used for infection , because specific mutations in CA make the virus less dependent on NUP153 and more dependent on NUP155 [14] . Furthermore , CA influences incorporation of certain tRNAs species into the viral particle , which promote HIV-1 entry into the nucleus , presumably by recruiting the intracellular viral complex into the so called tRNA retrograde transport pathway [15] , [16] . CA also impacts on post-nuclear entry events . HIV-1 mutants that maintain larger amounts of CA associated with their RTCs and pre-integration complexes ( PICs ) integrate less efficiently [9] . Furthermore , the restriction factors TRIMcyp and TRIM19 , which bind to CA , can block post-nuclear entry steps required for efficient integration [17] . This evidence supports a functional link between CA , uncoating , nuclear import and integration [9] , [11] , [17] , however a unifying picture is lacking and little is known of the events taking place between HIV-1 nuclear entry and integration . Interestingly , recent high throughput screenings showed that Transportin 3 ( Tnp3 ) ( Gene ID: 23534 ) is a host factor critical for some events occurring at or shortly after HIV-1 nuclear import [18] , [19] , [20] , [21] . Tnp3 belongs to the importin ß superfamily of nuclear transport receptors that bind RanGTP at their N-termini [22] and participate in nucleocytoplasmic transport of proteins and nucleic acids by interacting with specific FG-repeats present in many nucleoporins , the constituents of the NPC [23] . Members of the importin ß superfamily can act as nuclear import or export receptors ( or both ) depending on whether they bind or release the cargo in the presence of RanGTP . Nuclear import receptors bind their cargos in the cytoplasm and release them in the nucleus upon binding to RanGTP , whereas nuclear export receptors bind their cargos in the nucleus in complex with RanGTP and dissociate from them in the cytoplasm upon hydrolysis of RanGTP [24] . In the nucleus , RanGDP in enzymatically converted into RanGTP by the regulator of chromatin condensation-1 ( RCC1 ) , a chromatin-bound guanine nucleotide exchange factor , whereas on the cytoplasmic face of the NPC RanGTP is hydrolyzed into RanGDP by the Ran GTPase-activating protein ( RanGAP1 ) and RanBP1 [24] . Hence a concentration gradient of RanGTP ( high in the nucleus and low in the cytoplasm ) is maintained across th nuclear envelope , providing directionality to nucleocytoplasmic trafficking . We have investigated the role of Tnp3 in HIV-1 infection and found that it is important for the completion of a post nuclear-entry step . We show that Tnp3 binds to CA and tRNA species present in the viral particle in a RanGTP-dependent way , facilitates their nuclear export and may promote a maturation step of the PIC inside the nucleus required for efficient integration . Remarkably , we found that Tnp3 is also an export factor for certain cellular tRNA species lacking a complete 3′ CCA end .
We wanted to investigate the role of Tnp3 in cell types relevant to HIV-1 infection , including macrophages and CD4+ T-cells . To this end , Tnp3 was depleted in human embryonic stem ( ES ) cell-derived macrophages [25] by lentiviral delivery of an shRNA targeting Tnp3 mRNA or the DsRed mRNA as control [26] and cells were infected four days later with an HIV-1 vector bearing the green fluorescent protein expression cassette ( HIVGFP ) . Levels of Tnp3 appeared to be low in differentiated macrophages and even a fairly modest depletion of Tnp3 resulted in ∼10-fold inhibition of HIV-1GFP infection ( Figure 1A-C ) . A greater depletion of Tnp3 corresponded to a greater block to HIVGFP infection ( Figure 1A-C ) . Similar results were obtained in blood-derived macrophages ( Figure 1D , 1E ) , in agreement with Christ et al . [20] . Next , we depleted Tnp3 in CD4+ Jurkat T-cells using a MLV vector to deliver an shRNA targeting Tnp3 mRNA . Relative to macrophages , HIV-1GFP infection was modestly ( ∼3 fold ) but consistently inhibited in Jurkat Tnp3 knock-down ( KD ) cells , despite effective Tnp3 depletion ( Figures S1A , S1B ) . We also depleted Tnp3 in HeLa cells by using two different siRNAs . Depletion of Tnp3 in HeLa cells resulted in ∼7 fold inhibition of infection with the best siRNA ( Figure 2A , 2B ) in agreement with previous reports [18] , [20] . Hence Tnp3 supports efficient HIV-1 infection in relevant target cells although different cell types might require different levels of Tnp3 to that end . We used HeLa cells , which can be grown in sufficiently large quantity and showed a robust phenotype , to examine in more detail which step of the HIV-1 life cycle was affected by depletion of Tnp3 ( Figure 2 ) . Reverse transcription of the viral RNA genome was not impaired in HeLa Tnp3 KD cells , however we detected 10 fold less viral DNA one week post-infection ( Figure 2C ) , suggesting that viral nuclear import or integration ( or both ) were defective . To investigate viral nuclear entry , levels of 2LTRs circular DNA ( a viral DNA form that is generated by end-ligation inside the nucleus ) [27] were measured by TaqMan qPCR at 24 h and 48 h post-infection . Similar amounts of 2LTR circular DNA were detected in control and KD cells ( Figure 2D ) . Infected cells were also fractionated and the distribution of viral DNA in the cytoplasm and nucleus was examined by TaqMan qPCR . A similar distribution of viral DNA in the nuclear and cytoplasmic fractions of Tnp3 KD and control cells suggested that viral nuclear import was not defective ( Figure 2E ) . The quality of the fractionation was controlled two fold . First , 2LTR circular viral DNA was enriched >20 fold in the nuclear fractions , consistent with the notion that viral DNA is circularized inside the nucleus [27] ( Figure 2F ) . Second , the distribution of spliced cyclophilin A mRNA in each fraction showed that contamination of nuclei with cytoplasmic material was <5% [26] ( Figure 2G ) . This result also indicated that fractionated nuclei were mostly devoid of the external nuclear envelope layer , which is contiguous with the ER . To test if viral DNA could be bound to the nuclear envelope , we fractionated infected cells in the presence of DNAse I beads . Whereas DNAse beads could digest in part viral DNA in the cytoplasm , viral DNA in the nuclear fraction was unaffected , suggesting that it was inside the nuclei and protected from digestion ( Figure S2 ) . Since viral nuclear import was not significantly inhibited , we next measured integration by Alu-PCR Taqman qPCR . We found that the amount of integrated viral DNA was approximately 10 fold lower in Tnp3 KD cells compared to control cells [28] , [29] ( Figure 2H ) . Similar data were obtained with a near full length HIV-1 clone containing all accessory proteins ( HIV-1LAIDenv ) [12] ( Figure S3 ) . Although the block to HIV-1GFP infection was modest in Jurkat Tnp3 KD cells , we found that integration was the only step significantly inhibited in these cells , consistently with the results obtained in HeLa cells ( Figure S1 ) . These results showed that Tnp3 is required for a post-nuclear entry step leading to efficient integration . Tnp3 was shown to bind to HIV-1 integrase ( IN ) , but the biological relevance of this interaction is uncertain [19] , [20] . To investigate if Tnp3 bound to other viral elements in addition to IN and to gain a greater understanding of its function , purified virus was used in pull down assays with recombinant Tnp3 in the presence or absence of the RanQ69L-GTP mutant . The RanQ69L point mutant is hydrolyzed to the GDP form with a kinetic several orders of magnitude slower than wild type RanGTP [30] , making it more suitable for pull down assays . Addition of RanGTP to the in vitro binding assays is critical to understand if the nuclear transport receptor acts as an import or an export factor for the specific cargo and adds an important element of specificity to the assay . Viral stocks were prepared from stable HIV-1 vector producer cells pseudotyped with the amphotropic envelope [31] , purified through two sucrose gradients and analyzed by SDS-PAGE and silver staining . This procedure allowed visualization of several viral proteins including Env , capsid ( CA ) and matrix ( MA ) . tRNAs incorporated into viral particles [32] were also clearly visible as the typical yellowish band migrating at ∼20 kDa ( Figure 3A ) . Pull-down assays were performed with virus particles mildly disrupted by gentle sonication in the absence of detergents to limit the damage to viral cores . Viral tRNAs and CA bound to Tnp3 with greater affinity in the presence of RanGTP ( Figure 3B ) , implying that Tnp3 might be an export factor for these molecules [33] . Next we generated several Tnp3 deletion mutants ( see below ) and found that a mutant lacking the last C-terminal 98 residues bound both viral tRNAs and CA less well , suggesting that these viral components may recognize the same Tnp3 domain ( Figure 3C ) . To confirm the specificity of the interaction between Tnp3 and CA , we performed parallel pull-down assays with wild type Tnp3 with or without RanGTP , the C-term deleted Tnp3 and Exportin-t ( Figure 3D ) . Exportin-t ( Xpo-t ) is the main tRNA export receptor in mammalian cells and binds tRNAs with high affinity in the presence of RanGTP [34] , [35] , [36] , [37] . We confirmed that Tnp3 binds CA and tRNAs with higher affinity in the presence of RanGTP and that the C-term deleted Tnp3 did not bind efficiently to either . Importantly , Xpo-t did bind to viral tRNAs , consistent with the notion that viral particles contain a mix of normal and truncated tRNAs [15] , but did not bind efficiently to HIV-1 CA ( Figure 3D ) . The relevant tRNAs and CA gel bands were quantified using ImageJ software analysis tool , which confirmed the trend observed by visual inspection ( Figure 3E-G ) . Hence , the interaction between Tnp3 and CA is specific . Interestingly , we were unable to detect a significant interaction between Tnp3 and purified recombinant CA , similarly to Fv1 , TRIM5α and CPSF6 [14] . To test the biological significance of the pull down assays and the importance of the last 98 C-term residues of Tnp3 , we performed rescue experiments . A polyclonal population of 293T cells with a stable Tnp3 KD was generated , which showed a modest but highly reproducible defect for HIV-1 infection ( Figure 3H ) . The Tnp3 KD cells were then transfected with increasing doses of a plasmid expressing wild type or the C-term truncated Tnp3 with a point mutation to make the mRNAs resistant to the shRNA targeting . Cells were challenged with the HIV-1GFP vector 48h after transfection and then analyzed by flow cytometry . Transfection of the plasmid expressing full length Tnp3 partially rescued HIV-1 vector infection; in contrast , the plasmid expressing the C-term deleted Tnp3 showed no rescue phenotype at all ( Figure 3H ) . Tnp3 levels were monitored by Western blotting: 293T cells showed a substantial KD and even a modest recovery of Tnp3 expression above background levels was sufficient for a partial rescue of HIV-1 vector infection ( Figure 3I ) . In contrast , similar levels of expression of the C-term deleted Tnp3 did not have any effect on HIV-1GFP transduction ( Figure 3I ) . Hence , the last 98 residues of Tnp3 are important for CA , tRNA binding and HIV-1GFP infection . The pull down assays showed greater binding of Tnp3 to viral tRNAs in the presence of RanGTP , suggesting that Tnp3 may be an export factor for these molecules . We wanted to investigate this aspect further and understand if the putative Tnp3 export activity was limited to viral tRNAs or was broader and extended to cellular tRNAs as well . To test if cellular tRNAs bound Tnp3 we performed pull-down assays with high speed cytosolic extracts ( HSEs ) , which are devoid of ribosomes and polysomes but contain plenty of cellular tRNAs [15] . HSE was incubated with GST-Tnp3 beads and factors bound to the beads were eluted and analyzed by SDS-PAGE and silver staining . Several proteins in the HSE bound non-specifically to the GST-beads and served as a loading control but a number of other proteins specifically bound to GST-Tnp3 beads and were selectively enriched in the presence of RanQ69L-GTP ( Figure 4A ) . Notably , a yellowish band migrating at ∼20 kDa was also specifically recovered in the Tnp3 pull downs and clearly enriched in the presence of RanQ69L-GTP ( Figure 4A ) . This band was strikingly similar to the tRNAs band that we previously observed in HSE fractions active in HIV-1 RTC nuclear import [15] , hence we purified nucleic acids in the eluted fractions and re-analyzed the samples in a long denaturing urea gel , which was stained with a fluorescent dye specific for nucleic acids . Using this procedure we could confirm that the band observed by silver staining was indeed tRNAs ( Figure 4B ) . Certain tRNA species lacking a complete 3′ CCA end have been previously shown to promote HIV-1 nuclear import [15] . We therefore asked whether the same tRNAs would bind Tnp3 in the presence of RanGTP . To this end we performed pull down assays with in vitro synthesized tRNAlys1 , 2 lacking the 3′ CCA tail ( hereafter called G2 ) . To control for specificity , a mutant G2 tRNA with a 3′ TTT tail replacing the normal CCA tail ( hereafter called m2 ) was tested in parallel and assays were performed in the presence of RanGTP or RanGDP . Tnp3 bound G2 tRNA with greater affinity in the presence of RanGTP than RanGDP , in agreement with the results obtained with cellular tRNAs . Interestingly , the m2 mutant bound Tnp3 less efficiently than G2 , suggesting that the 3′ tail may modulate recognition ( Figure 5A and 5B left panel ) . To explore the role of the 3′ tail and to map other tRNA regions important for Tnp3 binding , we generated a panel of tRNA mutants and examined their ability to bind Tnp3 in pull down assays ( Table 1 , Figures 5 and 6 ) . As a control , selected mutants were also tested for their ability to bind Xpo-t , which has rather stringent structural requirements for tRNA recognition , including a full 3′ CCA tail [34] , [35] , [36] , [37] . Remarkably , a G2 variant having a complete 3′ CCA end ( mutant m2a ) bound Tnp3 poorly ( Figure 5A and 5B , middle panels ) . In contrast , the m2a tRNA bound well to Xpo-t ( Figure 5A and 5B , right panels ) . A G2 tRNA variant with the U55A point mutation in the T-loop region ( m5 ) also showed weaker binding to Tnp3 than G2 ( Figure 6 ) . This mutation disrupts the interaction between nucleotides U55-G18 , which is important for correct tRNA 3D folding [38] , suggesting that a certain degree of tertiary structure integrity is necessary . This was confirmed by generating the U55G/G18U double mutant ( m6 ) to restore the interaction between nucleotides 55 and 18 , which indeed showed an improved binding to Tnp3 , if compared to m5 ( Figure 6 ) . Single point mutation m10 , which disturbed the T-loop conformation , did not significantly impact on binding to Tnp3 while the affinity to Xpo-t was decreased ( Figure 6 ) . These observations suggested that tRNA binding to Xpo-t is more sensitive to disruptions of the T-loop tertiary structure then Tnp3 . Intriguingly , swapping the G2 anticodon sequence from lysine to aspartate ( m22 ) inhibited tRNA binding to Tnp3 but swapping to glutamate ( m20 ) did not ( Figure 6 and Table 1 ) . Overall these results indicated that substantial disruptions of the tRNA tertiary structure impair Tnp3 binding , and that the anticodon may be involved in recognition of Tnp3 . These findings also suggest that Tnp3 selectively binds tRNAs lacking a mature 3′CCA end , in contrast to Xpo-t where the intact 3′CCA tail is required for efficient interaction – an observation which reinforces the idea that Tnp3 and Xpo-t have different requirements for tRNA recognition . To map the Tnp3 domains important for tRNA binding , several N- and C- terminal Tnp3 deletion mutants were generated and tested in pull down assays using equimolar amounts of tRNAs and Tnp3 for cross-comparison ( Figure 7 ) . A deletion in the first 355 N-terminal residues of Tnp3 significantly inhibited binding to RanGTP , in agreement with a previous study [39] , and to tRNAs . An even greater loss of tRNA binding was observed when the first 443 N-terminal residues of Tnp3 were deleted ( Figure 7B ) . Unfortunately , further N-term Tnp3 deletion mutants were poorly soluble and could not be tested . Nonetheless , these results clearly showed that a critical RanGTP binding domain was located within the first 355 N-terminal residues of Tnp3 , with residual binding up to the first 443 N-terminal residues , and that binding of RanGTP was important for tRNA recognition . Two C-terminal Tnp3 deletion mutants were generated , DC880 and DC825 . Pull down assays with these two mutants showed that binding of tRNA was mainly dependent on residues between 825 and 880 of Tnp3 ( Figure 7B – right panel ) . Interestingly , the DC880 and DC825 Tnp3 mutants lost some affinity for RanGTP , suggesting that Tnp3 may form a ternary complex similar to the one recently described for Xpo-t , in which RanGTP makes contact with the C-term of Xpo-t [36] . These results are also consistent with the pull down results obtained using viral tRNAs ( Figure 3C and Figure 3D ) Export of tRNAs from the nucleus is a quality controlled process , whereby Xpo-t selectively binds fully mature tRNAs suitable for protein translation [34] , [35] . It was therefore surprising to find that Tnp3 might be an export factor for defective tRNAs lacking a complete 3′ CCA end , which hinted at the possibility that a parallel tRNA export pathway might exist in mammalian cells . To examine if Tnp3 could indeed export these tRNA species , an export assay was devised in permeabilized HeLa cells by adapting the classical nuclear import assay [40] ( Figure 8A ) . To this end , HeLa cells were permeabilized by digitonin and a standard nuclear import assay was carried out first in the presence of purified and fluorescently labeled tRNAs ( G2 ) and an energy regenerating system . After 10 minutes incubation at 37°C to allow tRNA nuclear accumulation [15] , cells were washed and incubated for a further 10 minutes at 37°C in the presence or absence of recombinant Tnp3 , an energy regenerating system and the Ran system . Following this second incubation , cells were washed , fixed and analyzed by confocal microscopy . A tRNA export receptor should reduce the fluorescent signal from pre-loaded nuclei and relocate tRNAs to the cytoplasmic side where they are washed out . This modified assay was initially tested with Xpo-t and provided an excellent readout for tRNA export ( Figure 8B ) . In the export assay , addition of Tnp3 clearly reduced the nuclear fluorescent intensity compared to controls and the effect was specific because it was observed only in the presence of the Ran system ( Figure 8B ) . The reduction in the fluorescent signal could not be explained by contamination of the protein preparation with RNAse because it was not observed upon addition of the individual components ( Figure 8B , 8C ) . Moreover , the export activity of the DN355 , DN443 and DC825 Tnp3 deletion mutants correlated with their affinity for RanGTP and tRNAs as detected in pull down assays ( Figure 8B , 8C ) . We found that the fluorescent signal was reduced in both nuclei and cytoplasm , presumably because tRNAs in complex with Tnp3 were more soluble and could be more easily removed from the cytoplasmic remnants of permeabilized cells by washing . These results confirmed that Tnp3 is a RanGTP-dependent export factor for some tRNAs species lacking a complete 3′ CCA end . To test if endogenous viral tRNAs were also exported by Tnp3 , we extracted tRNAs from purified viral particles and used them in the export assay . Similarly to the in vitro synthesized G2 tRNA , endogenous viral tRNAs were also exported by Tnp3 in the presence of the Ran system ( Figure 9 ) . In pull down assays with purified HIV-1 vector we found that Tnp3 bound to CA , suggesting that it is an important target for Tnp3 function . To further investigate the relevance of CA in influencing susceptibility to Tnp3 , we generated a panel of HIV-1 vectors with mutations in CA , which were previously shown to be defective for infection at a post-nuclear entry step [11] and examined their dependence on Tnp3 in stable HeLa Tnp3 KD cells ( Figure 10 ) . The N74D point mutant , which is independent of Tnp3 for infection [14] was tested in parallel . The T54A and the T54A/N57A capsid mutant HIV-1 vectors showed substantially reduced infectivity compared to wild type virus , however they were also less dependent on Tnp3 for infection ( Figure 10 ) . We also tested the A105S mutation , which conferred resistance to the antiretroviral compound Coumermycin-A1 [41] . Interestingly , Coumermycin-A1 was shown to inhibit HIV-1 integration [41] , similarly to the phenotype observed in Tnp3 KD cells , suggesting that the compound and Tnp3 may act on same pathway . The A105S mutant vector maintained near-wild type infectivity and was also clearly less dependent on Tnp3 for infection . Of all mutant vectors the N74D was the least dependent on Tnp3 for infection and maintained normal infectivity ( Figure 10 ) . Similar results were obtained using an HIV-1 vector bearing the gp120 envelope ( Figure S4 ) . These results confirmed that CA is an important target of Tnp3 [14] , [19] . Next we examined what the functional significance of the interaction between CA and Tnp3 might be . The pull down assays showed that Tnp3 binding to CA was stronger in the presence of RanGTP . We therefore asked if Tnp3 was required for the export of capsid from the nucleus of infected cells . We could not detect accumulation of recombinant HIV-1 CA into the nucleus of permeabilized cells , precluding the use of the nuclear export assay in this case . Therefore , control and stable Tnp3 KD cells were transduced at an MOI of 0 . 5 with the HIV-1 vector ( Figure 11A ) , fractionated into nuclear and cytoplasmic fractions 16 hours post-infection and the distribution of CA in each fraction was examined by Western blot ( Figure 11B ) . A relatively low MOI was used because control and Tnp3 KD cells maintained a clear phenotype with respect to HIV-1GFP infection in these conditions . Following fractionation , CA was detected in the nuclear fraction of Tnp3 KD cells and , to a lesser extent , control cells , whereas cytoplasmic CA was significantly less abundant in Tnp3 KD than control cells ( Figure 11E ) . The biological significance of this result was confirmed using an HIV-1 vector bearing the N74D mutation in CA , which is the least dependent on Tnp3 for infection [14] ( Figure 11B , 11C ) . The N74D mutant virus did not appear to accumulate CA in the nuclei in either control or Tnp3 KD cells ( Figure 11B ) . The ratio of nuclear to cytoplasmic CA was significantly higher in KD than control cells infected with wild type virus but remained essentially unchanged in cells infected with the N74D mutant ( Figure 11D ) . A different intracellular distribution of wild type and N74D CA was also observed by immunofluorescence ( Figure S5 ) . In this case , however , we had to increase the MOI by 10 fold to ≥5 to be able to see a specific intracellular p24 signal , which resulted in an almost complete loss of phenotype in Tnp3 KD cells relative to control cells upon infection with the HIV vector . To investigate if the temporal dynamics of nuclear accumulation of CA coincided with that of integration , we performed a time-course experiment in cells infected with the HIV-1GFP vector . Trace amounts of CA could be detected 6 h post-infection , then CA progressively accumulated into the nucleus of Tnp3 KD cells at 14 h and 24 h post-infection ( Figure 11F ) . Significantly less CA was detected in the nucleus of control cells 14 h and 24 h post-infection compared to KD cells ( Figure 11F ) . In contrast , the N74D CA mutant was not found accumulating into the nuclei of either control or Tnp3 KD cells ( Figure 11F ) . The timing of CA nuclear accumulation was consistent with the dynamics of viral integration in single-cycle infection assays [27] , [42] , supporting the notion that Tnp3 promotes a maturation step , possibly by displacing any CA and tRNAs still bound to the PIC .
Tnp3 was previously shown to bind to HIV-1 IN [20] , which provided an attractive explanation for the infectivity defect observed in Tnp3 KD cells . However the physiological relevance of the Tnp3/IN interaction remains uncertain , and the ability of Tnp3 to support HIV-1 infection mainly maps to CA [14] , [19] . Therefore we have taken an unbiased approach and performed pull down assays to examine which components bind to Tnp3 in the context of a mature viral particle . We detected binding of Tnp3 to CA and viral tRNAs and , to our surprise , the affinity of the interaction was greater in the presence of RanGTP ( Figure 3 ) . Tnp3 is a nuclear import receptor for some serine-arginine rich ( SR ) proteins , such as the splicing factor ASF and the human papilloma virus-5 ( HPV-5 ) E2 protein , which are released upon RanGTP binding [39] , [43] . The observation that Tnp3 bound viral tRNAs better in the presence of RanGTP suggested that it has both nuclear import and nuclear export activity , depending on the cargo . Other examples of this dual activity include importin 13 [44] , exportin 4 [45] , [46] and Mtr10p [47] , [48] , the hortologue of Tnp3 in S . cervisiae . Tnp3 bound and exported some cellular tRNAs as well as in vitro synthesized tRNAs lacking the 3′ CCA end . In this respect Tnp3 differs from Xpo-t , which binds preferentially mature tRNAs with a complete 3′ CCA end [34] , [35] , [36] . These differences are most likely related to function: Xpo-t is the main nuclear export receptor for mature tRNAs that participate in protein translation , whereas Tnp3 appears more likely to be involved in nuclear export of “defective” tRNAs . Tnp3 may be more similar to exportin 5 ( exp5 ) , which has more relaxed structural requirements , recognizing and exporting different double stranded RNAs , including tRNAs and miRNAs [49] , [50] , [51] , [52] , [53] . Recent evidence showed that regulation of the tRNA retrograde pathway is more complex than anticipated in S . cervisiae [16] , [54] , [55] . Whereas active nuclear import of tRNAs is constitutive , export of tRNAs is regulated by availability of nutrients and inorganic phosphates [56] , [57] . At least two parallel tRNAs export pathways have been detected: one for the initial and subsequent export of mature tRNAs , mediated by Los1p ( the yeast hortologue of Xpo-t ) , and a secondary export pathway , mainly mediated by Msn5 , the yeast hortologue of exportin 5 [58] , [59] . A similar scenario might be present in human cells , whereby several parallel tRNA export pathways serve different purposes . Although Mtr10p is implicated in tRNA nuclear import in yeast [54] , [59] , Tnp3 appears to be mainly implicated in export of “defective” tRNAs in human cells . Future work will elucidate the basis for this difference and if additional co-factors participate in the tRNA shuttling process in human cells . It is noteworthy that , in human cells , we have observed energy-dependent nuclear accumulation primarily of defective tRNAs lacking a complete 3′ CCA end [15] . However , whereas nuclear import of defective tRNAs may serve as a quality control process by withdrawing such tRNAs from the protein synthesis machinery , it is less clear at present why defective tRNAs should also be exported from the nucleus of human cells . One possibility is that defective tRNAs serve specific purposes , distinct from protein translation . Supporting this hypothesis , there is growing evidence showing that truncated or fragmented tRNAs are specifically generated in mammalian cells and contribute to the regulation of protein synthesis , cell division and cell growth [60] , [61] , [62] , [63] , [64] . Hence shuttling truncated tRNAs in and out of the nucleus may have a fundamental biological function , quite separate from HIV-1 infection . Another possibility is that shuttling to a different cellular compartment signals that a defective tRNA must be repaired . Tnp3 binding to tRNAs was influenced by structural features such as the lack of the complete 3′ CCA tail , similar to tRNA nuclear import in human cells [15] , suggesting that the two processes may be linked . Similar structural requirements for tRNA retrograde transport on the one hand and Tnp3-mediated tRNA nuclear export on the other hand as well as the involvement of CA proteins in both processes [14] , [15] point to a common pathway of which import and export may be two sides of the same coin . It may seem counter-intuitive for HIV-1 to depend on defective tRNAs for replication . However in metabolically active cells such as chick embryo fibroblasts , the rate of tRNA synthesis has been estimated at ∼2 . 4×105 molecules/min/diploid genome , with a steady state value of ∼109 tRNA molecules/diploid genome [65] . The tRNA half-life in dividing cells ranges between 50 h to 60 h [65] , [66] . Importantly , the 3′ CCA end is the most exposed portion of the tRNA molecule and the most sensitive to a variety of nucleases because it is single stranded . Therefore , the rate of tRNA molecules being hydrolyzed at the 3′ CCA end is likely to be very high yet steady state levels of tRNAs with defective ends are by far lower than normal tRNAs . The simplest explanation for this is that tRNA repair/degradation mechanisms , which presumably include tRNA shuttling , are extremely efficient . Therefore HIV-1 may in fact have evolved to use one of the most efficient cellular trafficking pathways available . In addition to tRNAs , Tnp3 also bound HIV-1 CA in pull down assays . The strength of this interaction was greater in the presence of RanGTP , suggesting that CA may be an export cargo for Tnp3 . The permeabilized cell assay could not be used to investigate this possibility because we could not detect nuclear import of recombinant CA . We therefore examined the nucleo-cytoplasmic distribution of CA after acute infection of Tnp3 KD and control cells . Remarkably , Tnp3 KD cells contained lower amounts of cytoplasmic CA and relatively greater amounts of nuclear CA compared to control cells . We also detected reduced levels of total ( nuclear + cytoplasmic ) CA in Tnp3 KD cells compared to controls . The reason for this is unclear but CA retained into the nucleus due to lack of Tnp3 may be targeted for rapid degradation in dividing cells , similarly to other nuclear factors [67] . A time course experiment showed that the dynamics of CA nuclear accumulation appear to overlap with that of HIV-1 integration in single cycle assays [27] , [42] . The simplest interpretation of this result is that Tnp3 exports CA from the nucleus of infected cells . The biological significance of this result is supported by experiments with the N74D CA mutant HIV-1 vector . Among the CA mutants tested , the N74D was the least dependent on Tnp3 for infection , suggesting that either the N74D CA was shed before nuclear entry or that it could exploit different factors for its export , becoming more promiscuous . In pull down assays , we have detected binding of Tnp3 to the N74D CA mutant ( Figure S5 ) . We have also detected CA in the nuclei of control cells 16 h post-infection by immunofluorescence whereas mutant N74D CA localized mostly outside the nuclei or at the nuclear envelope ( Figure S5 ) . The time course experiment shown in Figure 11 also suggested that N74D CA does not enter into the nucleus , at least for up to 24 h post-infection . Therefore it appears that the N74D CA is shed before nuclear entry of the RTC/PIC , making Tnp3 unnecessary . It will be interesting to see if such a premature loss of CA impacts on other downstream events . Accumulating evidence indicates that HIV-1 CA impacts on post-nuclear entry events [9] , [17] , [68] . Furthermore , a functional link between HIV-1 CA and integration has been recently described using a chemical genetic approach , whereby the small molecule Coumermycin-A1 impaired integration by targeting HIV-1 CA [41] . Interestingly , the A105S CA mutation made the virus insensitive to this block [41] . It is remarkable that the same mutation also makes HIV-1 independent of Tnp3 for infection , suggesting that Coumermycin-A1 and lack of Tnp3 perturb the same pathway . Moreover , the A105T mutation in CA was shown before to influence HIV-1 post-entry events in a cyclophilin A ( CypA ) dependent way [68] . However the mechanism by which CA impacts on post-nuclear entry events and integration is poorly understood . One hypothesis is that insufficient uncoating of the viral core in the cytoplasm may affect downstream events by making the pre-integration complex too bulky or unsuitable to bind specific host factors . Alternatively , following uncoating in the cytoplasm , some CA may remain bound to the pre-integration complex and help HIV-1 negotiate through the NPC [14] , [69] . In this case , some CA is likely to remain bound to the PIC after nuclear translocation . Indeed the presence of CA associated with the PIC inside the nucleus can be inferred from previous studies in which the restriction factors Fv-1 and members of the TRIM protein family were fused to CypA . The resulting fusion proteins maintained their specific ability to bind CA yet restricted HIV-1 at a post-nuclear entry step [70] , [71] . We propose a unifying hypothesis . Our results show that Tnp3 promotes the export of certain viral components from the nucleus of infected cells and that efficient HIV-1 integration depends on this activity . Therefore , several uncoating steps may be necessary for HIV-1 infection . The main uncoating step likely occurs in the cytoplasm , presumably starting shortly after initiation of reverse transcription [6] , [10] , and the last uncoating step occurs inside the nucleus . To permit efficient nuclear entry , some CA and tRNAs must remain associated with the viral complex [14] , [15] but once their function is exhausted , these same elements must be displaced to facilitate integration ( Figure 12 ) . We propose that Tnp3 is the displacing factor . The requirement for RanGTP ensures that Tnp3 binds to the viral elements with greater affinity after the PIC has entered into the nucleus , where RanGTP concentration is highest [24] . PICs that have not “matured” may fail to interact with host factors present in the nucleus , impairing integration both quantitatively and qualitatively . Our model is supported by recent evidence indicating that depletion of Tnp3 results in an aberrant pattern of HIV-1 integration [72] .
Blood was obtained from healthy volunteers after written informed consent according to the approved protocol of the UCL Ethics Committee ref . 0335/001 or from buffy coats obtained from the NHS National Blood Service according to Governmental ethics regulations . HUES-2 hES cells [73] were differentiated to monocytes via embryoid body formation followed by directed differentiation in Advanced DMEM , supplemented with 10% FCS , 100 ng/mL M-CSF ( R&D ) , 25 ng/mL IL-3 ( R&D ) , 2 mM l-glutamine ( Invitrogen GIBCO ) , 100 U/mL penicillin and 100 µg/mL streptomycin ( Invitrogen GIBCO ) , and 0 . 055 mM β-mercaptoethanol ( Invitrogen GIBCO ) . Monocytes emerging into the supernatant after 3–6 weeks were harvested and further differentiated to macrophages for 1 week at a density of 1 . 5×105 cells/cm2 , in culture medium consisting of RPMI ( Invitrogen GIBCO ) supplemented with 10% FCS , 100 ng/mL M-CSF ( R&D ) , 2 mM l-glutamine ( Invitrogen GIBCO ) , 100 U/mL penicillin and 100 µg/mL streptomycin ( Invitrogen GIBCO ) [25] . Macrophages were isolated from buffy coats from healthy donors by standard Ficoll-Hypaque density centrifugation . Cells were incubated at a density of 5×104/well in a volume of 100 µL in 96 well plates for 72 h in the presence of 20 ng/ml of GM-CSF before media change and infection at day 5 . HeLa and 293T cells were grown in Dulbecco's modified Eagle's medium ( DMEM ) ( Gibco Labs , Paisley , UK ) supplemented with 10% foetal calf serum ( FCS ) ( Helena Bioscience , Newcastle , UK ) and 2 mM glutamine at 37°C in 5% CO2 . Jurkat cells were grown in RPMI medium supplemented with 10% FCS at 37°C in 10% CO2 . Supernatants containing HIV-1 pseudotyped with amphotropic envelope were collected from stable producer cells [31] , centrifuged at 3000 g for 10 mins , filtered through a 0 . 45 µm filter and purified as described [15] . pHIVLAIΔenv and HIV-1GFP vectors were made and purified as described previously [2] , [26] . For HIV-1GFP plasmids pCSGW , pCMVΔR8 . 2 , ( expressing gag-pol ) and pMD . G expressing VSV-G were used [12] , [74] , [75]; pHIVLAIΔenv was pseudotyped using plasmid pMD . G . Reverse transcriptase ( RT ) activity was measured by the Lenti-RT™ Activity Assay ( Cavidi Tech , Uppsala , Sweden ) following the manufacturer's instructions . For infections , HeLa cells were plated onto 24 well plates at 3×104/well , infected 24 hours later with serial dilutions of viral stocks and analyzed by FACS from 24 hours to 2 weeks after infection . Jurkat cells were plated at 5×105/ml in 48-well plates , infected with serial dilutions of HIVGFP and analysed by FACS 48 hours later . For DNA analysis , Jurkat cells were plated at 5×105/ml in 6-well plates , infected at an MOI of 0 . 1 and DNA extracted 24 h , 48 h and 10 days later . Tnp3 was knocked down using two siRNAs; Tnp3-1 sense , GCAGUGAUAUUUAGGCAUAUU , antisense , UAUGCCUAAAUAUCACUGCUU; Tnp3-2 sense , GUACCAAAACUAACGAUGAAUU; antisense , UUCAUCGUUAGUUUUGUACUU [18] . HeLa cells were plated in 6-well plates at 1 . 5×105/well and transfected with siRNAs and oligofectamine ( Invitrogen ) the following day according to the manufacturer's instructions . To generate stable Tnp3 KD cells , Tnp3 oligonucleotides 5′-CTAGTCGGCGCACAGAAATTATACTTCCTGTCATATAATTTCTGTGCGCCGTTTTT-3′ and 5′-GTACAAAAATCGGCGCACAGAAATTATATGACAG GAAGTATAATTTCTGTGCGCCGA-3′ or scrambled oligonucleotides 5′-CTAGTCGGCGCAGTCTAATTATACTTCCTGTCATATAATTAGACTGCGCCGATTTTT-3′ and 5′-GTACAAAAATCGGCGCAGTCTAATTATATGACAGGAAGTATAATTAGACTGCGCCGA-3′ [20] were annealed and cloned into pSIREN vector ( Clontech ) . The resulting vector was used for virus production and infection of target HeLa , Jurkat and 293T cells . Differentiated macrophages were transduced with a lentiviral vector bearing the same shRNA expression construct . A silent mutation was introduced into the human Tnp3 cDNA to make it resistant to shRNA targeting by QuickChange II XL ( Stratagene ) following the manufacturer's instruction with primers C498A forward CGAATTGGAGCTAATCGGCGAACAGAAATTATAGAAGATTTG and C498A reverse CAAATCTTCTATAATTTCTGTTCGCCGATTAGCTCCAATTCG . Four clones were confirmed by sequencing and cloned into pCDNA3 to generate pTnp3-R for expression into mammalian cells . pTnp3-R was used as a template to generate ΔC825Tnp3-R by PCR using primers ΔC forward ATCATCGAATTCATGGAAGGAGCAAAGCCG and ΔC reverse ATCATCCTCGAGTCACACCTGTCCAATCAG . Clones were confirmed by sequencing . For the rescue assay , 293T cells were plated into 48-well plates at 2×105/well and infected the next day with serial dilutions of pSIREN shRNA-Tnp3 or pSIREN shRNA-Scramble MLV vector . Forty-eight hours post-infection , cells were transferred into 24-well plates and selected with 1 µg/ml puromycin . Puromycin-resistant cells were plated into 6 well plates at 106/well and transfected 24 h later with 1 µg and 2 µg Tnp3-R or ΔC825Tnp3-R plasmids complexed with Fugene-6 ( Roche ) . Cells were incubated for 48 h , infected with the HIV-1 vector and analyzed by FACS 24 h post-infection . The GST-Xpo-t expression plasmid was generated by PCR from pQE30-Xpo-t [37] as a template using forward primer 5′-ATCGGATCCATGGATGAACAGGCTCTATTAG-3′ and reverse primer 5′- . ATCGAATTCTCAGGGCTTTGCTCTCTG-3′ , and cloned into BamHI and EcoRI sites of pGEX-2T . GST-Xpo-t and GST-Tnp3 ( pGST-TRN-SR2 [39] ) plasmids were expressed in E . Coli strain BL21 ( DE3 ) by overnight incubation at 16°C on induction with 0 . 5 mM isopropyl ß-D-thiogalactoside . The GST-fusion proteins were purified by using glutathione-Sepharose beads and then dialyzed against binding buffer using a 50 kDa c/o membrane . Alternatively , GST was cleaved by incubating for 16 h at 4°C with 15 U/µg Thrombin ( Amersham Biosciences ) and eluted untagged proteins were dialyzed ( 50 kDa c/o ) in import buffer . Tnp3 deletion mutants were generated by PCR with Pfu DNA polymerase using plasmid pGST-TRN-SR2 [39] as template and the following primers: DN443 Tnp3 forward ATCGGATCCATGGCTGCTATAGCAAAGAG DN534 Tnp3 forward ATCGGATCCATGGCTCAGCACTTTAATG and reverse: CCGGAATTCTCATCGAAACAACCTGGTG DC880 Tnp3 reverse CCGGAATTCTCATGGCAAACCTTTTAAGG DC825 Tnp3 reverse CCGGAATTCTCACACCTGTCCAATCAG and Tnp3 forward ATCGGATCCATGGAAGGAGCAAAGC PCR products were cloned into pGEX-2T using BamHI and EcoRI , confirmed by sequencing and expressed in E . Coli strain BL21 ( DE3 ) by overnight incubation at 16°C on induction with 0 . 5 mM isopropyl ß-D-thiogalactoside . The GST-fusion proteins were purified by using glutathione-Sepharose beads and by gel filtration using a Superdex 200 column . The components of the Ran system and RanQ69L were expressed and charged with GTP or GDP as previously described [30] , [76] , [77] . The pellet from ∼109 HeLa cells was washed once in phosphate-buffered saline ( PBS ) and resuspended in 5 vols of hypotonic buffer ( 10 mM Hepes pH 7 . 9 , 10 mM KCl , 1 . 5 mM MgCl2 , 1 mM DTT , 20 µg/ml aprotinin and leupeptin ) on ice . The supernatant was centrifuged at 3300 g for 10 min at 4°C , resuspended in 5 vols of hypotonic buffer and incubated on ice for 10 min with gentle stirring . Cells were broken by Dounce homogenization on ice and centrifuged at 3300 g for 15 min at 4°C . The supernatant was centrifuged at 7500 g for 20 min at 4°C and then ultracentrifuged at 100 , 0001 g for 4 . 5 h at 4°C . The supernatant was passed through a glass wool filter and then through a 0 . 45 µm filter . Samples were concentrated using Vivaspin concentrator ( Amersham ) and ∼3 . 8 mg of 60S HSE was used in pull down assays . Pellets containing HIV-1 vector were resuspended into 200 µl of binding buffer . To disrupt virus particles , 160 µl purified viral stock was sonicated for 6 sec . at 40 output level in an Ultrasonicator Process machine and 50 µl were used in pull down assays . Mutant tRNA clones were obtained by PCR using mutagenic primers ( see Table S1 and Table S2 ) , generated by T7 polymerase and purified as previously described [15] . Pull-down assays were performed with ∼6 . 5 µg GST-Tnp3 or GST-Xpo-t , 3 µg tRNAs , 14 µg RanQ69L-GTP in a final volume of 30 µl binding buffer ( 50 mM Hepes pH 7 . 3 , 200 mM NaCl , 2 mM Mg ( Ac ) 2 and 10 µM GTP ) . The mix was incubated at 4°C for 30 mins with gentle mixing then 20 µl equilibrated Glutathione-sepharose 4B beads ( GE Healthcare ) were added and samples incubated for a further 2 h at 4°C with continuous rotation . Samples were washed with binding buffer ( 1 ml×4 washes ) , mixed with 30 µl 5x SDS-sample buffer and boiled for 10 mins , resolved by 10% SDS-PAGE and visualized by silver staining following the manufacturer's instructions ( Silver Staining Plus , BioRad ) . ImageJ 1 . 42q ( NIH ) was used to quantify the intensity and the total area of the bands in silver stained gels following thresholding to optimize linearity of the signal . Values of the pulled down bands were normalized by the input bands in each sample . Import assays were performed with fluorescently-labelled tRNAs as previously described [15] , [77] except that incubation of permeabilized cells with tRNAs and the energy regenerating system was at 37°C for 10 mins . Samples were washed 3 times in import buffer ( 20 mM HEPES pH 7 . 3 , 110 mM KAc , 5 mM Mg ( Ac ) 2 , 0 . 5 mM EGTA , 250 mM sucrose ) on ice then the export mix was added ( Tnp3 to 1 µM final concentration and 1x energy mix and 1xRan mix in 30 µl import buffer ) and samples incubated at 37°C for 10 mins . Following three washes in import buffer , samples were fixed in 2% paraformaldehyde in import buffer for 5 mins on ice , washed twice in import buffer and analyzed by confocal microscopy . Quantitative analysis of fluorescent signal in the nuclei was performed on confocal images by MetaMorph software version 4 . 5r4 ( Universal Imaging Corp . , Molecular Devices ) as previously described [15] . Rabbit polyclonal anti-importin 7 antibodies were previously described [77] , monoclonal antibody 3152C2a against Tnp3 was purchased from Abcam ( Cambridge , UK ) and used 1/500 dilution in TBST-T . Anti-p24/p55 monoclonal antibodies EH12E1 and 3D3 were obtained from the AIDS repository reagent programme EVA centre for AIDS reagents , UK , mixed and used at 1/300 dilution . Anti-β-actin monoclonal antibody ( AC-40 , Sigma-Aldrich ) was used at a 1/10 , 000 dilution . Anti-rabbit and anti-mouse IgG HRP-conjugated antibodies were purchased from Jackson Laboratories ( Bar Harbor , MN ) and from Sigma respectively . After SDS PAGE , the proteins were transferred overnight to a PVDF membrane ( Bio-Rad , Hercules , CA ) in transfer buffer pH 8 . 4 containing 0 . 01% SDS and 5% methanol and probed with the primary antibodies for 1 h at room temperature . HRP-conjugated secondary antibodies were used diluted 1/3 , 000 in 10% non-fat milk . Chemiluminescence ( ECL , Amersham ) was used to develop the blots as described by the manufacturer . Autoradiography films were exposed for different periods of time to ensure linearity of the signal . Approximately 106 HeLa cells were plated onto 10 cm plates . The next day cells were infected at an MOI of 0 . 2–0 . 5 for 22–24 hours , trypsinized and fractionation was performed in the presence of NP-40 as previously described [26] , [78] . For DNAse I digestion , 10 µl DNAse I beads ( MoBiTec , Göttingen , Germany ) were added to the fractionation buffer with 5 mM CaCl2 and incubated at 4°C for 5 mins with rotation , followed by washes as previously described . TaqMan qPCR was performed in an ABI Prism 7000 thermocycler as described [78] . For amplification of 2LTR circular DNA , the same conditions were used with primers 2LTRqPCRF: 5′-AACTAGAGATCCCTCAGACCCTTTT-3′ and 2LTRqPCRRC: 5′-CTTGTCTTCGTTGGGAGTGAATT-3′ and probe 5′-FAM-CTAGAGTTTTCCACACTGAC-0-TAMRA-3′ [26] . Standards were prepared by PCR amplification of DNA from acutely infected cells with primers 2LTRF 5′-GCCTCAATAAAGCTTGCCTGG-3′ and 2LTRRC 5′-TCCCAGGCTCAGATCTGGTCTAAC-3′ . The amplification product was cloned into TOPO vector , amplified and confirmed by sequencing . Detection of cyclophilin A cDNA was as described [26] . Alu-LTR Taqman qPCR was carried out as previously described [41] using primers ALU-forward , AAC TAG GGA ACC CAC TGC TTA AG and LTR1-reverse , TGC TGG GAT TAC AGG CGT GAG ( for first round amplification ) and ALU-forward AAC TAG GGA ACC CAC TGC TTA AG , LTR2-reverse , TGC TAG AGA TTT TCC ACA CTG ACT , ALU-probe , FAMRA – TAG TGT GTG CCC GTC TGT TGT GTG AC – TAM ( for second round Taqman qPCR ) .
We thank Woan-Yuh Tarn for the Tnp3 expression plasmid , Dirk Görlich for the Xpo-t expression plasmid , Michael Emerman , Adrian Thrasher , Didier Trono , and Masahiro Yamashita for the HIV-1 plasmids , Apsara Kandanearatchi for primary human macrophages , Ian Anderson and Luciano Vozzolo for assistance with experiments , Peter Cherepanov and Stephen Hare for helpful discussions , Greg Towers and members of his lab for reagents , Mahad Noursadeghi for help with image analysis . | HIV-1 , the causative agent of AIDS , is a virus that enters the nucleus of infected cells and must integrate its genome into the host cell DNA . Here we show that efficient HIV-1 integration depends on a host cell factor called Transportin 3 . We also show that Transportin 3 can export out of the nucleus the viral capsid proteins and viral transfer RNAs ( tRNAs ) and that this activity is important for HIV-1 integration . We propose that Transportin 3 facilitates a maturation process inside the nucleus by removing remaining capsid proteins and tRNAs still bound to the virus . The mature viral complex , free of any bulky component , can then more easily integrate . Transportin 3 also export certain cellular tRNAs out of the nucleus , presumably as a way to control cell metabolism . By feeding into this tRNA shuttling pathway , HIV-1 can complete its life cycle . Our work sheds new light into the biology of HIV-1 and points to the existence of a new pathway in human cells to shuttle certain tRNAs between nucleus and cytoplasm . | [
"Abstract",
"Introduction",
"Results",
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] | 2011 | Transportin 3 Promotes a Nuclear Maturation Step Required for Efficient HIV-1 Integration |
Model-based phylodynamic approaches recently employed generalized linear models ( GLMs ) to uncover potential predictors of viral spread . Very recently some of these models have allowed both the predictors and their coefficients to be time-dependent . However , these studies mainly focused on predictors that are assumed to be constant through time . Here we inferred the phylodynamics of avian influenza A virus H9N2 isolated in 12 Asian countries and regions under both discrete trait analysis ( DTA ) and structured coalescent ( MASCOT ) approaches . Using MASCOT we applied a new time-dependent GLM to uncover the underlying factors behind H9N2 spread . We curated a rich set of time-series predictors including annual international live poultry trade and national poultry production figures . This time-dependent phylodynamic prediction model was compared to commonly employed time-independent alternatives . Additionally the time-dependent MASCOT model allowed for the estimation of viral effective sub-population sizes and their changes through time , and these effective population dynamics within each country were predicted by a GLM . International annual poultry trade is a strongly supported predictor of virus migration rates . There was also strong support for geographic proximity as a predictor of migration rate in all GLMs investigated . In time-dependent MASCOT models , national poultry production was also identified as a predictor of virus genetic diversity through time and this signal was obvious in mainland China . Our application of a recently introduced time-dependent GLM predictors integrated rich time-series data in Bayesian phylodynamic prediction . We demonstrated the contribution of poultry trade and geographic proximity ( potentially unheralded wild bird movements ) to avian influenza spread in Asia . To gain a better understanding of the drivers of H9N2 spread , we suggest increased surveillance of the H9N2 virus in countries that are currently under-sampled as well as in wild bird populations in the most affected countries .
Phylogeographic methods can infer the migration history of sampled lineages based on genetic data . The discrete trait analysis ( DTA ) and structured coalescent model are commonly used probabilistic model-based phylogeographic methods . The DTA model treats the migration of lineages between different geographic locations as a per-lineage continuous-time Markov process , analogous to the DNA substitution process [1] . This approach achieves computational efficiency by integrating over all possible migration histories using the efficient tree pruning algorithm for computing phylogenetic likelihoods [2] . One drawback of this approach is the assumption of independence of the tree generating process and the migration process , which can lead to underuse of the data [3] . Another drawback is the potential biases in migration rates estimates when sampling is biased across sub-populations , since such a model assumes that the sample sizes across sub-populations are proportional to the subpopulation sizes ( sub-populations refer to different geographic locations in this study ) [3] . The structured coalescent on the other explicitly models how lineages coalesce within and migrate between sub-populations [4] . Additionally , the structured coalescent conditions on sampling and only assumes that the samples are drawn at random from a large population . This makes the structured coalescent more robust to sampling bias [3 , 6] . Exact inference under the structured coalescent is challenging [5] . Thus , approximations to the structured coalescent model by approximately integrating over all ancestral migration histories were proposed [3 , 6] . The marginal approximation of the structured coalescent ( MASCOT ) currently provides the closest approximation to the structured coalescent while being computationally efficient . This allows to analyse datasets with many different sub-populations [6 , 7] and currently enables to analyse datasets of up to 500 sequences and up to 14 different states [8] . The generalized linear models ( GLMs ) can be employed as an extension of phylogeographic inference to inform the pathogen migration rates between distinct geographical locations by predictor data [9] . Some authors used GLMs in both discrete and continuous phylogeographic models to investigate the impact of underlying environmental variables on the dispersal frequencies and velocity of a virus respectively [10] . But only univariate models can be considered in the current GLM implementation in continuous phylogeographic inference . The GLM model incorporating multiple predictors in DTA [9] gained popularity for its computational efficiency and user-friendly implementation in BEAST 1 . 10 [11] . Recently , DTA GLM models were also applied to inform the potential factors shaping the spatial dispersal of influenza A virus [9 , 12] , the Ebola virus [13] , the Foot-and-Mouth disease virus [14] and dengue virus [15] , and the underlying predictors contributing to host transmission dynamics of rabies virus [16] . But it unrealistically assumes time-homogeneous substitution processes between sub-populations . The epoch GLM has already allowed for time-dependence in both coefficients and predictors data to model the heterogeneous spatial diffusion processes through time in DTA [13 , 17] . However , a recent phylodynamic GLM only considered time-dependent coefficients ( rather than time-dependent predictors ) to inform the temporal dynamics in the spread of Ebola virus [13] . Very recently the GLM model with both time-dependent predictors and coefficients were proposed in MASCOT [8] . This allows , for the first time , the ability to quantify the contribution of both time-series and constant predictors to both migration rates and effective population sizes jointly in a structured population . H9N2 avian influenza viruses ( AIVs ) have spread into multiple Asian countries and became endemic in domestic poultry populations in some of these countries [18 , 19 , 20] . Its transnational geographic dispersal and exchange of genetic segments with other subtypes in poultry increase their potential for zoonotic threat to public health [18 , 21 , 22] . Of note , the multi-segmented H9N2 virus provided its internal gene materials to facilitate the genesis of the novel H7N9 AIVs that caused multiple outbreaks and high mortality in humans since 2013 in China [21] . Hereafter the underlying mechanism behind evolution and spread of the “donator” H9N2 virus raises wide concerns [23] . Poultry trade network is a potential source of avian influenza virus mobility in Asia and may help to explain the multiple introductions of H9N2 AIVs from the same genetic group into different countries [24 , 25] . Additionally , free-living birds played a limited role in dissemination of poultry-adapted AIVs for the specific host adaption [26] . The G1-like H9N2 virus isolated in middle eastern countries shares a common ancestor with the virus from China [27] , and the genetically related H9N2 viruses in geographic regions separated by long distances suggest a role for migration by poultry trade [28] . Further , the international poultry trade increased in recent decades to meet human demand on the cheap protein from poultry . The asymptomatic poultry carrying this low pathogenetic virus could be neglected during transportation process . Poultry transportation can bring together various host species from different regions in a high-density setting and provides an ideal environment for interspecies virus transmission and theretofore the reassortment of different viral segments [29] . Another potential source of AIV mobility is wild bird movements [30 , 31 , 32] . In 2005 , the outbreak of highly pathogenic AIV H5N1 in wild birds in Qinghai Lake , China and its subsequent rapid dissemination from Asia to Europe and Africa led to a great concern about the role of migratory birds played in virus dispersal [33 , 34] . Wild aquatic birds can act as natural reservoirs of influenza A viruses , since infection is often asymptomatic or mild , allowing them to migrate while carrying the virus [35] . The virus from wild bird can spread into the free-ranging poultry by frequent contacts in their sharing areas [36] . Viral transmission between domestic and wild birds commonly occurred within a region while mobile and migratory wild birds could transmit viruses between regions [24] . Although poultry trade may play a major role in H9N2 virus spread , the contribution of bird migration to the long-distance virus dispersal between poultry populations should therefore not be neglected . Nevertheless , we here focus on assessing the role of poultry trade and production in the prevalence and spread of H9N2 AIVs . By elucidating the evolutionary dynamics and the underlying factors that drive virus spread , we aim to better understand the ecology and spread mechanism of the viruses in Asia . In this study , we also described the spatial distribution and temporal dynamic of H9N2 virus isolates . The migration dynamics and their underlying mechanism of H9N2 AIVs between 12 Asian countries and regions were inferred by using two phylogeographic methods DTA and MASCOT in a Bayesian Markov chain Monte Carlo ( MCMC ) inference framework . Under MASCOT , we also jointly inferred predictors of the effective population size of the virus in each location , which is not possible in DTA . To do so , we used a GLM approach to parameterize migration rates and effective population sizes of H9N2 viruses by potential predictors , including time-dependent predictors ( annual live poultry trade , annual national poultry production , yearly mean temperature , yearly total rainfall , annual seasonality of temperature and rainfall , and yearly virus sample size ) and time-independent predictors ( e . g . geographic distance ) . The underlying mechanisms of virus migration and genetic diversity were evaluated and the sensitivity of our results was investigated by comparing models that included different predictors , different sub-sampling strategies of genetic data and alternative phylogeographic modelling assumptions .
We obtained all full-length haemagglutinin ( HA ) segment nucleotide sequences of avian-origin H9N2 from Asian countries and regions that were available in the GenBank Influenza Virus Database ( http://www . ncbi . nlm . nih . gov/genomes/FLU/FLU . html ) hosted by the National Center for Biotechnology Information ( NCBI ) . Identical sequences caused by duplicated submissions in the database ( i . e . same sequence and same isolate name ) , were reduced to a single sequence to avoid bias in rate estimates . Sequences without explicit isolation date or country information were excluded . These HA sequences from 1976 to 2014 were geocoded and pooled into groups according to their geographic location , host type ( chicken , duck , quail , turkey , wild bird , and others ) , and isolation date ( S5 Table ) . The virus sequences were aligned in MAFFT v7 software with default parameters [37] . We evaluated the temporal signal of the remaining heterochronous sequences with TempEst [38] and removed sequences that we identified as outliers . To get a more even distribution of samples through time and between different locations , we randomly sub-sampled the H9N2 sequences to keep at most 10 isolates per country/region per year . In order to avoid over-parameterization we discarded locations with less than 10 isolates in total . Finally , we added commonly used representative HA gene sequences to help the phylogenetic clade classification ( A/quail/Hong Kong/G1/97 represents the G1 lineage; A/chicken/Hong Kong/G9/97 or A/duck/Hong Kong/Y280/97 or A/chicken/Beijing/1/94 represents the G9 lineage; and A/chicken/Korea/38349-p96323/96 or A/duck/Hong Kong/Y439/97 represents the Korea lineage ) [39] . The final data set contained 526 HA sequences from 12 Asian countries/regions . We defined distinct locations on the country/region level as Bangladesh , mainland China , Hong Kong , South Korea , Japan , Vietnam , India , Pakistan , Iran , Israel , Saudi Arabia , and the United Arab Emirates . A dataset of this size is currently an upper limit of what can be analysed by using structured coalescent methods . In order to test the impact of sub-sampling strategy on our inferences , we additionally sub-sampled this dataset by randomly sampling at most 5 isolates per country/region per year , which resulted in a dataset of 385 HA sequences . More detail information on the viral sequences is provided in S5 Table . To inform the spread and genetic diversity of H9N2 viruses across the 12 locations , we chose several potential predictors . These are similar to ones previously used to describe the spread of H3N2 [9] or Ebola [13 , 8] . We used annual live poultry trade , annual poultry production , gross domestic product values ( GDP ) , geographic distance , a predictor describing if countries share a continental border , temperature , temperature seasonality , rainfall , rainfall seasonality , virus sample size and the latitude of centroid point of each country . All the country-level predictors were available from 1986 to 2013 . We downloaded live poultry trade and poultry production data ( including data related to chicken , duck , and turkey ) from FAOSTAT ( http://faostat3 . fao . org ) . Typically , poultry movements are driven by variation in the supply and demand for poultry , which are in turn commonly affected by economic , ecological and climatic conditions [40] . These , as well as practical considerations led us to chose the set of potential predictors used in this study . GDP statistics were collected to describe the economic level of each country . Annual mean temperature and annual total rainfall were gathered to describe the climatic condition in each country through time . Further , the annual variation of temperature and rainfall were described by temperature seasonality and rainfall seasonality respectively . Temperature seasonality is the standard deviation of the monthly mean temperatures in each year . Rainfall seasonality ( RS ) for year t is the ratio of the standard deviation of the monthly total precipitation ( sp ) over one plus the mean monthly precipitation ( pm ) : RS ( t ) = sp ( t ) / ( 1 + pm ( t ) ) . GDP , temperature , and rainfall data were collected from the World Bank database ( http://data . worldbank . org/ ) . The H9N2 isolates sampled from the same region commonly tended to gather in a phylogenetic group inferred by the Bayesian or maximum likelihood inference [23 , 28 , 41] . The geographic distance between each pair of locations was considered a potential factor of virus spread and calculated by the great circle distance based on the central latitude and longitude of each location . We also used a predictor with 1 or 0 to describe if two locations share their border on the continent or not , respectively . To test the impact of sampling effects , the number of H9N2 sequences in both origin and destination location was considered as two separate predictors . Finally the geographic central latitudes of locations were considered as a predictor to investigate latitude as a potential driver of H9N2 spread . To avoid excessive co-linearity among explanatory predictors , we removed the GDP , temperature seasonality and the latitude variables , since the Pearson correlation coefficients between temperature variable and each of them exceeded 0 . 7 . To eliminate the effect of the magnitude of different predictors , all predictors ( except binary predictors ) were transformed into log space and standardized so that their means are equal to 0 and standard deviation equals to 1 . This is standard practice when using generalize linear models to inform viral migration rates and effective population size ( e . g . [9] ) . More detail information on predictors is supplied in S2 Table . The DTA model treats movement of viral genes across discrete geographic locations as a continuous time Markov process in which the state space consists of the sampled locations [1 , 42] . This model treats the spread of viruses as statistically equivalent to the evolution of molecular substitutions at a site . The posterior probability distribution of parameters given data in the DTA model is shown in Eq 1 [1] . Here , the aligned sequences S and the sampling locations L are treated as observations , whereas the isolation dates of the sequence t are treated as boundary conditions . The phylogenetic tree T , the nucleotide substitution rate matrix μ , the forwards-in-time migration rate matrix f and the effective population size θ of the whole meta-population are random variables estimated in this model . The first term on the right is the likelihood of the sequences . This likelihood is calculated by integrating over all possible substitution histories using the pruning algorithm [2] . The second term is the likelihood of the sampling locations given the time-stamped genealogy and the instantaneous migration rate matrix . It is calculated by the same pruning algorithm , but using a migration rate matrix rather than a substitution matrix . The third term describes the probability density of the genealogy across the entire meta-population , approximated by a standard neutral coalescent prior for a well-mixed and unstructured population . The fourth term represents the prior distribution of three independent random variables . It should be noted that to the extent that θ can be interpreted in this model , it represents the effective population size for the entire meta-population across all locations in the dataset . P ( T , μ , f , θ | S , L ) ∝ Pr ( S | T , μ ) Pr ( L | T , f ) P ( T | θ ) P ( μ , f , θ ) ( 1 ) The structured coalescent jointly models how lineages coalesce within locations and migration between them . The posterior distribution of the parameters given the data in a structured coalescent phylogeographic inference is described in Eq 2 . Here , the meaning of parameters is the same as in the DTA model . However , migration is parameterized as a backwards-in-time migration rate matrix ( m ) and the effective population size θ → is modelled separately for each sub-population . The first term is the likelihood of sequences given genealogy and substitution model , which is computed using the pruning algorithm [2] . The second term is the probability density of the genealogy and migration history of lineages under the structured coalescent assumption given the migration rate matrix and effective population sizes . The third term represents the prior distribution of the model parameters . P ( T , M , μ , m , θ → | S , L ) ∝ P ( S | T , μ ) P ( T , M | L , m , θ → ) P ( μ , m , θ → ) ( 2 ) The structured coalescent likelihood can be computed analytically only when conditioned on a migration history ( M ) . Thus standard approaches have required augmenting the tree with a random-dimensional migration history which has restricted the application of this model to datasets with a small number of demes/locations [5] . However , when the details of the migration history are not of particular interest , the MASCOT model [7] can be used to approximate the integrated likelihood ( i . e . formally integrating over every possible migration history for each tree in the MCMC chain ) . This approximation is closely related to the exact structured coalescent , but still allows us to analyse a dataset with many different sub-populations . Since we seek to investigate the spread of H9N2 between 12 different countries/regions , we used MASCOT in our analyses . DTA and MASCOT have both been extended such that constant migration rates and time-series migration rates can be described using GLMs [7 , 9 , 17] . This allows us to infer the contribution of explanatory factors to migration rates between different sub-populations , and through time . We examined different sub-sampling scheme on our genetic data , and the effect of including and excluding isolate sample size as a predictor separately in our GLM models . We used a prior probability distribution on the number of active predictors such that 50% of the probability mass is no predictors being included in the GLM . Four variants were considered in GLM under DTA model to investigate the contribution of drivers to the constant migration rates among these unstructured sub-populations; both time-dependent and time-independent predictors were considered to model viral migration rates between different sub-populations in six GLMs using MASCOT ( S1 Table ) . Migration rates between locations are defined as log-linear combinations of coefficients , indicators , and predictors . Eqs 3 and 4 describe the time-independent and time-dependent parameterizations of the migration rates respectively . Here , mij represents the migration rates between location i and j; p m { i j } k represents the k-th predictor between location i and j; I m k represents the indicator and β m k represents the coefficient of the kth predictor . The indicator and coefficient describe if and to what degree each predictor contributes to explain the migration rates . Indicators are estimated by a Bayesian stochastic search variable selection ( BSSVS ) algorithm to describe the posterior inclusion probability for each predictor and use the priors on the number of active predictors to reduce over-fitting [43] . Further , mij ( t ) , p m { i j } k ( t ) , β m k ( t ) and I m k ( t ) represent the time-dependent version of corresponding parameters in this model . Because more than 10 predictors were chosen to model the migration rates , we removed the error terms in the GLM model to avoid having to infer too many parameters . log m i j = ∑ k = 1 n I m k β m k log p m { i j } k ( 3 ) log m i j ( t ) = ∑ k = 1 n I m k β m k log p m { i j } k ( t ) ( 4 ) In contrast to DTA , the structured coalescent models a process from the present backwards in time to the past . Therefore , all parameters of the model are backwards in time parameters , including the migration rates . To compute backwards in time migration rate m j i b ( t ) from forwards in time migration rates , we scale the forwards in time rates using the effective population sizes of the source and sink populations: m j i b ( t ) ≈ N e i ( t ) N e j ( t ) m i j f ( t ) ( 5 ) Effective population sizes within different sub-populations were modelled by both time-independent ( Eq 6 ) and time-dependent ( Eq 7 ) GLM models in MASCOT . Here , Nei represents the viral effective population size of location i; p N e { i } k represents the kth ( time-independent ) predictor at location i; β N e k and I N e k represent the coefficient and inclusion probability of the kth predictor respectively . αi represents the extra part of viral effective population size that could not be explained by the predictors in region i . Further , Nei ( t ) , p N e { i } k ( t ) , β N e k ( t ) and I N e k ( t ) represent the time-dependent version of corresponding parameters from 1986 to 2013 in the model . Eq 8 describes a specific instance of the model in Eq 7 . The first part of Eq 8 describes the relationship between viral effective population size and poultry production in each region jointly . Here , the number of predictors n is equal to the number of locations in the analysis . P N e { i } i ( t ) represents the poultry production in region i from 1986 to 2013 . In order to model the time before 1986 as a structured coalescent process with constant rates , we introduce predictors that are 1 for any of the locations before 1986 and 0 otherwise . One predictor therefore only predicts the Ne after 1986 and for only one location . This we do in order to avoid events that happened more than 28 years ago for which we do not have predictor information to bias our inference . log N e i = ∑ k = 1 n ( I N e k β N e k log p N e { i } k + α i ) ( 6 ) log N e i ( t ) = ∑ k = 1 n ( I N e k β N e k log p N e { i } k ( t ) + α i ) ( 7 ) log N e i ( t ) = { ∑ i = 1 n I N e i β N e i log P N e { i } i ( t ) + α i t = 1 , … , 27 , ∑ i = 1 n α N e { i } t = 28 . ( 8 ) Bayesian analyses of H9N2 AIVs using the DTA and DTA GLM models were conducted using BEAST v1 . 10 . 0 [11] . The MASCOT GLM analyses on the same data were conducted using BEAST v2 . 5 . 2 [44] and Coupled MCMC [45] . An HKY nucleotide substitution model with gamma site heterogeneity using 4 rate categories and a strict molecular clock model were employed to model sequence evolution in all analyses . The discrete phylogeographic analysis using DTA with asymmetric trait substitution model and Bayesian skyline tree prior was performed in 5 parallel runs . The convergence and mixing of MCMC chains in these runs were diagnosed by the RWTY package in R v3 . 4 . 3 [46] . DTA and MASCOT GLM models were used to estimate the contribution of potential predictors to the migration rates between each pair of locations . Further , the MASCOT analyses included population dynamic GLMs to identify the underlying factors driving virus population diversity in each sub-population . We performed at least 5 runs with 10-100 million iterations ( based on the convergence time of different analyses ) of different analyses to estimate the phylogenetic tree with location information and GLM parameters . We used Tracer v1 . 7 [47] to remove an appropriate burn-in ( 10%-20% of samples in most cases ) to achieve an adequate effective sample size ( ESS , at least 100 ) . The time scaled phylogenetic tree with the maximum probable location in each lineage was annotated and visualized in FigTree v1 . 4 . 3 ( http://tree . bio . ed . ac . uk/software/figtree/ ) and the ggtree package in R v3 . 4 . 3 [48] .
H9N2 viruses spread to at least 22 countries in Asia between 1976 and 2014 ( Fig 1a ) . These 22 countries are mostly located in tropic and temperate zones with lower latitudes , where the climatic conditions are suitable for poultry rearing . H9N2 viruses have been isolated from a wide variety of different hosts , including the major poultry species: chickens and ducks . Compared to the number of isolates from wild birds , H9N2 viruses were predominantly isolated from domestic poultry populations in Asia , especially from chicken . Since 1996 , H9N2 viruses have been isolated in more Asian countries and then persisted in birds in some of these countries for several years ( Fig 1b ) . The number of isolates shows an increasing trend and most were sampled from mainland China since the late 1990s , which is likely in part driven by a larger surveillance effort . The estimated effective population size of the virus however also substantially increased since 1996 ( Fig 1c ) . The evolutionary relationships and the migration history of lineages reconstructed using the two phylogeographic methods are shown in Fig 2 . As mentioned previously , the genealogies can be grouped into three lineages: G9-like , G1-like and Korea-like lineage [39] . These three lineages were established in the 1990s and continue to be isolated to date . Most isolates used in this study were found to be G9- or G1-like . Isolates from countries close by to each other were often genetically related ( Fig 2 ) . G9-like H9N2 viruses were mainly isolated in mainland China , Hong Kong , Japan , and Vietnam . G1-like viruses were mainly isolated in the Middle East and the Indian subcontinent . Korea-like viruses were predominantly isolated in South Korea , Japan , and Hong Kong . Hong Kong was inferred as the most likely source of H9N2 viruses circulating in Asia by both DTA ( 98% posterior probability ) and MASCOT ( 97% posterior probability ) ( Fig 2 ) . This is however likely driven by a lack of samples from other regions , for example mainland China , in this era . DTA inferred H9N2 to have spread from Hong Kong to East Asia in the 1980s . After , one part of the viral population continued to spread in countries in the East and Southeast Asia; Another part of the population was transmitted to several countries in the West and South Asia . MASCOT inferred that H9N2 viruses were directly transmitted into countries in the West and South Asia from Hong Kong , and via multiple introductions from Hong Kong to East Asia . Viral migration rates among Asian countries and regions were inferred by using DTA with asymmetric migration rates ( Fig 3 ) . The highest migration rates with the strongest support were inferred from Pakistan to Iran , between Hong Kong and mainland China , from Hong Kong to Vietnam , from Pakistan to the United Arab Emirates , from mainland China to Japan , from Pakistan to India , and from India to Bangladesh . All these pairs are locations within close geographic proximity . The migration rates among these pairs are 0 . 91 or higher , which means ∼ 1 or more migration event occurred between these locations per lineage per year . Additionally , the phylodynamic reconstructions on root state , location transition dynamics in evolutionary history , and migration rates of H9N2 are broadly consistent when we employed the sub-sampling pattern including 385 or 526 HA sequences ( S1 and S2 Figs ) . We next investigated the factors driving the spread of H9N2 viruses across several locations in Asia by using a GLM to model the relationship between potential predictors and viral migration rates ( Fig 4 , S3 and S4 Figs ) . A total of 10 different GLMs were used ( S1 , S3 and S4 Tables ) . We used both DTA and MASCOT and included time-dependent and time-independent predictors; 385 or 525 HA sequences . Further , we ran analyses with and without considering the number of isolates as a distinct predictor . Bayes factors ( BFs ) on inclusion probability of each predictor were calculated to explain how much the data informed the inclusion of a predictor [49] . BF is calculated as a ratio of the posterior odds for a predictor inclusion to the corresponding prior odds . A BF over 3 is typically considered suggestive and a BF over 20 is typically used as strongly supporting a predictor to be included into the GLM model [50] . In addition , we also evaluate the robustness and support of a predictor can inform virus migration rates by counting times each predictor was selected as a supportive one in all migration rate GLMs investigated . If a predictor was always selected as supportive regardless of the heterogeneous sampling intensity and model assumption , it can robustly inform virus spread . Geographic distance was identified as a strongly supported driver in all migration rate GLMs investigated and it consistently made a negative contribution to the virus spread ( S4 Table ) , meaning that migration is inferred to be stronger between closer countries . We inferred poultry trade to strongly predict viral migration in all GLMs except the time-independent ones under MASCOT . Predictors related to rainfall seasonality in destination location and boarder sharing were also inferred in more than half of all GLMs investigated to be strongly supported . Additionally , we inferred migration to be weaker into locations with strong seasonality in rainfall . Further , our time-dependent GLM of MASCOT was more sensitive as identified by predictor poultry production in origin and rainfall seasonality . These results were consistent and robust when we included viral sample size as a distinct predictor or when we used fewer samples ( S4 Table ) . In MASCOT , we also jointly inferred predictors of effective population sizes of H9N2 virus in the different locations and their changes through time . The effective population sizes in the different locations were modelled by multiple time-independent and time-dependent predictors in GLMs ( Fig 5 , S5 and S6 Figs ) . Poultry production was selected as a supportive predictor for the effective population size of H9N2 virus in GLM model with time-dependent predictors ( Fig 5 ) . This implies that the higher the poultry production in a country is , the larger the genetic diversity of H9N2 virus in that country . Furthermore , virus sample size was also considered as a positive and supportive predictor , and the inclusion of virus sample size into the GLM model lowered the contribution of poultry production to virus population diversity . This suggests that the number of viral samples through time was roughly proportional to the effective population sizes . No potential predictors of effective population sizes were supported when we assumed that the effective population sizes are constant through time ( S6 Fig ) . Additionally , we applied a GLM that used time-dependent poultry production data in 12 Asian countries or regions to model the virus population dynamics in each sub-population jointly ( S7 Fig ) . Poultry production in mainland China was strongly supported as a positive predictor of the local viral effective population size , but not in any other location .
In Asia , H9N2 viruses have spread into multiple countries which did not previously have documented viral isolation in the 1990s and persist in domestic poultry in some of these countries [18 , 19 , 20] . To reduce the cost and maximize the profit , poultry farms are often built in close proximity to one another and rearing facilities tend to be overcrowded [51] . Viruses can therefore spread easily and outbreaks in poultry pose great economic threats to some of these countries . Further , most of these countries are low- and middle-income countries with poor bio-security . Low sanitary standards and high density of poultry in farms and markets can additionally facilitate the transmission of viruses [52] . Multiple influenza subtypes simultaneously circulate in birds in these countries [28 , 54] , which increases the probability of reassortment of influenza segments . In this work , we investigated the evolutionary dynamics and the spread of avian influenza H9N2 in Asia and attempted to uncover factors that potentially predict this spread by using a GLM in two phylogeographic frameworks , DTA and MASCOT [9 , 8] . Alongside estimating the factors driving migration rates , we also jointly investigated potential drivers of virus effective population sizes in MASCOT . To do so , we used H9N2 viral HA sequences isolated from avian hosts and 12 locations in Asia between 1976 and 2014 . We used different predictor data to inform the viral migration rates between 12 countries/locations . These predictors however ignore other potential drivers of migration , such as wild bird migration , and different sanitation levels among countries . Typically , predictors adopted to explain the virus spread and diversity were scale-dependent . In the future , more exact and more high-resolution predictors could be included to test more detailed hypotheses and model influenza movements in a smaller and confined geographical region [9] . Since rate estimates of DTA are likely to be sensitive to the number of sequences sampled in each location [3] , we repeated analyses under two sub-sampling strategies . Additionally , we performed the GLM analyses with and without considering the viral isolate sample size in each country or region as a distinctive predictor to test the impact of heterogeneous sampling intensity . The results were mostly robust and consistent whether this predictor was included or not in DTA using 385 or 526 HA sequences . But the inclusion of isolate sample size in the model did reduce the support of the predictor rainfall seasonality in the destination location , which is negatively related to virus migration rates . Further , sharing a border was included as a suggestive predictor in the DTA GLMs with 385 sequences . These slight exceptions can result from the heterogeneous samples across locations or different information variance provided by two genetic data sets . We found geographic proximity between locations to be a strong driver of H9N2 migration rates in all GLM models investigated . Additionally , we found that whether two locations are neighbouring each other to be a strong predictor of migration . The contribution of geographic proximity to viral spread was intuitively recorded in the close evolutionary relationship among viruses sampled from nearby countries . Further , a consistent role for poultry trade was inferred in both a GLM with time-independent predictors in DTA and a GLM with time-dependent predictors in MASCOT . This suggests that poultry trade is probably a driver of the spread of avian influenza H9N2 viruses . Infected poultry , especially chicken , without strong clinical symptoms can easily be missed during the process of transportation . H9N2 viruses can therefore spread into native poultry . Increased surveillance of imported poultry and their products could decrease the spread of H9N2 across locations [53] . Illegal poultry trade across borders is another potential factor contributing to the spread of H9N2 [54] . However , even when controlling for poultry trade volumes and other potential predictors , we still found geographic proximity to be a key driver to migration rates . This may point to some factors directly linked to geographical distance to contribute to the viral spread of H9N2 across countries . Contact between domestic and wild birds is inevitable in the intensive and outdoor-reared livestock farms and two-way virus transmission has been documented between them [24 , 55] . Wild birds could therefore spread H9N2 , as they can easily cross borders and then transmit the viruses . The dispersal distribution of H9N2 isolates from wild birds on the phylogenetic tree supports the possibility of their movement facilitating virus spread across countries and across genetic groups ( Fig 2 ) . Future studies will however have to investigate if wild bird migration is really associated with the spread of H9N2 viruses . Further , the region with less monthly variance in rainfall volumes could provide stable feeding and habitat areas for birds and attract the birds carrying viruses . Active surveillance of migratory birds could therefore help to monitor the dispersal of H9N2 virus . Inferences of migration rate GLM variants we investigated can be divergent such that poultry trade has strong support in all GLM models except ones with time-independent predictors in MASCOT . This divergence shows that including time dependence can be important to identify predictors to inform the heterogeneous spatial diffusion the processes through time . In addition , different inferences of GLMs via DTA and MASCOT can result from diverse migration rate definitions and model assumptions . The DTA models migration rate as the frequency of migration events , while the structured coalescent model describes it by virus genetic diversity spreading among different locations [1 , 7] . To improve our understanding of what potentially drives genetic diversity of H9N2 , we also used a GLM approach to inform effective population sizes of H9N2 virus in each location and through time by using MASCOT [8] . Time-dependent poultry production was identified as a positive driver to virus divergence within each sub-population . When including the number of samples through time , the support for poultry production as an effective population size predictor decreased . This can be caused by a proportional relationship between the annual number of viral samples and the viral effective population size in a location over time . Mainland China was likely the main driver of poultry production being an effective population size predictor . Approximately 78% of H9N2 samples were isolated from China based on the HA gene sequences recorded in NCBI [56] . The positive correlation between an increasing poultry production and an increasing effective population size of the virus suggests that virus control measures in local poultry may not currently be sufficient . Surveillance of high pathogenic H5N1 AIVs in ducks has been actively carried out in several Asian countries [57] . Samples from chickens , wild bird , and the environment could also be collected to investigate the prevalence of H9N2 and other subtypes . If there are H9N2 cases in humans , having such samples readily available can help to track possible origins of these cases . Additionally , making surveillance of both high pathogenic and low pathogenic AIVs in poultry and humans routine can potentially help to improve our understanding of how these viruses jump into humans . Overall , the integration of temporal predictors into phylodynamics provides a powerful tool to test how disease spread within and between populations . | What drives the geographic dispersal and genetic diversity of avian influenza A virus H9N2 in Asia ? We used two model-based approaches , DTA and MASCOT , to reconstruct the phylogeographic dynamics of the virus genetic sequence and predictor data . To do so , we compiled multiple potential predictors to inform migration rates and effective population size in a generalized linear model framework . This allowed us to quantify which of these predictors most likely predicts the spread of avian influenza A/H9N2 in Asia . We found a positive association of international poultry trade and national poultry production time-series with virus migration rates and effective population sizes respectively . We also identified geographic proximity as a strongly supported driver to virus migration rates and this points to the potential role of wild bird populations in virus dispersal across countries . Our study is a practical example of the use of temporal information in predictors to model heterogeneous spatial diffusion and population dynamic processes and provides direction to H9N2 control efforts in Asia . | [
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"genet... | 2019 | Bayesian phylodynamics of avian influenza A virus H9N2 in Asia with time-dependent predictors of migration |
Yellow fever virus ( YFV ) is a mosquito-borne flavivirus that is a major public health problem in tropical areas of Africa and South America . There have been detailed studies on YFV ecology in West Africa and South America , but current understanding of YFV circulation on the African continent is incomplete . This inadequacy is especially notable for East and Central Africa , for which the unpredictability of human outbreaks is compounded by limitations in both historical and present surveillance efforts . Sparse availability of nucleotide sequence data makes it difficult to investigate the dispersal of YFV in these regions of the continent . To remedy this , we constructed Bayesian phylogenetic and geographic analyses utilizing 49 partial genomic sequences to infer the structure of YFV divergence across the known range of the virus on the African continent . Relaxed clock analysis demonstrated evidence for simultaneous divergence of YFV into east and west lineages , a finding that differs from previous hypotheses of YFV dispersal from reservoirs located on edges of the endemic range . Using discrete and continuous geographic diffusion models , we provide detailed structure of YFV lineage diversity . Significant transition links between extant East and West African lineages are presented , implying connection between areas of known sylvatic cycling . The results of demographic modeling reinforce the existence of a stably maintained population of YFV with spillover events into human populations occurring periodically . Geographically distinct foci of circulation are reconstructed , which have significant implications for studies of YFV ecology and emergence of human disease . We propose further incorporation of Bayesian phylogeography into formal GIS analyses to augment studies of arboviral disease .
Yellow fever virus ( YFV ) is a mosquito-vectored member of the family Flaviviridae , genus Flavivirus , which includes the causative agents of dengue fever , Japanese encephalitis , West Nile fever , and other prominent , arthropod-borne infections . The enveloped , 50 nm particle , encloses a single-stranded , positive-sense RNA genome of approximately 10 . 8 kb , bearing a 5′ cap , and 5′ and 3′ terminal untranslated regions ( UTRs ) , without 3′ polyadenylation . Translation occurs as a single open reading frame , the product of which is co- and post-translationally cleaved into 10 functional proteins . The three structural proteins , capsid ( C ) , pre-membrane/membrane ( prM/M ) , and envelope ( E ) , are upstream of 7 nonstructural proteins NS1 , NS2A , NS2A , NS3 , NS4A , NS4B , and NS5 . Many members of the genus Flavivirus participate in complex transmission cycles that involve mammalian and insect hosts [1] . The risk of periodic emergence into humans depends upon many factors , including vaccination status , elevation , dispersal patterns , and interaction with competent vector populations . Geographic distribution of YFV endemicity covers tropical areas of Africa and South America . For the African continent , human epidemics have been reported from western regions with more regular frequency than the east . This observation is almost certainly biased by regional differences in population density , vaccination coverage , and surveillance capacity [2] . However , ecological factors presumably govern some observed differences in outbreak severity and frequency of East and West African YFV outbreaks . The natural history of human YFV infection includes periodic emergence events in rainforest perimeter transition zones , following contact of humans with infected hematophagous mosquitoes of the Aedes genus . Prediction of YFV emergence in East and Central Africa is confounded both by overlapping distributions of competent vectors and uncertain dynamics of sylvatic maintenance in populations of arboreal nonhuman primates . Sylvatic vector burden for the region in question is dominated by the diverse A . africanus complex , which ranges across the entirety of sub-Saharan Africa . Regardless of the structure of maintenance cycles , proximity to contiguous vector habitat is a prime risk factor of infection for susceptible human populations , an ecological property originally supported by high rates of seroprevalence in residents of Ugandan villages adjoining forest galleries of known YFV endemicity [3] . Viral dispersal patterns may have tracked with human movement as a consequence of civil unrest , or with agricultural and population changes brought by the European colonial era [4] . The displacement of north Ugandan citizens into temporary camps during civil conflicts of the previous two decades is a potential example of such an alteration to the YFV transmission landscape [5] . Nucleotide sequence analysis of 3′UTRs of representative YFV genomes have inferred a shared ancestry for West and East/Central African genotypes of the virus , based on the variable presence of one , two or three 41-nucleotide repeat segments , which presumably form secondary RNA structures that contribute to host range adaptation [6] , [7] . Overall , phylogenetic and epidemiological analysis of the Flaviviridae supports a model of YFV emergence that is dependent upon both the presence of competent vectors and sylvatic nonhuman primate reservoir hosts [8] , [9] . Sampling history for YFV in Africa is limited , especially with respect to central and eastern regions of the continent . However , phylogenies derived from the use of the historical YFV isolates have yielded important data on the association of viral dispersal patterns with ecological features , including evidence for the existence of geographically associated YFV genotypes . The first phylogenetic evidence for geographic association of African YFV taxa resolved the bifurcation of African YFV circulation into east and west lineages [10] . Variable presence of repeat segments in the 3′ noncoding regions of YFV isolates was associated with the region of isolation ( East/West Africa and South America ) , indicating a role for repeat element structures in virus range adaptation [6] , [11] . A neighbor-joining analysis of African YFV strains , provided evidence for the presence of 5 prototype lineages in circulation in the continent , West Africa I , West Africa II , East Africa , East/Central Africa , and Angola [12] . Subsequent analysis using full-length YFV sequences provided confirmatory evidence for the putative geographic structure [13] . Bayesian estimation of divergence dates offers clarity to historical correlations of virus dispersal . Utilization of accurate clock models for arboviral phylogeny estimation is a matter of ongoing study . The reliability of this technique for measurements of viral evolutionary dynamics depends upon a combination of historical reference events , confirmation by independent data , and uncertainty of posterior estimates . In this manner , Bryant and colleagues estimated a plausible timescale of YFV divergence events , finding experimental support for the hypothesized introduction of YFV from West Africa to South America with the transatlantic slave trade [14] . Subsequently , relaxed clock analysis was used to estimate divergence histories for Trinidadian YFV isolates , producing evidence for enzootic maintenance of YFV on the island [15] . Significance of estimated dates was derived by correlation with observed recent history of YFV epizootic activity in nonhuman primate populations . A relaxed clock analysis was performed to determine phylogenetic placement of new sequence data from a Ugandan YFV outbreak that had occurred in 2010 [16] . This rare acquisition of east African sequence data resulted in a tree of topology and time estimates consistent with other published materials , using a combination of envelope gene and full-length sequences . Phylogeographic modeling incorporates distance and location under the presumption that these features are informative properties of the viral dispersal path . Most significantly , these methods permit direct testing of hypotheses on lineage dispersal and geographic association , including estimation of surface diffusion rates [17] . We believe that use of diffusion analysis is justified for reconstruction of African YFV phylogenies due to both the origin of sequence isolations from a contiguous landmass , and epidemiological guidance that continuous vegetation is a useful proxy for YFV infection risk [18] . The spatial relationships between extant African YFV lineages are currently unknown [4] . Determination of lineage orientation in physical space offers clarity to the structure of periodic and uncertain disease emergence , especially for isolates of east African origin . Our dataset includes a recent historical phylogeny of African YFV ranging in time of isolation from the prototype strain Asibi , isolated in 1927 , to a recent isolate from 2010 [16] . These analyses present the most complete Bayesian phylogeny of African YFV to date .
49 partial coding region YFV sequences comprising 670 nucleotides spanning prM , M and E genes were used in the study . Publicly available sequences were downloaded from NCBI/Genbank ( Table S1a ) . Seven novel Central and East African YFV isolates were obtained from the World Reference Center for Emerging Viruses and Arboviruses ( Galveston , TX ) amplified by RT-PCR , sequenced for the specified region , and added to the alignment . Sequences newly added to GenBank are as follows: JX012097 , JX012098 , JX012099 , JX012100 , JX012101 , JX012102 , JX012103 . The complete listing of accession taxa used in the study are compiled as supplemental data ( Table S1 ) . Amplification conditions were as follows: Reverse transcription was performed at 50°C for 30 min , followed by denaturation at 95°C for 2 min . ; 40 amplification cycles were performed at 95°C for 10 s , 55°C for 30 s , 68°C for 60 s . Final extension was at 68°C for 2 min . Reactions were cooled to 4°C before downstream use . Primers used for all amplifications were CAG ( GGTGTCCCGACTCAATGGAA ) and YF7 ( CCAAAGAGCCCACAACCATT ) as described previously [12] . Amplified fragments were agarose gel-purified and directly sequenced . All sequencing was performed at the Protein Chemistry Core Laboratory at the University of Texas Medical Branch ( Galveston , TX ) . Alignment was performed following translation using the MUSCLE algorithm as implemented in the SEAVIEW v . 4 platform [19] , [20] . The prepared alignment was screened for recombination with methods RDP , GENECONV , BOOTSCAN , and 3SEQ using the RDP3 platform [21]–[25] . Using a consensus of these methods , we found no significant evidence of recombination in the alignment . Bayesian Markov-Chain Monte Carlo ( MCMC ) analyses were performed using the BEAST 1 . 6 . 1 platform on the CIPRES computational resource ( CA , University of California San Diego ) [26] , [27] . A codon-based substitution model SRD06 was used for all inference calculations in the study [28] . A chain length of 30 million generations was used , discarding burn-in of 10 percent . Stable combinations of molecular clock and coalescent model were modeled , and assessed for relative significance by comparisons of marginal likelihood estimated by path-sampling and stepping stone sampling [29] . A consensus of these techniques supported the use of a lognormally distributed clock with a constant population coalescent prior . A Bayesian skyline coalescent prior was applied to a lognormally distributed clock model to obtain estimates of population change for the dataset . Analysis of posterior data , including assessment of convergence and Bayes factor ( BF ) comparisons , was performed using Tracer v1 . 5 [30] . Posterior results were summarized as maximum clade credibility ( MCC ) trees with TreeAnnotator v . 1 . 6 . 1 , and visualized using FigTree v . 1 . 3 . 1 . Confirmatory inference was performed using 20 partial coding sequences containing the C-terminal section of the NS5 gene and 3′ non-coding region ( 786 nucleotides ) . 3′ noncoding region sequences were aligned using the ClustalW algorithm and hand-verified using the BioEdit platform [31] . A neighbor-joining tree using observed distance and 1000 bootstrap replicates was computed for this alignment using SEAVIEW v . 4 . The MCMC chain was sampled for discrete state , bivariate continuous , and relaxed random walk ( RRW ) diffusion as implemented in BEAST v . 1 . 6 . 1 . Bayesian stochastic search variable selection ( BSSVS ) was used to provide evidence for statistically supported diffusion between state variables . BSSVS output and surfaces representing uncertainty for continuous diffusion processes were formatted as KML using the SPREAD utility [32] . Coordinate determination for taxon locations was performed using Google Earth v . 6 . 0 . 1 . For final rendering , KML files were imported and manipulated in ArcMap 10 . 0 [ESRI , Redlands CA] using an Albers equal-area conic projection for the African continent . Surfaces representing uncertainty for the diffusion process were observed as overlay with a maximum entropy raster considering the presence of Ae . africanus; the raster file was obtained from the MosquitoMap resource of the Walter Reed Biosystematics Unit [Suitland , MD] [33] . We performed selection analyses on the full dataset using Datamonkey server implementation of HyPhy [35] . We used the Single Likelihood Ancestor Counting ( SLAC ) , Fixed Effects Likelihood ( FEL ) , Internal Fixed-Effects Likelihood ( IFEL ) , and Random-Effects Likelihood ( REL ) algorithms to assay for the presence of selected sites in the prM/E alignment . Significance of SLAC , FEL and IFEL results used a p-value cutoff of 0 . 05 , and results of REL were assessed to be significant for Bayes Factors greater than 50 . 0 .
Nucleotide sequence information for African YFV is sparse , especially for eastern and central regions of the continent . We acquired and sequenced 7 unpublished YFV sequences fragments , now totaling 26 isolates from Central and East Africa , and comprising all known isolates from this region . Although the complete dataset remains sparse , requiring careful interpretation , the resultant phylogeny provides new information on regional dispersal patterns of YFV , supported by several methods of Bayesian phylogeographic inference . The maximum clade credibility tree ( Figure 1 ) supports the presence of two primary circulating lineages in western and eastern regions of the African continent . Each lineage is further bifurcated spatially into outlying and central genotypes ( Figure 1 ) . Posterior support was high for ancestral nodes of the primary lineages and subordinate genotypes ( Table 1 ) . Confirmatory neighbor-joining analysis based on the NS5-3′UTR sequences ( Figure S1; Taxa listed in Table S1b ) was found to have the same geographic topology as the Bayesian phylogeny constructed from the prM/E sequences ( Figure 1 ) . Divergence dates inferred by models under Bayes factor support were tabulated with accompanying 95% highest posterior density ( HPD ) intervals ( Table 1 ) . The branch time-corrected mutation rate was 2 . 8×10−4 substitutions/site/year , 95%HPD: 1 . 3×10−4 to 4 . 5×10−4 substitutions/site/year . In order from earliest inferred divergence and expressed as mean height in decimal years for the indicated node , the time of the most recent common ancestor ( TMRCA ) was dated to 1007 CE ( 95% HPD: 342 to 1608 ) . The divergence of the singular Angola isolate from the East lineage was dated to 1415 CE ( 95% HPD: 1017 to 1769 ) . The West lineage was dated to 1695 CE ( 95% HPD: 1520 to 1853 ) with the west/central genotype dated to 1820 CE ( 95% HPD: 1728 to 1902 ) and the west genotype dated to 1887 CE ( 95% HPD: 1840 to 1922 ) . The East lineage was dated to 1844 . 37 CE ( 95% HPD: 1751 to 1909 ) with the east/central genotype dated to 1736 CE ( 95% HPD: 1579 to 1872 ) and the east genotype dated to 1835 CE ( 95% HPD: 1740 to 1912 ) . Analysis using the SLAC algorithm detected a global ratio of nonsynonymous to synonymous nucleotide substitutions ( dN/dS ) of 0 . 0382 , which suggests the presence of predominantly purifying selection . Integrated results from all algorithms used found evidence for 192 sites under negative selection . FEL analysis detected evidence for one positively selected site , position 631 of the alignment , an envelope protein amino acid substitution G100S that was present in three isolates: Democratic Republic of Congo/1958 , Guinea-Bissau/1965 , and Senegal/1992 ( p = 0 . 045 ) . REL analysis also detected positive selection at this site ( BF = 55 . 43 ) . SLAC and IFEL analyses recovered no evidence of positively selected sites . Bayesian skyline analysis indicated a stable viral population over the length of time represented by the sample set ( Figure 2 ) . The early terminus of the plot , encompassing the date ranges of 1927–1940 , lacks adequate sampling frequency to resolve the skyline model at these timepoints , but the time period of the sample between 1940 and 2010 is adequately sampled and shows evidence of an unchanging population size during that period . Lack of sampling in some intervals ( see Figure 2 ) and the sensitivity of this coalescent model to short sequence length requires that any interpretation should be made with caution . Discrete state analysis returned a number of dominant transitions in the posterior sample , indicating focal dominance of Ethiopian , Ugandan , Nigerian , and Senegalese origin in four well supported and geographically associated clades , respectively ( Figure 1 ) . The highest posterior probability for the root state was Angola ( 0 . 083 ) . BSSVS models returned significant BF values for a number of state transitions . Under the criteria ( BF>5 , Indicator>0 . 5 ) , significance for certain traits was maintained with the use of a linearly relative distance penalty ( Table 2 ) . All significant transitions returned from the BSSVS model span the longitudinal axis of the African continent , connecting the east and west lineages ( Figure 3 ) . The transition between Nigeria and the Central African Republic received the greatest relative support by the BF criterion ( BF = 22 . 79 , uncorrected ) . Cauchy-distributed diffusion rates were highly supported by BF comparisons of all available models ( Table S2 ) . For Cauchy-distributed RRW diffusion , the mean root position was inferred at 4 . 2°N , 10 . 5°E ( Central Cameroon ) . Mean lateral diffusion rate was 10 . 6 km/yr , 95% HPD = 4 . 9 to 17 . 4 km/yr . The 80% HPD interval containing diffusion uncertainty coverage for the most recent common ancestor of the tree encompasses a broad region of the mass of the African continent ( Figure 4 , grey polygons ) .
Two West African genotypes were resolved as described previously ( West Africa I and West Africa II ) [12] . Based on the current findings we redefine West Africa I and West Africa II genotypes as the West/Central genotype and Western genotype , respectively . The West/Central genotype is defined by predominant sampling of isolates of Nigerian origin while the Western genotype is defined by isolates originating primarily from Senegal , Guinea-Bissau , and the Gambia . Incorporation of sequence information from seven additional isolates in this study enabled us to find evidence for structure in the East African lineage that has so far been poorly resolved . As presented , the inferred phylogeny supports the presence of both East and East/Central genotypes with high posterior probability . The proposed East/Central genotype is defined primarily by sampling from Ethiopia and the Democratic Republic of the Congo . The proposed east genotype is represented by isolates primarily recovered from Sudan and Uganda . Results from multiple independent models were used to support claims of geographic structure in the dataset . Posterior estimates for time structure of the dataset are consistent with patterns observed in previous studies . The mean substitution rate estimate of 2 . 8×10−4 substitutions/site/year , ( 95%HPD 1 . 3 to 4 . 5×10−4 substitutions/site/year ) overlaps a previously estimated range 1 . 0×10−4 to 3 . 3×10−4 substitutions/site/year , ( mean = 2 . 1×10−4 ) computed from an alignment of complete E gene sequences [36] . The estimated rate from this study is not directly comparable to that of Sall et al . [36] , as the alignments used are derived from different regions of the YFV genome . However , the overlap of posterior density does not permit significant discrimination of the means derived from either study . Additionally , our estimation is similar to that provided by Auguste , et al . , using the criterion of 95% HPD overlap and based primarily on isolates from the Americas [15] . Comparison of dates for significant nodes shows broad agreement with previous findings . The common ancestor of all African lineages was estimated to have arisen in 1003 years before the most recent taxon , a later divergence than proposed by Bryant et al . ( 742 ) [14] , but earlier than that proposed by Sall et al . ( 1262 ) [36] , or McMullan et al . ( 2188 ) [37] , however , overlap of posterior density is considerable for estimations of Bryant et al [38] and Sall et al . [36] , indicating agreement . Estimations of divergence dates were indistinguishable for the common ancestors of both east and west lineages , potentially indicating simultaneous dispersal of these lineages to the eastern and western regions of the African continent from ancestral populations . Most significantly , ancestral nodes of the subordinate genotypes were estimated to have arisen in a spatially biased sequence , with earlier nodes belonging to the east/central and west/central genotypes . This finding suggests that spatially outlying genotypes were the most recently established , and that ancestral populations of YFV were located centrally on the continent . Sall et . al . [36] inferred a topology in which YFV taxa isolated from the Central African Republic are represented in both east and west lineages , implying a pattern of multiple YFV introductions from central reservoirs . Across the phylogeny , several instances of relatively high substitution rates are recovered in the earliest-emerged taxa of several clades ( Nigeria/1948 , Uganda/1948 , Central African Republic/1977 , Kenya/1993 ) ( Figure S2 ) . The appearance of these higher rates may be a result of periodic local expansions , and consequent increases in sequence diversity . This property exists in east and west lineages , and in all cases appears to precede diversification of a stable clade . Previously described YFV sequence data supports ecological features of geographic niche association . 3′ UTRs of YFV contain specific repeat regions that occur with frequencies reflecting the major region of origin [39] . The presence of three noncoding tandem repeat sequence elements in West African YFV isolates , and two in East African isolates , allows a parsimonious interpretation that one repeat segment is gained or lost in lineage diversification between East and West Africa . Discrete model estimates inferred in this study substantiate the longitudinal orientation of African lineage diversification ( Figure 3 ) . This finding provides a context to the paradigm of viral transition between contiguous vegetative ecosystems , a geographic proxy measure for human YFV infection risk [18] . The discrete model permits time-reversible transitions , or free lineage exchange between location states without preconditioned directionality . This aspect of the model mirrors the reality of sylvatic YFV circulation , in which epizootic foci are hypothesized to be geographically dynamic [40] . Spatial boundaries represented by sequence features will only be fully resolved by rigorous surveillance . The observation of positive selection for the envelope protein mutation G100S in the fusion loop has been demonstrated for prM/E alignments of YFV sequence data containing combined taxa of African and South American origin [14] , [41] . The significance of the mutation G100S for African YFV emergence dynamics is unknown , but it arises independently three times in our expanded African dataset . Times between isolations of this mutation are seven years ( Democratic Republic of Congo/1958 and Guinea Bissau/1965 ) and 27 years ( Guinea Bissau/1965 and Senegal/1992 ) , respectively . Selection at this site would be expected to be biologically significant . In aggregate , experimental evidence suggests that flavivirus envelope protein mutations in the fusion loop play a significant role in antigenicity and host entry . Mutational analysis of this region provided evidence for sequence conservation to facilitate effective fusion with host intracellular membranes [42] . A comparison of flavivirus fusion loop sequences found this mutation to occur mostly in YFV isolates , with rare occurrence in other members of the genus [43] . The apparent mutational diversity at E100 has been found in temporally and geographically diverse YFV isolate groups , but the result of a selection pressure that has not been identified . Significantly , the results of our BSSVS analysis strongly supports transitions that span the most ancestral node of the inferred tree , providing evidence for connection of the ecosystems of eastern and western circulation . Long-distance ( Senegal-Ethiopia , The Gambia-Kenya ) transitions were reconstructed from the West to each of the subordinate Eastern clades , implying a level of independence for the east and east/central genotypes for the seeding of virus from unobserved reservoirs . This property contrasts with the transitions reconstructed from the Western lineage , in which local diffusions are supported . Transitions to Angola were not supported by BSSVS significance criteria used in the study . This is not unexpected , considering that transition to Angola from eastern circulation is not consistent with the pattern of east-west diffusions so far described . However , the highest root state probability was estimated to have originated from Angola , implying that the 1971 urban outbreak in Luanda was seeded from a more ancestral population than is represented in the phylogeny to date . The virus Angola/1971 may have entered the population through a rare dispersal mechanism , such as traveler movement or population displacement . Disruptions to population or infrastructure provoked by the Nigerian Civil War ( 1967–1970 ) or the Angolan War of Independence ( 1961–1975 ) may represent appropriate historical correlations to the outbreak; however this interpretation must considered with caution as is it is based on a single isolation event . Coalescent demographic analysis indicates stability of the viral population across the most adequately sampled timescale of the dataset ( Figure 2 ) . Biologically , this finding supports a classic model of persistent sylvatic arboviral maintenance . Campaigns of reactive mass vaccination presume risk to community members following the appearance of human cases , and is a response to presumed static dynamics of YFV spillover events during response timescales [37] , [44] . Irregular East African YFV emergence is a result of human intrusion into the range of viral circulation , including human movement as a consequence of civil unrest ( a recent example is a 2005 Sudanese YFV outbreak , of which commentary suggested that previously blocked north/south pastoralist migration routes had opened due to relief in civil tensions , exposing immunologically naive individuals to fatal YFV infections [45] ) and with agricultural and population changes brought by the European colonial era [4] . Low sampling frequency preceding 1948 does not permit resolution of demographic trends for the early time interval . This alternative model of YFV emergence is supported by the estimated posterior density intervals for the divergence dates of east and west lineages , which was estimated to have occurred between 1733 and 1802 CE , suggesting that alterations or intrusions to continental ecology may have been concurrent with European colonization of YFV-endemic regions ( Table 1 ) . A plausible reconstruction for this time period of geographic movements is supported by studies on human immunodeficiency virus 2 ( HIV-2 ) that suggest population movements during this time period [46] . Again , flat demographic estimates offer a constant paradigm from which to model variable intrusions of humans into regions of YFV circulation . Bayes factor support for Cauchy-distributed viral diffusion rates suggests the existence of a heterogenous dispersal process . It is possible that the irregularities seen in emergence of human cases in Central and East Africa are the combined result of wandering epizootic transmission with variability of interaction between susceptible humans and regions of sylvatic maintenance . Forty-three of the 49 isolates used in the study were obtained from human cases , so this property is relatively uniform in the dataset . Local-scale geographic averaging produced by discrete state modeling offers some clarity to this question . By including all isolates within a politically defined landmass , the discrete model may provide some correction to sampling errors introduced by the transit of human cases from the location of infection before discovery of illness . Estimated connection of the east and west lineages ceased during the 30-year interval starting in 1644 CE ( Figure 4 ) . Our modeled reconstruction of YFV lineage phylogeography independently supports topotype mapping derived from ecological data , and expands upon this information to suggest the presence of overlapping foci of circulation [34] . Assuming a centered spatial average for node location estimates , diffusion analysis supports the overlap of East African genotypes in recent history . Posterior diffusion uncertainty was visualized against a publicly available model for predicting the presence of Ae . africanus , the major vector of sylvatic YFV in Africa ( Figure 4 ) [33] . Considering the timescales of the diffusion analyses , it is possible that past spatial mixing of the virus obscures aspects of the dispersal path from basal nodes . This is especially true for the root of the inferred phylogeny , which at the timescales represented occupies a central section of the African continent ( 80%HPD ) . The inference of divergence events presented in this study remedies gaps in the historical understanding of YFV emergence , and offers further opportunity to correlate viral dispersal events with human social and demographic history . Evidence for stable maintenance , in a context of irregular emergence , highlights the need for further surveillance and acquisition of YFV sequence information . The results of this study have potential to be incorporated into design of regional surveillance efforts and implementation of vaccination strategies . | Yellow fever virus ( YFV ) is a mosquito-transmitted pathogen of great public health significance , which is endemic to tropical areas of Africa and South America . Despite the availability of an effective vaccine , and programs that exist in many endemic areas to reduce populations of mosquitoes , YFV continues to circulate and emerge in regions with developing public health infrastructures . Periodic outbreaks of YFV into humans are unpredictable and merit thorough investigation of the ecology and genetic diversity of the virus . Our analyses improve the current understanding of African YFV evolution in several respects . We have included unpublished viral sequence data from Central and East Africa , which is significant because the availability of YFV isolates from these regions is extremely limited . We present a modeled geographic structure of African YFV dispersal , and propose a new model for the spread of YFV based on concurrent historical movement of the virus from reservoirs in central African jungles to both eastern and western regions of the continent . Our results provide evidence for the presence of unique genotypes of the virus in both central and east African circulation . The presented findings not only provide insight to estimations of outbreak risk for the regions in question , but also contribute to rational GIS analysis and approaches to vaccination campaigns . | [
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] | 2013 | Phylogeographic Reconstruction of African Yellow Fever Virus Isolates Indicates Recent Simultaneous Dispersal into East and West Africa |
Insufficient or dysregulated energy metabolism may underlie diverse inherited and degenerative diseases , cancer , and even aging itself . ATP is the central energy carrier in cells , but critical pathways for regulating ATP levels are not systematically understood . We combined a pooled clustered regularly interspaced short palindromic repeats interference ( CRISPRi ) library enriched for mitochondrial genes , a fluorescent biosensor , and fluorescence-activated cell sorting ( FACS ) in a high-throughput genetic screen to assay ATP concentrations in live human cells . We identified genes not known to be involved in energy metabolism . Most mitochondrial ribosomal proteins are essential in maintaining ATP levels under respiratory conditions , and impaired respiration predicts poor growth . We also identified genes for which coenzyme Q10 ( CoQ10 ) supplementation rescued ATP deficits caused by knockdown . These included CoQ10 biosynthetic genes associated with human disease and a subset of genes not linked to CoQ10 biosynthesis , indicating that increasing CoQ10 can preserve ATP in specific genetic contexts . This screening paradigm reveals mechanisms of metabolic control and genetic defects responsive to energy-based therapies .
ATP is the key energy-carrying molecule in all cells , and failure to maintain adequate ATP levels may be critical in many diseases , ranging from mitochondrial disorders to cancer and neurodegeneration [1–3] . Despite the importance of ATP , we understand little about the contributions of genes and pathways that maintain its levels and how they are regulated . Genes that modulate ATP levels may be therapeutic targets for diseases of energy failure or candidates for novel inborn errors of metabolism . Finding these genes would provide insight into how energy failure contributes to disease . However , that analysis has been limited by a lack of tools to screen the genome at high throughput for modifiers of ATP levels . One innovative study assayed ATP with arrayed adherent cells and identified genes that , when knocked down by RNA interference ( RNAi ) , decreased or increased cellular ATP content [4] . Although an important starting point , the study was limited by a small number of library short hairpin RNAs ( shRNAs ) per gene , and it assayed total ATP normalized to DNA content and so could not differentiate changes in cell size or cell cycle from ATP concentration . The primary sources of ATP are mitochondrial oxidative phosphorylation and cytoplasmic glycolysis . However , which pathway predominates and the genetic control points are determined by poorly understood factors . In addition , functional assays are needed to match specific energy-based therapies to disorders that are likely to benefit from these therapies . For example , cofactor therapies , such as coenzyme Q10 ( CoQ10 ) supplementation , have been used therapeutically in disorders of CoQ10 biosynthesis [5] , but with limited efficacy in other mitochondrial disorders [6] . Genetically encoded fluorescent biosensors specifically report the level of their target metabolite in living cells , including ATP [7] , NADH [8] , lactate , and glucose [9] . Measuring metabolites in individual cells instead of cell lysates has produced important observations of the temporal , spatial , and subcellular regulation of cellular metabolism . However , high-throughput technologies , such as flow cytometry , have yet to be combined with fluorescent biosensors to detect metabolites in screens and other large studies . Here , we report a novel screening paradigm to identify genes that maintain cellular ATP levels . We combined fluorescence resonance energy transfer ( FRET ) and fluorescence-activated cell sorting ( FACS ) to measure ATP in single living cells . This screen shows that FRET and FACS can be used to screen a metabolite based on the real-time level and to provide valuable insight into key pathways and therapeutic strategies to maintain ATP in human cells .
FRET describes the process by which photons emitted from an excited fluorophore are transferred to a second fluorophore with an efficiency dependent on the physical proximity of those fluorophores . FRET-based biosensors operate on the principle that the binding of a metabolite to a specific protein domain tethered to 2 fluorophores changes the sensor’s confirmation and the distance and orientation of the fluorophores , thus fluorescently reporting the concentration of that metabolite . FRET-based biosensors for metabolites have been used with fluorescence microscopy , but flow cytometry also detects FRET [10] . To our knowledge , sorting cells based on the real-time levels of a metabolite has not been done . We started with an established sensor with modified cyan fluorescent protein ( CFP ) and monomeric Venus ( mVenus ) ( AT1 . 03YEMK , CFP-Venus ATP ) separated by an ATP-binding domain [7 , 11] . This sensor shows ATP-dependent changes in FRET at physiological ATP concentrations and is resistant to pH changes [7 , 11] . We found that it also reports ATP levels by flow cytometry as it had a higher FRET/donor signal than a mutant sensor with no ATP affinity ( ATR122K/R126K , CFP-Venus Dead ) [7] ( Fig 1A and S1A Fig ) . Furthermore , drugs blocking oxidative phosphorylation and glycolysis ( to stop all ATP synthesis ) reduced the FRET signal ( S1B Fig ) . Unfortunately , the FRET signal varied based on expression level of the sensor , limiting its ability to detect differences in ATP between cells ( S1C Fig , see also Materials and methods ) . This limitation would confound population-based measurements needed for a screen . We hypothesized that this artifact is due to the relative dimness of CFP and the poor overlap of CFP with available FACS laser excitation wavelengths ( 405 and 488 nm , with Emax of CFP approximately 433 nm ) , causing reduced detection of FRET at low expression levels . To improve the sensor , we substituted fluorophores using the same interposed ATP-binding domain [12] . We selected Clover ( donor ) and mApple ( acceptor ) for their brightness , spectral overlap , and compatibility with standard flow cytometer laser and emission filter wavelengths [13 , 14] . The Clover-mApple sensor had the expected cytosolic localization in cells ( S1D Fig ) and produced a narrow distribution of FRET that reported ATP levels ( Fig 1A ) . The FRET signal was decreased by substituting the dead mutant that disrupts ATP binding ( Clover-mApple Dead , S1E Fig ) or by adding drugs to inhibit oxidative phosphorylation and glycolysis ( S1F Fig ) . The signal was independent of expression of the sensor ( S1G Fig ) . In vitro , the dynamic range of the CFP-Venus ATP sensor approached saturation approximately 3 mM ATP ( S1H Fig ) . Because cytosolic ATP levels are estimated in the low mM range [11 , 15] , the CFP-Venus sensor may be saturated at baseline ATP levels [7] , which precludes detection of increased ATP and likely reduces sensitivity to small decreases in ATP . Substituting green and red fluorophores for CFP and Venus can reduce the affinity of the sensor for ATP [12] , and the Clover-mApple ATP sensor FRET saturated close to 6-mM ATP ( Fig 1B ) , indicating an improved capacity to detect higher ATP levels versus the CFP-Venus sensor . The Dead sensor showed little response to exogenous ATP ( Fig 1B ) and can thus be a control to identify artifactual FRET changes independent of ATP binding . The Clover-mApple sensor showed limited sensitivity to pH similar to the CFP-Venus sensor [7] ( Fig 1C ) . Because of the improved brightness , compatibility with standard flow cytometry lasers , dynamic range , and lack of FRET variation based on sensor expression level compared with the CFP-Venus sensor , the Clover-mApple ATP and Dead sensors were used for the high-throughput measurement of ATP in individual cells . Clustered regularly interspaced short palindromic repeats interference ( CRISPRi ) technology allows the knockdown of individual genes in a large population of cells . We generated K562 cells that express a dead CRISPR-associated protein 9 ( dCas9 ) -Kruppel-associated box ( KRAB ) fusion protein , which allows CRISPRi to reduce gene expression [16 , 17] , followed by the Clover-mApple ATP FRET sensor ( hereafter , ATP FRET sensor ) or the Dead sensor ( Fig 2A ) . We then transduced these cells with pooled lentivirus containing approximately 28 , 000 single guide RNAs ( sgRNAs ) targeting 2 , 231 genes in a mitochondrial gene-enriched subgenome library . The library contained 10 distinct sgRNAs per gene and 1 , 400 non-targeting control sgRNAs . This many controls could not be tested by lower-throughput methods . ATP can be derived from oxidative phosphorylation , glycolysis , or both . To know the source of ATP , the screen was conducted 3 times , wherein cells were acutely exposed to different drug and substrate conditions that forced ATP synthesis to derive from ( 1 ) oxidative phosphorylation only , ( 2 ) glycolysis only , or ( 3 ) from both . We forced cells to use oxidative phosphorylation—the “respiratory” condition—by incubating cells with high-dose 2-deoxyglucose ( 2DG; 10 mM ) to inhibit glycolysis and 10 mM pyruvate to support aerobic respiration without glucose . To limit homeostatic mechanisms or cell death , cells were switched to these treatments only about 30 minutes before sorting , leaving cells little time to adapt . In the respiratory condition , ATP levels declined modestly , as assessed by luciferase and flow cytometry FRET ( Fig 2B , blue line and box and whiskers , respectively , and S2A Fig an independent repetition ) , showing that FRET is in the dynamic range of the sensor during screening and that cellular energy was acutely stressed but not depleted . The relative changes in ATP as measured by FRET or luciferase were similar , indicating that our ATP-FRET sensor reliably reflects ATP levels . Adding 5 μM oligomycin to block oxidative phosphorylation in combination with high-dose 2DG resulted in rapid and complete loss of ATP ( Fig 2B , red line [luciferase] and box and whiskers [FRET] ) , confirming that , in 2DG alone , ATP was actively being made and that all detected ATP was from mitochondrial oxidative phosphorylation . The Dead sensor showed only a minimal reduction in FRET upon inhibiting glycolysis and oxidative phosphorylation ( S2B Fig ) , although ATP levels measured by luciferase decreased as expected ( S2C Fig ) , indicating that the Dead sensor is a suitable control for genetic manipulations that alter FRET independent of ATP . To restrict ATP production to glycolysis only—the “glycolytic” condition—cells were exposed to 2 mM glucose and 5 μM oligomycin ( S2D Fig ) . A low dose of 2DG ( 3 mM ) was also added to the glycolytic condition to reduce ATP from baseline because ATP levels did not acutely decrease in K562 cells with oligomycin alone in glucose . As with the respiratory condition , reducing ATP below baseline levels during cell sorting was necessary to ensure that ATP levels were within the dynamic range of the sensor and to confirm that cellular energy was acutely stressed . Increasing 2DG to 10 mM in the glycolytic condition resulted in loss of ATP ( S2C Fig ) , confirming that detected ATP was derived from glycolysis . Finally , we screened a “basal” condition with PBS , 5 mM pyruvate , 10 mM glucose , and no inhibitory drugs . Because FACS occurs over time , we noted the stability of ATP levels in each metabolic condition over the timeframe ( 30–60 minutes post treatment ) needed to sort cells . For cells in respiratory or glycolytic conditions , ATP levels assessed by the ATP FRET sensor were stable for 90 minutes as detected by luciferase or flow cytometry FRET ( Fig 2B and S2D Fig ) . To conduct the screen , cells expressing the ATP FRET sensor , dCas9-KRAB , and a single sgRNA were exposed to acute metabolic conditions as above , and the highest and lowest quartiles of ATP as reported by the ATP FRET sensor were collected ( Fig 2A ) . To control for ATP-independent changes in FRET ( e . g . , from unexpected effects of the sgRNA on the synthesis or turnover of the sensor fluorophores ) , we conducted a parallel screen with the Dead FRET sensor . We collected approximately 200 cells per sgRNA ( average of 10 sgRNAs/gene , approximately 2 , 000 cells/gene ) ; with this method , about 1 , 000 genes can be screened and sorted per hour . Each collected quartile was deep sequenced , and the number of each sgRNA in each FRET quartile was determined by counting the sequencing reads for that sgRNA in that quartile [17] . We generated a phenotype ( fold-enrichment in the high- versus low-FRET quartile ) for each sgRNA . The screen was repeated 3 times for each of the 3 metabolic contexts ( respiratory , glycolytic , basal ) with the ATP FRET sensor , and twice with the Dead sensor in each context . The phenotype of each individual sgRNAs was averaged over the repetitions . The final gene phenotype was reported as the average of the 3 sgRNAs with the strongest phenotype per gene . We examined the raw phenotypes for separate sgRNAs for each repetition and found that the reproducibility of each sgRNA compared with non-targeting sgRNAs was good ( S3 Fig , select genes , S1 Table for all average phenotypes , S2 Table for all individual sgRNA phenotypes for the respiratory condition for all 3 repetitions ) . To assess the specificity of our screen for true hits that alter ATP levels , we considered the distribution of non-targeting sgRNAs . To simulate the analysis for the active CRISPRi sgRNAs , we tested random combinations of 10 sgRNAs from among the 1 , 400 non-targeting sgRNAs , each combination representing a simulated gene , and graphed the average of the 3 sgRNAs with the strongest phenotypes for each simulated gene ( Fig 3A and 3B grey dots ) . The phenotypes of these simulated genes were tightly clustered around 1 ( equal distribution in the high- and low-FRET fractions ) , thus representing the “noise” in the screen . A gene was defined as a “hit” in the respiratory condition if there were 2 or more sgRNAs yielding phenotypes >2 SDs beyond the mean phenotype of the non-targeting sgRNAs , averaged over the 3 independent repetitions . Using these criteria , none of the simulated genes was identified as a hit under any of the 3 metabolic conditions ( fold-enrichment in high versus low-ATP fractions all approximately 1 ) ( Fig 3 and S1 , S2 Tables ) , suggesting the specificity of the screen for detecting sgRNAs that affect gene expression . Consistent with this , many genes targeted by active sgRNAs were strikingly enriched in the low-ATP fraction when sorted under respiratory conditions ( Fig 3A ) . To control for artifactual hits that might impact the FRET signal independent of the ATP level , the entire sublibrary was also rescreened 2 times with the Dead sensor , and the same analysis was performed ( Fig 3A and 3B ) . Potential hits that similarly altered FRET with the Dead sensor were removed from analysis . Under respiratory conditions , most hits that decreased the FRET signal with the ATP sensor had little effect on the FRET signal of the Dead sensor ( Fig 3A ) , indicating that they are true low-ATP hits . However , occasional genes did influence the FRET signal of the Dead sensor , especially under glycolytic conditions ( Fig 3B ) , indicating that ATP-independent effects can also impact the FRET signal and underscoring the importance of a dead mutant control . An analogous control is not available for luciferase-based assays . To determine the effect of metabolic context of gene knockdown on ATP levels , we compared the phenotypes of each gene in respiratory and glycolytic conditions . While few genes decreased the ATP FRET signal in glycolytic conditions , knockdown of some genes caused small increases in the ATP FRET signal in glycolytic conditions ( Fig 3B , S3 Table ) . Among these , some gene-targeting sgRNAs also increased the FRET of the Dead sensor ( Fig 3B ) . These artifacts were due to the effect of gene knockdown rather than general effects of CRISPRi because artifacts were not observed with non-targeting sgRNAs . In the glycolytic condition , 65 of 144 ( 45 . 1% ) of FRET-increasing hits were removed because they also increased FRET in the Dead sensor control . ATP-independent artifacts seemed to be pathway specific: 20 of 29 ( 60 . 0% ) of cytosolic ribosomal hits were removed because they increased FRET in the Dead sensor , but only 4 of 25 ( 16 . 0% ) of mitochondrial ribosomal subunits were removed for that reason . Of the 79 hits remaining in the glycolytic condition after removing artifacts , 43 ( 54 . 4% ) that protected ATP in glycolysis also decreased ATP in the respiratory condition , indicating opposite effects of knockdown of the same genes in opposite metabolic contexts ( Fig 3C ) . Only 6 genes reduced the FRET signal of the ATP sensor in basal conditions , but none decreased FRET from the Dead sensor ( S1 , S2 Tables , S4 Fig ) . While 60 genes produced small increases in the ATP FRET signal in basal conditions , only 8 ( 13 . 3% ) did not also increase FRET from the Dead sensor ( S3 Table ) . The magnitude of phenotypes observed in the basal condition was smaller than seen in the respiratory or glycolytic conditions ( Fig 3D , and S4 Fig ) . We conclude that very few genes alter ATP levels in basal condtions , consistent with the hypothesis that maintaining ATP levels is of primary importance in cells , and that cells adjust their metabolism and growth to maintain ATP levels . The acute metabolic perturbation in the respiratory and glycolytic conditions was thus essential to uncovering genes critical to maintaining ATP levels . Our sublibrary contained genes from 3 cellular processes ( vesicular trafficking , cell motility , and mitochondrial function ) that behaved differently under respiratory conditions . As expected , few trafficking or motility genes affected ATP ( Fig 3D ) , suggesting that many functions are not needed to maintain ATP . Genes in the nonmitochondrial pathways that did affect ATP ( S3 Table ) are potentially unrecognized mitochondria-related genes . Among genes involved in mitochondrial structure or function , specific pathways were clearly enriched ( Figs 3D and 4 ) . A total of 85% of protein subunits of mitochondrial ribosomes reduced ATP when knocked down . A similar pattern was seen with other genes involved in transcription and translation from the mitochondrial genome . A smaller fraction of respiratory chain genes impacted ATP when knocked down ( 23% , Figs 3D and 4 ) compared with the mitochondrial ribosome . Among these respiratory chain genes identified as hits , there was an enrichment for those involved in CoQ10 biosynthesis and copper insertion into complex IV ( Fig 4 and S3 Table ) . In 2 previous studies , the effects of knocking down genes on ATP levels [4] and cell death as a function of metabolic substrate [18] were assessed by complementary methods . We compared our results to these screens and found overlap with our screen and the “death screen” conducted by Arroyo and colleagues , which examined genes necessary for cell survival in galactose , which forces reliance on mitochondrial metabolism ( Fig 5 ) . The agreement between our screen and that of Arroyo and colleagues suggests that many genes for ATP maintenance are also needed for cell survival in substrate that forces reliance on oxidative phosphorylation . While we found about two-thirds of the hits from the Arroyo screen , half of our hits were not found by Arroyo and colleagues , suggesting that some cells survive with impaired respiratory function and may not be identified by a survival or growth screen . There was very little overlap with the study by Lanning and colleagues from either our screen or that of Arroyo and colleagues . We expected the pyruvate condition from Lanning and colleagues to overlap with our respiratory condition . Instead , even though our library and that of Lanning and colleagues screened 384 genes in common , there was an overlap of only 6 ATP-decreasing genes ( Fig 5 ) . Many ATP-decreasing genes in our study increased ATP in the study by Lanning and colleagues , overall suggesting that the assays produced different results . To further validate our assay , we generated stable cell lines expressing single sgRNAs against selected genes identified as low-ATP hits under respiratory conditions , selected for transduced cells with puromycin , and further verified gene knockdown in a subset of lines by quantitative real-time reverse transcription PCR ( qRT-PCR ) ( S5 Fig ) . As expected , knockdown of MRPL10 , a strong low-ATP hit , decreased mitochondrial-derived ATP as detected by FACS/FRET ( Fig 6A ) and by luciferase versus a negative-control sgRNA ( Fig 6B ) but did not alter ATP in the glycolytic condition by either method ( Fig 6A and 6B ) . Luciferase analysis of additional low-ATP hits from the primary screen also confirmed decreased ATP in the respiratory condition , while glycolysis-derived ATP was unchanged or protected ( Fig 6C ) , confirming by a second method that the FRET-based screen identified true changes in ATP , both increased and decreased . The magnitude of ATP change detected by luciferase , however , was somewhat smaller overall than that detected by FRET , suggesting that the FRET screen is more sensitive than standard luciferase assays . We hypothesize that the increased sensitivity of the screen is due to the large number of cells analyzed individually compared with pooling cells in a lysate as is required by luciferase analysis . We also used a Seahorse analyzer to determine whether an impairment in aerobic respiration underlies decreased mitochondrial-derived ATP . Consistent with this , several genes identified as decreasing ATP , particularly KPNB1 , which has no known role in respiration , showed decreased maximal respiration ( following carbonyl cyanide-4- ( trifluoromethoxy ) phenylhydrazone [FCCP] ) ( Fig 6D and 6E ) . PAF1 knockdown did not significantly decrease maximal respiration ( Fig 6D ) , suggesting either another role in ATP regulation for this gene or a milder effect on respiration below the sensitivity of this assay . Impaired respiration might result either from an intrinsic deficit in respiration or decreased mitochondrial content . However , all lines had similar mitochondrial mass ( S6 Fig ) , as assessed by western blot on cell lysates against translocase of outer membrane 20 kDa subunit ( Tom20 ) and NADH:ubiquinone oxidoreductase subunit S4 ( NDUFS4 ) , suggesting that mitochondrial-derived ATP levels are decreased due to an impairment in intrinsic mitochondrial function . To determine what energy sources support cell growth , we analyzed our ATP phenotype data from different conditions ( respiratory , glycolytic ) compared with data from a published growth screen of the same CRISPRi library in K562 cells performed under basal conditions that permit both respiration and glycolysis [17] . When growth data are overlaid with our ATP data under respiratory conditions ( ATPresp ) , genes organize into classes that differentially impact ATP and growth ( Fig 7A ) . For example , knockdown of genes encoding components of the cytosolic ribosome slowed growth but did not affect ATP levels , but lower ATP levels did correlate with poorer growth for mitochondrial ribosomal genes ( Fig 7A ) . In contrast to the respiratory condition , comparing growth with ATP levels under glycolytic conditions ( ATPgly ) ( Fig 7B ) demonstrated no relationship between growth and ATP levels for mitochondrial ribosomal genes . Therefore , among genes identified to impact growth , the ATP screen distinguishes genes that slow growth independent of energy levels from those that influence growth due to effects on mitochondrial function . Because cells in the growth screen had access to glucose without drugs ( untreated ) , and we observed that few genes impact ATP levels in the similarly untreated basal condition ( ATPbasal ) ( S4 Fig ) , we conclude that mitochondrial function is important for K562 cell growth even when respiratory and glycolytic substrates are present and ATP levels are normal . In contrast to the respiratory condition , when growth was compared with the corresponding ATP levels under basal conditions ( S7 Fig ) or glycolytic conditions ( Fig 7B ) , there was no relationship between growth and ATP levels . To further understand how genes influence growth through energy-independent and -dependent mechanisms , we compared ATP and growth under matching metabolic conditions . We performed separate growth screens in respiratory ( GROWTHresp ) , glycolytic ( GROWTHglyc ) , and basal ( GROWTHbasal ) conditions and compared the growth phenotypes to the ATP phenotype in the corresponding metabolic condition . Mirroring what we observed when comparing respiratory ATP with growth in a basal condition ( Fig 7A and 7B ) , we observed that in respiratory conditions for both ATP and growth , knockdown of genes encoding components of the cytosolic ribosome showed no relationship between ATP and growth , indicating that these genes inhibit growth independent of energy level . In contrast , after knockdown of genes encoding components of the mitochondrial ribosome , the extent of decline in ATPresp correlated with the decrease in GROWTHresp ( Fig 7C ) . In contrast , there was no relationship between ATP and growth in the glycolytic or basal conditions ( Fig 7D and S7 Fig ) . These data suggest that knockdown of mitochondrial ribosomal genes impairs growth by affecting mitochondrial energy metabolism , either directly ( ATP ) or indirectly ( redox status from respiratory chain function ) rather than by other effects on mitochondria ( e . g . , Ca2+ buffering or reactive oxygen species [ROS] production ) , although these effects could also be regulated in a substrate-specific manner . Gilbert and colleagues ( 2014 ) noted more genes that slowed growth than we did . The difference might be in the number of cell divisions . They included only a basal condition , which permitted many cell divisions . We found that cells grew poorly in respiratory and glycolytic conditions , limiting the total number of divisions . The ability to screen for phenotypes that are not compatible with robust cell growth is another advantage of FRET-based over growth-based screening . Finally , although modest ATP decreases correlate with lower growth , several genes ( e . g . , PAF1 , KPNB1 , or ISCA2 , Fig 7C ) decrease ATP despite normal growth in respiratory conditions , again indicating that growth-inhibiting effects of decreased ATP can be overcome . Such genes that impair mitochondrial energy metabolism without slowing growth would be missed by growth-based screens but can be identified by a functional assay for ATP , as shown here . Modulating ATP levels may have therapeutic potential for diseases with high biosynthetic requirements , such as cancer . Yet little is known about how diverse cancers respond to metabolic therapies , and metabolic therapies are not yet major strategies employed in the clinic . By identifying energy-modulating genes in human tumors , our screening approach may help to identify those cancer-related mutations that are critical to cellular ATP levels and thus support cancer cell growth . Based on the results of our growth screen , we reasoned that hits that disrupt mitochondrial-derived ATP would negatively impact cancer cell growth and examined 1 hit that decreased ATP in respiratory conditions named METTL17 , which is implicated in breast cancer [19] . We cloned the sgRNA targeting sequence associated with the greatest ATP alterations for the METTL17 ( sequence provided in Materials and methods ) . Lentivirus expressing these or negative-control sgRNAs was transduced into HCC827 human lung cancer cells expressing dCas9-KRAB . After 1 week , total METTL17 protein levels were more reduced in cells transduced with sgRNA targeting this protein than in negative controls ( S8A Fig ) . Anticancer therapies inhibit tumor growth by affecting proliferation , and to determine whether METTL17 silencing altered cell proliferation , we performed trypan blue staining of HCC827 cells expressing either negative-control sgRNA ( Neg 1 ) or stable silencing of METTL17 that were exposed to either control medium or medium containing 20 mM 2DG over 48 hours . METTL17-silenced cells yielded significantly fewer viable cells at basal condition than negative controls ( S8B Fig ) , while the number of nonviable cells ( trypan blue–positive ) did not differ significantly between the groups . Inhibition of glycolysis with 2DG further decreased the total number of viable cells after 24 hours in both negative-control and METTL17-silenced lines ( S8B Fig ) . To determine whether reducing METTL17 protein expression compromises cell cycle progression either at baseline or during glycolysis inhibition , unsynchronized HCC827 cells in standard culture conditions were collected at 24 hours after exposure to control medium or medium with 10 or 20 mM 2DG . Collected cells were fixed , stained with propidium iodide , and analyzed by FACS . Baseline G1 and G2 fractions were similar between control and METLL17 cells at baseline ( S8C Fig ) . Forcing control HCC827 cells to increase mitochondrial energy production by inhibiting glycolysis with 2DG led to an accumulation of cells in G1 . This shift was absent in cells lacking METTL17 ( S8C Fig ) , suggesting that knockdown of METTL17 decreases the overall growth rate , potentially to preserve ATP levels . We specifically assessed apoptosis by analyzing Annexin V using FACS , which quantified <6% Annexin V positivity across all cells and conditions , indicating that death was not prominent under basal or 2DG conditions ( S8D Fig ) . Although METTL17 did not itself impact growth in K562 cells , it was nonetheless identified in our ATP screen performed in K562 human leukemia cells; these subsequent studies link METTL17 to a cancer-relevant endpoint in a different cancer cell line—in this case , human lung cancer cells—in which stable silencing of METTL17 significantly suppresses basal cancer cell proliferation without producing increased apoptosis . This finding parallels observations from our screen that indicate a negative relationship between genes that inhibit mitochondrial function and basal cell growth . From the standpoint of cancer therapeutics , these data raise the possibility of pharmacologic inhibition of METTL17 function or other genes critical to mitochondrial-derived ATP suppressing tumor growth , and future studies are needed to assess this possibility and its potential to preferentially target tumor cells . Our screen might be used to identify specific genetic disorders that respond to targeted energy-based therapies . We focused on CoQ10 , a lifesaving therapy for a subset of mitochondrial disorders caused by CoQ10 deficiency [5 , 20] , widely used with many other mitochondrial disorders though with less consistent clinical benefit [6 , 21] . To assess the capacity of CoQ10 to restore ATP levels , we developed a mini-library of 180 sgRNAs consisting of 1 to 2 sgRNAs per gene , with the strongest ATP-lowering phenotypes in the primary respiratory screen , including 3 CoQ10-biosynthetic genes from the primary screen that lowered ATP in the respiratory condition ( PDSS1 , PDSS2 , COQ2; CRISPRi against 7 other CoQ10 biosynthetic genes did not lower ATP in our main screen in respiratory conditions and were not included [see S1 Table for each gene phenotype] ) as well as 20 non-targeting sgRNAs that had no effect on ATP level ( see S4 Table for sequences of included sgRNAs ) . Cells were pre-incubated with 50 μM CoQ10 for 5 days and switched to respiratory conditions before sorting , as in the primary screen . CoQ10 supplementation protected ATP levels in all 3 CoQ10 biosynthesis genes identified as reducing ATP in the primary screen in the respiratory condition ( PDSS1 , PDSS2 , COQ2 ) ( Fig 8A ) , demonstrating the specificity of the assay to mitochondrial function and that our assay is able to detect interventions that protect ATP . CoQ10 had no effect on the decrease in ATP produced by CRISPRi against most genes not in the CoQ10 pathway . One notable exception , COX11 ( a component of cytochrome c oxidase [22] not known to be involved in CoQ10 biosynthesis ) , responded to CoQ10 supplementation , suggesting that CoQ10 rescues bioenergetic function by some mitochondrial genes not known to be involved in CoQ10 biosynthesis . We then repeated the full subgenome library screen 3 times with CoQ10 supplementation and twice without . Known CoQ10 biosynthetic genes again showed improved ATP with CoQ10 supplementation , and a subset of low-ATP hits with no known direct function in CoQ10 biosynthesis including COX11 showed improved ATP with CoQ10 supplementation ( Fig 8B , S5 Table ) . To support these findings , we measured ATP levels with and without CoQ10 in a subset of these genes by luciferase and confirmed rescue of ATP levels in several genes ( PDSS1 , PDSS2 , COX11 ) and no rescue in others ( COX16 , ATP5MPL ) ( Fig 8C ) , consistent with what was observed by FRET ( Fig 8A and 8B ) . No improvement in ATP was detected with CoQ10 supplementation in the glycolytic condition by luciferase ( Fig 8C ) , confirming the specificity of CoQ10 to the respiratory metabolic context . CoQ10 could rescue ATP levels by correcting a primary or secondary CoQ10 deficiency or by augmenting mitochondrial function through other mechanisms . To distinguish between these possibilities , we measured cellular CoQ10 levels with and without CoQ10 supplementation in cells with a CoQ10 biosynthetic gene knocked down ( PDSS2 ) or selected low-ATP hits with no known function in CoQ10 biosynthesis ( COX11 , ATP5MPL , COX16 ) . We observed that baseline CoQ10 levels were low in cells lacking PDSS2 , but CoQ10 levels in cells lacking COX11 , ATP5MPL , or COX16 were not different from control cells ( Fig 8D ) . Supplementation with CoQ10 increased CoQ10 levels in all cells ( Fig 8D ) . Supplementation also increased the fraction of total CoQ10 in the oxidized versus reduced state in control cells and in cells with single-gene knockdown ( Fig 8D ) . From these data , we conclude that CoQ10 supplementation can improve mitochondrial ATP production by correcting a primary CoQ10 deficiency , but the rescue of COX11-induced ATP deficiency results either from additional improvements in respiration or other undefined benefits of supraphysiologic CoQ10 levels .
Our screen identified genetic factors that regulate ATP with high specificity . None of the >2 , 000 simulated genes we screened produced hits , and 87 . 2% ( 136/156 ) of our total hits that decreased ATP under respiratory conditions had known mitochondrial functions . The other 12 . 8% ( 20 genes ) likely also function in energy metabolism and warrant further study . CRISPR and RNAi screens using whole-genome libraries are common and powerful tools to dissect genetic pathways in mammalian cells . Yet most assay an endpoint ( e . g . , survival , growth , reporter gene expression ) that may reflect heterogeneous molecular intermediates . Measuring a metabolite in real time at a scale compatible with genome-wide screening is novel and enables a mechanistic dissection of detailed biochemical processes . Genes identified in our screen had little overlap with the only screen published on genetic regulators of ATP ( Lanning and colleagues ) [4] . This study did not observe decreased ATP phenotypes under a respiration-only condition ( pyruvate ) with inhibition of most mitochondrial ribosomal genes , and in fact , 6 such genes increased ATP by more than 25% when knocked down , and they found increased ATP with inhibition of a majority of screened CoQ10 biosynthetic genes . While some differences may be attributable to the different cell types used ( K562 cells in this study; HeLa in Lanning and colleagues’ study ) , the mitochondrial ribosome and CoQ10 biosynthesis are believed to be critical to aerobic respiration across different cell types [20 , 23] . The discrepancies between these studies warrant further consideration . Lanning and colleagues measured ATP in many cells concurrently , and ATP was normalized to total DNA content . This method would be susceptible to artifacts caused by differences in cell size ( altering the absolute ATP content ) or cell cycle ( impacting the ATP/DNA ratio ) that would not reflect changes in ATP concentration . Other differences include the use of 2 siRNAs per gene by Lanning and colleagues versus 10 sgRNAs per gene in this study , and the use of an ATP-insensitive control to remove ATP-independent artifacts in this study . We believe that the methodologic advantages detailed here—including our detection of nearly all genes in one mitochondrial compartment ( i . e . , the ribosome ) and the gene-specific rescue of ATP by CoQ10—demonstrate that our FRET-based method of screening for ATP modifiers adds substantially to the nascent field of metabolite-based screening . The high-throughput screen described here required fast-growing cells capable of effective CRISPRi-mediated gene knockdown . Any cell type selection , particularly a transformed cell line , will likely impact the results of a screen . K562 cells have successfully revealed important biology in other related screens [17 , 18] , and here , they demonstrated clear pathway-specific effects , e . g . , from knockdown of mitochondrial ribosomal subunits . These effects are biologically plausible , are supported by additional investigation ( e . g . , rescue by CoQ10 ) , and likely can be generalized to other cell types . Knowledge of essential and novel mitochondrial processes enables future detailed studies in targeted disease models . Effects of pH on the fluorescent ATP biosensor are unlikely to underlie the findings reported here . We employed a Dead sensor control to eliminate artifacts due to ATP-independent effects on the fluorophores , and many hits identified in this study were validated by luciferase assay , a completely orthogonal methodology . We found differences in the classes of genes required to maintain ATP levels . Mitochondrial-derived ATP levels were decreased by knockdown of 40% ( 27/67 ) of mitochondrial transcription/translation genes and 85% ( 57/67 ) of mitochondrial ribosomal genes . In contrast , knockdown of only 23% ( 21/90 ) of screened respiratory chain genes decreased ATP levels . The specific respiratory chain genes identified in this screen may be critical to maintaining ATP levels . Indeed , mutations in some of the genes identified in our screen , e . g . , PDSS2 and COX10 , are believed to cause human mitochondrial disorders by depleting ATP [24 , 25] . Our screen also assayed other classes of mitochondrial genes with known roles in promoting respiration , but only a subset of the genes tested were identified as hits . For example , our screen identified select components of the mitochondrial contact site and cristae organizing system ( MICOS ) complex , which plays a critical role in maintaining cristae morphology ( IMMT [26] , MINOS1 [27] ) , and a subset of mitochondrial fusion and fission proteins ( e . g . , neither Mfn1 or Mff were low-ATP hits ) . In one case , the fission receptor encoded by MIEF1 ( Mid51 ) was a hit , while the related gene MIEF2 ( Mid49 ) was not , despite functioning in the same pathway . There are likely several reasons why certain genes with known functions in respiration were not hits . First , in many cases , these genes are likely less critical for maintaining ATP levels than those genes identified as hits . For example , although mitochondrial dynamics proteins influence respiration , mitochondria can continue to respire with different morphologies [28] , and the impact on respiration may only be observed in cell types and conditions with high energy requirements [29] . In other cases , there may be compensatory pathways that maintain respiration or delayed effects on ATP levels that would be missed in our assay system . We examined the capacity to maintain ATP after only a few days of gene knockdown and could have missed genes that might influence ATP levels more slowly , e . g . , by compromising processes such as mitochondrial DNA maintenance [30] . In other cases , there may have been insufficient knockdown by CRISPRi , or gene function may be preserved even with substantial knockdown . As such , future studies will be needed to determine whether specific pathways ( e . g . , individual respiratory chain complexes ) are more or less critical for ATP synthesis . Our finding that a majority of mitochondrial ribosomal subunits were identified as hits suggests that most genes critical for ATP maintenance would be identified in this screen and that many genes not hit may indeed be less critical for maintaining ATP in the conditions screened in this study . Why are ATP levels so sensitive to disruption of genes involved in mitochondrial protein synthesis ? Genes in the mitochondrial genome are retained evolutionarily , including proteins at the core of respiratory chain complexes [31] , whereas peripheral subunits were more easily moved to the nucleus , perhaps due to differences in protein hydrophobicity . Disrupting nuclear-encoded genes that impair mitochondrial transcription and translation may affect mitochondria more than individual respiratory chain components because they interfere with expression of multiple core , mitochondrially encoded subunits . This observation suggests that combined respiratory defects cause disease due to more severe energy failure than dysfunction of a single respiratory chain complex . Mutations in only a few mitochondrial ribosomal proteins cause human disease [23] . Others have likely not yet been linked to disease due to rarity or early lethal phenotypes [32] . Indeed , whole-exome sequencing has led to the discovery of mutations in mitochondrial ribosomal subunits as the cause of neonatal lethal diseases [33] . From our findings , loss of these proteins may produce disease due to energy failure , but their loss could also compromise other mitochondrial functions , including Ca2+ metabolism and ROS production , and deficits in these functions may contribute to disease . The screen presented here was not exhaustive of all mitochondria-localized genes ( see S1 Table for complete gene list ) . For example , most components of the tricarboxylic acid ( TCA ) cycle and some respiratory chain subunits , including many complex III subunits , were not included , and further investigation is required to define the bioenergetic requirement for these genes and for comparisons of individual respiratory chain complexes . We identified genes without recognized mitochondrial functions that , when knocked down , reduced ATP only from mitochondria . The strongest such hit was KPNB1 , which encodes a component of the nuclear pore complex . No human disease is yet associated with this gene , but it may be needed for mitochondrial biosynthesis [34] because Drosophila larvae lacking Importin-Beta had fewer mitochondria . This may be due to impaired nuclear import of Nrf2 [35] . Our studies show impaired mitochondrial function with KPNB1 knockdown ( Fig 6C and 6D ) , indicating a conserved role in mitochondrial metabolism but normal mitochondrial mass ( S6 Fig ) , suggesting that the defect is not due to decreased mitochondrial biosynthesis . Also , PAF1 , which regulates nuclear gene transcription [36] , has no known link to energy metabolism , but its knockdown also decreases ATP only when ATP is produced by the mitochondria as detected by FRET and luciferase ( S1 , S3 Tables , S3 Fig , Fig 6C ) . Overall , our findings demonstrate the capacity of functional screens to identify mitochondria-related genes whose protein products may not necessarily be localized to the mitochondria as well as demonstrate that further study of putative mitochondrial genes may give insight into how energy levels are maintained , contribute to disease , and/or can be therapeutic targets . Knocking down most mitochondrial ribosomal protein genes affected mitochondria-derived ATP levels but did not decrease ATP when cells used glycolysis alone or both respiration and glycolysis . Therefore , glycolysis may compensate for decreases in aerobic respiration , and/or energy consumption may be decreased to maintain normal ATP . In support of the former , when cells were forced to use only glycolysis-derived ATP , knockdown of some mitochondrial ribosomal proteins actually enhanced the capacity of cells to maintain ATP levels above those of control cells in the same condition ( Fig 3C and 3D ) . This likely reflects up-regulated glycolysis when aerobic respiration was compromised by gene knockdown . Which metabolic context is most relevant to in vivo human diseases of energy failure ? This is poorly understood . Local substrate conditions are often not known and differ among cell types , energy requirements , stressors ( e . g . , starvation ) , and perhaps genetic modifiers of metabolism . Developmental stage is a consideration; e . g . , the in utero environment is more hypoxic than after birth , leading to increased reliance on glycolysis during development [37] . In our experiments , knockdown of only a few genes caused chronic basal ATP deficiency . More were important to maintaining ATP levels during acute metabolic perturbations . We hypothesize that disease more often results from acute insults to ATP homeostasis in a setting that is sensitized by an underlying deficiency in mitochondrial function than a chronic deficit in ATP levels . Acute exposure to respiratory or glycolytic conditions may not seem representative of chronic metabolic disease , but many disorders of energy failure manifest as intermittent acute clinical episodes rather than chronic disease . For example , patients with Leigh syndrome or mitochondrial encephalopathy , lactic acidosis , and stroke-like episodes ( MELAS ) present with acute stroke-like episodes [38] . Similarly , hypoxia/ischemia , rhabdomyolysis due to mitochondrial disease , and hypoglycemia due to inborn errors in fatty acid oxidation are acute forms of energy failure and injury [39 , 40] . The relative contributions of chronic versus intermittent ATP perturbation to metabolic disease need more study . What are the cellular consequences of insufficient ATP ? ATP is required for many cell functions ( e . g . , neuronal activity , myocardial contractility , insulin release , and cell growth ) [41] . However , it is rarely proven that insufficient ATP itself underlies deficits in these functions . Growth-based screens in human cells with shRNA or CRISPR libraries [17 , 42 , 43] create a “black box” between the gene and growth change that may not reflect a metabolic etiology . To investigate this , we studied the relationship of ATP level and growth under different metabolic conditions . Our data suggest that specifically insufficient ATP ( i . e . , energy failure ) , not loss of other functions , explains why decreasing mitochondrial ribosomal proteins compromise growth in respiratory conditions . Moreover , cells may decrease growth as a means of preserving ATP levels when ATP production is decreased . The relationship between ATP level and cell growth also raises the possibility of an energy threshold for growth . In addition , we identified several genes for which CRISPRi markedly decreased ATP without compromising growth , or slowed growth without affecting ATP . These findings highlight the significant utility of the ATP screen in beginning to define the contribution of metabolism to cellular functions . An important strength of our screen is the ability to identify ATP-modulating genes under distinct metabolic conditions ( basal , respiratory , glycolytic ) . Moreover , we hypothesize that genes identified in these specific contexts ( metabolic and/or cellular ) will exert similar effects in other cellular and experimental contexts . For example , our data indicate that METTL17 loss significantly suppresses human lung cancer cell growth and support the idea that the identification of energy-modulating genes could inform the development of cancer-relevant biochemical approaches . Moving forward , defining the precise cellular and metabolic contexts amenable to metabolic-based therapy will require cancer-type–specific studies , as our findings suggest . To our knowledge , we report here the first high-throughput screen of responsiveness to CoQ10 ( or any drug ) for single-gene causes of mitochondrial dysfunction . Of at least 18 known CoQ10 biosynthetic genes [44] , 10 were screened in our library , and 5 are known to cause human disease . Among these 5 , knockdown of PDSS1 , PDSS2 , and COQ2 decreased ATP specifically in the respiratory condition in this screen . CoQ10 supplementation prevented the drop in ATP in respiratory conditions in all 3 of these genes as detected by repeat FRET-based screening ( Fig 8A and 8B ) and by luciferase ( Fig 8C ) . However , many mitochondrial disorders have not responded clinically to CoQ10 supplementation [6] . We also identified several genes in which mitochondrial-derived ATP increased with CoQ10 , even in the absence of a known role in CoQ10 biosynthesis . In particular , COX11 consistently showed improved ATP with CoQ10 supplementation ( Fig 8A–8C ) . Moreover , cells lacking COX11 did not have lower CoQ10 content , suggesting that the ATP rescue must be due to a role of CoQ10 in other mitochondrial functions . We speculate that supraphysiologic levels of CoQ10 may improve mitochondrial ATP production , e . g . , by improving the efficiency of the respiratory chain [45] or increasing mitochondrial biogenesis [46] . Indeed , CoQ10 has been reported to partially prevent ATP depletion from mitochondrial stressors in the absence of a known defect in CoQ10 biosynthesis [47] . Alternatively , CoQ10 also has known functions as an antioxidant that could decrease energy requirements or indirectly increase energy production . Although COX11 is not yet associated with disease , we hypothesize that COX11 deficiency , if found , may respond to CoQ10 treatment . If supraphysiologic CoQ10 can boost mitochondrially derived ATP , why is CoQ10 not more broadly effective ? Perhaps there are 2 CoQ10 thresholds to increase ATP: one to restore deficient levels in disorders of CoQ10 biosynthesis and a second to boost mitochondrial ATP production though other mechanisms at supraphysiologic levels . Difficulty achieving high enough CoQ10 levels may explain the lack of clinical efficacy in humans , in which the bioavailability of CoQ10 is limited , especially in certain tissues , such as the brain [48] . Secondary CoQ10 deficiency may also underlie some mitochondrial disorders [49] , contributing to the degree of CoQ10 responsiveness . Our findings here suggest that the beneficial effects of CoQ10 supplementation on mitochondrial-derived ATP levels may also go beyond simply restoring CoQ10 levels to normal . Moreover , our observation that CoQ10 rescues ATP levels caused by knockdown of some genes and not others has important implications for the utility of high-throughput screening for targeting therapies to mitochondrial disease and other disorders of energy failure . There are hundreds of single-gene mitochondrial disorders , and until now , no mechanism existed to screen for effective interventions for each genetic deficiency individually . The high-throughput nature of our technology should permit preclinical screening of any number of candidate therapies in different substrate/drug combinations , adding tremendously to our understanding of the interaction between single-gene mitochondrial dysfunction and external stresses and therapies . Furthermore , just as we combined our ATP data with other large-scale growth screens to achieve new insights , metabolite screening for other molecules with fluorescent biosensors ( e . g . , ROS , nicotinamide adenine dinucleotide ) may advance a genome-scale understanding of the regulation of metabolism .
The ATP screen was conducted using K562 cells provided by Jonathan Weissman’s lab , identical to those used by Gilbert and colleagues 2014 , with stable integration of dCas9-KRAB as described ( ATCC 536 [RRID:CVCL_0004] , a female cell line ) [17 , 50] . K562 cells were maintained at 37 °C in RPMI-1640 with 25 mM HEPES , 2 . 0 g/L NaHCO3 , 0 . 3 g/L L-glutamine supplemented with 10% fetal bovine serum ( FBS ) , 2 mM glutamine , 100 units/mL penicillin , and 100 mg/mL streptomycin . Lentivirus expressing the Clover-mApple ATP or Dead sensors was produced by the UCSF Viracore and transduced with polybrene ( 8 μg/mL ) into K562-dCas9-expressing cells via spinfection . Two days after transduction , cells were selected by FACS that expressed both fluorophores ( Clover and mApple ) . To avoid artifacts from potential mutations or variations in a single cell , a small polyclonal population of ATP FRET sensor-expressing cells was expanded for all subsequent experiments . The use of a polyclonal population was very unlikely to have affected screen results for several reasons , as follows: post-FACS comparisons showed no difference in FRET distribution from small or large collections of ATP FRET sensor-expressing cells; sufficient cells were screened such that each sgRNA was screened in approximately 200 cells , which would average out any cell-to-cell variability; and , as described above , the Clover-mApple sensor did not differ in FRET signal based on sensor expression level . For the CRISPRi pooled library , lentivirus was transduced as above , and 2 days after transduction , puromycin selection ( 0 . 65 μg/mL ) was started and maintained for 4 to 5 days total , followed by 1 to 3 days of recovery from puromycin . Experiments were performed using the pooled sgRNA-expressing cells surviving antibiotic selection . Cell lysates used for in vitro FRET experiments were obtained from COS7 cells originally obtained from Robert Edwards ( UCSF ) ( RRID:CVCL_0224 ) . COS7 cells were grown at 37 °C in high-glucose DMEM supplemented with 110 μg/mL penicillin and streptomycin and 10% FBS . The human lung adenocarcinoma HCC827 cell line was originally obtained from Trever Bivonas ( UCSF ) ( ATCC 2868 , 39-year-old Caucasian female individual ) . HCC827 cells were grown at 37 °C in RPMI medium with 10% FBS , 1% penicillin/streptomycin , 1 . 5 mM pyruvate , and 0 . 05 mg/mL uridine . Lentivirus expressing dCas9-KRAB and sgRNA of interest was produced by the UCSF Viracore and transduced with polybrene ( 8 μg/mL ) into HCC827-dCas9–expressing cells over 2 consecutive days . At 48 hours after transduction , puromycin selection ( 1 μg/mL ) was started and maintained for 7 days total . Experiments were performed using the pooled cells surviving antibiotic selection . The cells were authenticated based on the knockdown of gene expression . CFP-Venus ATP ( AT1 . 03YEMK ) and Dead ( ATR122K/R126K ) FRET sensors ( which use a variant of CFP [mseCFP] as the donor fluorophore and YFP [circularly permuted mVenus] as the acceptor fluorophore , surrounding an ATP-binding protein ) were kind gifts from Hiromi Imamura ( Kyoto University ) and Hiroyuki Noji ( Osaka University ) [7] . Clover-mApple ATP and Dead FRET sensors were constructed using Clover ( Addgene number 40259 ) , which was circularly permuted by PCR to begin at residue 173 with a linker ( GGSGG ) as described [51] . To the 3′ of a PCR product encoding the permuted Clover was ligated the ATP or Dead sensor domains , followed by mApple . The entire 5′-Clover-sensor-mApple-3′ construct was then subcloned into the lentiviral vector FUW2 . COS7 cells were electroporated with DNA encoding the ATP FRET sensor , Dead sensor , or the individual fluorophores using a GenePulser ( Bio-Rad , Hercules , CA ) set for mammalian cells , 250 V , 950 μF , and allowed to grow for 2 to 3 days after transfection . Cells were then collected by scraping the plates and sonicating cells in SH buffer ( 10 mM HEPES , 0 . 32 M sucrose [pH 7 . 4] ) and pelleting the remaining debris . Lysates were saved at −80 °C until use and were not refrozen . Thawed lysates were distributed in a 96-well plate , and fixed concentrations of MgATP ( A9187 , Sigma , St . Louis , MO ) were added , all diluted in SH buffer . The pHs of the SH buffer and MgATP stock were adjusted with NaOH or HCl . Fluorescence was detected on a Molecular Devices SpectraMax M5 plate reader using wavelengths ( ex/em ) 550/610 ( acceptor ) , 490/550 ( donor ) , and 490/600 ( FRET ) . Bleed-through into the FRET channel was subtracted by measuring single fluorophores [52] . To examine the effect of individual gene knockdown on cell growth , sgRNA from the CRISPRi sublibrary for mitochondria , motility , and trafficking represented in a K562 cell population were compared before and after 7 days of growth . Six million cells were collected before and after 7 days of culture in medium containing different metabolic substrates , maintained at a cell density between 250 k/mL and 1 M/mL . Medium consisted of RPMI-1640 ( includes 11 mM glucose ) and 50 μg/mL uridine , along with 2 . 5 mM pyruvate ( basal ) , 20 mM 2DG and 2 . 5 mM pyruvate ( respiratory ) , or 1 μM oligomycin ( glycolytic ) . Growth phenotypes were calculated similar to ATP phenotype , as the average of the highest 3 log2 ratios for each gene , post-7 days’ growth versus pregrowth . Luciferase measurements were performed using the CellTiterGlo 2 . 0 kit ( Promega , Madison , WI ) , and luminescence was measured on a Biotek H4 plate reader . K562 cells transfected with dCas9-KRAB , ATP FRET sensor , and CRISPRi pooled library were cultured with 50 μM CoQ10 or N , N-dimethylformamide ( DMF ) vehicle alone for 5 days . The cells were maintained at a cell density between 250 k/mL and 1 M/mL prior to sorting 6-million–cell high- and low-ATP fractions under acute respiratory conditions as described above . Genomic DNA was processed and sequenced for sgRNA quantification as described . Three CoQ10-treated replicates and 2 vehicle-treated replicates were performed . ATP phenotype rescue was calculated as the difference between each CoQ10-treated gene’s ATP phenotype and its average vehicle-treated ATP phenotype . A gene was determined to have strong rescue if it had either a t test p-value of less than 0 . 05 and rescue larger than 1 SD from the average rescue of non-targeting guides , or a t test p-value of less than 0 . 1 and a rescue larger than 2 SDs from the average rescue of non-targeting guides . Individual sgRNAs were selected to create a small library that could be screened more rapidly with analysis of fewer cells . From the primary screen , 1 to 3 sgRNAs/gene were selected based on robust decreased ATP phenotype ( see S4 Table for list of sgRNAs and genes ) . This small library consisted of 161 sgRNAs targeting 68 genes and 20 non-targeting sgRNAs . sgRNAs were cloned by synthesizing 2 oligonucleotides with complementary sequences per sgRNA with necessary overhangs , annealing the oligonucleotides , and individually ligating into the sgRNA lentiviral backbone plasmid as used by Gilbert and colleagues ( 2014 ) . Sequences were checked by Sanger sequencing of individual clones . DNA from individual sgRNAs was then pooled in approximately equal amounts , and the presence of all sgRNAs was confirmed by deep sequencing of the pooled plasmids after PCR amplification of the sgRNA regions . Lentivirus was created , and cells were selected for integration of the sgRNA-bearing sequence as in the primary screen . To identify genetic causes of mitochondrial dysfunction responsive to bioenergetic therapy , K562 cells containing 1 sgRNA from the above small library were screened as in the primary screen in respiration-only substrates after they were grown in culture media containing either no supplement or 50 μM CoQ10 for at least 72 hours . The cells were maintained at a cell density between 250 k/mL and 1 M/mL and then sorted by flow cytometry into high- and low-ATP fractions of 1 million cells each under acute treatment of respiration-only substrate . Genomic DNA was processed and sequenced for sgRNA quantification as described . ATP phenotype rescue was calculated as the difference between each untreated gene’s ATP phenotype and its ATP phenotype when cultured with 50 μM CoQ10 . Below are the sequences of individual sgRNA used in experiments . Control 1 ( non-targeting ) GGCCGTGGTACTGTAAAGA Control 2 ( non-targeting ) GAGGGAGCTTGGTCCAACCCC MRPL10 GGCTTCCGTCCATTCTTCCGG METTL17 GGCGTTGGGACTGAGGGTCAC PDSS2 GTGCCGCGGGAAACAAACCAG K562 cell lines with single-gene knockdown were cultured for 5 days with or without 50 μM CoQ10 . One million cells of each line were pelleted , followed by flash freezing in liquid nitrogen . CoQ10 and ubiquinol were isolated from cell pellets by 1-propanol extraction , and the masses of each species per cell pellet were quantified by HPLC and mass spectrometry by the Vanderbilt Neurochemistry Core . Total CoQ10 pool size was calculated as the sum of CoQ10 and ubiquinol masses per cell pellet , and percent oxidation was calculated as the mass of CoQ10 divided by the total mass of CoQ10 and ubiquinol . Gene functional categories within the mitochondria were assigned based on manual evaluation of Online Mendelian Inheritance in Man ( omim . org ) and GeneCards ( genecards . org ) . Respiratory and glycolytic rates in K562 cell lines were measured with the Seahorse Extracellular Flux ( XF ) Analyzer 96-well plate reader . Cells were seeded at 150 , 000 cells per well in Seahorse assay medium ( Agilent Technologies numer 103335–100 ) , supplemented with 10 mM pyruvate , in a 96-well culture plate precoated with 22 . 4 μg/mL Cell-Tak ( Corning number CB40240 ) . Cells were then adhered to the plate surface by centrifugation at 200 g for 5 minutes , followed by a 40-minute incubation at 37 °C without CO2 . Respiration and glycolysis were simultaneously measured based on oxygen consumption rates ( OCRs ) and extracellular acidification consumption rate ( ECAR ) , respectively . OCR and ECAR were measured 3 times before injection and 3 times after sequential injection of oligomycin ( 1 μM ) or FCCP ( 1 μM ) and rotenone ( 1 μM ) . The measurements at each time point were normalized to the value of the first time point on a well-by-well basis . qRT-PCR gene relative expression quantifications were performed using 7900HT Fast Real-Time PCR System ( Applied Biosystem ) and using FAM-MGB TaqMan Gene Expression Assays ( ThermoFisher , assay ID: Hs01680112_mH—COX11; Hs01550960_g1—COX16; Hs01043634_m1—ATP5MPL; Hs00372008_m1—PDSS1; Hs01047689_m1—PDSS2; and Hs00204417_m1—NDUFA8 ) , together with VIC-MGB human ACTB ( β-actin ) ( ThermoFisher number 4326315E ) as endogenous control . cDNAs and PCR reactions were prepared according to the protocol for Cells-to-CT kit ( ThermoFisher number AM1728 ) , using the standard reverse transcription cycle ( 37 °C for 6 minutes , inactivation at 95 °C for 5 minutes , hold at 4 °C ) , and qRT-PCR conditions ( UDG Incubation: 50 °C for 2 minutes; enzyme activation: 95 °C for 10 minutes; PCR cycle: 95 °C for 15 seconds , 60 °C for 1 minute—repeat 40 cycles ) . All reactions were performed in a 384-well plate , in duplicate and from 2 to 3 independent experiments . CT ( threshold cycle ) values of each gene were averaged and calculated relative to CT values of β-actin using the the 2−ΔΔCT method [53] . Protein levels were assessed by western blotting . HCC827 cells were collected and lysed in lysis buffer ( 1% sodium deoxycholate , 0 . 1% SDS , 25 mM Tris-HCl , 150 μM NaCl , 1% TritonX , 0 . 2 mM EDTA , 2% NaF , 1% sodium vanadate , 4% protease inhibitor , and 0 . 1% calyculin A ) . Equal amounts of proteins ( 20 μg/lane ) were separated by SDS-PAGE and transferred onto polyvinylidene fluoride ( PVDF ) membranes ( Trans-BlotTurbo Transfer Pack , Bio-Rad ) . K562 cells were collected and lysed in RIPA buffer ( ThermoFisher number 89900 ) . Equal amounts of proteins ( 40 μg/lane ) were separated by SDS-PAGE and transferred onto nitrocellulose membranes using iBlot 2 Dry Blotting System ( ThermoFisher number IB23001 and IB21001 ) . Primary antibodies used recognized human anti-METT11D1 antibody ( ab103318 , 1:1000; Abcam ) , β-actin mAbs ( number 3700 , 1:1500; Cell Signaling Technology ) , human anti-β-actin antibody ( ab20272 , 1:2000; Abcam ) , anti-NDUFS4 antibody ( ab55540 , 1:1500; Abcam ) , and anti-Tom20 antibody ( sc-11415 , 1:500; Santa Cruz Biotechnology ) . An anti-mouse IgG , HRP-linked secondary antibody ( number 7076 , 1:3000; Cell Signaling Technology ) was used for HCC827 cells . Blots were developed using the Amersham ECL Western Blotting Detection Reagents ( GE Healthcare ) , and bands were visualized using the ChemiDoc Touch Imaging System ( Bio-Rad ) . IRDye 800CW Goat anti-Mouse IgG and IRDye 680RD Goat anti-Rabbit IgG secondary antibodies ( 1:10000; LI-COR Biosciences ) were used for K562 cells . Blots were visualized using the Odyssey infrared imaging system and analyzed using Image Studio Lite Software ( LI-COR Biosciences ) . Cell cycle was assessed using propidium Iodide staining and FACS as described [54] . HCC827 cells were seeded in quadruplicates in 6-well plates and incubated overnight . Cells were treated with 0 , 10 , or 20 mM of 2DG in medium supplemented with 1 μg/mL puromycin , 1 . 5 mM pyruvate , and 0 . 05 mg/mL uridine . Samples were collected after 24 hours and fixed in 70% ethanol . Fixed cells were treated with RNase and PI for 2 hours and analyzed by flow cytometry ( BD Biosciences FACS Verse ) . The FITC Annexin V apoptosis detection kit ( catalog number 556547; Becton Dickinson ) was used per manufacturer’s instructions , and cells were analyzed by flow cytometry ( BD Biosciences FACS Verse ) . The results were analyzed using FlowJo V . 10 . 1 software ( FlowJo , LLC , Ashland , OR ) . Cells were plated at a density of 2 , 000 cells/well in 6-well plates , and 24 hours after plating , medium was changed to control or 2DG-containing media . Cells were cultured under standard conditions over 48 hours , at which point cells were trypsinized , centrifuged , washed with PBS , and resuspended in 1 mL of PBS . The collected cell suspension for each replicate was stained with 0 . 4% ( w/v ) trypan blue ( catalog number 15250061; Thermo Fisher Scientific ) in 1:1 ratio , and cells were counted using a hemocytometer . All statistical analyses—including n , what n represents , description of error bars , statistical tests used , and level of significance—are described in the figure legends . FACS analyses were graphed with box plots , with the top and bottom of the box indicating the interquartile range , the whiskers indicating the 5th and 95th percentiles , and the center line in the box indicating the median . One- or two-way ANOVA followed by multiple comparisons tests were used to compare ATP levels , assessed with either luciferase or the ATP FRET sensor-based assays , with p < 0 . 05 considered significant . Correlation analyses were performed with linear regression and determined to have non-0 slopes via F-test . For the respiratory condition , we defined a hit as a gene with 2 or more sgRNAs >2 SDs beyond the phenotype of the non-targeting sgRNAs after averaging the 3 repetitions of the respiratory-condition screen . We removed hits that showed a phenotype by these same criteria in the same direction with the Dead mutant sensor . For the glycolytic and basal conditions , which had narrower distributions of the non-targeting sgRNAs , we defined a hit as having 3 or more sgRNAs >2 SDs beyond the average phenotype of the non-targeting sgRNAs , and we removed hits with 2 or more sgRNAs >2 SDs beyond non-targeting sgRNAs with the Dead sensor . Phenotypes ( mean of best 3 sgRNAs averaged over all repetitions ) for all screened genes in all conditions are provided in S1 Table . GraphPad Prism and FlowJo were used to generate graphs , and GraphPad Prism was used for statistical analyses . Software for processing and analysis of sequencing reads from pooled library was the same as that used by Gilbert and colleagues ( 2014 ) . Gene names used were HUGO Gene Nomenclature Committee ( HGNC ) -approved symbols ( https://www . genenames . org ) . For the ATP FRET-based screen , we used the “rho” calculation comparing the high and low FRET pools as described . We required at least 50 reads in 1 of the 2 pools for an sgRNA to be included in analysis . Gene phenotypes were calculated as the mean of the 3 sgRNAs with the strongest phenotype . No growth value was used in the FACS data analysis because this was not a growth screen . Phenotypes ( average of strongest sgRNAs ) for all genes screened in all conditions are provided in S1 Table . Sequences for all sgRNAs were published by Gilbert and colleagues ( 2014 ) . | There is abundant evidence that insufficient energy , or energy failure , contributes to the pathophysiology of many inherited and degenerative diseases , cancer , and aging . However , we understand little about how cellular energy levels are regulated . To begin to address this gap , we developed a screening approach that uses a fluorescent biosensor to measure relative levels of ATP in individual cells and used this approach to identify those genes that are most essential to maintaining energy levels . We screened approximately 2 , 200 genes and found that mitochondrial ribosomal proteins are particularly critical in maintaining energy levels and enabling cell growth . We also used this approach to identify genetic targets that could be amenable to an energy-based therapy via supplementation with the mitochondrial cofactor coenzyme Q10 ( CoQ10 ) . These included CoQ10 biosynthetic genes associated with human disease as well as a subset of genes not previously linked to CoQ10 biosynthesis . Our screening paradigm thus reveals mechanisms by which cellular energy levels are controlled . It can also be used to identify genetic defects that will be responsive to energy-based therapies . Application of this approach may additionally enable powerful screens for other key metabolites to further advance a genome-scale understanding of the regulation of metabolism . | [
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"organ... | 2018 | A high-throughput screen of real-time ATP levels in individual cells reveals mechanisms of energy failure |
The PhoPR two-component system is essential for virulence in Mycobacterium tuberculosis where it controls expression of approximately 2% of the genes , including those for the ESX-1 secretion apparatus , a major virulence determinant . Mutations in phoP lead to compromised production of pathogen-specific cell wall components and attenuation both ex vivo and in vivo . Using antibodies against the native protein in ChIP-seq experiments ( chromatin immunoprecipitation followed by high-throughput sequencing ) we demonstrated that PhoP binds to at least 35 loci on the M . tuberculosis genome . The PhoP regulon comprises several transcriptional regulators as well as genes for polyketide synthases and PE/PPE proteins . Integration of ChIP-seq results with high-resolution transcriptomic analysis ( RNA-seq ) revealed that PhoP controls 30 genes directly , whilst regulatory cascades are responsible for signal amplification and downstream effects through proteins like EspR , which controls Esx1 function , via regulation of the espACD operon . The most prominent site of PhoP regulation was located in the intergenic region between rv2395 and PE_PGRS41 , where the mcr7 gene codes for a small non-coding RNA ( ncRNA ) . Northern blot experiments confirmed the absence of Mcr7 in an M . tuberculosis phoP mutant as well as low-level expression of the ncRNA in M . tuberculosis complex members other than M . tuberculosis . By means of genetic and proteomic analyses we demonstrated that Mcr7 modulates translation of the tatC mRNA thereby impacting the activity of the Twin Arginine Translocation ( Tat ) protein secretion apparatus . As a result , secretion of the immunodominant Ag85 complex and the beta-lactamase BlaC is affected , among others . Mcr7 , the first ncRNA of M . tuberculosis whose function has been established , therefore represents a missing link between the PhoPR two-component system and the downstream functions necessary for successful infection of the host .
Mycobacterium tuberculosis , the etiologic agent of tuberculosis in humans , is arguably the world's most important intracellular pathogen . The bacterium disseminates via the aerosol route from open pulmonary lesions of an infectious case and on reaching the alveoli of a susceptible individual is phagocytosed by resident macrophages . After infection , the tubercle bacillus resides asymptomatically for years in 95% of the infected persons . This latent state can be life-long but disease develops when the immune system weakens as a consequence of HIV co-infection , aging or malnutrition [1] . M . tuberculosis encounters a variety of environmental conditions and has to adapt to both the extracellular milieu and to the intracellular niche in order to survive [2] . Improved understanding of how this pathogen fine-tunes gene expression to support active growth and non-replicating persistence , and of how it copes with stresses encountered within the host would not only shed light on bacterial pathogenesis and biology but also aid the design of novel intervention strategies . Well-known adaptation mechanisms in bacteria include two-component signal transduction systems ( TCSS ) . These consist of a sensor protein that , upon reception of specific signal ( s ) , activates its cognate transcription factor resulting in transcriptional regulation of a defined set of genes . The number of TCSS in M . tuberculosis is lower than typically found in bacteria of similar genome size , possibly reflecting the evolution of the bacillus as a human pathogen adapted to a predominantly intracellular environment [3] . Of the 11 TCSS present in M . tuberculosis H37Rv , the PhoPR TCSS is essential for virulence [4] , as demonstrated in ex vivo and in vivo infection models , where inactivation of phoP led to greatly impaired growth [4] , [5] . Consistent with these observations , a single nucleotide polymorphism ( S219L ) in the DNA binding domain of PhoP , which affected the ability of the regulator to control gene expression of the ESX-1 secretion system , was responsible for the reduced virulence of the attenuated strain H37Ra [6] , [7] . Biochemical analyses conducted on the phoP mutant revealed that the synthesis of the cell wall components diacyltrehaloses , polyacyltrehaloses and sulfolipids , specific to pathogenic mycobacterial species , was also diminished as compared to wild type M . tuberculosis , thus providing an additional mechanism for attenuation [8] . Loss of these phenotypes is the basis of candidate vaccine strains carrying deletions in phoP , such as the recently developed MTBVAC , which showed great promise upon preclinical evaluation [9] . The PhoP protein is part of the PhoB/OmpR subfamily of transcription factors , characterized by an N-terminal regulatory domain and a DNA binding domain at the C-terminus . The crystal structure revealed the dimeric nature of the regulator and predicted PhoP to bind to direct repeats [10] , in agreement with in vitro investigations of the PhoP-DNA interactions at a few operator regions [11] , [12] , [13] . Microarray-based transcriptomic studies of wild type and phoP mutant strains have been performed to identify the regulon controlled by PhoP . Genes belonging to lipid and intermediary metabolism , to the PE , PPE and PE_PRGS families and to the transcriptional regulator categories were found to be deregulated in phoP-deficient bacteria [5] , [14] , while a very recent publication [15] identified the genes under direct control of PhoP by means of ChIP-seq experiments on a PhoP-overexpressing strain . Despite this body of knowledge , several questions remain unanswered . These include defining the external stimulus sensed by the PhoPR TCSS , performing ChIP-seq experiments in physiological conditions , obtaining a single-nucleotide resolution transcriptomic map , and identifying transcriptional regulators acting downstream of PhoP . In this study , we used a systems biology approach , combining ChIP-seq , RNA-seq and in-depth proteomics , to thoroughly investigate the role of PhoP in the biology of M . tuberculosis . We identified a ncRNA , encoded by the mcr7 gene , as a major target of PhoP and showed its involvement in controlling secretion of Twin Arginine Translocation ( Tat ) substrates .
The PhoP regulon has been characterized previously by transcription profiling using microarrays in both the H37Rv [5] and MT103 strains [14] of M . tuberculosis . Those studies indicated that approximately 2% of genes are regulated by PhoP at the transcriptional level . However , little is known about the biophysical interactions between PhoP and the promoter regions of the genes controlled with the exception of a few well-characterized promoters [13] . Here , we applied ChIP-seq ( chromatin immunoprecipitation with anti-PhoP antibodies followed by ultra-high throughput DNA sequencing ) to locate PhoP binding sites across the M . tuberculosis H37Rv [16] chromosome . To avoid false positive signals we included an isogenic phoP mutant [11] , which served as a control and reference sample in all experiments . ChIP-seq analysis of cultures grown to exponential phase led to the identification of 35 significantly enriched ( p<0 . 0001 , FDR 0 . 00% ) regions in H37Rv compared to the phoP mutant ( Table 1 ) . Several of these peaks were localized between divergently transcribed open reading frames ( ORF ) or upstream of validated or predicted operons [17] , [18] , [19] , thus increasing the number of genes potentially affected by PhoP binding directly . These targets were randomly distributed along the M . tuberculosis genome ( Figure 1A ) and predominantly located upstream of ORF ( Figure 1B ) . However , we also observed PhoP binding sites in the 3′-end of ORF , as shown for the hddA-ldtA genes ( Figure 1B ) . All of the functional categories in which the M . tuberculosis ORFs have been grouped were represented in the ChIP-seq results , although clear prevalence of regulatory proteins was observed ( 12% of the total number of signals as compared to 5% representation in the genome ) . Remarkably , of the 35 regions detected by ChIP-seq , a number of them had not been described previously [5] as being associated with PhoP-regulated genes ( i . e . mcr7 , PE27 , PPE43 , PE31 , Rv3778c , lpdA ) . The distance between the PhoP peak and the ORF start site was calculated for each gene and plotted as reported in Figure 1C . The majority ( 83% ) of the PhoP peaks were between 0 and 200 bp upstream of the ORF start site , with 50% of them within the first 100 bp . Two binding sites were considerably further away from the closer ORF: these were the cases of lipF ( >500 bp ) and rv1535 ( 472 bp ) . Only one case was observed with the PhoP peak lying within the ORF: rv2137c , where the summit was located 97 bp downstream of the ATG start codon . To gain insight into the interplay between PhoP and the transcriptional complex , we compared previous ChIP-seq data of RNA polymerase ( RNApol ) [17] with the PhoP profile obtained here . We observed that PhoP distribution mirrored that of RNApol at the putative promoter regions ( Figure 1B ) . Closer examination indicated that PhoP binding sites precede those of RNApol . Additional confirmation came from calculation of the distance between the PhoP and RNApol signals , which was between 0 and 100 bp for most of the genes ( Figure 1D ) . This might indicate a role of PhoP in positioning RNApol as a prerequisite for transcriptional control . Exceptionally , the PhoP binding region upstream of PE8 lies downstream of the RNApol binding site ( Figure 1B ) . Curiously , we noticed that some strong PhoP peaks lacked a concomitant RNApol signal as illustrated by mihF ( Figure 1B ) . In order to confirm the ChIP-seq data independently , we quantified a selection of PhoP binding sites from the immunoprecipitated DNA of H37Rv , its phoP mutant and a control experiment performed without antibody , obtained from biological replicates . The results validated those obtained in ChIP-seq experiments ( Figure S1 ) . We used the MEME suite to identify the PhoP consensus sequence from ChIP-seq signals . Two hundred bp surrounding the summit of the peaks were scanned and a motif was found in 83% of the instances ( p-value between 5 . 88e-09 and 4 . 40e-05 , Figure 1E and Worksheet 1 in Table S1 ) . Additional , though more divergent , copies of the same consensus sequence were found in 11 peaks ( 31% ) ( Worksheet 1 in Table S1 ) . Overall , six ChIP-seq signals ( rv1535 , PPE43 , lpdA , rv3767 , whiB1 and the region between yajC and gabT ) were not found to be associated with the identified motif , suggesting either higher divergence of the sequences or indirect PhoP binding , i . e . mediated by other proteins . While the 5′ to 3′ orientation of the motif generally corresponded to the orientation of the gene associated with the ChIP-seq signal ( 22 out of 29 cases ) , we observed 7 exceptions , the most notable being the well-known PhoP-regulated gene pks3 [13] , [14] . In this case the motif was localized on the opposite strand as compared to the direction of transcription of pks3 . To explore the relationship between binding of PhoP and transcriptional control on the target genes , we performed deep transcriptomic analysis by RNA-seq under the same in vitro conditions employed for ChIP-seq . We compared exponentially growing H37Rv wild type to the isogenic phoP mutant and quantified gene expression according to the functional categories in the TubercuList database ( http://tuberculist . epfl . ch ) , generating results reported in Worksheet 1 in Table S2 . An arbitrary 3-fold threshold was applied to the dataset for further analysis . Integration of the ChIP-seq and RNA-seq data revealed that 19 PhoP binding sites were associated with altered expression of the flanking gene ( s ) ( Table 1 ) . Since some of these ORFs are part of predicted operons [17] , [18] , [19] , the total number of genes under direct control of PhoP was found to be at least 30 in the experimental conditions tested ( Table 1 ) . The region showing the most remarkable affinity for PhoP ( 162-fold enrichment in ChIP-seq ) lay between rv2395 and PE_PGRS41 . This signal correlated with the expression of a small transcript ( Mcr7 ) in the intergenic region that was severely affected by deletion of PhoP . This small transcript is further characterized later in this work . Other examples are represented by the operons composed of pks3-pks4-papA3-mmpL10 and rv2633c-rv2632c , which were more expressed in the wild type strain , whereas the PE8-PPE15 transcriptional unit was induced upon deletion of phoP , thus demonstrating the dual role of the regulator . On the contrary , 15 ChIP-seq peaks did not correlate with the presence of deregulated transcripts in their vicinity . The most striking signal in this group was the one upstream of mihF . Deeper inspection of the RNA-seq results uncovered 140 transcripts whose expression underwent changes in the phoP mutant ( Worksheet 1 in Table S2 ) . Since 30 of these were part of the aforementioned operons , the remaining 110 were likely to be indirectly controlled by PhoP through regulatory cascades . It is worth recalling that PhoP binds upstream of genes encoding several transcriptional regulators ( espR , whiB1 , whiB3 , whiB6 ) that may act downstream . Interestingly , an almost equal proportion of genes was found to be activated ( 68 ) or repressed ( 72 ) as a consequence of the mutation . Upon clustering these transcripts into functional categories , we observed significant enrichment for the “lipid metabolism” group among the up-regulated genes ( p = 0 . 0016 , Fisher's Exact test ) and for the “PE/PPE” category among the down-regulated genes ( p = 0 . 0024 , Fisher's Exact test ) . Further discussion of the PhoP regulon will be presented elsewhere . Independent validation of the RNA-seq data was obtained for a subset of genes by quantitative reverse transcription PCR ( qRT-PCR ) , which confirmed the excellent correlation between high-throughput results and targeted quantification ( Figure S1 ) . The most prominent PhoP binding site in the genome lay between genes rv2395 and PE_PGRS41 ( Figure 2A ) but , surprisingly , transcription of neither gene differed between strain H37Rv and its phoP mutant ( Worksheet 1 in Table S2 ) . However , in a previous study of ncRNA in M . bovis BCG , the mcr7 gene encoding a 350 nt transcript , was located within this region [20] . Northern blot analysis was performed on RNA extracted from wild type M . tuberculosis , phoP mutants and complemented strains in two different genetic backgrounds: the H37Rv laboratory strain and GC1237 , a clinical isolate belonging to the Beijing family [21] . We detected an RNA of approximately 350 nt in length in wild type and complemented strains ( Figure 2B ) , whose 5′-end could be mapped from the RNA-seq profile to coordinate 2 , 692 , 165 in the H37Rv genome . In contrast , we were unable to identify this RNA in the phoP mutants even when 10-times more RNA was used in Northern blot experiments ( Figure S2 ) . These results were also confirmed by qRT-PCR showing barely detectable levels of Mcr7 in the M . tuberculosis phoP mutant ( Figure S2 ) . The complete lack of expression of this ncRNA in the M . tuberculosis phoP mutant validates the findings obtained by ChIP-seq and RNA-seq and confirms the phoP mutant as an Mcr7-deficient strain as well . Next , we sought to establish whether Mcr7 is a primary transcript or conversely processed from a longer RNA . Detection of an mcr7 transcript of approximately 350 nt in length in the primary transcriptome of H37Rv ( Figure 2B ) ruled out the latter possibility and indicated that Mcr7 is a primary , unprocessed RNA . We then investigated the presence of mcr7 in the Mycobacterium genus by bioinformatic analysis and found that it is predicted to be restricted to the M . tuberculosis complex . We therefore obtained expression data for mcr7 in five representative species . Surprisingly , the Mcr7 ncRNA is only weakly expressed in M . africanum , M . bovis , M . caprae and M . microti as compared to the high expression levels in M . tuberculosis ( Figure 2C ) . After demonstrating the strict PhoP regulation and the predominant expression of mcr7 in M . tuberculosis species , we tried to assign a biological role to this ncRNA . Most trans-acting ncRNA act by limited complementarity with their target mRNA , which results in post-transcriptional regulatory mechanisms [22] . A highly structured fold of Mcr7 with a 33-nt free loop ( Figure S3 ) was predicted using the RNAfold server . A bioinformatic search for putative targets of Mcr7 resulted in 18 candidates with complementarity in their 5′-end portion ( Figure S3 ) . Given that some ncRNA exert their regulatory function through interaction with their loop structures [23] , we focused on mRNAs that annealed with the 33-nt loop . As our previous unpublished results suggested that secretion of Tat-dependent substrates was affected in the phoP mutant strain , we focused on the predicted interaction between tatC and Mcr7 . Interestingly , the 5′-end of the tatC mRNA is predicted to base pair with the major loop of Mcr7 ( Figure 2D ) . The interacting region includes the putative ribosome binding site ( RBS ) and the first 6 codons of the tatC mRNA , suggesting that Mcr7 probably prevents ribosome loading and , consequently , translation of tatC mRNA . The tatC gene is essential for M . tuberculosis [24] , and encodes a transmembrane protein that is part of the TatABC general secretory apparatus required for export of proteins with a twin arginine motif ( RR ) in their signal peptide [25] ( Figure 2E ) . TatC recognizes the RR motif prior to protein translocation through the TatA channel ( Figure 2E ) . Our prediction suggested that Mcr7 might regulate tatC at the post-transcriptional level by occlusion of the RBS and the consequent translational down-regulation ( Figure 2D ) . Consequently , we studied the secretome from exponentially grown cultures of strain H37Rv , its phoP mutant and a phoP complemented mutant by in-depth proteomics . The enrichment ratio for each protein in the secreted fraction was calculated as the log2 of normalized peptide abundance between the desired strains . Results are presented in Worksheet 1 in Table S3 . Upon applying a cutoff based on the Significance B value ( B<0 . 05 ) , 37 proteins were found to be more secreted in the phoP mutant compared to the wild type strain . Sixteen of these ( 43 . 24% ) exhibited an RR motif within the first 50 aminoacids . On the contrary , 6 out of 35 proteins , that were more abundant in the wild type displayed the RR motif ( 17 . 14% ) . These encouraging findings prompted us to compare the abundance of previously predicted Tat substrates [24] , [26] , [27] in our secretome experiments . Results indicated that these were significantly more present in the secreted fraction of the phoP mutant relative to wild type and complemented strains ( Figure 3A ) . In addition , we compared the relative secretion levels of EsxA ( ESAT-6 ) , EsxB ( CFP-10 ) , EspA and EspC since these proteins are well-known PhoP-dependent ESX-1 secretion substrates [7] and thus serve as controls . As expected , the secretome of the phoP mutant contained very low amounts of EsxA , EsxB , EspA and EspC , thus showing the opposite trend to Tat-dependent substrates ( Figure 3A ) . Next , we validated these results by Western blot analysis of Ag85C [26] and Rv2525c [24] as known Tat-dependent substrates and EsxA as a PhoP-dependent ESX-1 substrate . The results corroborated the proteomic studies: the secreted fraction of the phoP mutant showed higher levels of Ag85C and Rv2525c proteins compared to the wild type and complemented mutant strains . On the contrary , EsxA secretion was undetectable in the phoP mutant compared to the strains harboring a wild type phoP allele ( Figure 3B ) . Taken together , these results are consistent with a regulatory model involving PhoP , Mcr7 and tatC mRNA since the absence of Mcr7 in the phoP mutant would result in more efficient TatC translation and therefore increased secretion ( Figure 3C ) . In order to confirm that mcr7 , but no other PhoP-dependent genes , influenced secretion of Tat substrates via post-transcriptional regulation of tatC mRNA , we restored Mcr7 production in the M . tuberculosis phoP mutant that we have previously demonstrated to be mcr7 deficient ( Figure 2 ) . The mcr7 gene was cloned downstream of the promoter for the 16S rRNA gene and the resultant construct was integrated into the chromosome of the H37Rv phoP mutant , thereby obtaining the mcr7-complemented strain . Northern blot experiments confirmed the authenticity and length of the mcr7 transcript ( Figure 3D and Figure S4 ) . Detection of TatC in whole-cell lysates of H37Rv , its phoP mutant , the phoP-complemented mutant and the mcr7-complemented strain demonstrated increased production of this protein in the phoP mutant in agreement with our proposed model ( Figure 3E ) . Reintroduction of phoP complemented this phenotype as expected . Ectopic expression of mcr7 in the phoP mutant was sufficient to restore TatC levels to the wild type condition ( Figure 3E ) , indicating that mcr7 per se was able to modulate expression of TatC , presumably by regulating translation of tatC mRNA . To examine the phenotypic effect caused by reintroducing mcr7 , we first showed by qRT-PCR that expression of the ncRNA from the surrogate promoter was only about 6-fold higher in the complemented mutant relative to the wild type strain ( Figure 4A ) . Furthermore , we demonstrated that reintroduction of mcr7 in a phoP mutant did not influence the expression of the PhoP regulon that remained at undetectable levels in both the phoP mutant and mcr7-complemented strains ( Figure 4A ) . Additionally , we proved that transcription of the tatC mRNA in the phoP mutant and in the mcr7-complemented strains showed no significant difference as compared to H37Rv ( Figure 4A ) . Overall , these data ruled out a transcriptional impact of mcr7 on gene expression and supported the notion of a post-transcriptional effect exerted by the ncRNA on tatC . We then investigated whether reintroduction of mcr7 in the phoP mutant restored secretion of Tat-dependent substrates to wild type levels . Western blot analysis of Ag85C in the whole-cell lysate and secreted fractions showed that while Ag85C is produced at very similar levels in all strains , secretion of this protein was more pronounced in the phoP mutant compared to the wild type and mcr7 complemented strains ( Figure 4B ) . By contrast , inspection of ESX-1 substrates showed no detectable secretion of EsxA and EspD in either the phoP mutant or the mcr7-complemented mutant strains ( Figure 4B ) . Therefore , the Mcr7 ncRNA did not impact the activity of the ESX-1 secretion apparatus whereas it did affect protein secretion through the Tat system . Finally , we measured the enzymatic activity of a Tat-dependent substrate , the well-characterized BlaC [28] beta-lactamase using the chromogenic cephalosporin substrate nitrocefin . The results indicated faster reaction kinetics in the phoP mutant relative to wild type ( Figure 4C ) , a finding correlated with the protein secretion levels observed in proteomic studies . Again , complementation with mcr7 was sufficient to successfully restore BlaC activity to wild type levels ( Figure 4C ) . Since no deregulation of blaC transcription was observed in the phoP mutant ( Worksheet 1 in Table S2 ) and in the mcr7-complemented strain ( Figure 4A ) , we attributed this effect to post-transcriptional regulation of TatC by Mcr7 .
High-resolution systems biology is helping greatly to unravel the complexities of the M . tuberculosis “regulome” . Recent works have uncovered a plethora of ncRNA [29] and reconstructed the hypoxia regulatory network [15] in this pathogen . In this study we integrated data from complementary high-throughput sequencing technologies and obtained extensive knowledge on PhoP-dependent transcriptional regulation in the tubercle bacillus . Specifically , ChIP-seq identified the PhoP binding sites along the M . tuberculosis chromosome ( Figure 1 ) , whereas strand-specific , single-nucleotide resolution transcriptomic analyses revealed previously unknown features of the PhoP regulatory network in vitro . Although good overlap was observed between RNA-seq data and published transcriptomic analyses ( [5] , see Worksheet 2 in Table S2 for comparison ) , major progress has been made as compared to traditional microarray-based approaches as indirect regulatory effects present in former studies [5] , [14] have been unmasked . Importantly , we found many genes that were deregulated in the phoP mutant despite the absence of a PhoP binding signal in the respective promoter regions . In this regard , PhoP was found to control expression of several other regulatory proteins ( e . g . EspR , WhiB1 , WhiB3 , WhiB6 ) , which act in downstream regulatory cascades [30] , [31] . Independent confirmation for this conclusion was presented recently in a regulatory model predicting production of acyltrehalose-derived lipids to be coordinated by a PhoP-WhiB3 network via regulation of pks2 and pks3 [15] . Additional proof was obtained upon comparing the transcriptome of the phoP mutant with the predicted binding sites of WhiB1 , WhiB3 and WhiB6 ( information available at http://www . tbdb . org/ ) . Indeed , the overlap was found to include rv0996 , rv1004c , rv1040c , rv2274c and rv3289c for WhiB1 , rv1040c for WhiB3 , and rv2396 for WhiB6 . Concerning EspR , deregulation of the espACD operon was reported in an espR knockout strain [32] , where a binding site for EspR was demonstrated [30] . Interestingly , PhoP was shown to bind upstream of lipF and of lppL , where EspR is also present [30] , thereby increasing the complexity of the regulatory machinery at these loci . The small ncRNA Mcr7 can also be considered as an intermediate regulator in the PhoP global network , although it likely exerts its function at the post-transcriptional level . We will come back to Mcr7 later in the discussion . Comparison of ChIP-seq and RNA-seq profiles uncovered several genes associated with a PhoP binding site but whose expression was not altered in a PhoP-deficient strain . We hypothesize that these genes may be subjected to additional layers of regulation or may respond to yet unexplored environmental conditions . This is exemplified by the mihF gene , which , despite its upstream PhoP binding site , was not found to be deregulated . Since the signal sensed and the downstream components of the PhoPR two-component system have not yet been completely elucidated , it is conceivable that signal transduction originating from PhoR may involve other factors than PhoP , thereby fine-tuning gene expression in M . tuberculosis in different conditions . Galagan and colleagues recently mapped the binding sites of 50 transcription factors , including PhoP , in M . tuberculosis [15] by exploiting a tetracycline-inducible promoter system to overexpress the FLAG-tagged version of the protein of interest and using anti-FLAG antibodies in ChIP-seq experiments . Contrary to their approach , we worked in physiological conditions and performed immunoprecipitation assays using antibodies directed against native PhoP , thus avoiding artifacts due to abnormal expression levels or to biased protein-antibody interaction . In addition , use of the phoP mutant allowed false positive signals to be avoided . Interestingly , the number of peaks pinpointed in our work ( 35 ) was considerably smaller than that reported in Galagan et al . [15] , where several signals were detected in intergenic as well as in intragenic regions . This could reflect the different methods employed , since artificial expression of PhoP may have increased binding to low affinity sites . Head-to-head comparison revealed that all but two of the peaks ( yajC-gabT and lpdA-rv3304 ) identified here were also present in the other study ( see Worksheet 2 in Table S1 and Worksheet 3 in Table S1 for detailed comparison ) . The position of the PhoP peak with respect to the ORF start site merits discussion . We noticed that in the case of lipF and rv1535 , the binding site was located >400 bp upstream of the translation start codon . This is consistent with previous results of footprinting assays for lipF [13] and with the presence of long 5′-UTRs , with presumptive regulatory roles , for lipF and rv1535 in the respective RNA-seq profiles . Interesting observations were made upon alignment of the PhoP and RNApol ChIP-seq profiles . PhoP was located upstream of the enzyme in most cases , suggesting a role as a positive regulator , later confirmed by RNA-seq data . On the contrary , PE8 was the only gene associated with a PhoP signal downstream of the RNApol peak , indicating potential steric hindrance and thus prevention of RNApol progression throughout the coding sequence . ChIP-seq analysis can therefore provide clues as to the role fulfilled by a transcription factor depending on the position of its binding site with respect to RNApol . Inspection of the PhoP targets uncovered unusual binding sites in the 3′-end of ORF such as the one between hddA and ldtA . Since this peak is at the end of two convergent genes , it likely corresponds to an unmapped small RNA . Closer inspection of this intergenic region revealed the presence of a novel small transcript downregulated in the phoP mutant . Another case is represented by rv2137c , where the PhoP interacting region was mapped within the ORF , suggesting an alternative , PhoP-dependent start codon . Indeed , a polypeptide starting at the ATG codon at nucleotide 106 , in frame with the currently annotated start site , shows more than 85% identity with the corresponding proteins in all other mycobacteria whose genomes have been sequenced . The bipartite PhoP consensus sequence derived from ChIP-seq analysis is consistent with the crystal structure of the dimeric PhoP regulator that is predicted to bind to direct repeats [10] . It also agrees with previous footprinting experiments demonstrating binding of PhoP upstream of its own gene [11] , [13] and in the promoter regions of lipF , fadD21 , pks2 [13] . On the other hand , the divergent orientation of the PhoP binding motif relative to the pks3 gene can be subjected to different interpretations . It could be that the adjacent rv1179c is the gene directly controlled by PhoP while pks3 undergoes indirect regulation . Alternatively , transcriptional regulation in that locus might be independent of directional positioning of the transcription factor . The last years have witnessed increased attention to ncRNA in prokaryotic organisms , including Salmonella enterica [33] , Legionella pneumophila [34] , Listeria monocytogenes [35] and M . tuberculosis [29] . These molecules have been predicted to exert their function at the post-transcriptional level by modulating translation of RNAs [36] . This process has important implications when bacteria face environmental stresses since it allows faster responses than classical transcriptional regulation . In this study we disclose the Mcr7 ncRNA encoded by the mcr7 gene , located between rv2395 and PE_PGRS41 ( Figure 2 ) . The latter was described as highly repressed in a phoP mutant from microarray experiments by Walters and co-workers [5] . Thanks to the increased resolution provided by RNA-seq , we can now identify the heavily deregulated gene as mcr7 rather than PE_PGRS41 . The position of the probes in the microarray assay probably did not allow such precision . A similar observation can be made for a study that characterized the transcriptional differences between the avirulent strain H37Ra and H37Rv [6] . The gene encoding PE_PGRS41 ( and likely the associated intergenic region carrying mcr7 ) was found to be the most highly deregulated . Importantly , H37Ra is a natural mutant in the phoP gene since it carries a polymorphism in the DNA binding domain [7] , [11] , thus indicating that reduced virulence is associated with lack of PhoP activity and impaired expression of the locus encoding mcr7 . We confirmed this prediction by measuring the expression levels of mcr7 in H37Ra by qRT-PCR . The ncRNA was found to be poorly detectable as compared to H37Rv ( Figure S5 ) . In the same genomic region of the CDC1551 strain , Abramovitch and colleagues postulated the existence of the aprABC locus with aprC corresponding to PE_PGRS41 and aprA and aprB corresponding to ORFs MT2466 and MT2467 [37] . These ORFs were not predicted in strain H37Rv as neither their codon usage nor positional base composition are typical of true protein coding sequences [16] , [38] . The mcr7 gene completely overlaps the hypothetical aprA . A major PhoP binding site precedes the mcr7 gene but there is none immediately upstream of PE_PGRS41 . Our results from Northern blot experiments clearly showed one prominent band of approximately 350 nt in length corresponding to Mcr7 , that was first described in M . bovis BCG [20] . This genomic locus is restricted to species belonging to the M . tuberculosis complex , including M . canettii , and was not identified in M . kansasii and in M . marinum , although expression of mcr7 was found to be particularly prominent in M . tuberculosis only . The expression pattern of mcr7 tallies with the proposed evolutionary pathway of the tubercle bacilli [39] . Indeed , those lineages that evolved from a common M . tuberculosis-like ancestor by multiple deletions ( M . africanum , M . microti , M . caprae and M . bovis ) express low-levels of the ncRNA as compared to the M . tuberculosis strain . In light of our findings , it is tempting to speculate that modulation of the activity of the Tat secretion system by means of a small RNA has played a role in shaping the adaptation of tubercle bacilli and/or in restricting their host spectrum . We investigated the potential role played by Mcr7 in virulence by performing ex vivo and in vivo infections . Complementation of the phoP mutant with mcr7 alone did not restore the wild type virulence and the strain was more attenuated than the phoP mutant ( Figure S6 ) . This phenotype may be related to the ectopic overexpression of Mcr7 , which was indeed associated with small colony size ( data not shown ) . The predicted folding model of Mcr7 revealed the presence of a 33-nt loop with the potential to anneal to three candidate mRNAs: rv2767c , rv2053c and tatC ( Figure S3 ) . Since our results provided convincing evidence for increased secretion of the Tat substrates , Ag85A and Ag85C , in phoP mutants , we prioritized the study of Tat-dependent secretion . However , we cannot exclude a post-transcriptional impact of Mcr7 on expression of the hypothetical membrane proteins Rv2767c and Rv2053c , although their role in M . tuberculosis physiology is questionable since previous proteomic experiments failed to detect them in the total proteome or in cellular subfractions [40] , [41] , [42] , [43] ) . Proteomic analysis demonstrated that proteins secreted through the Tat system are more abundant in the extracellular fraction of the PhoP-deficient strain ( Figure 3 ) . A genetic approach relying on complementation of the phoP mutant with mcr7 proved the involvement of the ncRNA in the regulation of Tat-dependent secretion at the post-transcriptional level while no impact on the amount of mRNA was observed ( Figures 3 and 4 ) . This is the first report describing the function of a ncRNA in M . tuberculosis . Notably , M . tuberculosis phoP mutants display pleiotropic phenotypic effects including impaired secretion of ESX-1 substrates [7] , compromised production of sulphatides ( SL ) , diacyltrehaloses ( DAT ) and polyacyltrehaloses ( PAT ) [8] and reduced virulence in the macrophage and mouse models of infection [4] , [5] . Mcr7 was found to be sufficient to re-establish the wild type phenotype with respect to secretion of Tat substrates whereas ESX-1 substrates were unaffected , thus evoking a specific regulatory cascade where Mcr7 acts downstream of PhoP . Overall , this work refined the role played by PhoP in control of gene expression in M . tuberculosis . A previous study reported that PhoP is involved in the regulation of the ESX-1 secretion system [7] but no direct evidence had been provided so far . Here we uncovered the existence of a novel regulatory cascade composed of at least two regulatory factors , PhoP and EspR , that ultimately controls ESX-1 functions , such as secretion of EsxA , via regulation of the espACD locus [7] , [30] . In addition , we demonstrated a role for the PhoP-dependent ncRNA Mcr7 in Tat-dependent secretion of well-known M . tuberculosis antigens , namely the immunodominant Ag85 complex . PhoP could therefore also mediate antigenicity and pathogenesis via the Ag85 complex itself and/or through trehalose 6 , 6-dimycolate , an abundant glycolipid in the mycobacterial cell wall whose biosynthesis is catalyzed by Ag85 proteins . The Ag85 complex is also involved in binding to human fibronectin , important for cell adhesion and invasion [44] , [45] . Mcr7 could therefore represent the missing link between PhoP and the downstream processes required for successful infection of the host . Finally , our findings provided a new molecular basis to explain the better protection against tuberculosis conferred by the candidate vaccine strain MTBVAC , which carries a deletion in phoP [9] . While the reduced virulence results mainly from abrogation of the ESX-1 secretion system and possibly from lack of complex lipids , its efficacy may be ascribed to improved antigenicity properties following silencing of Mcr7 and the ensuing increase in secretion of Tat substrates such as the Ag85 proteins .
All animal work has been conducted according to the national and international guidelines . The protocols for animal handling were previously approved by University of Zaragoza Animal Ethics Committee ( protocol number PI43/10 ) . Mycobacterium tuberculosis H37Rv [16] , GC1237 [21] wild type strains and their isogenic phoP mutants were previously described [11] . The growth rate of the wild type and of the mutant strains were similar ( Figure S7 ) . M . bovis AF2122/97 [46] , M . caprae M57 [39] , M . microti 15496 and M . africanum MAF419 [47] were used as representative strains of the M . tuberculosis complex . Mycobacterial strains were grown at 37°C in 7H9 medium ( Difco ) supplemented with 0 . 05% Tween 80 and 10% albumin-dextrose-catalase ( ADC , Middlebrook ) or on 7H10 plates supplemented with 10% ADC . For M . tuberculosis complex strains different from M . tuberculosis , 40 mM sodium pyruvate was added to the medium . Escherichia coli DH5α used for cloning procedures was grown at 37°C in LB broth or on LB agar plates . Kanamycin ( 20 µg/ml ) and hygromycin ( 20 µg/ml ) were used as appropriate . All chemicals were purchased from Sigma-Aldrich , unless otherwise stated . Immunoblotting was performed with mouse monoclonal anti-EsxA antibodies ( Hyb 076-08 , Abcam ) , mouse monoclonal anti-Ag85C antibodies ( HYT27 , Abcam ) , mouse monoclonal anti-GroEL2 antibodies ( BDI578 , Abcam ) , rabbit polyclonal anti-Rv2525c antibodies [24] , rat polyclonal anti-EspD antibodies ( kindly provided by Jeffrey Chen ) and rabbit polyclonal anti-TatC ( Eurogentec ) antibodies . Polyclonal antibodies to the transcriptional regulator PhoP of M . tuberculosis were obtained from rabbits that received five doses of PhoP ( 0 . 5 mg ) , at weeks 0 , 4 , 8 , 12 and 16 , respectively . These anti-PhoP antibodies were validated by ELISA ( ZEU-Immunotec Zaragoza , Spain ) . Sequences of the oligonucleotides used in this study will be provided upon request . The pAZ31 plasmid was kindly provided by Ainhoa Arbues . The pWM222 plasmid was used for phoPR complementation in northern blot experiments and was constructed as follows . A 2 . 7 kbp region spanning the phoPR operon was amplified by PCR using primers ( 5′-ATACTAGTGGCATCACCCAACGCTTGTT-3′ ) and ( 5′-ATACTAGTGGTGAGCCAGCTGATCGG-3′ ) . This PCR product was digested with SpeI and subsequently transferred into a pMV361 [48] derivative deleted from the phsp60 promoter . In this construct , the phoPR operon is expressed from its native promoter . Plasmid pLZ11 used for mcr7 expression was constructed by inserting a transcriptional fusion of the rrs ( 16S rRNA ) promoter with the mcr7 transcript following a similar strategy to that described in [29] . This transcriptional fusion was accomplished using an overlapping two-step PCR strategy . Briefly , the rrs promoter was PCR amplified using primers rrsOV Fw: GACGTCCCGCAGCTGTCGAGCGCT and rrsOV Rv: GGGCCGCCGGCCCTGCCAGTCTAATACAAATCC . The mcr7 region was amplified using primers mcr7Ov Fw: GACTGGCAGGGCCGGCGGCCCGACACA and mcr7Ov Rv: AAGCTTCCACCTTCTCGTTACCCGCCTCTG . Both PCR products overlap in 21 bp ( underlined nucleotides ) and were used as self-templates in a PCR reaction . The entire transcriptional fusion was amplified by PCR using the flanking primers rrsOV Fw and mcr7Ov Rv , digested with HindIII and EcoRI and introduced between the HindIII and EcoRI sites of pMV361 . The resulting construct was introduced in mycobacteria by electroporation and colonies carrying a chromosome-integrated vector were checked by PCR . Chromatin immunoprecipitation experiments were performed as previously described [49] with the following modifications . We performed two independent ChIP-seq experiments with the wild type strain H37Rv and one experiment with the control phoP mutant . Briefly , M . tuberculosis cultures were grown to exponential phase ( optical density at 600 nm of 0 . 4 ) and cross-linked with 1% formaldehyde for ten minutes at 37°C . Cross-linking was quenched by addition of glycine ( 125 mM ) . Cells were then washed twice with Tris-buffered saline ( TBS , 20 mM Tris-HCl pH 7 . 5 , 150 mM NaCl ) , resuspended in 4 ml immunoprecipitation ( IP ) buffer ( 50 mM Hepes-KOH pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 0 . 1% sodium deoxycholate , 0 . 1% SDS , protease inhibitor cocktail from Roche ) and sonicated to shear DNA using Bioruptor ( Diagenode ) . Cell debris was removed by centrifugation and the supernatant used in IP experiments . Nucleo-protein extracts were incubated with 50 µl of rabbit polyclonal anti-PhoP antibodies at 4°C for 2 days on a rotating wheel . Complexes were subsequently precipitated with Dynabeads ( Dynal , anti-rabbit , Invitrogen ) for three hours at 4°C . Beads were washed twice with IP buffer , once with IP buffer plus 500 mM NaCl , once with buffer III ( 10 mM Tris-HCl pH 8 , 250 mM LiCl , 1 mM EDTA , 0 . 5% Nonidet-P40 , 0 . 5% sodium deoxycholate ) , once with Tris-EDTA buffer pH 7 . 5 . Elution was performed in 50 mM Tris-HCl pH 7 . 5 , 10 mM EDTA , 1% SDS for 40 minutes at 65°C . Samples were finally treated with RNAse A for one hour at 37°C and cross-links were reversed by incubation for two hours at 50°C and for eight hours at 65°C in 0 . 5× elution buffer with 50 µg Proteinase K ( Eurogentec ) . DNA was purified by phenol-chloroform extraction and quantified by Nanodrop and Qubit fluorometer according to the manufacturer's recommendations ( Invitrogen ) . DNA fragments obtained from the immunoprecipitation procedure were used for library construction and sequencing with the ChIP-Seq Sample Preparation Kit ( Illumina ) , according to the protocol provided by the manufacturer . One lane per library was sequenced on the Illumina Genome Analyzer IIx at the Lausanne Genomics Technologies Facility using the SR Cluster Generation Kit v2 and SBS 36 Cycle Kit v2 . Data were processed with the Illumina Pipeline Software v1 . 40 . All analyses in this study were carried out using the M . tuberculosis H37Rv annotation from the TubercuList database ( http://tuberculist . epfl . ch/ ) , which includes 4019 protein coding sequences ( CDS ) , 73 genes encoding for stable RNAs , small RNAs and tRNAs . In order to quantify protein occupancy and transcription across the entire genome , 3080 intergenic regions ( regions flanked by two non-overlapping CDS ) were included , resulting in a total of 7172 features . ChIP-seq analysis was performed using the HTSstation pipeline at EPFL ( http://htsstation . epfl . ch/ ) . Briefly , the single-ended sequence reads generated from ChIP-seq experiments were aligned to the M . tuberculosis H37Rv genome ( NCBI accession NC_000962 . 2 ) using Bowtie [50] with options “-l 28 -best -strata” . Peaks were analysed using MACS v . 1 . 4 [51] with parameters “-bw 200 -m 10100” . Alignment files were converted to bigWig format for visualization in the UCSC genome browser Mycobacterium tuberculosis H37Rv 06/20/1998 Assembly [52] . To determine the level of ChIP-seq enrichment for each feature , an enrichment ratio ( ER ) was calculated by dividing the read count for the ChIP-seq sample in the wild type strain by the read count for the mutant ( control ) sample . PhoP binding site motifs were searched using the MEME Suite ( http://meme . nbcr . net/meme/ ) in sequence regions encompassing 100 bp upstream and 100 bp downstream of the predicted peak summit . Motif sequence logo was obtained using WebLogo3 ( http://weblogo . threeplusone . com/ ) . Mycobacterial cultures ( one for the wild type strain and one for the phoP mutant ) were grown to exponential phase ( OD600 = 0 . 5-0 . 6 ) and pelleted by centrifugation . To minimize RNA degradation bacteria were resuspended in 1 ml RNA Protect Bacteria Reagent ( Qiagen ) , incubated for 5 min at room temperature and then centrifuged . Bacterial pellets were resuspended in 0 . 4 ml lysis buffer ( 0 . 5% SDS , 20 mM NaAc , 0 . 1 mM EDTA ) and 1 ml phenol:chloroform ( pH = 4 . 5 ) 1∶1 . Suspensions were transferred to tubes containing glass beads ( Qbiogene ) and lysed using a ribolyser ( Fast-prep instrument ) with a three-cycle program ( 15 sec at speed 6 . 5 m ) including cooling the samples on ice for 5 min between pulses . Samples were then centrifuged and the homogenate was removed from the beads and transferred to a tube containing chloroform:isoamylalcohol 24∶1 . Tubes were inverted carefully before centrifugation and the upper ( aqueous ) phase was then transferred to a fresh tube containing 0 . 3 M Na-acetate ( pH = 5 . 5 ) and isopropanol . Precipitated nucleic acids were collected by centrifugation . The pellets were rinsed with 70% ethanol and air dried before being re-dissolved in RNase-free water . DNA was removed from RNA samples using Turbo DNA free ( Ambion ) by incubation at 37°C for 1 h . RNA integrity was assessed by agarose gel electrophoresis and absence of contaminating DNA was checked by lack of amplification products after 30 PCR cycles . Primary , unprocessed RNA from H37Rv was prepared as indicated in [53] . Briefly , 10 µg total RNA were treated with 10 U of Terminal 5′-phosphate dependent Exonuclease ( Epicentre ) for 24 h at 30°C followed by phenol extraction and isopropanol precipitation . Successful preparation of primary transcriptome was confirmed by lack of 23S/16S rRNA bands in agarose gels . 100 ng of total RNA were mixed with 5× Fragmentation buffer ( Applied Biosystems ) , incubated for 4 minutes at 70°C and then transferred immediately on ice . RNA was purified using RNAClean XP beads ( Beckman Coulter ) , according to the manufacturer's recommendations , and subsequently treated with Antarctic phosphatase ( New England Biolabs ) . RNA was then re-phosphorylated at the 5′-end with polynucleotide kinase ( New England Biolabs ) and purified with Qiagen RNeasy MinElute columns . In order to ensure strand-specificity , v1 . 5 sRNA adapters ( Illumina ) were ligated at the 5′- and 3′-ends using RNA ligase . Reverse transcription was carried out using SuperScript III Reverse Transcriptase ( Invitrogen ) and SRA RT primer ( Illumina ) . Twelve cycles of PCR amplification using Phusion DNA polymerase were then performed and the library was finally purified with AMPure beads ( Beckman Coulter ) as per the manufacturer's instructions . A small aliquot ( 2 . 5 µl ) was analyzed on Invitrogen Qubit and Agilent Bioanalyzer prior to sequencing on Illumina HiSeq 2000 using the TruSeq SR Cluster Generation Kit v3 and TruSeq SBS Kit v3 . Data were processed with the Illumina Pipeline Software v1 . 82 . The single-ended sequence reads generated from RNA-seq experiments were aligned to the M . tuberculosis H37Rv genome ( NCBI accession NC_000962 . 2 ) using Bowtie2 with default parameters [54] . Read counts for all annotated features were obtained with htseq-count program ( http://www-huber . embl . de/users/anders/HTSeq/doc/count . html ) . Regions where genes overlapped were excluded from counting . Reads spanning more than one feature were counted for each feature . Since the RNA library was strand-specific , the orientation of sequence reads had to correspond to the orientation of annotated features to be counted . Analysis of differential gene expression was carried out using the DESeq package [55] . One microgram of M . tuberculosis RNA was converted to cDNA using SuperScript III Reverse Transcriptase ( Invitrogen ) according to the manufacturer's recommendations . All PCR primers were designed using Primer Express software ( Applied Biosystems ) . The 10 µl PCR reaction consisted of 1× Sybr Green PCR Master Mix ( Applied Biosystems ) , 0 . 25 µM of each primer and 1 µl of 1∶10 diluted cDNA or IP DNA from immunoprecipitation reactions . Reactions were carried out in triplicate in an Applied Biosystems StepOnePlus Sequence Detection System ( Applied Biosystems ) according to the manufacturer's instructions . Melting curves were constructed to ensure that only one amplification product was obtained . In the case of qRT-PCR for RNA-seq data confirmation , normalization was obtained to the number of sigA molecules in each sample . Regarding the qPCR for ChIP-seq data validation , the number of target molecules was normalized to the mutant ( control ) sample , after subtraction of the background represented by the mock-IP ( no antibody control ) . Northern blot was performed using the DIG Northern starter kit ( Roche ) following the manufacturer's recommendations . Briefly , total RNA was separated using denaturing 1% agarose gels in 1× MOPS buffer containing 2% formaldehyde . RNA was transferred by capillary blotting to Hybond-N+ nylon membranes ( Amersham ) and UV-crosslinked prior to incubation with the desired probe . Digoxigenin ( DIG ) -labelled probes were synthesized to detect rrf ( 5S rRNA ) and mcr7 transcripts using the primer pairs NB-5S-rRNA-fw ( ttacggcggccacagcgg ) /NB-T7-5S-rRNA-rv ( taatacgactcactatagggtgtcctacttttccacccggagggg ) , NB-mcr7-fw ( ccggcggcccgacacatg ) /NB-T7-mcr7-rv ( taatacgactcactatagggacccgctcaagcaggtcg ) respectively . The T7 promoter used for in vitro transcription and labeling of RNA is underlined . RNA transcripts complementary to each probe were detected by Western-blot using an anti-DIG antibody conjugated to alkaline phosphatase and the chemiluminescent substrate CDP-Star . The secondary structure fold of mcr7 was predicted using the RNAfold web server ( http://rna . tbi . univie . ac . at/cgi-bin/RNAfold . cgi ) . Prediction of mcr7 putative targets was performed using TargetRNA ( http://cs . wellesley . edu/~btjaden/TargetRNA2/index . html ) allowing antisense complementarity from -80 to +20 relative to ORF translation start sites of M . tuberculosis H37Rv . A minimum hybridization seed of 7 nt and a p-value threshold of 0 . 05 were required for target transcripts . In order to avoid albumin contamination in the secreted protein fraction , cultures were grown in 7H9 ( Difco ) 0 . 05% Tween 80 supplemented with 0 . 2% dextrose , 0 . 085% NaCl . After 2-3 weeks incubation at 37°C , cultures were pelleted by centrifugation . The supernatant containing secreted proteins was incubated with 10% trichloroacetic acid ( TCA ) for one hour in ice and then centrifuged at 4°C for 30 min . Pelleted proteins were rinsed with cold acetone and then resuspended in 150 mM TrisHCl pH 8 . Protein integrity and absence of albumin contamination was checked by SDS-PAGE and Coomassie staining . The pelleted fraction of bacterial cultures was used for extraction of whole-cell proteins . The pellet was resuspended in PBS containing 1% triton ×100 and a cocktail of protease inhibitors ( Roche ) and sonicated for 30 minutes at 4°C using a Bioruptor ( Diagenode ) . Samples were then centrifuged and the upper phase containing whole-cell lysate was used in downstream experiments . To prepare whole-cell extracts for detection of TatC by Western blot , proteins were further solubilized with 9 M urea , 70 mM DTT and 2% Triton X-100 followed by TCA precipitation and final resuspension in 150 mM TrisHCl pH 8 . Each sample ( 8 µg ) was reconstituted in 50 µl of 4 M Urea , 10% acetonitrile and buffered with Tris-HCl pH 8 . 5 to a final concentration of 30 mM . Proteins were reduced using 10 mM dithioerythritol ( DTE ) at 37°C for 60 min . Cooled samples were subsequently incubated in 40 mM iodoacetamide at 37°C for 45 min in a light-protected environment . Reaction was quenched by addition of DTE to a final concentration of 10 mM . A two-step digestion was performed using Lys-C ( 1∶50 enzyme: protein ) for 2 hours at 37°C . The lysates were first diluted 5-fold and samples were again digested overnight at 37°C using Mass Spectrometry grade trypsin gold ( 1∶50 enzyme: protein ) and 10 mM CaCl2 . Reaction was stopped by addition of 2 µl of pure formic acid ( FA ) and peptides were concentrated by vacuum centrifugation to a final volume of 70 µl . Samples were dimethyl-labeled as previously described [56] . The sample H37Rv phoP- was labeled with light dimethyl reactants ( CH2O + NaBH3CN ) , the sample H37Rv was labeled with medium reactants ( CD2O + NaBH3CN ) and the sample H37Rv phoP- complemented was labeled with heavy methyl reactants ( 13CD2O + NABD3CN ) . As a final step of labeling procedure , samples were mixed in a 1∶1∶1 [ ( Light: Medium: Heavy ) ratio and extensively lyophilized . Technical replicates were obtained . SAX fractionation was performed as previously described [57] . The eluted fractions were dried by vacuum centrifugation and used for LC-MS analysis . Each SAX fraction was resuspended in 2% acetonitrile , 0 . 1% FA for LC-MS/MS injections and then loaded on a homemade capillary pre-column ( Magic AQ C18; 3 µm by 200 Å; 2 cm×100 µm ID ) and separated on a C18 tip-capillary column ( Nikkyo Technos Co; Magic AQ C18; 3 µm by 100 Å; 15 cm×75 µm ) . MS/MS data was acquired in data-dependent mode ( over a 4 hr acetonitrile 2–42% gradient ) on an Orbitrap Q exactive Mass spectrometer equipped with a Dionex Ultimate 3000 RSLC nano UPLC system and homemade nanoESI source . Acquired RAW files were processed using MaxQuant version 1 . 3 . 0 . 5 [58] and its internal search engine Andromeda [59] . MS/Ms spectra were searched against M . tuberculosis strain H37Rv database R23 ( http://tuberculist . epfl . ch/ ) [60] . MaxQuant default identification settings were used in combination with dimethyl-labeling parameters . Search results were filtered with a false-discovery rate of 0 . 01 . Known contaminants and reverse hits were removed before statistical analysis . Relative quantification within different conditions was obtained by calculating the significance B values for each of the identified proteins using Perseus [58] . Protein samples were quantified using the RC DC protein assay ( BioRad ) and equal amounts of protein preparations were loaded per well . Proteins were separated on SDS-PAGE 12–15% gels and transferred onto PVDF membranes using a semidry electrophoresis transfer apparatus ( Bio-Rad ) . Membranes were incubated in TBS-T blocking buffer ( 25 mM Tris pH 7 . 5 , 150 mM NaCl , 0 . 05% Tween 20 ) with 5% w/v skimmed milk powder for 30 min prior to overnight incubation with primary antibodies at the dilution indicated below . Membranes were washed in TBS-T three times , and then incubated with secondary antibodies for 1 h before washing . Antibodies were used at the following dilutions: 1∶2 , 000 for anti-EsxA , 1∶5 , 000 for anti-Ag85C , 1∶500 for anti-GroEL2 , 1∶1 , 000 for anti-Rv2525 , 1∶1 , 000 for anti-EspD and 1∶1 , 000 for anti-TatC . Horseradish peroxidase ( HRP ) conjugated IgG secondary antibodies ( Sigma-Aldrich ) were used at a 1∶20 , 000 dilution . Signals were detected using chemiluminescent substrates ( GE Healthcare ) . Bacterial cultures were grown to OD 600 nm 0 . 6–0 . 8 and pelleted . Nitrocefin was added to culture supernatants at 50 mM final concentration and absorbance was measured at 486 nm ( Synergy HT BioTEK ) every 10 minutes for 3h . Slope of linear range was measured and normalized against total CFUs of the culture . Virulence of the different M . tuberculosis strains was evaluated in J774A . 1 murine macrophages according to a previously published procedure [61] , [62] . Briefly , cells were grown in DMEM medium containing 10% fetal bovine serum at 37°C under 5% CO2 . 10 , 000 macrophages per well were seeded into a 384-well plate in a total volume of 45 µl and incubated at 37°C for 30 minutes before infection . Cells were infected at an MOI of 10 with titrated stocks of H37Rv , phoP mutant , phoP-complemented and mcr7-complemented strains . On day 3 , macrophage survival was measured by exposing the infected cells to PrestoBlue Cell Viability Reagent ( Life Technologies ) for 1 hour . Fluorescence was read using a TECAN Infinite M200 microplate reader and statistical analysis was performed with the unpaired T-test method . C57BL/6 mice were infected intranasally with an inoculum of 2 . 5×104 cfu/ml ( 6 mice per group ) . Four weeks post-infection mice were euthanized and lungs were plated on 7H11 plates supplemented with 0 . 5% glycerol , 10% albumin-dextrose-catalase ( ADC , Middlebrook ) , polymixin B 50 U/ml , trimethoprim 0 . 02 mg/ml and amphotericin B 0 . 01 mg/ml . Unpaired T-test was used for statistical analysis . The protocols for animal handling were previously approved by University of Zaragoza Animal Ethics Committee ( protocol number PI43/10 ) . The ChIP-seq and RNA-seq datasets have been deposited in NCBI's Gene Expression Omnibus [63] under accession number GSE54241 . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium ( http://www . proteomexchange . org ) via the PRIDE ( Proteomics Identification Database ) partner repository [64] with the dataset identifier PXD000698 . | One of the best characterized two-component systems in Mycobacterium tuberculosis is represented by the PhoPR pair , with PhoR being the transmembrane sensor kinase and PhoP playing an essential part in controlling expression of virulence-associated genes , such as those encoding the ESX-1 secretion apparatus . Previous studies showed that mutations in phoP resulted in attenuation in the mouse model of infection , thus providing the basis for the development of a novel live attenuated Mycobacterium tuberculosis vaccine carrying a deletion in phoP which is today in clinical trials . To thoroughly investigate the role of PhoP in M . tuberculosis , we undertook a systems biology approach comprising ChIP-seq and RNA-seq technologies . We demonstrated binding of PhoP to at least 35 targets on the M . tuberculosis chromosome and direct impact on expression of 30 genes , while further amplification of the signal is provided by regulators acting downstream . The strongest binding site was located between rv2395 and PE_PGRS41 , where transcription of the non-coding RNA ( ncRNA ) Mcr7 was demonstrated . Expression of Mcr7 was found to be restricted to M . tuberculosis species and totally silenced in a phoP mutant . Genetics and proteomics approaches proved that Mcr7 controls activity of the Twin Arginine ( Tat ) secretion system , thus modulating secretion of the immunodominant antigen Ag85 complex and the BlaC beta-lactamase . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"biology",
"and",
"life",
"sciences",
"microbiology",
"genomics"
] | 2014 | The PhoP-Dependent ncRNA Mcr7 Modulates the TAT Secretion System in Mycobacterium tuberculosis |
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